rafmacalaba/datause-encoder-data huggingface dataset:
The intervention was successful in altering the intention of parental leave usage among fathers. However, we find no effect of the intervention on planning nor effective use among both women and men.<sup>15</sup> There is evidence of a significant increase in the probability of using it only himself for fathers in experiment 2 who received the role model message compared to the planning message.<sup>16</sup> The emphasis on the father's role as caregiver appears to have had a positive effect on the intention to use the parental leave by men.

Table 8. Effects on intention to use parental leave, experiment 2

||||**Panel A**<br>|**: Men**<br>||
|---|---|---|---|---|---|
||Planning|Use both|Use onlyme|Use only partner|Effective use|
|Role model (M2)|0.046|0.007|0.045|-0.046|0,003|
||(0.078)|(0.072)|(0.045)|(0.077)|0,016|
|Planning (M3)|-0.041|0.071|-0.039|-0.022|0,014|
||(0.081)|(0.077)|(0.039)|(0.080)|1,017|
|Observations|302|294|294|294|304|
|R-squared|0.105|0.192|0.136|0.149|0,544|
|Control mean|0.511|0.407|0.0659|0.440|0,011|
|Diff M2-M3|0.0872|-0.0641|0.0832**|-0.0239|-0,011|
|p-val|0.253|0.385|0.0387|0.753|0,642|
|||**Pa**<br>|**nel B: Women**<br>|||
||Planning|Use both|Use onlyme|Use only partner|Effective use|
|Role model (M2)|-0.002|-0.029|-0.006|0.017|0,010|
||(0.041)|(0.037)|(0.039)|(0.011)|0,039|
|Planning (M3)|0.009|-0.048|0.041|0.004|-0,003|
||(0.042)|(0.036)|(0.038)|(0.010)|0,039|
|Observations|922|928|928|928|936|
|R-squared|0.056|0.092|0.097|0.147|0,115|
|<br>Control mean|0.502|0.304|0.647|0.0162|0,571|
|Diff M2-M3|-0.0110|0.0190|-0.0464|0.0127|0,012|
|p-val|0.789|0.598|0.228|0.307|0,754|

Notes: The table shows the results of regressing the variables regarding intention to use parental leave on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,242 individuals in experiment 2 (304 men and 938 women). Source: Postintervention survey.

# _4.3. Robustness checks_

Finally, we estimate some alternative model specifications in order to provide robustness to the main results.

We first re-estimate the main specification excluding the set of control variables. Then we estimate the same

> 15 As shown in

> Table B. 8, the specification with standard errors clustered at strata level shows that the message about couple planning (M3) helped increase the effective use of parental leave among fathers compared to the information message. The magnitude of this effect (0.057) is quite important given the low take-up among fathers in the control group (0.011).

> 16 This effect holds when estimating the results using a multinomial logit for a 4-category variable instead of separate binary outcomes. Results available upon request.

20
Fi ure 3b: Correlation in female activit for unor anized manufacturin b industr <u>g y g g y y</u> <!-- Start of picture text --> A. Female-owned firms vs. female employees<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Share of establishments in industry that are female-owned<br>B. Female-owned firms vs. segmentation in female-<br>owned firms<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Share of establishments in industry that are female-owned<br>C. Female-owned firms vs. segmentation in male-<br>owned firms<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Share of establishments in industry that are female-owned<br>female<br>Share of employees in industry that are<br>establishments<br>Share of female employees in female-owned<br>establishments<br>Share of male employees in male-owned<br><!-- End of picture text --> <!-- Start of picture text --> D. Female employee share vs. segmentation in female-<br>owned firms<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>Share of employees in industry that are female<br>E. Female employee share vs. segmentation in male-<br>owned firms<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>Share of employees in industry that are female<br>F. Segmentation in female-owned vs. male-owned<br>firms<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>0% 20% 40% 60% 80% 100%<br>Share of female employees in female-owned establishments<br>establishments<br>Share of female employees in female-owned<br>establishments<br>Share of male employees in male-owned<br>establishments<br>Share of male employees in male-owned<br><!-- End of picture text -->
trends of the middle class over time, produces outcomes that don‘t align with our general conceptual definition of the middle class. Applying the thresholds defined by Ravallion and B&D to Chile, Mexico and Peru we observe that the middle class has decreased over the whole period of time analyzed in this paper. Conversely, the middle class has increased in times of economic downturn moving in parallel with poverty levels.

**Figure 1: People living on $2-10 a day in Chile, Mexico and Peru (B&D’s measure of middle class)**

_Percentage of households_ <!-- Start of picture text --> 60.6<br><!-- End of picture text --> <!-- Start of picture text --> 56.7<br><!-- End of picture text --> <!-- Start of picture text --> 56.7<br>51.8<br><!-- End of picture text --> <!-- Start of picture text --> 36.5<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases.

**Figure 2: People living on $2-13 a day in Chile, Mexico and Peru (Ravallion’s measure of middle class)**

_Percentage of households_ <!-- Start of picture text --> 64.3<br>62.9<br>61.0<br><!-- End of picture text --> <!-- Start of picture text --> 49.9<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases.

As figures 1 and 2 show, the size of the middle class increased in Mexico during the _tequila crisis_ of 1995 because the incidence of poverty also increased from 23.2 percent in 1994 to 38 percent in 1996. In Peru, the economy suffered a triple blow during 1998-99 with President Fujimori‘s political collapse, the financial turmoil in BRIC‘s countries, and a serious damage caused by _El Niño_ phenomenon. As a result, the incidence of poverty increased from 21.8 percent in 1998 to 25.1 percent in 1999 and to 28.1 percent in 2000; surprisingly the middle class also increased. In the following years, in spite of Peru‘s economic performance being among the best in Latin America since 2002, the unemployment rates were still high and

- 7 -
Policy Research Working Paper 7506

# **Abstract**

This paper studies the use of psychometric tests, which were designed by the Entrepreneurial Finance Lab as a tool to screen out high credit risk and potentially increase access to credit for small business owners in Peru. The analysis uses administrative data covering the period from June 2011 to April 2014 to compare debt accrual and repayment behavior patterns across entrepreneurs who were offered a loan based on the traditional credit-scoring method versus the Entrepreneurial Finance Lab tool. The paper finds that the psychometric test can lower the risk of the loan portfolio when used as a secondary screening mechanism for already banked entrepreneurs—that is, those with a credit history. For unbanked entrepreneurs—those without a credit history—using the Entrepreneurial Finance Lab tool can increase access to credit without increasing portfolio risk.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mbruhn@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
learning on the job where the young are concerned. Translating freshly derived educational attainment into output of goods and services is not accomplished overnight; rather it can be likened to the maturing of wine. The waiting involved will reduce the earnings, and hence the opportunity costs, of primary school graduates; but it will also delay the stream of expected returns from all education. What the net effect turns out to be is a matter of more than academic interest.

# **References**

- [1] Azam, J.-P., and Daubr ́ee, C. (1997), _Bypassing the State: Economic Growth in Kenya, 1964-90_ , Development Centre, O.E.C.D., Paris.

- [2] Altig, D. Auerbach, A.J., Kotlikoff, L.J., Smetters, K.A., Walliser, J. (2001), “Simulating Fundamental Tax Reform in the United States”, _American Economic Review_ , 91(3): 574-595.

- [3] Appleton, S., Bigsten, A. and Manda, D.K. (1999), “Educational expansion and economic decline: returns to education in Kenya, 1978-1995”. CSAE Working Paper Series, Working Paper 90, Oxford.

- [4] Bell, C., Gersbach, H., Bruhns, R., and V ̈olker, D. (2004), “Economic Growth, Human Capital and Population in Kenya in the Time of AIDS: A Long-run Analysis in Historical Perspective”, mimeo, University of Heidelberg.

- [5] Bell, C., Devarajan, S., and Gersbach, H. (2006), ‘The Long-run Economic Costs of AIDS: A Model with an Application to South Africa’, _The World Bank Economic Review_ , forthcoming.

- [6] Corrigan, P., Glomm, G., and M ́endez, F. (2004), “AIDS, Human Capital and Growth”, Indiana University, Bloomington, IN.

45
- Ruhm, C. J. (2004). Parental employment and child cognitive development. _The Journal of Human Resources_ , 39(1):155–192.

- Schirle, T. (2015). The effect of universal child benefits on labour supply. _Canadian Journal of Economics/Revue canadienne d’ ́economique_ , 48(2):437–463.

- Schultz, T. P. (1993). Returns to women’s education. _Women’s education in developing countries: Barriers, benefits, and policies_ , pages 51–99.

- Seim, K. (2006). An empirical model of firm entry with endogenous product-type choices. _The RAND Journal of Economics_ , 37(3):619–640.

- Shonkoff, J. P., Garner, A. S., Siegel, B. S., Dobbins, M. I., Earls, M. F., McGuinn, L., Pascoe, J., Wood, D. L., on Psychosocial Aspects of Child, C., Health, F., Committee on Early Childhood, A., Care, D., et al. (2012). The lifelong effects of early childhood adversity and toxic stress. _Pediatrics_ , 129(1):e232–e246.

- UNICEF (2017). Building the future: Children and sustainable development goals in rich countries. _UNICEF Innocenti Report Card 14_ .

- Urquiola, M. and Verhoogen, E. (2009). Class-size caps, sorting, and the regressiondiscontinuity design. _The American Economic Review_ , 99(1):179–215.

- Xiao, M. (2010). Is quality accreditation effective? evidence from the childcare market. _International Journal of Industrial Organization_ , 28(6):708–721.

- Zhou, J., Heckman, J. J., Liu, B., Lu, M., et al. (2022). The impacts of a prototypical home visiting program on child skills. Technical report, Institute of Labor Economics (IZA).

44
Results of the fixed effect model show that a 10 percent increase in human and intangible capital leads to a 4 percent increase in real GDP per capita, holding other things constant. Also, a 10 percent increase in physical capital, land capital and energy capital results in an increase in real GDP per capita by 12 percent, 0.7 percent and 1 percent, respectively, _ceteris paribus_ .

Second, we evaluate the impacts of natural disasters on income by: obtaining the predicted mean values of the two types of capital (human and intangible capital and land capital) _with and without_ the impacts of a particular disaster in terms of the average number of people affected per capita<sup>11</sup> ; and using these values along with the parameter estimates in Table 12 to estimate equation 10. These impacts are expected to differ in countries due to their strengths of institutions, size of the economy and other characteristics. A comparison of selected countries and groups of countries is therefore presented here. Tables 13 and 14 present the estimated losses in real GDP per capita due to the impact of a particular natural disaster, _ceteris paribus_ . In particular, we focus on earthquakes and storms because they consistently show significant impacts on capital across the different models that we estimated, as compared to floods and droughts.

<u>Earthquakes: In Table 13, the expected loss in income due to the impacts of earthquakes, holding</u> other variables constant, is estimated in the range of about US$0.32 to US$1,022 per capita in 1995, US$0.39 to US$127 per capita in 2000 and US$0.31 to US$81 per capita in 2005. The largest losses in income are estimated to be highest in Japan during 1995 and 2005, and in China during 2000. The 1995 and 2005 earthquakes in Japan affected about 205 per 100,000 people and 3 per 100,000 people, respectively; while the earthquake in China during 2000 affected about 144 per 100,000 people.

_Table 13. Estimates losses in real GDP (RGDP) per capita with and without_ _<u>the impacts of earthquakes, in 2005 constant US$, selected countries, 1995-2005.</u>_

|SelectedCountries|1995|2000|2005|
|---|---|---|---|
|Average country*|5.95|8.48|12.46|
|High Income Non-OECD**|16.94|20.62|0.31|
|High Income OECD**|16.64|19.52|0.40|
|Low Income**|0.32|0.39|0.51|
|Low Middle Income**|3.36|4.12|22.25|
|Upper Middle Income**|6.70|8.17|23.64|
|Good Governance***|12.03|3.74|1.476|
|Average Governance***|6.79|15.01|6.32|
|Poor Governance***|0.35|6.31|1.40|
|Japan|1,022.23|35.43|80.70|
|China|55.22|127.43|57.82|
|Philippines|36.03|0.02|0.00|
|Australia|12.65|0.00|0.00|
|Thailand|0.00|0.00|18.01|

> 11 See Appendix 2. In particular, we use Model 2 in Table A2.2 for human and intangible capital (HR), and Model 3 in Table A2.4 for land capital (L).

18
# **1 Introduction**

Suppose that two nations with different levels of income per capita integrate their goods and factor markets. What are the implications for industrial performance in the less developed economy? In the standard Ricardian model of comparative advantage, both countries would gain from the reallocation of productive resources towards the industries in which they are relatively more efficient. However, integration may also induce capital flows to the less developed country, which may contribute to narrow the efficiency gap within industries. If the scope for capital flows and technology transfer is greater in the industries in which the advanced economy is specialized, the less developed country may plausibly observe stronger productivity and output growth in these sectors.

In this paper, we study the interplay between specialization patterns and foreign acquisitions in shaping the effects of deeper integration on industrial performance in less advanced economies. In particular, we investigate the extent to which this interaction played an important role in driving the patterns of catch-up growth observed in Poland following accession to the European Union in 2004—and the consequent deepening of goods and factor market integration with Germany. Using detailed data on Polish manufacturing firms during 1995-2013 and a difference-indifferences approach, we examine if and how the evolution of German acquisitions and firm performance in Poland, following EU accession, was mediated by predetermined measures of industrial specialization in Germany. If the scope for capital movements and technology transfers was higher in sectors in which Germany was relatively more specialized at the moment of accession, improvements in performance would be stronger among Polish firms operating in those sectors. Poland’s integration experience offers several interesting features for this analysis. Following the collapse of the Soviet Union in the late 1980s, a comprehensive reform program enabled the country to transform its socialist-style planned economy into a market economy. Like other post-communist nations, Poland experienced slumps in social and economic standards during this transition. But it became the first post-communist country to reach its pre-1989 income levels, which it achieved by 1995 following a period of strong economic growth. In the years preceding the accession in 2004, Poland observed a sharp increase in the degree of openness to international trade. A significant share of this rise was accounted for by the growth of trade flows with its higher-income neighbor, Germany—Europe’s major center of high-tech industrial production. Following accession, Poland became an important destination for Germany’s FDI, and experienced a period of remarkable catch-up growth: GDP per capita increased from about 18% of Germany’s in 2004 to about 29% in 2013.

2
productivity falls far short of meeting national food needs, and approximately 70 percent of Mauritania’s demand for cereal is met by imports. More specifically, while livestock provides a surplus for exports, crops are generally insufficient to cover national demand and are highly dependent on external shocks (such as prices and climate). Total arable land in Mauritania accounts for roughly 500,000 ha. Within this figure, rain-fed land accounts for a large portion, although highly volatile depending on the yearly rainfall (it can shift between 5’000 ha to over 200,000 ha). Mauritania is also among the countries most hit and concerned by desertification; according to FAO more than 70 percent of the territory is menaced by this phenomenon. Agriculture and livestock together employ roughly two thirds of the Mauritanian labor force<sup>17</sup> , but due to low productivity and structural inefficiencies the sector represents barely 16 percent of GDP.<sup>18</sup> As a Sahelian and largely desert country, Mauritania is vulnerable to climate shocks, which heighten the risk of food insecurity and put upward pressure on food prices. The government has given a high priority to agriculture as reflected in its Poverty Reduction Strategy Papers and in the focus of much bilateral and multilateral support, including that provided by the World Bank Group. Mauritania’s ability to manage its renewable resources, to sustainably increase the productivity of the primary sector and adopt policies that ensure broad-based participation in the growth of agriculture, livestock and fisheries will be critical to realizing its economic and social development objectives.

> 17 Source: Labor Survey, ONS, 2013: approximately 57% of employed workforce is concentrated in the rural sector.

> 18 World Bank staff estimate, in nominal terms, 2013.

7
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―rich people‘s problems.‖ We do not know exactly how many cases it takes before the government changes its policies. But our analysis allows us to set the outside bounds of the outcome of interest: if all the effects are direct effects on litigants, the benefited population is the litigant population; if the effects fully generalize to those similarly situated through indirectpolicy effects, then the benefited population includes all Brazilians who share an illness with the litigants. As will be seen below, our estimate of the benefited population closely matches the results of the only existing surveys of the litigants themselves, so it is unlikely that we are very far off in our estimates of the proportion of poor people who benefit.

Who benefits from medications litigation in Brazil, then? When they do, these medications generalize through the public health system (the Sistema Único da Saúde, or SUS). We know that the vast majority of SUS users have low incomes: Ribeiro et al (2006) show that only 9.2% of SUS users come from families with per capita monthly incomes above 440 Reais ($200) – the cut-off for the fifth income quintile in Brazil. But it is not the case that only a narrow slice of Brazilian society uses the SUS. Until we get to the fifth quintile in the income distribution, the various socio-economic strata are approximately equally represented: the 20% of the population with the lowest income accounts for 22.8% of users, the next 20%, for 23.8%, and the next two for 22.4% and 21.8%, respectively. It is only the top quintile that is sharply underrepresented. Thus for low cost medications, we simply assume that the indirect beneficiaries mirror the demographics of typical SUS users, so that 46.6% of them come from the bottom two quintiles (and thus have per capita family incomes below about $60 USD/month, according to Ribeiro).

For high cost treatments, however, we assume that the SE profile of SUS users of these treatments roughly reflects the social distribution of these diseases in society. This is a more 20
# **Appendix Table 8: Intent-to-Treat Effects of Daycare Attendance, Separately by Gender**

|**Appendix Ta**|**ble 8: Inte**|**nt-to-Tre**<br> <br>**Economic**|**at Effects**<br> <br>**Preferences**|**of Daycare**|**Attendan**<br>|**ce, Sepa**<br><br>**Decision-M**|**rately by**<br><br>**aking Abilities**|**Gender**<br>|
|---|---|---|---|---|---|---|---|---|
|||_verson o_|_verson o_||_oaons_|_oaons_|_omnae_|_ransv_|
||_Risk_<br>_Tolerance_|<br>_Disadvan._<br>_Inequality_|<br>_Advan._<br>_Inequality_|_Delayed_<br>_Gratification_|_Transitivity_<br>_(Toys)_|_Transitivity_<br>_(Sharing)_|_Risk_<br>_Choices_|_y_<br>_or Monoto-_<br>_nicity (Risk)_|
|Treatment × Boys|-0.056|0.034|0.017|-1.463|-0.334|-0.127|-0.144|-0.177|
||(0.122)|(0.048)|(0.045)|(0.548)|(0.183)|(0.071)|(0.111)|(0.123)|
||[0.908]|[0.891]|[0.908]|[0.074]|[0.429]|[0.429]|[0.712]|[0.712]|
|Treatment × Girls|-0.201|0.190|-0.002|0.330|-0.027|0.071|0.045|0.278|
||(0.133)|(0.056)|(0.044)|(0.564)|(0.184)|(0.075)|(0.119)|(0.156)|
||[0.613]|[0.003]|[0.989]|[0.944]|[0.989]|[0.864]|[0.973]|[0.387]|
|Boys|0.209|0.137|-0.156|1.104|-0.354|0.066|0.035|0.035|
||(0.137)|(0.049)|(0.046)|(0.531)|(0.189)|(0.074)|(0.118)|(0.156)|
|Mean_Y_Cntrl|5.309|1.239|1.522|19.820|2.858|0.744|2.485|1.149|
|<br>_Observations_|<br>2,113|<br>2,113|<br>2,113|<br>1,858|<br>2,113|<br>2,113|<br>2,113|<br>2,113|

_Notes_ : This table estimates intent-to-treat effects of daycare attendance on economic preferences and on decisionmaking abilities, separately by gender. All regressions include dummies specific for each childcare center × age group. Robust standard errors between parentheses. Romano-Wolf p-values which adjust for multiple hypothesis testing between brackets (Romano and Wolf 2005a; 2005b).

43