Economic Impact Study, 2001 to 2013

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About this publication

Publication author : Statistics Canada

Publish date : April 13, 2017

Summary :

In 2014, CED commissioned Statistics Canada to undertake a study on the impact of its programs, to understand whether its assistance to firms in Quebec over the period 2001 to 2013 had a significant impact on firm performances.

Table of Contents

  1. Highlights
  2. 1. Introduction
  3. 2. A CED clients' list and matching process
  4. 3. Selection of comparison group
  5. 4. Hypotheses
  6. 5. The model
  7. 6. The empirical results
  8. 7. Conclusions
  9. References

Highlights

1. Introduction

The Canada Economic Development Agency for the Regions of Quebec (CED) is a federal agency whose mandate is regional development in Quebec. CED promotes local economic development and innovation by providing financial assistance to small and medium-sized enterprises (SMEs) in Quebec.

In 2008, CED and Statistics Canada (STC) began a joint initiative to identify and measure the economic impact of the enterprises that used CED financing services from 2002 to 2008. In 2012, STC and CED extended the analysis from 2001 to 2010 and performed the study for two different programs: Regional Strategic Initiatives Program—Community Diversification (RSI-CD) and Innovation, Development, Export and Assistance for entrepreneurship—Business and Regional Growth (IDEA-BRG). The second study is referred to thereafter as CED 2013.

For the purpose of the present study, STC and CED agreed to revisit the work by extending the analysis from 2010 to 2013 and by performing the study for three different groupings of programs and seven different “program components”. The programs being studied are: Regional Strategic Initiatives Program—Community Diversification (RSI-CD), Innovation, Development, Export and Assistance for entrepreneurship—Business and Regional Growth (IDEA-BRG) and Strengthening of Quebec’s Forest Economies (IPREFQ). The programs and the “program components” being studied are: Commercialisation and Exports, Innovation and Technology Transfer, Development Strategies, Productivity and Expansion, Community Adjustment Fund (FAC), Economic Development Initiative (IDE), and Strengthening of Quebec’s Forest Economies (IPREFQ).

This document describes the methodology used to perform the analysis and provides the findings. Overall, it should be mentioned that the methods are the same as CED 2013.

Following this introduction, which also highlights the strengths and limitations of this analysis, section 2 presents the matching process. Section 3 explains the method employed to select the comparison group. Section 4 presents the hypotheses that were tested. Section 5 presents the adopted methodology. The empirical results are presented and discussed in Section 6. The last section concludes the report.

1.1. Methodological change compared to the previous study (CED 2013)

Overall, we use the same methods to be consistent and to allow comparability with the results of the previous study. The comparison groups are slightly different because some of the old comparison businesses became CED clients in 2010-2013; therefore, they were removed from the comparison groups and replaced with other comparable businesses. Also, some CED clients’ data and comparison businesses’ data were updated in Statistics Canada databases. That resulted in slightly different survival rates in the current study compared to the previous study.

A novelty of this study, compared to the previous, was to perform the empirical analysis for different “program components”, which has not been done in the previous study. In addition, this study also presents the survival rates of CED clients by “program components”.

1.2. Limitations and relevant implications

A caveat that should be kept in mind is about the selection of the comparison group. It is acknowledged that the selection of the comparison group is of crucial importance. In an ideal scenario, this group should be composed of businesses that applied to CED funding but were not ultimately selected for funding.

In this study, the comparison groups were selected from across Quebec. It should be kept in mind that some CED programs, such as IPREFQ, specifically targeted certain municipalities in Quebec. The selection of the comparable business, for each of CED client, could not be drawn from the same local context as the pool of potential comparable matches would be too small.

Also, the current study does not account for the fact that some businesses received multiple funding or benefited from other sources or types of funding. These limitations do suggest areas for future research. For instance, it would be useful, in the future, to have a list of enterprises that applied to CED programs and did not succeed, to see if the pattern of enterprises that did not get funding had different characteristics vis à vis to those in the comparison group.

Moreover, the comparison groups for the cohort 2001-2009 needed to be updated and were not identical to those used in the CED 2013 study; specifically, some of the businesses in those comparison groups joined CED clients in 2010-2013. Therefore, new businesses replaced them in the comparison groups for the cohorts 2001-2009; however, the analysis done in the CED 2013 study was not adjusted for this new comparison group.

The last data limitation concerns the size of some of “program components” such as Economic Development Initiative, Community Adjustment Fund and Development Strategies, which reported a relatively small number of beneficiaries.

2. A CED clients' list and matching process

An Excel file containing 568 businesses that benefited from CED financing services between 2010 and 2013 was submitted to the CSBP, Statistics Canada, by CED. This file contained one row for each funded project, with some businesses (identified on the file by clients ID) having multiple rows. The rows referring to the same clients ID after the initial year of assistance were removed. For instance, if an enterprise received CED support in 2010 and 2011, only the initial year of assistance, 2010 was kept.

We started with 568 records. After removing records that were in CED 2013, 395 records (“new” records) were retained. After removing multiple instances of the same business (duplicates), there were 388 businesses retained in the dataset (Figure 1).

Figure 1
Removal of duplicates from the CED client list

Figure 1 Removal of duplicates from the CED client list

2.1 Matching the clean file to the business register

After cleaning the CED file, a total of 388 CED records remained. Of these, 386 were successfully matched to the Business Register to obtain their Enterprise ID (a Statistics Canada unique identifier). No match was found for 2 clients. This corresponds to a match rate of 99.5%.

There are a number of reasons why a CED client may not be identified in Statistics Canada’s Business Register. One of the main reasons is the administrative changes related to ownership, mergers and acquisitions, or partnership arrangements that can result in the issuing of new business numbers or the alteration of other key identifiers associated with an enterprise. Table 1 provides the match rate by year and type of program. The match rate is calculated as the total number of successful matches to the Business Register divided by the total number of the client’s records in the clean file.

As can be seen from Table 1, a high match rate is found for all types of programs and for all years considered in the analysis. The match rate for Regional Strategic Initiatives Program—Community Diversification (RSI-CD) is 100% in all three years. Depending on the year considered, the match rate ranges from 99.0% to 100% for Innovation, Development, Export and Assistance for entrepreneurship—Business and Regional Growth (IDEA-BRG) program, while it ranges from 97.1% to 100% for Strengthening of Quebec’s Forest Economies program. The average match rate for all programs combined is about 99.5%.

Table 2 presents the results of matching by type of “program components”. Match rates are high for all types of “program components” and for all years considered in the analysis. The match rate for Commercialisation and Exports, Entrepreneurship Support, Community Adjustment Fund, Economic Development Initiative and Innovation and Technology Transfer is 100% in all three years. Matching rates vary between 97.7% and 100% for two other programs: Productivity and Expansion, and Strengthening of Quebec’s Forest Economies. Match rate for all “program components” combined, on average, is about 99.5%.

Table 1
Matching rate to Business Register, by year and type of program
RSI-CD IDEA-BRG
First year of funding Records Matched Match rate Records Matched Match rate
2010 22 22 100% 51 51 100%
2011 8 8 100% 111 111 100%
2012 2 2 100% 105 104 99.0%
Total 32 32 100% 267 266 99.6%
Strengthening of Quebec’s Forest Economies All programs
First year of funding Records Matched Match rate Records Matched Match rate
2010 11 11 100% 84 84 100%
2011 44 44 100% 163 163 100%
2012 34 33 97.1% 141 139 98.6%
Total 89 88 98.9% 388 386 99.5%
Table 2
Matching rate to Business Register, by year and type of program components
Commercialisation and Exports Entrepreneurship Support
First year of funding Records Matched Match rate Records Matched Match rate
2010 4 4 100% 17 17 100%
2011 19 19 100% 24 24 100%
2012 20 20 100% 34 34 100%
Total 43 43 100% 75 75 100%
Community Adjustment Fund Economic Development Initiative
First year of funding Records Matched Match rate Records Matched Match rate
2010 26 26 100% 2 2 100%
2011 7 7 100% 2 2 100%
2012 2 2 100%
Total 33 33 100% 6 6 100%
Strengthening of Quebec’s Forest Economies Innovation and Technology Transfer
First year of funding Records Matched Match rate Records Matched Match rate
2010 11 11 100% 5 5 100%
2011 43 43 100% 15 15 100%
2012 34 33 97.1% 7 7 100%
Total 88 87 98.9% 27 27 100%
Productivity and Expansion All programs
First year of funding Records Matched Match rate Records Matched Match rate
2010 19 19 100% 84 84 100%
2011 53 53 100% 163 163 100%
2012 44 43 97.1% 141 139 98.6%
Total 116 115 99.1% 388 386 99.5%

2.2 Match rate of key variables

For the modeling to be robust, a high percentage of records with useable values is required. The key variables of interest for the CED impact study are revenue, employment and productivity (computed as sales over employment). For the three key variables mentioned, an enterprise is considered to have a “usable value” if the enterprise has a NAICS code on the BR and the value for the key variable is neither zero nor missing. The requirement of having a BR NAICS code is part of the counterfactual analysis methodology.

Table 3 presents the number and percentage of usable records for each key variable of interest. For the variables revenue, sales, and employment, the matched clients have on average over 70% of useable values; i.e., they have either a value or a value equal to zero. Revenue has the higher percentage of useable values (75%) followed by employment (75%) and sales (72%).

The results show the match rate and the availability of the key variables of interest were sufficiently high to complete the modeling analysis of the impact study.

Table 3
Usable values in the variables of interest, both programs combined
First year of funding Number of CEDQ client names (NC) Number of records matched to BR Number of records with BR NAICS Total revenue Total sales Average employment
Number of usable values (NT) Match rate (100 * NT/NC) Number of usable values (NS) Match rate (100 * NS/NC) Number of usable values (NE) Match rate (100 * NE/NC)
2010 84 83 76 51 61% 49 58% 52 62%
2011 163 164 161 131 80% 124 76% 129 79%
2012 141 139 138 108 77% 107 76% 110 78%
Total 388 386 375 290 75% 280 72% 291 75%

Note(s): The “Number of usable values” for revenue, sales, employment and RDCI means usable revenue, sales and employment in the year of first funding.
“Number of records matched to BR” and “Number of records with BR NAICS” just means matched to the BR in some year (not necessarily the funding year).

3. Selection of comparison group

The comparison group (in this report also referred to as non-clients group) was selected using Statistics Canada’s imputation software Banff, which employs a nearest-neighbour approach. The methodology is consistent with CED 2013. CED client records were flagged with a variable indicating the year in which they first received funding. For each year, a comparison enterprise was selected using the following criteria:

To identify the most similar record to a CED client record, the following numeric matching variables were used:

Additionally, the comparison record to a particular CED client record was required to have the same first two digits of NAICS.

From the 386 study group records matched to Business Register, any study group record or potential comparison group record missing NAICS or any of the five bulleted variables above was excluded from the process, resulting in counts shown in Table 4. Table 4 also indicates the ratio of the number of study records to the number of potential comparison records.

Table 4
Number of potential comparison records by first year of funding
First year funding Study records Potential comparison records including in-scope units only Ratio
2010 50 70,261 0.07%
2011 128 69,346 0.18%
2012 106 68,931 0.15%
Total 284 208,538 0.14%

It should be noted that for the purpose of the present analysis, the 2,004 CED clients that were used in CED 2013 study were also retained to follow up the results of these same cohorts (2001 to 2009) in the most recent years (2010-2013). For these 2,004 businesses, the same comparison group was retained as in the CED 2013 study (except for the changes already outlined in section 1.1).

4. Hypotheses

A series of hypotheses were tested to measure the performance outcomes of the CED clients and similar enterprises that did not receive CED funding. This is regarded as the performance effect. The hypotheses examined were:

Hypotheses: CED financing generates the following outcomes:

  1. Higher revenue growth
  2. Higher employment growth
  3. Higher labour productivity growth
  4. Higher survival rate

The CED mission is to help regional economic development through businesses financing of small and medium-sized enterprises. This study was conducted to measure and assess the performance of enterprises that used CED’s services to similar enterprises that did not, during the period 2010 to 2013. The empirical literature dealing with enterprise performance often cites factors such as revenue growth, employment growth and labour productivity as relevant indicators of performance. As such, the four above mentioned hypotheses were tested to assess the effectiveness of the CED in influencing the economic performance of its clientele.

5. The model

The estimation models used in this study replicate the specifications used in CED 2013 study. The same specification is applied to all cohorts. The differences in revenue, productivity and employment growth between CED-supported enterprises and non-supported enterprises are investigated based on the following general empirical model:

lnyit+1 - lnyit = a +β CEDQit + d Controlitit

where i is the index of the enterprise, t is the index of the year, y is the hypothesized variable (e.g. revenue growth, employment growth and labour productivity growth), lnyit+1 - lnyit is the growth of y , CEDQ is a dummy variable for CED clients (takes 1 if the enterprise benefited from CED funding in year t, 0 else), Control is a vector of control variables and εit is an error term.

The variable of interest, the coefficient β captures the effect for enterprises that received CED support. For instance, if the estimated β in the model (1) is equal to 0.06 and is statistically significant at a usual error level, that means, supported enterprises experience on average 6% higher growth compared to non–clients for the given performance indicator y and reference period.

It should be noted that in section 7.2.1 the estimations are based on the data for all of CED programs pooled together. However, in section 7.2.2, the estimations are based on the data on different selected CED programs.

To test for the impact of the program on revenue growth, the following general specification is used:

lnRevit+1 - lnRevit = a + ß CEDQit + d1TAit + d2LTLit + d3LPit + d4GPit + εit

where the specific variables included in the control vector are: total asset (TA) and long term liabilities (LTL), which control for the financial status of the firm; labour productivity (LP) computed as total sales over employment, and gross profit/loss (GP), which control for changes in firm performance. All the explanatory variables are expressed in growth terms and not in level terms.

To test for the impact of the program on employment growth (Empl), the general specification is used:

lnEmplit+1 - lnEmplit = a + β CEDQit + d1TSGSit + d2TAit + d3GPit + d4TEit + εit

where total asset (TA) controls for the financial status of the firm, total sales of goods and services (TSGS) and gross profit/loss control for the performance indicators; and TE is the total expenses.

Lastly, the effect on labour productivity (LP) is investigated using the following general specification is used:

lnLPit+1 - lnLPit = a + β CEDQit + d1TAit + d2GPit + d3TEit + d4AEMPLit + εit

where AEMPL is average employment, which controls for firm characteristics such as size, and TA, GP and TE are as specified above.

The regression equations (2), (3) and (4) are intended to be an empirical model to explain revenue growth, employment growth and labour productivity growth between the CED clients and non-clients.

The impact of a financing program is unlikely to be observable within the same reference year, for both operational as well as statistical reporting reasons. To investigate the impact of CED financing, the following lead structure of response variables was used to study the impact of CED services for the reference years:

t = 2001: L → [t +10, t+11, t+12]

t = 2002: L → [t +9, t+10, t+11]

t = 2003: L → [t +8, t+9, t+10]

t = 2004: L → [t +7, t+8, t+9]

t = 2005: L → [t +6, t+7, t+8]

t = 2006: L → [t +5, t+6, t+7]

t = 2007: L → [t +4, t+5, t+6]

t = 2008: L → [t +3, t+4, t+5]

t = 2009: L → [t +2, t+3, t+4]

t = 2010: L → [t +1, t+2, t+3]

t = 2011: L → [t +1, t+2]

t = 2012: L → [t +1]

For instance, for a firm that received funding for the first time in 2010, its performance is compared in terms of revenue, employment and labour productivity growth to the performance of the potential comparison group for the first year after the funding (t+1) and all subsequent years until 2013 (that is, t+3).

For each cohort, a unique identifier (operating entity number) is used to track the business revenue, employment, assets, liabilities, gross profit/loss, total expenses etc, over the years; this permits following the two groups of businesses for several years after the initial funding. Control variables were allowed to vary with the lead structure, being taken from one year behind the lead period for which the impact was assessed. For example, when the lead period was 2011, control variables were calculated based on the change between 2010 and 2011. This allowed the impact of ongoing changes in the control variables on the response variable to be isolated.

The most common regression model used to investigate the effect of the independent variables on the dependent variable is the Ordinary Least Square (OLS) estimator. However, when outliers are present in the data such as vertical outliers, bad leverage points, and good leverage points, the OLS estimation may be biased. The OLS estimator is known to be vulnerable to the presence of outliers. One way to deal with outliers is to exclude the 1st and the 99th percentiles distribution of the variable of interest from all computations. An alternative way is to use a robust regression.

To overcome the outliers’ problem, Huber (1981) proposes the M-estimators which is robust with respect to vertical outliers but is not in the presence of bad leverage points. To correct for outliers’ issue, Yohai (1987) proposes a general measure that minimizes the dispersion of the residuals that is less sensitive to extreme values. Therefore, Yohai (1987) introduces an estimator that combines a high breakdown point and a high efficiency, the MM-estimator. This estimator is known to yield unbiased estimators because it corrects for all types of outliers that influence the OLS estimator. For robustness purpose, the OLS, the M-estimator and the MM-estimator have been applied; it was found that the MM-estimator has the desirable properties. Therefore, the modeling results presented in this report are based on the MM-estimator.

6. The empirical results

6.1 Profiling

In this section, we compare the distribution of CED-supported enterprises between 2010 and 2013 with non-supported enterprises that represent the comparison group. The comparison is made with respect to revenue and employment, as recorded one to three years before the supported enterprises began receiving financing. To put it differently, we assess whether today’s assisted enterprises are different from non-assisted enterprises based on past values of selected indicators, when all of them did not benefit from the program. The backward tracking time-frame that we consider is three years. The empirical strategy used here to test for the difference between the two groups is the nonparametric test for first order stochastic dominance of one distribution over another that was introduced by Kolmogorov–Smirnov.

Kolmogorov–Smirnov performs a robustness check on the similarity between the study group and comparison group. This robustness is intended to explore the characteristic of CED clients and non-clients prior to CED contributions using the Kolmogorov–Smirnov test. This contribution is relevant because it ensures that the selected comparison group is closely comparable to the study group and, secondly, it provides further ground to attribute the change observed in the analysis to the CED program. It is expected that firms receiving financial contribution will show higher performance indicators than a comparison group of firms with similar characteristics. But without full knowledge of the two groups, the comparison could yield biased estimates and thereby misleading interpretation.

An attractive feature of the Kolmogorov–Smirnov test is that it considers all the moments rather than focusing on testing the differences in the mean value for both groups. Hence, the Kolmogorov–Smirnov test is the most appropriate test that compares the empirical distribution differences between two groups.

The null hypothesis for the Kolmogorov–Smirnov test is that the distribution of employment for the two groups considered are identical against the alternative hypothesis that the distribution for the second group first-stochastically dominates the distribution of the first group. If the p-value falls below the specified alpha level, then there is evidence to suggest that the two populations are not identical.

The data set used in this empirical investigation covers the years 2010-2013. The analysis examined each cohort of enterprises that started benefiting from CED program for the first time in 2001. For 2003-2012, it was possible to go back three years. For instance, if an enterprise starts to receive funding for the first time in 2011, we extracted data on revenue and employment for the CED-supported enterprises as well as the comparison group three years back (i.e. for 2008, 2009 and 2010). The analysis was limited to three years time intervals to preserve the sample size.

In sum, the nonparametric method described above was applied to determine if there exists a significant difference in revenue and employment among two groups of enterprises.

6.1.1. Kolmogorov–Smirnov tests results

Table 5 presents the results for the Kolmogorov–Smirnov tests for employment differential between the CED-clients (study group) and non-clients (comparison group). In this table, the first column indicates the reference year in which the funding was first received. The second and third columns identify the Kolmogorov–Smirnov test statistics (KS test statistics) and the probability value (P-values) for year t-1 prior to first funding. As can be observed from Table 5, the null hypothesis of equality between the distributions of the two groups cannot be rejected at any reasonable level for all years considered.

Following Table 5, Table 6 presents the hypotheses test statistics of revenue differential between the CED-clients and non-clients. First, in the test applied to the group of businesses that benefited from CED and non-supported enterprises from 2010 to 2012, we are not able to reject the null hypothesis of equality of distributions at any significance level and any year before the first financing. For the cohort of 2010, the null hypothesis of equality of revenue distributions is rejected at the 0.10 significance level for year t-2 and t-3 before the first funding, suggesting some heterogeneity between the two groups of businesses with regard to their revenue in year t-2 and t-3.

Overall, our empirical results can be interpreted as evidence supporting the similarity of the two groups under study.

Table 5
Employment distribution differences between study and comparison groups, hypothesis test statistics
KS test statistic P-values KS test statistic P-values KS test statistic P-values
First year funding Year t-1 Year t-2 Year t-3
2010 0.018 [0.999] 0.021 [0.999] 0.027 [0.980]
2011 0.022 [0.999] 0.046 [0.701] 0.028 [0.993]
2012 0.026 [0.999] 0.030 [0.994] 0.031 [0.992]

Note: Compare the p-values in the tables with [0.01], [0.05] significance levels. If the p-value in the tables is less than the chosen level of significance, there is evidence to suggest that the two populations are not the same.

Table 6
Revenue distribution differences between study and comparison groups, hypothesis test statistics
KS test statistic P-values KS test statistic P-values KS test statistic P-values
First year funding Year t-1 Year t-2 Year t-3
2010 0.058 [0.182] 0.068 [0.086] 0.068 [0.100]
2011 0.069 [0.178] 0.072 [0.160] 0.041 [0.820]
2012 0.052 [0.638] 0.059 [0.467] 0.029 [0.997]

Note: Compare the p-values in the tables with 1%, 5% significance levels. If the p-value in the tables is less than the chosen level of significance, there is evidence to suggest that the two populations are not the same.

6.1.2. Remarks

This section examined differences between CED-clients and non-clients prior to funding. These differences were examined using enterprises over the period 2010-2012. The empirical strategy is to compare differences in employment and revenue distributions of groups of businesses prior to the year in which the clients received funding from CED. Results can be summarized as follows.

The data suggest that, generally, there is no significant difference of employment or revenue between CED-supported enterprises and non-supported enterprises in year t-3, t-2 and t-1, where t is the reference period of the first funding received by the enterprise. Some difference (at relatively low level of statistical significance) is observed for the 2010 cohort in terms of revenue.

Hence, it can be concluded that, overall, the empirical analysis presented in this section confirms that CED-clients are similar to non-clients prior to first funding in terms of employment and revenue.

6.2. Regression results

This section plots the estimated coefficient ß which captures the performance of CED clients relative to the comparison group with respect to revenue, employment and labour productivity growth. It is important to keep in mind that the two groups were compared in terms of revenue and employment up to three years before the CED clients began receiving financing (see section 6.1 for the cohort 2010 to 2012, and CED 2013 for previous cohorts). Results showed that the two groups were similar prior to CED funding. Therefore differences in outcomes between the two groups can be more clearly attributable to CED funding.

The performance for each cohort from 2001 to 2012 relative to the period 2010 to 2013 is presented in terms of revenue, employment and labour productivity growth.

6.2.1. Impact on revenue, employment and labour productivity

Figure 2 shows the performance in revenue, employment and labour productivity growth for the 2001 cohort from 2010 to 2013; the figure shows the value of the coefficient ß estimated with equation 2, 3, and 4 for the three indicators, respectively. Hence, a value of 12.1% indicates that CED clients reported, on average, 12.1 percentage point higher growth on that performance indicator.

Differences between the two groups that are not statistically significant (at 0.10 significance level or lower) are not reported in this and the following figures.

Figure 2
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2001 cohort

Figure 2 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2001 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

The results indicated that there is no statistical difference in revenue growth between the two groups in the tenth and eleventh years following funding. In the twelfth year, CED clients outperformed non-clients in terms of revenue growth by 7.2%. The empirical results of the previous study (CED 2013) indicated that there is no statistical difference in revenue growth between the two groups of this cohort in the first year following funding. After this, CED clients outperformed non-clients for three consecutive years in terms of revenue growth. Analysis showed no difference in revenue growth between the two groups in the seventh, eighth and ninth year following funding.

This study showed CED clients outperformed non-clients in terms of employment growth in the eleventh year by 8.2%. With respect to employment growth, the results of the previous study had showed that the growth of employment for CED clients outperformed that of the comparison group in the year after the initial funding; then, no significant difference between the CED-clients and the comparison group was observed for the three following years. In the fifth and sixth year, the CED clients experienced negative employment growth compared to the comparison group. The rate increased to 13.7% in favour of CED clients in the ninth year.

This work found that CED clients also outperformed non-clients in terms of labour productivity growth in the tenth, eleventh, twelfth year by 12.1%, 13.7% and 4.7% respectively.Reference 3 On the labour productivity front, looking at the first year after initial funding, the results of the previous study showed that the impact of the program yielded no significant difference between the study group and the comparison group. However, in the second year after financing, CED clients experienced an additional growth of labour productivity (19.2%) compared to the non-clients. CED clients’ labour productivity remained higher than that of the comparison group until the sixth year. The data showed no significant difference between the two groups in the seventh, eighth and ninth years. CED clients showed 6.8% higher labour productivity growth compared to similar enterprises in the ninth year.

Figure 3
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2002 cohort

Figure 3 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2002 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

Figure 3 shows results for the 2002 cohort. This study showed that the difference in revenue growth was not significant for the ninth, tenth and eleventh year following funding. The previous study showed that the 2002 cohort experienced higher revenue growth than the comparison group for two consecutive years after clients received funding. There was no difference between the groups recorded in the third year. After the third year, CED clients’ started showing higher growth than their counterparts for two years. The difference in revenue growth was not significant in the sixth, seventh, eighth year following funding.

The new study exhibited 2.8% higher employment growth than similar enterprises in the tenth after funding (figure 3). According to the previous study, the 2002 cohort experienced higher employment growth than similar enterprises in the year after funding. From the second to the fourth year, the difference between the CED funded enterprises and their counterparts was not statistically significant. In the seventh year after funding, the coefficient that captures CED clients’ performance was found to be negative, meaning that the non-clients outperformed the clients group in that year.

The results for the cohort 2002 showed that the difference in labour productivity growth was not significant for the ninth, tenth and eleventh year following funding (Figure 3). The previous study showed that CED beneficiaries reported a higher rate of labour productivity growth than the comparison group for four consecutive years. Then non-clients outperformed CED clients in the fifth and seventh years. The program showed a positive impact for the clients group in the eighth year.

Figure 4
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2003 cohort

Figure 4 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2003 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

Figure 4 reports the performance in revenue, employment and labour productivity growth for the 2003 cohort. This study showed that the difference in revenue growth was not significant for the eighth, ninth and tenth year following funding. The CED 2013 study showed that enterprises that received funding for the first time in 2003 had a higher revenue growth than non CED-clients for four consecutive years. There was no difference in revenue growth between CED clients and non-clients in the fifth and sixth years but CED clients outperform non-clients again in the seventh year.

The current study shows that the difference in employment growth was not significant for the eighth, ninth and tenth year following funding. With respect to employment growth, the previous study showed that CED-clients experienced positive growth in two years (first and seventh year) after initial funding.

The new study showed that CED clients also had a higher productivity growth than non CED-clients in the ninth year. The previous study showed that the 2003 cohort had a higher growth in labour productivity than those in the comparison group in the first and second year following funding. Then, the estimated coefficient was not statistically significant from the third year to the sixth year. In the seventh year CED clients outperformed non-clients on the productivity growth performance measure.

Figure 5
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2004 cohort

Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2004 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2004 cohort, CED clients reported a higher rate of revenue growth than the comparison group for the seventh and eighth year (5.2% and 3.8%, respectively). The earlier study found that the CED funding program started showing impact on revenue growth in the second year following the initial year of funding. CED clients recorded a higher performance on revenue in the second and third year respectively.

Between businesses that used the CED financing for the first time in 2004 and the comparison group, this study showed that the difference in employment growth was not significant for the seventh, eighth and ninth year following funding. The previous study had showed that the businesses that used the CED financing for the first time in 2004 displayed higher employment growth than similar businesses that did not receive CED funding in the third year following funding. During the economic downturn of 2008, non-clients performed better than CED clients in terms of employment growth.

The 2004 cohort of CED clients reported higher labour productivity growth (5.1%) than their counterparts in the seventh year after receiving funding, respectively (Figure 5). The earlier study showed that labour productivity of the CED clients outperformed that of the comparison group in the first and second year following the initial year of funding only.

Figure 6
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2005 cohort

Figure 6 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2005 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2005 cohort (Figure 6), the analysis shows that CED clients did better than non-clients in the comparison group in the sixth and seventh year in revenue growth (3.1% and 3.2%, respectively). The previous study had revealed that a revenue growth was recorded in the first and second years after the year of funding. There was no significant difference between the two groups for the period 2007-2008 and 2008-2009. In the fifth year, CED clients outperformed non-clients.

This study shows that enterprises that used the CED financing for the first time in 2005 had 3.7% higher employment growth than the comparison group in the seventh year. There was no significant difference between CED clients and their comparison group in the sixth and eighth year. The earlier study in 2014 showed that CED clients had on average higher employment growth than the comparison group in the first year following the initial funding. CED clients did worse in the fourth year.

The 2005 cohort of CED clients record higher labour productivity than their counterparts in the sixth and seventh year after receiving funding (3.8% and 3.2% respectively, Figure 6). The previous study found that CED clients outperform their comparison group in the first year after funding and the third year, which coincides with the economic downturn of 2008, CED clients showed lower labour productivity growth than similar enterprises in the comparison group. A higher labour productivity growth for CED client was estimated for the fifth year.

Figure 7
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2006 cohort

Figure 7 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2006 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2006 cohort, this study shows that CED clients outperformed the non-clients in the fifth and sixth year after the funding in revenue growth. Figure 7 shows a 6.8% and 4.8% higher employment growth in favour of the study group in the fifth and sixth year, respectively, following the initial year of funding. There was no statistically significant difference in the seventh year in the employment growth. The previous study showed that the funding had a positive impact on revenue growth in the first year following the initial year of funding. There was no difference in revenue growth between CED clients and non-clients in the second and third years. In the fourth year, CED clients had higher revenue growth than the comparison group.

With regard to employment, the analysis shows higher employment growth in favour of the comparison group in the fifth year following the initial year of funding (-2.3%). There was no significant difference in the average employment growth between the clients and the comparison group in the sixth and seventh years. The earlier study showed a higher employment growth in favour of CED clients in the second year following the initial year of funding. CED clients were outperformed by the comparison group in the third year after funding and then increased in the fourth year.

This analysis shows that CED clients experienced a higher (7.7%) labour productivity growth than non-clients in the fifth year after funding (Figure 7). No statistically significant difference found between CED clients and non-clients in the sixth and seventh year. The previous study showed that there was no significance difference in labour productivity growth between the two groups after the two years following the funding. In the third year, CED clients registered a lower labour productivity growth than the comparison group. In the fourth year, CED clients had a labour productivity growth of 6.2% higher than the comparison group.

Figure 8
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2007 cohort

Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2007 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2007 cohort, CED clients had a higher revenue growth (3.1%) than non-clients in the fourth year after funding (Figure 8). No significant difference was observed between CED clients and the comparison group in revenue growth in the fifth year and sixth years after the funding. The earlier study found that the funding had a positive impact on revenue growth in the first year following the year of funding. CED clients had revenue growth 4.2% higher than the comparison group. There was no statistically significant difference in revenue growth between the two groups in the 2008-2009 period (second year after funding). A higher revenue growth (2.7%) was estimated for CED clients in the third year.

This study shows that the difference in employment growth was not significant for the fourth, fifth and sixth years following funding.The previous study showed a higher employment growth (3.0%) in favour of the CED clients for the year following the funding. There was no statistically significant difference in employment growth in the second and third year after the funding.

In terms of labour productivity, no significant difference is observed between CED clients and the comparison group in the labour productivity growth in the fourth, fifth and sixth years after the funding (Figure 8). The CED 2013 study showed that 2007 CED clients registered a lower performance (-6.3%) in the first year following the year of funding. However, they outperformed non-clients in labour productivity growth by 3.8% in the second year.

Figure 9
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2008 cohort

Figure 9 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2008 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2008 cohort (Figure 9), CED clients showed a higher rate of revenue growth than the comparison group for the third and fourth years (7.3% and 5.6, respectively). There was no significant difference in revenue growth in the third and fifth years after the first year of funding. The earlier study found that the CED funding program started showing impact on revenue growth in the second year following the initial year of funding (2.3%).

In terms of labour productivity, no significant difference is observed between CED clients and the comparison group in the employment growth in the third, fourth and fifth years after the funding (Figure 9). The previous study showed that there was no statistically significant difference in employment between the two groups after the first year of funding. However, a higher employment growth (4.4%) was recorded in the second year following funding.

CED clients showed a higher rate of labour productivity growth (7.2%) than the comparison group for the fourth year. There was no significant difference in labour productivity growth in the third and fifth year after the initial year of funding. The previous study showed that there was no significant difference in labour productivity growth between the two groups in the first two years.

Figure 10
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2009 cohort

Figure 10 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2009 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2009 cohort, this study showed that there was no statistically significant difference in employment growth between the two groups in the 2010-2013 period. There was significance difference in labour productivity growth between the two groups after the three years following the funding (9.3%). The previous study found that CED clients outperformed non-clients in terms of both employment and labour productivity growth on the first year.

For the 2009 cohort, this study shows that there was a significant difference in revenue growth between CED clients and non-clients in the second, third and fourth years (4.1%, 5.1% and 2.8%). The previous study found that CED clients had comparable performance with non-clients, in terms of revenue growth, on the first year.

Figure 11
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2010 cohort

Figure 11 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2010 cohort

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2010 cohort, the funding had a positive impact on revenue growth in the first year following the initial year of funding. In the first year, CED clients had higher revenue growth (5.5%) than the comparison group. There was also significant difference in revenue growth between CED clients and non-clients in the second and third years (4.6% and 4.7%).

With regard to employment, figure 11 reveals that there was no significant difference in employment growth between the two groups after the first, second and third years of funding.

There was a significant difference in labour productivity growth between the two groups after the first year following the funding (3.3%) in favour of the CED clients. However, there was no statistically significant difference in labour productivity growth between the two groups for the last year.

Figure 12
Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2011 and 2012 cohorts

Figure 12 Difference in revenue, employment and labour productivity growth between CED-clients and non-clients from 2010 to 2012, 2011 and 2012 cohorts

Note: “N.S.” stands for no significant difference between the client and non-client groups.
Source: Authors’ estimations.

For the 2011 cohort (Figure 12), there was no significant difference in employment growth between the two groups after the first and second year of funding. Figure 12 also reveals that in the first two years following funding a higher productivity growth (3.7% and 4.4%) was recorded. There was significant difference in revenue growth between the two groups in the first two years (6.3% and 5%) in favour of the CED clients.

For the 2012 cohort, the analysis found that CED clients outperformed non-clients in terms of both revenue and employment growth (5.4 and 3.6%) in the first year (Figure 12).

6.2.2. Impact of different programs and program components on revenue, employment and labour productivity

This section discusses the performance of CED clients in different programs or “program components” relative to the comparison group with respect to revenue, employment and labour productivity. The performance for each cohort between 2010 and 2012 is presented in terms of revenue, employment and labour productivity.

It is important to remember that some of these programs such as IPREFQ targeted specific regions in Quebec. However, the businesses comparison groups could be chosen from any region in Quebec.

Also, a business could receive different types of CED funding but it is placed in one group of the programs only. In other words, it will be placed in the group from which it has received the first funding from. Therefore, new funding programs are underrepresented as their participants are placed in the older programs.

IDEA-BRG

Strengthening of Quebec’s Forest EconomiesReference 4

Commercialisation and Exports/Innovation and Technology Transfer

Productivity and Expansion

Community Adjustment Fund/Economic Development Initiative/Strengthening of Quebec’s Forest EconomiesReference 5

6.3. Survival rates

This section compares the survival rates of CED clients and non-clients over the subsequent years following the reference year of funding. The survival rate was calculated by following enterprises overtime. For each cohort considered in the period of the study, if the firm did not report any employment and any revenue and did not change status (mergers/acquisitions), then this firm was considered having exited the market.

In order to generate an aggregate measure of business survival, a cumulative survival rate was calculated by aggregating cohorts for equivalent time frames, regardless of the calendar year in which the business entered the market. For instance, the business survival rate after the n-th year of entering the market was calculated by dividing the number of firms still alive after the n-th year following the first funding year, by the number of active businesses at the beginning of that period, for all cohorts with a possible life span equal or greater than n.

Figure 13
Cumulative survival rates, all cohorts

Figure 13 Cumulative survival rates, all cohorts

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- The last cumulative year (year 11) includes only the cohort 2001. The cohort 2001 comprises a small number of observations (see Appendix 9); hence, small changes in the number of survived businesses correspond to relatively large change in survival rates (percentage change). For this reason, the magnitude of the survival rate difference for the year 11 should be interpreted with caution.
- Some of the businesses in the comparison groups became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison groups. Therefore, the comparison groups are slightly different from the ones used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison groups in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
Source: Authors’ estimations.

Figure 13 shows the cumulative survival rates for all cohorts. The estimated survival rates for CED clients lie above the survival rates of non-clients throughout the entire time-period analyzed. For instance, the percentage of CED clients that were still in business five years after receiving funding (regardless of the first year in which received funding) was 88.1%; conversely, the same percentage for the comparable group of non-clients was 83.7%.

Figure 14
Survival rates, 2001 cohort

Figure 14 Survival rates, 2001 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- The cohort 2001 comprises a small number of observations; hence, small changes in the number of survived businesses correspond to relatively large change in survival rates (percentage change). For this reason, the magnitude of survival rate differences for this cohort should be interpreted with caution.
- Some of the businesses in the comparison group of the cohort 2001 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2001 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
- In most cases, survival rates declined between consecutive years. However, small increases in survival rates between consecutive years are observed in a few cases; these are due to businesses that exited the market in one year and re-entered (reported some employment or revenue) in the following year. Source: Authors’ estimations.

Figure 14 shows the survival rates for the 2001 cohort. The estimated survival rates for CED clients lie above the survival rates of non-clients throughout the entire time-period analyzed. Results showed that the proportion of the 2001 cohort that was still in the business eleventh years after the initial year of funding was 19.3 percentage points higher than the survival rates of non-clients.

Figure 15
Survival rates, 2002 cohort

Survival rates, 2002 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- Some of the businesses in the comparison group of the cohort 2002 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2002 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
- In most cases, survival rates declined between consecutive years. However, small increases in survival rates between consecutive years are observed in a few cases; these are due to businesses that exited the market in one year and re-entered (reported some employment or revenue) in the following year.
Source: Authors’ estimations.

Figure 15 shows the same rates for the 2002 cohort. Overall, CED clients compare favourably with that of the non-clients, despite of the length of time examined. CED had higher survival rates, with nearly 69.8% of the CED enterprises still in the business ten years after the initial year of funding (compared to almost 64.6% for the non-clients group).

Figure 16
Survival rates, 2003 cohort

Figure 16 Survival rates, 2003 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- Some of the businesses in the comparison group of the cohort 2003 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2003 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
Source: Authors’ estimations.

Survival rates for the 2003 cohort are shown in Figure 16, almost 80.2% of CED clients survived after operating nine years in the marketplace compared close to 71.8% of non- clients.

Figure 17
Survival rates, 2004 cohort

Figure 17 Survival rates, 2004 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- Some of the businesses in the comparison group of the cohort 2004 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2004 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
Source: Authors’ estimations.

Figure 17 shows the survival rates of the 2004 CED clients. At the end of the study period CED clients experienced a 82.6% survival rate compared to 80.1% for the comparison group.

Figure 18
Survival rates, 2005 cohort

Figure 18 Survival rates, 2005 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- Some of the businesses in the comparison group of the cohort 2005 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2005 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
Source: Authors’ estimations.

As seen in Figure 18, CED clients outperformed non-clients in each year subsequent to funding. The results show more CED supported enterprises survived compared to those in the comparison groups after seven years in business (88.8% to 84.4).

Figure 19
Survival rates, 2006 cohort

Figure 19 Survival rates, 2006 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- Some of the businesses in the comparison group of the cohort 2006 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2006 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada micro databases are revised and updated periodically.
Source: Authors’ estimations.

Figure 19 shows how CED clients of 2006 compare with enterprises that are in the comparison group. In terms of survival rate after six years, 94.0% of the CED clients were in the market the sixth year compared with 89.3% of the comparison group.

Figure 20
Survival rates, 2007 cohort

Figure 20 Survival rates, 2007 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- Some of the businesses in the comparison group of the cohort 2007 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2007 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
Source: Authors’ estimations.

For the 2007 cohort, figure 20 shows that the rate of survival for the CED group was 91.7% after five years following funding while the survival rates was higher for the comparison group, at 93.2%.

Figure 21
Survival rates, 2008 cohort

Figure 21 Survival rates, 2008 cohort

Notes:
- The year (horizontal axis) represents the year after receiving funding.
- Some of the businesses in the comparison group of the cohort 2008 became CED clients during the period 2010-2013 and new nearest-neighbour businesses replaced them in the comparison group of this cohort. Therefore, the comparison group for the cohort 2008 is slightly different from the one used in the CED 2013 study. This resulted in minor changes in the survival rates of comparison group in the current study. Also, the survival rates of the CED clients have slightly changed as Statistics Canada’s micro databases are revised and updated periodically.
Source: Authors’ estimations.

Figure 21 shows the survival rates of the 2008 CED clients. At the end of the study period CED clients experienced a 95.8% survival rate compared to 93.0% for the comparison group.

Figure 22
Survival rates, 2009 cohort

Figure 22 Survival rates, 2009 cohort

Note: The year (horizontal axis) represents the year after receiving funding.
Source: Authors’ estimations.

Survival rates for the 2009 cohort are shown in Figure 22; the difference between the two groups are marginal, considering also the short period of time for which survival rates can be computed for this cohort.; almost 98.5% of CED clients survived after operating three years in the marketplace compared close to 97.5% of non-clients.

Figure 23
Survival rates, 2010 and 2011 cohorts

Figure 23 Survival rates, 2010 and 2011 cohorts

Note: the year (horizontal axis) represents the year after receiving funding.
Source: Authors’ estimations.

Figure 23 displays the survival rates for the 2010 and 2011 cohorts. Also in this chase the time frame is short, therefore the results should be interpreted with caution. For the 2010 cohort, it can be seen that CED clients have the same survival rate as non-clients in the first and second year. For the 2011 cohort, the rate of survival for the CED group was 99.2% after one year following funding while the survival rate was higher for the comparison group, at 100%.

7. Conclusions

This report presents a counterfactual analysis of the economic impact of the CED programs. It is aimed at studying the impact of CED assistance on firm performances. The performance measures of revenue, employment and labour productivity growth were used as dependent variables in regression analysis. Using robust regression technique, coefficients were estimated for clients that benefited from programs delivered between 2001 and2012. Overall for the cohorts studied, CED clients’ revenue and labour productivity growth tended to be higher than the non-clients’ during 2010-2013.

The difference in performance that was associated with the status of CED clients varied from year to year and from cohort to cohort. For instance, after controlling for other business characteristics, the 2010 cohort of CED clients reported 5% higher revenue growth than non-clients in the first year following funding; in dollar terms this translated into an average increment of $75,200 in revenue that was associated with the CED client status. This was the largest annual increment in revenue that was associated with client status across all cohorts. The smallest increment associated with client status was recorded by the 2007 cohort in their fourth year after funding, with 3% higher revenue corresponding to approximately $13,000 higher revenues on average.

The labour productivity growth differential also varied from year to year and from cohort to cohort; in most cases, when the difference was statistically significant, the differential was in favour of CED clients. The largest differential was recorded for the 2001 cohort, eleven years after funding, when CED clients had a 14% higher labour productivity, translating to approximately $1,700 higher sales per employee, on average; the smallest differential in favour of CED client was recorded by the 2001 cohort twelve years after funding, with a higher growth of 5% translating into approximately $100 higher sales per worker, on average.

For the employment growth differentials, the pattern of association for client and non-client status was less clear than that seen for revenue and labour productivity. The largest difference in favour of the CED client group translated into an increment of 0.11 job per client which was reported by the 2001 cohorts in 2011-2012.

Overall, CED clients experienced higher survival rates than non-clients at the end of the study periods for most cohorts. This can be also observed for cumulate survival rates, which were obtained by combining all cohorts. Generally, CED clients had higher survival rates than non-clients throughout the entire time period that was analyzed. For instance, five years after funding the cumulate survival rate of CED clients was 88.1% compared to 83.7 for the non-clients group.

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