rafmacalaba/datause-encoder-data huggingface dataset:
whereas under the alternative labor market models, a temporary subsidy can have long-term impacts on firm size.

We find that 24 percent of firms take the subsidy to hire a worker, resulting in an increase in employment in the treated firms during the subsidy period. However, using 12 rounds of survey data to track the dynamics of adjustment for four years post-subsidy, we show there is no lasting effect on employment, firm profitability, or sales. A combination of shedding of workers by treated firms and additional hiring by control firms completely eliminates the employment gap within two years. The only long-term effect is that the subsidy appears to have increased survival rates for firms that initially had low-capital and low profitability.

We use the data generated by the experiment to differentiate between competing views of urban labor markets for small firms. A combination of detailed survey data and an analysis of heterogeneous treatment effects yields no evidence that owners are learning more about their ability to manage workers, and suggests that search is not excessively costly for such firms. Complementary treatments providing either capital or training show that the lack of long-term effect does not appear to be due to lack of complementary capital or skills. Instead, the estimated return to additional labor during the subsidy period appears similar in magnitude to the subsidy offered, suggesting additional workers bring no more value to the firm than their unsubsidized labor cost. As such, our results imply that labor markets appear to be functioning reasonably well for microenterprises, and do not appear to be the constraint to their growth that some theories might suggest.

This paper contributes to a recent experimental literature on frictions in urban labor markets in developing countries. Much of this literature focuses on interventions to help particular job-seekers find jobs, by directly offering the job seekers wage subsidies (Galasso et al., 2004, Groh et al. 2016, Levinsohn et al., 2014); and/or by trying to improve the search and matching process through transport subsidies and skill certification (Groh et al, 2015; Abel et al, 2016; Abede et al, 2016). Some, but not all, of these studies have found modest improvements in formal employment as a result of this assistance, consistent with constraints to workers finding jobs in larger, more formal firms. These studies have not explicitly focused on helping workers find employment in microenterprises, and have not typically found significant effects on informal employment.

3
sales (more than US$10,000) and 9 entrepreneurs with more than 15 employees.<sup>6</sup> This left 1,782 eligible entrepreneurs who were invited to come to demonstration sessions conducted at the JBDC facilities that were closest to the business of the respondent.

From August to November 2017, 457 entrepreneurs came to the demonstration sessions and completed the BDM elicitation method (BDM sample). From February to May 2018, 374 entrepreneurs came to demonstration sessions and received TIOLI offers (TIOLI sample). Table A1 compares the characteristics of those who enter into the demand study by attending the demonstration sessions to those who did the baseline survey by phone, but then did not attend any demonstration sessions. Overall, we see that the BDM sample is similar on many characteristics (e.g. age, gender) to those who were invited and did not show up. However, those who attended demonstration sessions have higher reported willingness to pay for the course and expect higher returns for it; they are more likely to have attended a previous course, have higher internet access and consider themselves worse than those who did not attend in terms of proactiveness and business practices (the topics of the course). In the richer TIOLI sample, we see that most characteristics are similar across those who attended and those who did not attend demonstration sessions, though we have a larger share of females and fewer takers of previous courses in the former group.

# **2.4 Sample Characteristics**

Demographic and business characteristics of the two samples are provided in Table 1. Just over half (56-58 percent) of business owners in both samples are female, with an average age of 46 in the BDM sample and 41 in the TIOLI sample. Education levels are high, with 88 percent having completed at least secondary education in the BDM sample and 95 percent in the TIOLI sample. This compares to only 59 percent of the overall Jamaican population aged 25 to 44 having completed at least secondary education in 2010 (Barro and Lee, 2013), and reflects positive selection into entrepreneurship on education. In the TIOLI sample entrepreneurs are significantly wealthier than in the BDM sample as measured by a wealth index.<sup>7</sup> In both samples, the firms cover a wide mix of industries, with the majority of businesses in the BDM sample being in services (38 percent), retail (30 percent) and manufacturing (23 percent), while in the TIOLI sample we have a larger share in services (53 percent), a

> 6For the BDM sample we removed 17 firms with more than 10,000 dollars in monthly sales. From the TIOLI sample we removed 3 firms with more than 35,000 dollars in monthly sales, allowing in the sample firms with more than 15 employees and more than 10,000 dollars in sales.

> 7 The wealth index is the principal component of four variables: owning a vehicle, having access to the internet, being able to finance J$20,000 and reservation wage.

8
The balanced panel is more appropriate to assess the impact of benefits provided that the attrition rate, which is very large over the four years, does not bias the sample. Table A1 in the Annex reports the means for all variables and the t-test for means differences between balanced and unbalanced panels. The difference in means is generally rather small and below 10% across variables with most variables showing a gap below 20%. The t-test shows a significant difference between the balanced and unbalanced panels only for the rural and summer variables. In particular, for the variables of main interest (y, poor, B and P) the difference in means is never very significant. Moreover, the unbalanced panel largely 'pollutes' time effects with incomplete spells which is a serious shortcoming if annual macroeconomic shocks are responsible for explaining most of the variation in welfare which seems to be our case.

The Hausman tests reported on the bottom of Tables 6a and 6b speak also in favor of the FE model rather than the RE model and in the FE model used with the unbalanced panel the intensity of benefits variable ( _B_ ) is not significant. In substance, when we use the most appropriate model (FE), the most appropriate sample (balanced) and when we control for macroeconomic trends social assistance does not seem to have any positive and significant effect on welfare or poverty.

# **8. Conclusion**

Between 2001 and 2004, Moldova experienced sustained growth and poverty reduction. The system of social assistance continued to focus on selected categories of beneficiaries including mainly children, the disabled and the war veterans irrespective of welfare status. Relative expenditure on the bottom quintile and expenditure per beneficiary decreased during the period in favor of increased overall expenditure and a sharp rise in population coverage.

This approach does not seem to have improved household welfare, all other factors being equal. If we ignore the questions of selection bias, behavioral responses, unobserved heterogeneity, endogeneity and measurement error, an incidence analysis would suggest that social benefits enhance the probability of moving out of poverty and reduce the probability of moving into poverty. However, double difference estimates and regression analyses which are able to control for at least some of these factors would suggest that social benefits have not contributed to improve welfare overall.

Double difference estimates show a negative impact on welfare. The main difference between the incidence and the double difference analyses as we structured them in this paper is that the incidence analysis simply compares the beneficiaries and non beneficiaries ignoring the question of comparability of the two groups whereas we used a matching procedure to carry out the double difference estimations. Comparing two homogeneous groups with the double difference procedure allowed us to observe that the non beneficiaries of social assistance benefits have performed better than the beneficiaries.

There may be both demand and supply side reasons which could explain the negative values 15
this approach, a mean willingness to pay for a specific level of water service can be estimated for each household. By comparing this WTP information with the cost information from the supply side, the potential economic efficiency issue in deciding the level of service and price can also be addressed.

A survey of 1,500 households was conducted in five districts of the Chongqing municipality, China. The MBDC format was employed. The analyses show that the willingness to pay for improved water service is low—between 2.5 to 3.3 yuan/ton on average, or 1.5 to 2 percent of income—but is significantly higher than the current price, which is about 2.2 yuan/ton on average. The poorer households are willing to pay less in absolute terms, but higher as a percentage of income, reaching as high as 6 percent for the lowest income group. In general, the higher the household income is, the higher the willingness to pay. The higher the current water price is, the higher the WTP. Urban households and male respondents are willing to pay more.

This study also gave a further assessment of household acceptability and burden of water expenditure at three prices—2.5, 3, and 3.5 yuan/ton. The results show that even at the lowest price, 2.5 yuan/ton, about 20 percent of households would strongly reject the price increase. The current water prices in the surveyed regions are between 1.80 yuan/ton and 2.50 yuan/ton. Rejection rates are much higher with the two higher prices. However, the average economic burden to the households is only 1.5 percent, 1.8 percent, and 2.1 percent respectively. The economic burden to the lowest income group—with a monthly income less than 400 yuan—is about 3.3 times as high as the average.<sup>12</sup> While there is clearly room for a water price increase in the surveyed areas, special attention needs to be paid to poor people and to those who strongly reject the increases. A targeted subsidy policy and public awareness campaigns or other public accountability measures may be warranted in order to make the price increases more acceptable.

The MBDC format can help conveniently collect information about households’ acceptability of different water prices and willingness to pay for improved water services. However, the results can potentially suffer from biases caused by the order of presenting the water prices to the respondents. This study provided a test on the potential ordering effect and found that the ordering effect did exist. Estimated WTP would be lower if lower prices are presented first, and higher if the higher prices are offered at the beginning. A strategy of randomizing the starting prices is recommended. Further research on this issue is warranted.

> 12 This is in contrast with the OECD countries, where, according to OECD (2003a), the burden of household water consumption over household income varied between 2.24 percent in Scotland to 4 percent in England and Wales, with an outlier of .066 percent in America in 1997–2000. The ratio of the lowestincome-group burden to average burden was 1.3–3.1.
WPS3784

**Table 1. Poverty Incidence and Poverty Gap in Rwanda**

||**Headcount Index (%)**<br>**P0**|**Poverty Gap**<br>**P1**|
|---|---|---|
|National|42.92|0.16|
|Urban|4.09|0.01|
|Rural|46.06|0.17|

Note:

1. The headcount index, P0, is the proportion of population with per capita expenditure below the poverty line

The poverty-gap, P1, estimates the average distance separating the poor from the poverty line, as a proportion of that line 2. Poverty line: 50,145.73 RwF = Income per capita of the last individual in the fourth decile of expenditure ranked by adult equivalent 3. All farm households in urban provinces are considered as "rural"

**-** **<u>Table 2. Determinants of the Probability of being non poor in Rwanda</u>**

|||**Standard**<br>|**Wald 95%**<br>|**Confidence**<br>|<br>|<br>|
|---|---|---|---|---|---|---|
|**Parameter**|**Estimate**|**Error**|**Lim**|**its**|**Chi-Square**|<br>**Pr> Chi-Sq**|
|Intercept|-0.3879|0.0178|-0.4229|-0.353|472.47|<.0001|
|Dummy Access to Road|0.1077|0.0059|0.0962|0.1192|336.26|<.0001|
|Distance to market|-0.018|0.0003|-0.0187|-0.0173|2673.26|<.0001|
|Electricity dummy|0.2431|0.0075|0.2285|0.2577|1064.67|<.0001|
|Access to credit dummy|0.0297|0.0035|0.0229|0.0366|72.83|<.0001|
|H Head's education (years)|0.0885|0.0009|0.0866|0.0903|9077.28|<.0001|
|Dependents ratio|-0.2029|0.0007|-0.2043|-0.2014|74556.8|<.0001|
|Land area detained|0.1733|0.0017|0.1701|0.1766|10893.6|<.0001|
|Livestock heads detained|0.1304|0.0006|0.1291|0.1317|41019.7|<.0001|
|Occupation: Farmers only|0.0663|0.0066|0.0534|0.0793|100.31|<.0001|
|Occupation: Agri wage worker|-0.0851|0.0099|-0.1046|-0.0657|73.77|<.0001|
|Occupation: Non-farm wage<br>worker|0.9515|0.0097|0.9325|0.9705|9678.01|<.0001|
|Occupation: Non-farm self<br>employed|0.9175|0.0122|0.8936|0.9415|5641.28|<.0001|
|DummyProvince: Butare|-0.413|0.0088|-0.4302|-0.3958|2219.65|<.0001|
|DummyProvince: Byumba|-0.1436|0.0089|-0.1611|-0.1261|258.35|<.0001|
|Dummy Province: Cyangugu|-0.0079|0.0093|-0.0262|0.0104|0.72|0.3973|
|Dummy Province: Gikongoro|-0.6147|0.0095|-0.6334|-0.5961|4172.71|<.0001|
|DummyProvince: Gisenyi|0.3932|0.0092|0.3752|0.4112|1835.89|<.0001|
|Dummy Province: Gitarama|0.087|0.0087|0.0699|0.1041|99.07|<.0001|
|DummyProvince: Kibungo|0.2772|0.009|0.2596|0.2949|944.15|<.0001|
|DummyProvince: Kibuye|-0.2396|0.0098|-0.2589|-0.2203|593.72|<.0001|
|DummyProvince: Kigali|-0.3153|0.0086|-0.3321|-0.2984|1342.69|<.0001|
|Dummy Province: Ruhengeri|-0.2806|0.0087|-0.2977|-0.2635|1034.2|<.0001|

# **Note:**

Excluded categories: “No occupation” and “other occupation”; “Kigali City” and “Umutara” 20
- Goux, D., and E. Maurin. 1999. “Persistence of Inter-Industry Wage Differentials: A Reexamination Using Matched Worker-Firm Panel Data.” _Journal of Labor Economics_ 17(3): 492–533.

- Greenaway, D., and R. Kneller. 2007. “Firm Heterogeneity, Exporting and Foreign Direct Investment.” _Economic Journal_ 117(517): F134–F161.

- Gruetter, M., and R. Lalive. 2009. “The Importance of Firms in Wage Determination.” _Labour Economics_ 16(2): 149–60.

- Haanwinckel, D., and R. R. Soares. 2016. “Workforce Composition, Productivity, and Labor Regulations in a Compensating Differentials Theory of Informality.” IZA Discussion Papers 9951. Bonn, Germany: IZA - Institute of Labor Economics.

- Helpman, E., O. Itskhoki, M. A. Muendler, and S. J. Redding. 2017. “Trade and Inequality: from Theory to Estimation.” _Review of Economic Studies_ 84(1): 357–405.

- Jaumotte, F., S. Lall, C. Papageorgiou, and P. Tapalova. 2007. “Technology Widening Rich Poor Gap.” Washington, DC, United States: International Monetary Fund.

- Kambourov, G., and I. Manovski. 2009. “Occupational Specificity of Human Capital.” _International Economic Review_ 50(1): 63–115.

- Katz, L., and K. Murphy. 1992. “Changes in Relative Wages, 1963–87: Supply and Demand Factors.” _Quarterly Journal of Economics_ 107: 35–78.

- Kovak, B. K. 2013. “Regional Effects of Trade Reform: What is the Correct Measure of Liberalization?” _American Economic Review_ 103(5): 1960-76.

- Krueger, A. 2012. “The Rise and Consequences of Inequality.” Presentation made to the Center for American Progress, Washington, DC, United States.

- Kumler, T., E. Verhoogen, and J. A. Fr ́ıas. 2015. “Enlisting Workers in Improving PayrollTax Compliance: Evidence from Mexico.” NBER Working Paper 19385. Cambridge, United States: National Bureau of Economic Research.

- Kugler, M., and E. Verhoogen. 2012. “Prices, Plant Size and Product Quality.” _Review of Economic Studies_ 79(1): 307–39.

- Lakner, C., and B. Milanovic. 2013. “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession.” _World Bank Economic Review_ 30(2): 203–32.

- Lee, D. 1999. “Wage Inequality in the United States during the 1980s: Rising Dispersion or Falling Minimum Wage?” _Quarterly Journal of Economics_ 114(3): 977–1023.

- Lee, J. and D. Wie. 2015. “Technological Change, Skill Demand, and Wage Inequality: Evidence from Indonesia.” _World Development_ 67: 238–250.

- ——— 2017. “Wage Structure and Gender Earnings Differentials in China and India.” _World Development_ 97: 313–329.

- L ́opez-Calva, L., and N. Lustig, editors. 2010. _Declining Inequality in Latin America: A Decade of Progress?_ Washington, DC, United States: Brookings Institution and United

41
# **Figure 12: Corporate Vulnerability Index by Industry**

# **1. CVI Industry trends I**

# **2. CVI Industry trends II**

<!-- Start of picture text --> VI VI<br>0.16 0.16<br>0.14 0.14<br>0.12 0.12<br>0.1 0.1<br>0.08 0.08<br>0.06 0.06<br>0.04 0.04<br>0.02 0.02<br>0 0<br>20062007200820092010201120122013201420152016 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Basic Materials Consumer Goods Health Care Industrials<br>Consumer Services Energy Technology Telecommunications<br>Source: Authors’ calculations Source: Authors’ calculations<br>Country median per industry Country median per industry<br><!-- End of picture text -->

- 4.2 Association between the Corporate Vulnerability Index and Socio-Economic Outcomes

Financial vulnerability in the non-financial corporate sector can have significant macroeconomic consequences as corporate distress may adversely impact the financial sector (e.g. through increases in non-performing loans, larger volatility of asset prices, rise of borrower risk); the public sector (e.g. through lower tax revenues, potential public support in case of public nonfinancial firms); other firms (e.g. through financial and/or trade interlinkages); and households (e.g. through the labor market).<sup>12</sup> This section investigates the leading indicator properties of the CVI with regards to socioeconomic outcomes. We use weighted logit regression models to establish whether an increase in unemployment; a GDP recession; or a reduction in welfare is associated with an increase in the Corporate Vulnerability Index CVIt, subject to controls (Xj): The respective dependent variables of the three logit regressions are three dummy variables that respectively take the value of 1 if (1) the unemployment rate increases from one year to the next;

> 12 See Gray et al (2006), and Ruscher and Wolff (2012).

19
<!-- Start of picture text --> Demographic scenario Income evolution<br>7 30<br>B65 28<br><!-- End of picture text --> <!-- Start of picture text --> 25 6 26<br>3 e<br>s 55 x 24<br>6¢ 5 2<br><!-- End of picture text --> <!-- Start of picture text --> 4 18<br>2015 «2020 «2025 «22030 «20385 «2040 «2045 = 2050 2015 2020 2025 2030 2035 2040 2045 2050<br>Cost of traveling 100km by car Public transport fare for short distances<br><!-- End of picture text --> <!-- Start of picture text --> 1 aus<br>= tos 2<br>Ss = 07<br><!-- End of picture text --> <!-- Start of picture text --> 10 ”<br>9s 0.65<br>es Car cost/100km mscheapest public transport fare<br>9 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text --> <!-- Start of picture text --> Evolution of socio-demographic variables<br>70<br>60 ——= transport costs<br>income<br>50 —= population<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>s<br>30<br>20<br><!-- End of picture text --> <!-- Start of picture text --> 0 -<br>2015 2020 2025 2030 2035 2040 2045 2050<br><!-- End of picture text -->
adapting this strategy in keeping with the availability of labor and the characteristics of the shed (e.g., the piece rate paid, fringe benefits offered, and grapes processed) (Newman and Jarvis).

Table 1, taken from Jarvis and Vera-Toscano, reports general characteristics of the workers surveyed at the time of the interview (Jan-Mar 1992). The labor force was relatively young, with most between 15–34 years old. Men tended to be slightly younger than women. As all women in our sample were engaged in piece rate work in 1992, the data suggest that most piece rate workers sought wage work or dropped out of the labor force as they aged.<sup>7</sup> The labor force was also relatively well educated; 66% of women and 85% percent of men had completed secondary school. Table 2 shows that the average level of education declined as age rose. This changing educational profile probably reflected the steady improvement over time in rural educational opportunities (especially for women) during the last 35 years. That women with secondary education accepted these temporary, manual jobs suggests that employment opportunities are limited for many rural females even after acquiring considerable schooling.

Female workers have a significantly higher number of children, on average, than their male counterparts and about half of female workers and about a third of male workers are married. A small proportion of workers are students, 12% of women and 15% of men, who generally worked only during the summer.

Each worker surveyed was asked to sequentially list all periods of employment during 1991 (first and last day of work in each job), the task performed, the location and

> 6 Workers of both sexes were seen in every task. Survey respondents were asked whether someone of the opposite sex could perform their task and a high proportion said yes.

- 8 -