rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5186

# **Abstract**

Unlike social programs targeting individuals, few enterprise support programs have been rigorously evaluated, and existing evaluations have mostly been done in high-income countries such as the United States and Europe. Mexico spends a large share of government resources on small and medium enterprise programs each year. How effective these programs have been in achieving their objectives is unclear. In Mexico, impact evaluations of small and medium enterprise programs are rare, and most are qualitative in nature. This is the first paper evaluating these programs in Mexico using firm-level panel data. The continuous and ten-year panel data—from the 1994–2005 period—allow the authors to address selectivity bias and unobserved firm heterogeneity by applying a generalization of differences-in-differences models combined with propensity score matching methods. This study finds evidence that participation in small and medium enterprise programs is associated with improvements in key variables such as value added, gross production, and wages. Furthermore, the study finds evidence that some of the positive effects can take several years to realize. The results also call for streamlining and greater efficiency in Mexico's small and medium enterprise programs.

This paper—a product of the Poverty and Gender Unit, Latin American and the Caribbean Region—is part of a larger effort in the department to increase knowledge on impact evaluation and on firm’s productivity. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gacevedo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
annual reports to construct a measure of induction risk by month of birth, divided by the number of individuals at risk or: Where RAm,y is the risk at age 18 for those of age A born in month m in year y. We calculate the denominator, _Size of Cohort at Risk,_ using the induction reports for the official number of individuals who have registered for selective service and are between the ages of eligibility (e.g. 181⁄2 to 26) for the given period.<sup>11</sup> We calculate risk separately for ages 181⁄2, 19, ..., 26, which we then sum to calculate induction risk from 181⁄2 to 26. We focus on 181⁄2 to 26 rather than college years (e.g. 19-22), which was the focus of previous research (Card and Lemieux 2001, de Walque 2007, Grimard and Parent 2007, Buckles et al. 2016), because ages 181⁄2 - 26 were the actual, legislated ages at which an individual was at risk of induction.<sup>12</sup> We calculate the total risk as follows, taking into account that one remains at risk of induction only if one was not previously inducted:

> 11 We also perform this calculation using measures of the cohort of age 17 year olds as reported by the Department of Education (and creating measures of those between the ages of 181⁄2 - 26, by assuming the cohort of 17 year olds in a given year will be the correct number of 18 year olds in the following year). Our results are robust to these multiple methods, so we use the official number of individuals registered via the selective service throughout.

> 12 For years in which the draft lottery system was in place, we only consider eligibility for those in the birth cohorts at risk.

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# **B.2 Robustness**

In the first robustness check, we estimate the average treatment effect in levels as percentage changes from the controlled mean using a negative binomial. Specifically, we estimate the following model: where _yij,t_ is the count number of patent applications. _Zi_ is the number of pre-determined, time-invariant firm characteristics that might affect firms’ patenting capability. Following Blundell et al. (1999), we use the average pre-sample patent applications to control for firms’ fixed effects in the negative binomial model. We use the firm’s patent age and patent stock to approximate firm _i_ ’s patent capability. Table A.7 displays the estimated coefficients and the implied average treatment effect, _exp_ ( _β_ + _ψ_ ) _−_ 1.

Table A.7: Estimated Average Treatment Effect using Negative Binomial

||(1)<br>Total|(2)<br>**Patent Ap**<br>International|(3)<br>**plication**<br>Triadic|(4)<br>High-Tech|
|---|---|---|---|---|
|Treatment_×_Post|-0.365***|-1.249***|-3.389***|-0.571***|
||(0.057)|(0.299)|(0.360)|(0.108)|
|Treatment|0.125***|0.266**|0.561***|0.400***|
||(0.040)|(0.135)|(0.051)|(0.108)|
|Implied ATE|-0.213***|-0.630***|-0.941***|-0.157*|
||(0.022)|(0.077)|(0.023)|(0.086)|
|No. Obs.|6,377|6,377|6,377|6,377|
|R-squared|0.110|0.065|0.089|0.109|
|Sector-year FE|Yes|Yes|Yes|Yes|
|Firm FE|No|No|No|No|

Note: We estimate the coefficients using negative binominal regressions. We control for firm fixed effects using the pre-sample patent applications. Implied ATE is calculated as _exp_ ( _β_ + _ψ_ ) _−_ 1. Robust standard errors clustered at the 2-digit ISIC level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

In our second robustness analysis, we estimate the treatment effect on intensive and extensive margins separately. Specifically, we re-run the regression 1 on a set of firms with 55
# **A Data details**

## **A.1 List of sample countries**

Table 9: Regional definitions and country membership

|Latin America, Caribbean|East Asia, Pacifc|Europe, Central Asia|Sub-Saharan Africa|
|---|---|---|---|
|Argentina|China|Armenia|Burundi|
|Bahamas, The|Fiji|Bulgaria|Cameroon|
|Belize|Hong Kong, China|Croatia|Central African Republic|
|Bolivia|Indonesia|Cyprus|Congo, Dem. Rep.|
|Brazil|Malaysia|Czech Republic|Cote d’Ivoire|
|Chile|Papua New Guinea|Georgia|Equatorial Guinea|
|Colombia|Philippines|Hungary|Gabon|
|Costa Rica|Samoa|Macedonia, FYR|Gambia, The|
|Dominica|Singapore|Moldova|Ghana|
|Dominican Republic|Solomon Islands|Poland|Lesotho|
|Ecuador|Thailand|Romania|Malawi|
|Grenada|Tonga|Russian Federation|Nigeria|
|Guyana||Slovak Republic|Sierra Leone|
|Mexico||Turkey|South Africa|
|Netherlands Antilles||Ukraine|Uganda|
|Nicaragua|||Zambia|
|Paraguay||||
|Peru||||
|St. Kitts and Nevis<br>St. Lucia||||
|St. Vincent and the Grenadines<br>Togo<br>Trinidad and Tobago<br>Uruguay<br>Venezuela, RB||||
|South Asia|Middle East|North Africa|High income|
|India|Iran, Islamic Rep.|Algeria|Australia|
|Pakistan|Israel<br>Kuwait|Egypt, Arab Rep.<br>Morocco<br>Tunisia|Canada<br>European Union<br>Iceland<br>Japan<br>Korea, Rep.<br>New Zealand<br>Norway<br>Switzerland<br>United States|

## **A.2 Data sources**

With the exception of the PTA database, all variables used in the analysis can be easily obtained from the World Bank’s World Development Indicators (WDI) database and from Centre d’Etudes Prospectives et d’Informations Internationales (CEPII ). The WDI database was accessed by the author through an internal World Bank server, but the data are also available on CD-ROM at subscribing libraries. In addition, the CD-ROM can be purchased from the World Bank bookstore, either in person or on-line. The CEPII distance data ( `dist_cepii.xls` ) may be freely downloaded 45
from industrial fish meal plants<sup>24</sup> . These plants currently process around 17,000 tonnes of frames per day, accounting for 59 percent of the total supply.

# **4.2. Food safety controls in the Nile perch sector**

As described in Section 8, significant efforts have been made in recent years to upgrade hygiene and other food safety controls along the Nile perch supply chain in Kenya, largely as a result of the imposition of restrictions on exports to the EU. This section, however, provides an assessment of the state of food safety capacity in Kenya in 1997, largely before these reforms were implemented. It draws upon the inspection reports of the European Commission and other previously published assessments (for example Landell Mills, 2001).

In general, the basic elements of a food safety control system were in place in the mid-1990s, although in many cases these were outdated and/or inadequate for the task of complying with food safety requirements in industrialised country markets (Table 3). Thus, for example, although there was an established system for the inspection and licensing of fish processing facilities to ensure minimum levels of hygienic practices, these were poorly enforced. Further, controls on imports and the control and certification of exports were in place, but inadequate. Many industrial fish processors had not implemented complete quality management systems, including HACCP. Frequently, pest control was rudimentary and general hygiene procedures and standards were inadequate. At landing beaches there was a lack of awareness, ability and/or unwillingness to comply with basic hygiene procedures.

Although no specific legislation relating to hygiene in the processing and marketing of fish and fishery products was in place, various acts and regulations specified including: Public Health Act; Food, Drugs and Chemical Substances Act; Food Hygiene Regulations; Fisheries Act; and Standards Act. Further, the Kenya Bureau of Standards (KEBS) had established a Code of Hygiene Practice for the fish and fishery sector in 1986. However, in most cases legislation was out-dated and not compatible with international standards.

Perhaps the more fundamental weakness was in the implementation and enforcement of existing regulatory requirements. In general, the number of inspectors and other personnel was inadequate to inspect and monitor compliance on landing beaches and industrial fish processing plants. Indeed the number of fish scouts, for example, had declined due to budgetary constraints. Whilst inspectors were generally well trained, they were in need of up-dating, for example in the principles of HACCP, and in some cases lacked access to basic infrastructure and equipment. For example, inspectors often had to request transport from the fish processors that they were charged to enforce. Generally, laboratory facilities were poorly functioning and otherwise outdated. Frequently, levels of staffing were insufficient and/or personnel were in need of retraining.

> 24 It is estimated that over 2,000 people were involved in this sub-sector in 1990 (Abila and Jansen, 1997).

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policy changes and in identifying dimensions that should call for attention in priority? This section will present two approaches. Similar to the time-series literature, the standard deviation of variables will be used to calibrate the shock to the system under the assumption that variables with a higher standard deviation experience more change and would be easier thus to alter. As an alternative, gaps with an aspirational reference group of countries will be used.

Results of the first exercise are shown in Figures 3 and 4. The results of the full model (Columns 11, Tables 1 and 2) are used to simulate the effects on the income growth of the two poorest quintiles. The obtained parameters are multiplied with the standard deviations of the respective variables. To the extent that the standard deviation could be interpreted as the expected change of a random variable, the resulting magnitude will give a rough estimate of the expected effect of a regular change in a variable on income developments. The results are displayed in Figure 3. To control for any indirect effect of the policy variables through the growth of mean income, a reduced model was estimated linking mean growth to the variable in question and controlling for the initial income level (results are presented in Appendix D). The results of this estimation were added to the previous exercise (Figure 4).

**Figure 3: Economic magnitude of estimated parameters**

(percentage points increase in growth rates)

**Figure 4: Magnitudes from direct effects and through the mean**

(percentage points increase in growth rates) <!-- Start of picture text --> Life expectancy Life expectancy<br>School enrollment Connectivity<br>Connectivity School enrollment<br>Inflation Democratic accountability<br>Democratic accountability Credit/ GDP<br>Inflation variation Inflation variation<br>Credit/ GDP Inflation<br>-2 -1 0 1 2 3 4 -4 -2 0 2 4 6 8 10<br>Bottom 20 Bottom 40 Bottom 20 Bottom 40<br><!-- End of picture text --> _Source:_ authors’ estimates _Source:_ authors’ estimates.

The poorest quintile seems to be the most affected or the biggest beneficiary of good policies. In both exercises, reasonable improvements in human and physical capital seem to be associated with faster income growth for the bottom 20 than for the bottom 40. Similarly, an average deterioration in the macroeconomic environment (or a credit expansion favoring the richest segments of the population) is associated with a slower income growth for the bottom 20 than the bottom 40.

Health policies thus potentially offer considerable income gains. As already suggested by the earlier discussion, life expectancy is quantitatively important for increasing the potential for

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Diener, E. and Lucas, R.E. (1999) Personality and subjective well-being, in Khaneman, D., Diener, E. and Schwarz, N. (Eds.), Foundations of Hedonic Psychology: Scientific perspectives on enjoyment and suffering, Chapter 11, New York: Russell Sage Foundation Di Tella, R., McaCulloch, R. and Oswald, A.J. (2003) The macroeconomics of happiness, Review of Economics and Statistics, vol. 85, 809-827 Di Tella, R., MacCulloch, R. and Haisken-deNew, J.P. (2005) Happiness adaptation to income and to status in an individual panel, Harvard Business School Working Paper.

Duesenberry, J.S., (1949) Income, Saving and the Theory of Consumer Behavior. Harvard University Press, Cambridge, MA.

Easterlin (1974; updated in 1995) Does economic growth improve the human lot? Some empirical evidence, n: David, P.A.,Reder, M.W. (Eds.), Nations and Households in Economic Growth, Essays in Honor of Moses Abramowitz, Academic Press, NY, pp. 89– 125.

Easterlin, R.A., (1995). Will raising the incomes of all increase the happiness of all? Journal of Economic Behavior and Organization 27 (1), 35– 47.

Ferrer-i-carbonell (2005) Income and well-being: An empirical analysis of the comparison income effect, Journal of Public Economics, vol. 89, 997-1019 Ferrer-i-Carbonell and Frijters (2004) The effect of methodology on the determinants of happiness. The Economic Journal 114, 641– 659.

Frijters, P., Shields, M.a. and Haisken-DeNew, J.P. (2004a) Money does matter! Evidence from increasing real incomes in East Germany following reunification. American Economic Review, 94, 730-741 Frijters, P., Shields, M.A. and Haisken-DeNew, J.P. (2004b) Changes in the pattern and determinants of life satisfaction in Germany following reunification, Journal of Human Resources, vol. 39, 649-674 Frank R.H. (1985) The demand for unobservable and other non-positional goods. The American Economic Review, 75, 101– 116.

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# **URBAN Administrative Units**

**Figure A3: Standard Error as Percentage of point estimate: Urban Area Azerbaijan, FTG(0). Comparing survey based stratum-level estimates to census based administrative units-level estimates (140 administrative units).**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>Stratum ratio (from survey)<br>15<br>10<br>5<br>0<br>0 20 40 60 80 100 120 140<br>Ranking by(s.e./point estimate)<br>Percentage<br><!-- End of picture text -->

**Figure A4: Standard Error as Percentage of point estimate: Urban Area Azerbaijan, monthly mean adult equivalent consumption Comparing survey based stratum-level estimates to census based administrative units-level estimates (140 administrative units).**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15 Stratum ratio (from survey)<br>10<br>5<br>0<br>0 20 40 60 80 100 120 140<br>Ranking by (s.e./point estimate)<br>Percentage<br><!-- End of picture text --> 42
# **II. Overview of the Model and Key Data**

## **Production and Geographic Structure**

There are 30 sectors in the model; these are listed in table 1. There are three types of sectors: perfectly competitive goods and services, imperfectly competitive goods sectors and imperfectly competitive business services sectors.

The geographic decomposition of our model of Russia is shown in table 2. In the first instance, we obtain data from the publication the _Regions of Russia_ by Rosstat on 88 regions of Russia. The 88 regions have several names in Russian; the most common legal jurisdiction is referred to in Russian as an “oblast.” Oblasts are analogous to states in the United States or provinces in Canada. But there are also jurisdictions known as territories, federal cities, autonomous districts and an autonomous region. Since we want to use the term “region” for another purpose in the model, we use the Russian term “oblast” for all of these 88 geographic areas,<sup>6</sup> with the understanding that they are not all oblasts in the Russian sense of the term.

The Russian Federation under President Putin has established seven federal okrugs that are aggregations of contiguous oblasts. The mapping of oblasts into federal okrugs is also shown in table 2. In this paper, we shall analyze effects at the level of the federal okrug. Descriptive data on value-added, exports and imports by sector by federal okrug are presented in tables 3-11.

We assume that firms and sectors operate at the okrug level, primary factors of production are not able to move between okrugs. Wage rates and the rental rates on capital adjust in each federal okrug so that there is no change in aggregate employment or use for any primary factor of production.

Russia as a whole, represented as an aggregate of the federal okrugs, must satisfy a typical economy-wide balance of the trade constraint. The real exchange rate adjusts to assure that any change in the aggregate value of regional imports from the rest of the world, is matched by an equal change in the value of aggregate exports to the rest of the world. Each region also has a balance of trade constraint so that any change in the value of imports (either from the rest of the world or another federal okrug within Russia) is matched by an increase in the value of exports.

> 6 Several of the territories are part of oblasts, so it was necessary to adjust the data to avoid double counting of the territories.

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export propensity are insignificant.<sup>27</sup> After controlling for industry fixed effects, Haddad et al. (1996) also found average foreign ownership to have an insignificant influence on firm entry. Average capital intensity and sector output growth is found to have an insignificant effect on firm entry.

The location-scale effect is no longer significant. Firms are not attracted to areas with a large amount of existing activity, and are actively discouraged from areas with high concentration in the sector. This reinforces the finding above that the agglomerating effects appear to be more negative than positive. Diversity, on the other hand, has little impact. However, the competition effect is large, negative and significant. Thus competition serves to increase exit, but what is somewhat surprising is that entry rates are lower where competition is fiercer (i.e. where market share concentration is lower). This could be due to a congestion effect. However, together, these effects over time will serve to decrease the concentration of firms in the location-sector, thereby serving to erode the effect.

Interestingly, the effect of productivity is not significant at the province level. Either the geographic unit is too large to capture the potential positive spillovers of having productive neighbors. Or, that would indicate that firms prefer to enter locations with generally less efficient firms and where there is no one firm clearly outperforming the rest so that they will be better able to compete. The comparison with the commune level data should be instructive.

> 27 All the firm characteristic results are not presented due to space constraints.  The full tables are available on request.

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computer skills (for jobs that require word or data processing skills, such as those needed for Microsoft Office); and (3) soft skills (for jobs that require teamwork or organizational skills).

For descriptive purposes, 52 percent of jobs in the study database required French, 32 percent English, and 2 percent other languages. For computer skills, 63 percent required knowledge of Microsoft Office, and 25 percent required other computer skills. For soft skills, 10 percent required strong teamwork skills, and 8 percent strongly emphasized organizational skills.

Table 9 summarizes the results. Overall, women appear more likely to receive a callback for job postings requiring French language skills when compared with jobs that do not require these skills. Women also are more likely to receive a callback for jobs that require knowledge of Microsoft Office. By contrast, men are more likely than women to receive a callback for jobs that require other computer skills. That said, interpretation of these results should take into account the potential confounders of some of these characteristics. For example, French is overwhelmingly required in marketing positions (about 75 percent) but not in the other fields, where it is required about 40 percent of the time. Similarly, other computer skills are required in IT over 90 percent of the time compared with 19 percent in finance, 27 percent in marketing, and 59 percent in engineering. In summary, these findings are in line with earlier evidence about heterogeneity of gender discrimination by field.

**Table 9 Effects of gender on differences in callback rates by job requirement**

|Marginal increase in gender<br>callback gap due to skill<br>requirement:|Percent of<br>jobs requiring<br>skill|Coefficient|Standard<br>error|P-value|[95% con<br>interval]|fidence|
|---|---|---|---|---|---|---|
|French|52|0.064***|0.023|0.01|0.018|0.110|
|English|32|-0.055**|0.028|0.05|-0.109|-0.001|
|Other languages|2|0.070|0.062|0.26|-0.053|0.192|
|Microsoft Office|63|0.089***|0.028|0.01|0.035|0.144|
|Other computer skills|25|-0.145***|0.021|0.00|-0.187|-0.104|
|Teamwork|10|-0.017|0.029|0.55|-0.074|0.039|
|Organizational skills|8|0.029|0.033|0.37|-0.035|0.094|

_Note_ : The table reports the results from ordinary least squares regressions in which the callback dummy was regressed on an interaction term between the female dummy and the required group of skill dummies, following the approach in equation (3). Only the coefficient on the interaction term between the female dummy and the skill requirement dummy is reported in the table ). iijj * _p_ < 0.10 ** _p_ < 0.05 *** _p_ < 0.01. (ββ 33