rafmacalaba/datause-encoder-data huggingface dataset:
|P126576|2012|2018|7/11/2017|11 Closed|Chad|
|---|---|---|---|---|---|
|P126579|2013|2013|2/22/2013|1 Active|Eastern Africa|
|P126579|2013|2014|10/6/2013|2 Active|Eastern Africa|
|P126579|2013|2014|4/28/2014|3 Active|Eastern Africa|
|P126579|2013|2015|11/11/2014|4 Active|Eastern Africa|
|P126579|2013|2015|6/26/2015|5 Active|Eastern Africa|
|P126579|2013|2016|1/4/2016|6 Active|Eastern Africa|
|P126579|2013|2016|4/29/2016|7 Active|Eastern Africa|
|P126579|2013|2017|11/18/2016|8 Active|Eastern Africa|
|P126579|2013|2017|5/14/2017|9 Active|Eastern Africa|
|P126579|2013|2018|1/31/2018|10 Active|Eastern Africa|
|P126579|2013|2019|8/1/2018|11 Active|Eastern Africa|
|P126579|2013|2019|2/11/2019|12 Active|Eastern Africa|
|P126579|2013|2020|8/14/2019|13 Active|Eastern Africa|
|P126579|2013|2020|2/18/2020|14 Active|Eastern Africa|
|P126580|2013|2013||0 Closed|Philippines|
|P126580|2013|2015|7/23/2014|1 Closed|Philippines|
|P126583|2013|2013|10/10/2012|1 Active|Colombia|
|P126583|2013|2013|6/9/2013|2 Active|Colombia|
|P126583|2013|2014|12/24/2013|3 Active|Colombia|
|P126583|2013|2015|7/25/2014|4 Active|Colombia|
|P126583|2013|2015|2/13/2015|5 Active|Colombia|
|P126583|2013|2016|11/19/2015|6 Active|Colombia|
|P126583|2013|2017|1/17/2017|7 Active|Colombia|
|P126583|2013|2018|5/17/2018|8 Active|Colombia|
|P126583|2013|2019|6/27/2019|9 Active|Colombia|
|P126583|2013|2020|6/26/2020|10 Active|Colombia|
|P126586|2012|2012||0 Closed|West Bank and Gaza|
|P126586|2012|2013|10/13/2012|1 Closed|West Bank and Gaza|
|P126586|2012|2013|2/25/2013|2 Closed|West Bank and Gaza|
|P126586|2012|2013|6/8/2013|3 Closed|West Bank and Gaza|
|P126586|2012|2014|8/24/2013|4 Closed|West Bank and Gaza|
|P126586|2012|2014|3/18/2014|5 Closed|West Bank and Gaza|
|P126586|2012|2015|10/29/2014|6 Closed|West Bank and Gaza|
|P126586|2012|2015|4/28/2015|7 Closed|West Bank and Gaza|
|P126586|2012|2016|11/6/2015|8 Closed|West Bank and Gaza|
|P126591|2011|2011||0 Closed|Maldives|
|P126591|2011|2012||0 Closed|Maldives|
|P126591|2011|2013||0 Closed|Maldives|
|P126594|2012|2012||0 Closed|Uganda|
|P126594|2012|2013||0 Closed|Uganda|
|P126596|2014|2014|3/15/2014|1 Active|Samoa|
|P126596|2014|2015|7/27/2014|2 Active|Samoa|
|P126596|2014|2015|12/19/2014|3 Active|Samoa|
|P126596|2014|2015|5/8/2015|4 Active|Samoa|
|P126596|2014|2016|12/31/2015|5 Active|Samoa|
|P126596|2014|2016|6/29/2016|6 Active|Samoa|
Source: Authors’ calculation based on Enterprise Surveys Similarly, the costs of labor per unit of value added increased over the same period on average (Figure 16). All else being equal, this increase in the average costs of labor per unit of value added can be interpreted as a factor for loss of competitiveness of Nigerian firms.

However, there is significant heterogeneity in the indicator by firm size, as it decreased for large firms, while it rose in small and medium firms. This would imply that larger firms were more productive between 2008 and 2014, while small and medium firms face a lost competitiveness over the same period. This striking difference in this key indicator points at the need of analyzing productivity behavior controlling for numerous factors, including size of the firm, location, and sector of operation to be able to explain which are the main determinants of productivity of Nigerian firms.

14
ascribed to a large increase in the poverty line going from 2019 to 2022, particularly in the non-food element. If we apply either the 2019 or the 2022 poverty line and fix its real value over time, we observe a decrease in cross-sectional poverty from 2019 to 2022.

Figure 1 shows our estimated population shares of the four poverty transition outcomes over time under the main scenario. Headline transition results are qualitatively the same, irrespective of which of the three poverty lines is used.<sup>17</sup> This means that all three scenarios show a consistent image of declining chronic poverty. In the main scenario, we observe a decrease from 22-28 percent of the population in chronic poverty over the 2004-2007 interval to a 2-3 percent share over 2019-2022. Moreover, the share of the population that is consistently non-poor increased to around 88 percent of the population. Over time, the share of the population that escapes poverty is decreasing, which is not surprising given that a much smaller share of the population lives in poverty. Similarly, the share of the population that falls into poverty has also decreased much, to between 3-4 percent of the population. Together, these result in much less chronic poverty. Further below, we investigate to what extent these results hold across different groupings of Malaysians.

It should be noted that social assistance policies instituted in response to the Covid-19 pandemic will have influenced these results. For example, we could have hypothesized that the pandemic would have led to higher poverty entry figures. However, we do not observe such an outcome over the 2019-2022 interval. Obviously, we cannot draw any conclusions about the years in between, when the Covid-19 pandemic hit hardest. Still, it seems that Covid-19 did not lead to largescale permanent poverty and it is likely that government relief programs have contributed to this. Total budgets of relief packages for households and businesses equaled 23 percent of GDP in 2020 and 15 percent in 2021 (World Bank, 2021). For example, a World Bank survey indicated that about 75 percent of households with monthly income below RM 4,000 had received cash transfers between June and November 2021 (World Bank, 2021). Even so, this leaves an important share of the eligible population without assistance.

> 17 For comparison, the same graph based on the highest poverty line (RM 618 under scenario 3) is given in the Appendix.

11
Cecchi, D. and Peragine, V. (2005). “Regional Disparities and Inequality of Opportunity: The Case of Italy,” IZA Discussion Papers 1874.

- --------. (2010), “Inequality of Opportunity in Italy,” Journal of Economic Inequality, 8: 429-450.

Cohen, G., (1989). “On the Currency of Egalitarian Justice,” Ethics, 99: 906-944.

Currie, J., and Thomas, D. (2001). “Early test scores, school quality and SES: Long-run effects on wage and employment outcomes,” Worker Wellbeing in a Changing Labor Market, 20: 103132.

- Davis-Kean, P. (2005). “The influence of parent education and family income on child achievement: the indirect role of parental expectations and the home environment,” Journal of family psychology, 19(2): 294-304.

- de Carvalho, M., Gamboa, L., and Waltenberg, F. (2012). “Equality of educational opportunity employing PISA data: taking both achievement and access into account,” Universidad del Rosario, Serie Documentos de Trabajo 130.

- Dworkin, R. (1981). “What is Equality? Part I: Equality of Welfare; Part 2: Equality of Resources,” Philosophy and Public Affairs, 10: 185–246; 283–345.

Eccles, J. (2005). “Influences of parents’ education on their children’s educational attainments: The role of parent and child perceptions,” London Review of Education 3(3) : 191-204.

- Ferreira, F. (2001). “Education for the masses? The interaction between wealth, educational and political inequalities,” The Economics of Transition, The European Bank for Reconstruction and Development 9 (2): 533-552.

- Ferreira, F., and Gignoux, J. (2014). “The Measurement of Educational Inequality: Achievement and Opportunity,” The World Bank Economic Review 26(3): 1-37.

Fleurbaey, M. (2008). “Fairness, responsibility, and welfare,” Oxford: Oxford University Press.

- Foster, J., Lopez-Calva, L., and Szekely, N. (2005). “Measuring the Distribution of Human Development: methodology and an application to Mexico,” Journal of Human Development and Capabilities, 6(1): 5-25.

- Heckman, J. (2011). “The economics of inequality: The value of early childhood education,” American Educator, Spring 2011.

- Hoyos, A., and Narayan, A. (2011). “Inequality of opportunities among children: how much does gender matter?” Background Paper for WDR 2012. Manuscript.

- Molina, E., Narayan, A., and Saavedra, J. (2013). “Outcomes, opportunity and development: why unequal opportunities and not outcomes hinder economic development,” The World Bank Policy Research Working Paper Series 6735.

19
6 migration formal education on self-employment upon return. The authors find that both overseas savings and the duration of stay overseas increase the probability of being self-employed among literate returnees to Egypt. Among illiterate returnees, overseas savings alone exerts a positive impact on the likelihood of becoming an entrepreneur.

Dustmann and Kirschkamp (2002) develop a model where migrants decide simultaneously on the optimal migration duration and their activities after return. They find that Turkish migrants in Germany are more likely to engage in self-employment upon their return to Turkey, relying on savings from abroad to finance their businesses. McCormick and Wahba (2004) also suggest that financial and human capital accumulated abroad promote investments in small non-farm household enterprises among Egyptian return migrants, with the largest share of investments going towards urban areas. Mesnard (2004) models migration as a way to overcome credit constraints in the presence of capital market failures, accounts for the potential endogeneity of financial capital from abroad, and demonstrates that the majority of entrepreneurial projects started by Tunisian returnees were financed through overseas savings. Finally, Gubert and Nordham (2008) investigate the determinants of entrepreneurship among return migrants by using survey data from Morocco, Tunisia, and Algeria. Without accounting for the potential endogeneity of return migration, the authors show that vocational training overseas is positively and significantly associated with entrepreneurship for Moroccan and Tunisian returnees. Their results indicate that time spent abroad exerts a positive impact on the outcome of interest only in the Tunisian case.

In comparison to the first strand of literature on occupational choice among return migrants, the number of studies that _compare return migrants with non-migrants_ and estimate the impact of past international migration experience on productive investments and labor market outcomes in source countries is fairly limited. In terms of analyzing the impact of past migration experience on productive investments, Zhao (2002) use survey data from China and find that in comparison to non-migrants, return migrants invest significantly more in productive farm assets, but are no more likely to undertake non-farm investments. Similar to Zhao’s approach, Mansuri (2007) uses cross sectional data from Pakistan, attempts to deal with the endogeneity of return migration, and recovers evidence for the positive relationship between household return migration status and agricultural land purchases as well as investments in non-farm enterprises and farm assets. For Albania, Kilic et al. (forthcoming) use the same data of this paper, and find
This possibility leads to our final specification estimating equation (2), shown in Panel B. Without additional controls, the DDD estimates follow immediately from the results in Panel A, indicating a slightly smaller but still substantial effect of the economic shock on HAZ, corresponding to a large 15 percentage point increase in the prevalence of stunting but no significant effects on WAZ or underweight. The introduction of controls slightly reduces the point estimates further, but they remain significant.

While the cross-sectional and panel approaches examine the effect of the economic crisis following different groups of children, the estimated effects tell a consistent story for the longerterm measure of nutritional status, with large negative effects on HAZ as well as large, but only marginally significant estimates for the prevalence of stunting. For WAZ, the cross-sectional approach that examines the younger cohort in 2012 is also negative and significant, about twice as large as for the panel cohort. As the comparison groups, and thus relevant common trends assumptions, differ for each approach, the similarity across the approaches for HAZ suggests that the results may not suffer from substantial bias stemming, for example, from non-parallel trends. This provides assurance that, while large, the estimated effects are not negatively biased. Nevertheless, the SD effect sizes are larger than many reported in the literature, in particular for other studies of aggregate shocks, where in several cases there were no or only small significant persistent effects (Stillman and Thomas 2008; Ferreira and Schady 2009) even after substantial effects in the shorter-term (Frankenberg et al. 2013, 2017; Frankenberg and Thomas 2017).<sup>xxiii</sup> The magnitudes we estimate, however, are on par with other related work including Hoddinott and Kinsey (2001), who examined the effect of a substantial drought on child growth longitudinally in Zimbabwe and found reductions of 1.5–2 centimeters (0.5–0.66 SD) for
<!-- Start of picture text --> Chart 9. Share i Total Private Capital Flws to Developing Countries, 1991-94<br>(In percent)<br>35<br>30<br>25<br>25 - 1|1 |~~~~Totall2coun rs8 %<br>20 E<br>15<br>10<br>5<br>- -<br>0 ^?<br>Souce World Bank (1 995a)<br>Chart 10. Saving in Industrial Countries, 1970-94<br>(Percent of GDP)<br>30<br>25 Gross Natoxul Sawing<br>20<br>15 _N >\rivate<br>10<br>5 P-ubllc<br>0<br>1970 1974 1978 1982 1986 1990 1994<br>Souce: IMF<br><!-- End of picture text -->

16
_Costing and Record-Keeping Practices_ : coded as 1 for each of the following that the business does:

- R1: Keeps written business records

- R2: Records every purchase and sale made by the business

- R3: Able to use records to see how much cash the business has on hand at any point in time

- R4: Uses records regularly to know whether sales of a particular product are increasing or decreasing from one month to another

- R5: Works out the cost to the business of each main product it sells

- R6: Knows  which goods you make the most profit per item selling

- R7: Has a written budget, which states how much is owed each month for rent, electricity, equipment maintenance, transport, advertising, and other indirect costs to business

- R8: Has records documenting that there exists enough money each month after paying business expenses to repay a loan in the hypothetical  situation that this business wants a bank loan

_Financial Planning Practices_ : coded as 1 for each of the following:

- F1: Review the financial performance of their business and analyze where there are areas for improvement at least monthly

- F2: Has a target set for sales over the next year

- F3: Compares their sales achieved to their target at least monthly

- F4: Has a budget of the likely costs their business will have to face over the next year

- F5: Has an annual profit and loss statement

- F6: Has an annual statement of cash flow

- F7: Has an annual balance sheet

- F8: Has an annual income/expenditure sheet

Note that F5 through F8 were not asked in the Kenya and Nigeria datasets, so the business practices score for these two countries is the proportion of the other 22 practices implemented.

# **Appendix 2: Additional Details on Surveys**

_Bangladesh:_ The survey was conducted between March and May 2010 as part of a World Bank project intended to investigate the differences between formal and informal firms in Bangladesh (McKenzie, 2010). The data consists of 1725 enterprises, selected from a sample frame formed by a census of 55,817 firms in the randomly selected areas in urban parts of the 19 old districts. The sample was stratified by firm size (in terms of full-time employment) and broad industry (manufacturing, trade or services. Oversampling of firms with 10-99 full-time workers was done to ensure sufficient sample sizes of these firms. Only one round of this survey was taken.

## _Chile_

23
Table 8: Effect of savings default on temptation spending

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Defn<br>Alcohol a<br>Amount|ition 1:<br>nd tobacco<br>Share|Defn<br>D1 + sug<br>Amount|ition 2:<br>ars and fats<br>Share|Def<br>D2+ pre<br>Amount|nition 3:<br>pared foods<br>Share|
||Panel A:|One week af|ter transfe|r|||
|Savings Default|19.056|-0.000|43.064|0.005|61.353|0.005|
||(30.316)|(0.003)|(41.079)|(0.004)|(49.105)|(0.005)|
|Observations|472|471|472|471|472|471|
|Mean of dependent variable in Cash group|106.78|0.01|284.94|0.02|414.87|0.03|
|SD of dependent variable in Cash group|405.86|0.03|565.35|0.04|685.35|0.06|
|R-squared|0.29|0.15|0.31|0.07|0.35|0.08|
||Panel B:|Two weeks a|fter transf|er|||
|Savings Default|46.818*|0.002|76.756|0.007|112.750*|0.011|
||(28.211)|(0.004)|(53.163)|(0.006)|(63.829)|(0.008)|
|Observations|312|309|312|309|312|309|
|Mean of dependent variable in Cash group|60.13|0.01|234.18|0.03|361.05|0.05|
|SD of dependent variable in Cash group|285.40|0.04|564.04|0.05|675.35|0.07|
|R-squared|0.40|0.24|0.31|0.16|0.38|0.19|

Panel A shows regressions with outcome variables measured one week after the transfer, Panel B uses the same outcomes measured two weeks after the transfer. The sample for Panel B is smaller since two-week follow-up data were only collected for respondents who received transfers immediately or with one day delay. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 <u>perUSD.</u> Robust standard errors in <u>parentheses.</u> * _<u>p <</u>_ 0 _._ 1, ** _<u>p <</u>_ 0 _._ 05, *** _<u>p <</u>_ 0 _._ 01.

Table 9: Effect of delayed transfers on expenditures

||(1)<br>Total|(2)<br>Food|(3)<br>Non-<br>durables|(4)<br>Durables|(5)<br>Transfers<br>and fees|
|---|---|---|---|---|---|
|1-day delay|735.111|-254.224|725.832**|375.094|-77.818|
||(1343.973)|(510.050)|(281.049)|(888.467)|(329.711)|
|8-day delay|-1042.097|-189.175|3.378|-26.334|-713.099**|
||(1260.499)|(552.714)|(249.935)|(942.140)|(288.540)|
|Observations|472|472|472|472|472|
|Mean of dependent variable in Immediate group|15318.73|5337.47|2139.36|5959.10|1515.09|
|SD of dependent variable in Immediate group|14635.63|5392.90|2552.21|9019.54|3145.75|
|R-squared|0.253|0.240|0.232|0.097|0.135|
|P-value: 1 day delay = 8 day delay|0.179|0.906|0.014|0.660|0.013|

‘Non-durables’ is the sum of spending on non-food non-durables. ‘Durables’ is the sum of spending on durable goods, assets, livestock and farm inputs. ‘Transfers and fees’ is the sum of spending on ceremonies, funerals, school fees, loans given, formal insurance, fines and fees. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 perUSD. Robust standard errors in parentheses. * _<u>p <</u>_ 0 _._ 1, ** _<u>p <</u>_ 0 _._ 05, *** _<u>p <</u>_ 0 _._ 01.

36
Table 4: Effects of forecast errors and forecast growth on worker compensation: high vs low earners

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:|||log mont|hly wage|||
|High vs. low earners|high|low|high|low|high|low|
|Definition|5%|95%|15%|85%|25%|75%|
|Weighted forecast error|0.0172**|0.0073**|0.0128**|0.0063**|0.0098*|0.0051*|
||(0.0068)|(0.0030)|(0.0059)|(0.0029)|(0.0055)|(0.0029)|
|Weighted forecast growth|0.0062|0.0015|0.0037|0.0014|0.0021|0.0008|
||(0.0043)|(0.0020)|(0.0036)|(0.0020)|(0.0033)|(0.0021)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|20888|20888|20888|20888|20888|20888|
|N (firms)<br>|8745|8745|8745|8745|8745|8745|
|Adj. R<sup>2</sup>|0.00868|0.0193|0.0110|0.0214|0.0127|0.0223|
|RSS|1602|293|1106|272|908|258|

Notes: In each column, the dependent variable is the change between the averages of each 3-year period. Standard errors are clustered at firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
# <u>Table A2. Analysis of differential attrition (Panel C)</u>

||N|(1)<br>**Full data**<br>Mean[SD]|N|(2)<br>**No skills measures**<br>Mean[SD]|N|(3)<br>**Full attrition**<br>Mean[SD]|t-test<br>Diff<br>(1)-(2)|t-test<br>Diff<br>(1)-(3)|Total obs.|
|---|---|---|---|---|---|---|---|---|---|
|**Individual - 2003**||||||||||
|Respondent age|4391|9.79<br>[2.99]|565|10.64<br>[2.78]|909|10.13<br>[2.85]|-0.85***<br>(0.13)|-0.34***<br>(0.10)|5865|
|Respondent is female|4391|0.49|565|0.33|909|0.50|0.16***|-0.01|5865|
|||[0.50]||[0.47]||[0.50]|(0.02)|(0.02)||
|Highest grade completed|4267|2.10<br>[2.22]|552|2.76<br>[2.30]|879|2.22<br>[2.30]|-0.67***<br>(0.12)|-0.13<br>(0.10)|5698|
|Respondent with disability|4236|0.01<br>[0.12]|550|0.03<br>[0.16]|863|0.05<br>[0.22]|-0.01<br>(0.01)|-0.03***<br>(0.01)|5649|
|How good is respondent health, max is 16|4308|15.62<br>[1.26]|557|15.63<br>[1.40]|881|15.60<br>[1.47]|-0.01<br>(0.08)|0.02<br>(0.07)|5746|
|How intelligent is respondent, max is 5|4279|3.19<br>[0.69]|554|3.19<br>[0.74]|884|3.10<br>[0.71]|-0.00<br>(0.03)|0.09**<br>(0.03)|5717|
|How hardworking is respondent, max is 5|4279|3.06<br>[0.76]|554|2.98<br>[0.81]|884|2.99<br>[0.79]|0.08**<br>(0.04)|0.07*<br>(0.03)|5717|
|Was tested in 2003|4391|0.15<br>[0.36]|565|0.18<br>[0.38]|909|0.15<br>[0.36]|-0.02<br>(0.02)|0.00<br>(0.02)|5865|
|English IRT Score (2003)|670|-0.55<br>[1.16]|99|-0.88<br>[1.31]|138|-0.44<br>[1.02]|0.33**<br>(0.16)|-0.11<br>(0.13)|907|
|Math IRT Score (2003)|670|-0.31<br>[0.96]|99|-0.44<br>[1.04]|138|-0.41<br>[1.07]|0.13<br>(0.13)|0.11<br>(0.11)|907|
|Urdu IRT Score (2003)|670|-0.49<br>[1.18]|99|-0.77<br>[1.24]|138|-0.49<br>[1.13]|0.28**<br>(0.12)|0.01<br>(0.12)|907|
|Mean IRT Score (2003)|670|-0.45|99|-0.69|138|-0.45|0.25**|0.00|907|
|||[0.93]||[1.00]||[0.94]|(0.12)|(0.10)||

* p<.10  ** p<.05  *** p<.01

_Notes_ . Columns (1) to (3) display the means for the group that answered all parts of the survey, the group surveyed that did not complete the cognitive and socioemotional assessments (either because they were surveyed over the phone or information was collected indirectly), and the group who attrited fully. Standard deviations are shown in brackets. Column (4) is the difference between the mean of respondents with complete data and those who do not have skills measures. Column (5) is the difference between the mean of respondents with complete data and the mean of attritors. Panel A shows differences in variables measured in this latest round of data collection, in 2018, so we cannot report data on full attritors. Panel B reports data measured during the tracking exercise of 2016. Panel C reports data measured in 2003 at the individual level. Panel D reports data measured in 2003 at the household level. Differences in means are computed by OLS regressions. All standard errors in parentheses are clustered at the village level.

78