rafmacalaba/datause-encoder-data huggingface dataset:
natives of each age ~~-s~~ kill type due to the changes in education levels; and (4) changes in the number of natives of each age ~~-s~~ kill type due to ageing. We should note that it is not simple to separate the ageing effect (4) from the changes in the education levels (3) in the data ~~—~~ we nevertheless undertake this exercise later in Appendix C. Younger cohorts tend to have higher education levels, leading to increased education levels as time passes. Ageing occurs as the relative share of the older group in the labor force increases. This is due to each successive cohort of natives having fewer number of people, mainly due to demographic reasons such as declining fertility rates.

In equilibrium, the wages and employment levels of workers of each type adjust as they respond to these demographic changes. For example, the ratios of the wages between natives and immigrants, between young and old workers, or between high ~~-s~~ killed and low ~~-s~~ killed workers are given by:

# 2.5 Alternative labor market structures

Our benchmark model assumes that unemployment is voluntary and linked to the en ~~-~~ dogenous labor force participation decision of workers. We account for the difference in employment rates between groups by introducing heterogeneity in the preference param ~~-~~ eters in the utility functions in equation (9). Labor force data reveal that differences in employment rates are mainly governed by differences in participation rates (Burzynski et al., 2018). This motivates our choice to endogenize participation rates and disregard invol ~~-~~ untary unemployment. However, as a robustness check, we relax certain assumptions and use an alternative specification, which involves smaller responses to soci ~~o-~~ demographic changes.

We first assume that unemployment can be partly involuntary. At each moment in time, active workers face exogenous job separation and matching rates, implying that they spend an exogenous fraction (1 — ugay) of their active time in employment, and the remaining fraction ug, in unemployment while searching for a job.° Working and searching for a job induce the same level of disutility. Job separation and matching rates differ across groups, implying that unemployment rates are group- ~~s~~ pecific.

~~5A~~ <u>more realistic modelling of the unemployment</u> response to soci ~~o-~~ demographic shocks can be found in Chassamboulli and Palivos (2014) or Battisti et al. (2018).

11
# IV. THE EFFECTIVZNESS OF PROJECTS

The previous section dealt with project design. This section deals more with the actual experience of projects.

# A. Criteria for Measuring Effectiveness

The ultimate purpose of Bank lending in the population sector is to assist national development by reducing population growth. A complex causal chain links Bank activities with these outcomes:

- (1) policy commitment

- (2) institution building

- (3) expenditure on programs, additionality

- (4) management efforts to organize programs

- (5) intermediate outputs such as construction

- (6) the quantity and quality of provider-client transactions

- (7) observed behavior of beneficiaries as measured by:

- a. the demand for contraception

- b. acceptance of family planning or health services

- c. contraceptive prevalence or health status

- d. fertility and mortality

- e. economic and social objectives of development.

in principle, the effectiveness of the Bank's projects could be assessed against any of these criteria, but in practice, the complexity of projects and programs, the influence of non-project factors and lengthy time-lags between project initiatives and final effects makes the use of the more advanced criteria difficult. The original goal of the first Kenya project was a modest reduction in fertility. During the ten years after the agreement was signed fertility rose, and while it is fair to conclude from this that the effect of the project on fertility was not as planned, it is hardly fair to hold the project accountable for the increase. In practice, supervisory missions tend to emphasize measures of project accomplishment such as levels of expenditure, training sessions run, buildings constructed, etc. We believe it would be helpful to give more attention to measures of program activity such as the quantity and quality of transactions or of intermediate output measures such as prevalence.

We have used a variety of sources for this section: personal interviews, project completion and audit reports, and published reviews of country experience.

40
# **<mark>Figure 2. Maternal employment and children’s attendance at child care centers</mark>**

Panel A. Percentage of women working and child care attendance Panel b. Percentage of women have a wage job and child care attendance <!-- Start of picture text --> 98.9<br>96.6<br>95.094.7 57.2 58.2<br>92.8<br>91.3 91.6<br>90.7<br>44.1<br>36.2<br>30.5 32.8 31.7<br>24.1<br>2 3 4 5 2 3 4 5<br>Age of children Age of children<br>Attending child care center Not attending child care center Attending child care center Not attending child care center<br>Panel C. Percentage of women with a formal job Panel D. Working hours of women and child care<br>and child care attendance attendance<br>49.3<br>3.2<br>41.6<br>3.0<br>30.0 2.9<br>20.6 21.0 2.7<br>19.2 2.7 2.7<br>16.0 2.6 2.6<br>13.5<br>2 3 4 5 2 3 4 5<br>Age of children Age of children<br>Attending child care center Not attending child care center Attending child care center Not attending child care center<br>Panel E. Hourly wage of women and child care Panel F. Monthly wage of women and child care<br>attendance attendance<br>5.2 5.2 8.4<br>5.2<br>5.1 8.3<br>5.1 5.1<br>5.1 8.1<br>5.0<br>8.0<br>7.9<br>7.9<br>7.9<br>7.8<br>2 3 4 5 2 3 4 5<br>Age of children Age of children<br>Attending child care center Not attending child care center Attending child care center Not attending child care center<br>100.0 70.0<br>60.0<br>95.0<br>50.0<br>90.0<br>Percentage Percentage40.0<br>85.0<br>30.0<br>80.0 20.0<br>50.0 3.3<br>3.2<br>40.0 3.1<br>3.0<br>30.0 2.9<br>Percentage<br>2.8<br>Log of hourly wage<br>20.0 2.7<br>2.6<br>10.0 2.5<br>5.3 8.6<br>5.2 8.4<br>5.1 8.2<br>5.0 8.0<br>Log of monthly wage<br>Log of monthly working hours4.9 7.8<br>4.8 7.6<br><!-- End of picture text --> <mark>Note: The vertical axis indicates the employment variables of the parent, and the horizontal axis gives the children’s age.</mark> <mark>Sources: Authors’ estimation from VHLSS 2010, 2012, 2014 and 2016.</mark> 28
Table 1. Model Without Interaction Between Inequality and Initial Income

|Dependent Variable is:|||l|n(yit)|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Inequality Variable is:|Market Gini|Net Gini|Market Gini|Net Gini|Market Gini|Net Gini|
|Sample:<br>|All Cou<br>|ntries<br>|Excluding<br>Coun<br>|Transition<br>tries<br>|Excluding<br>Coun<br>|East Asian<br>tries<br>|
|Inequalityit|1.23***<br>(0.14)|1.18***<br>(0.20)|1.24***<br>(0.17)|1.15***<br>(0.21)|1.14***<br>(0.15)|1.06***<br>(0.17)|
|ln(yit-1)|0.79***<br>(0.03)|0.82***<br>(0.04)|0.79***<br>(0.03)|0.83***<br>(0.04)|0.76***<br>(0.03)|0.79***<br>(0.03)|
||||<br>First Stage fo|<br>r Inequalityit|||
|Residual Inequalityit|0.85***<br>(0.02)|0.90***<br>(0.02)|0.85***<br>(0.02)|0.90***<br>(0.01)|0.86***<br>(0.02)|0.91***<br>(0.02)|
|Kleibergen Paap F-<br>statistic|2556|3658|2400|3642|2637|3707|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|768|768|725|725|738|738|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

26
# Inclusive Statistics: Human Development and Disability Indicators in Low- and Middle-Income Countries

## **<mark>Sophie Mitra, Jaclyn Yap, Justine Hervé, Wei Chen</mark>**

Originally published in the <u>Policy Research Working Paper Series on</u> _April 2021._ This version is updated on _May 2021._ To obtain the originally published version, please email prwp@worldbank.org.

JEL classifications: I32, J14, R20 Keywords: Disability; Functional Difficulties; Human Development; Inequalities; SDGs.

**Acknowledgments.** Wei Chen, Justine Hervé, Sophie Mitra and Jaclyn Yap are affiliated with Fordham University. This paper is a product of the World Bank Development Data Group (DECDG)’s Inclusive Data and Statistics Project (P170339), led by Buyant Erdene Khaltarkhuu and Akiko Sagesaka. Wei Chen, Justine Hervé, Sophie Mitra and Jaclyn Yap were consultants at the World Bank for this project. The authors can be reached at mitra@fordham.edu.

This study <mark>received funding from the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) and from the Wellspring Philanthropic Fund. 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. We are grateful for insightful comments on an earlier draft from Jose Cuesta, Mari-Helena Koistinen, Mitchell Loeb, Daniel Mont and Deepti Raja.</mark> 1
**Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria**<sup>#</sup>

David McKenzie<sup>1</sup> Dario Sansone<sup>2</sup>

**Keywords** : entrepreneurship, machine learning, business plans, Nigeria

**JEL** : C53, L26, M13

> # We are grateful to Eric Auerbach, Keisuke Hirano, William Maloney, Givi Melkadze, Allison Stashko and participants in seminars at Georgetown University, The World Bank and UIUC for their helpful comments. Funding from the Strategic Research Program (SRP) of the World Bank is gratefully acknowledged. The usual caveats apply.

> 1 Development Research Group, The World Bank Group. E-mail: dmckenzie@worldbank.org

> 2 Georgetown University. E-mail: ds1289@georgetown.edu
7

**Table 3: The Crowding Out of Private Investment in Mexico**

**(1)** A[ **_I]_** = -54.67 + 0.548AY + O.OOACG0 - 0.455AIG - 0.032 Ae (13.06)* (0.084)* (0.072) (0.238)** (0.099)

Unadjusted R<sup>2</sup> = 0.81, D.W. = 0.92, F-stat = 21.79, No. of obs. = 26

- (2) A[ **1P]** = -66.71 + 0.628AY + 0.062ACG - 0.573AIG - 0.054 ARe (22.17)* (0.051)* (0.059) (0.162)* (0.080)

Adjusted for autocorrelation R _=_ 0.89, D.W. =1.79, F-stat= 30.51, No. of obs. = 25, AR(1) =0.69* (3) A[ IP) = -50.42 + 0.487AY -0.041ADEFG (16.66)* (0.043)* (0.060) Adjusted for autorcorrelation R<sup>2</sup> = 0.82, D.W. = 1.99, F-stat = 32.15, No. of obs. = 25, AR(1) = 0.48*

- (4) A[ Ip] = -101.45 + 0.697AY + 0.040ACG - 0.627AIG - 0.016ARG + 47.0 DUM - 0.721DUM*AIG (19.37)* (0.065)* (0.060) (0.162)* (0.080) (23.93)** (0.446)

Adjusted for autocorrelation R' = 0.92, D.W. = 1.45, F-stat = 28.14, No. of obs. = 25, AR(1) = 0.53* Notes. These regressions are based on annual data from 1970-1996, obtained from the same source as Table 1. The figures in parentheses denote standard errors. The asterisk * (**) indicates that the coefficient is significantly different from 0 at the 5% (10%) level. A denotes differences.

- Regression equations (1) and (2) indicate that public investment has a negative and significant impact on private investment, suggesting that it exerted a crowding out, rather than crowding in effect in Mexico. Furthermore, the size of the crowding out effect has been significant: Regressions (1) and (4) indicate that private investment is crowded out by a factor of around one-half of the change in public investment.<sup>8</sup>

- To examine whether the relation between private and public investment might be nonlinear, the change in public investment was added in quadratic form to the list of explanatory variables in all the equations in Table 3, but the associated coefficients were not significant.

- 8While the regression equations in Table 3 gave the best fit, it is necessary to point out that the crowdingout coefficient is very sensitive to the inclusion or exclusion of other variables, notably GDP, among the explanatory variables. This suggests that there may be significant cyclical covariation in the variables.
We examine the extent to which these proposed variables coming from baseline survey measures are associated with future business outcomes in the remaining columns of Tables 1-3 and A1. Columns 2 and 6 use the variables selected in McKenzie (2015), while Columns 3 and 7 attempt to match as closely as possible the variables used in Fafchamps and Woodruff (2017). Appendix A.1 describes how these variables were constructed in detail. These regressions allow us to examine whether the proposed models are useful for predicting business outcomes within sample – in the next section we will examine out-ofsample performance to allow comparison with machine learning methods. We also include the business plan score in these regressions so that we can test whether these survey measures add value beyond that contained in the judges’ scores, and conversely, whether the judges’ scores now contain information once we have conditioned on other features of the applicants.

Consider predicting outcomes for the non-winners. We can strongly reject that the McKenzie (2015) set of regressors, and the Fafchamps and Woodruff (2017) set of regressors, are not associated with each of our four business outcomes (p<0.001). This greater performance relative to the judges comes largely from a few characteristics of the owner. First, consistent with a broad body of literature (e.g. Boden and Nucci 2000; Bruhn 2009; Robb and Watson 2012; McKenzie and Paffhausen 2017), female applicants who do not win are less likely to start or continue businesses than men, and operate firms that are smaller in size and less profitable.

Second, while the business plan competition was designed for youth, it maintained an expansive definition of youth which ranged from age 18 to 40, with a median age of 30 among our non-winners. We see that older applicants were more likely to be running firms three years later among the non-winners, and to run firms of larger size, sales, and profitability. This is consistent with a return to work and life experience, and with evidence from other developing countries that businesses have the lowest failure rates when the owners are middle-aged (McKenzie and Paffhausen 2017). In contrast to pitching situations where judges see the applicants, the anonymized business plans used for scoring meant that judges did not observe gender or age, and therefore did not consider it in their assessments (and indeed, concerns about sex- or agediscrimination would occur if they did directly consider it).

Third, higher ability of the owner is associated with better performance in all four outcomes for the nonwinners. Both the McKenzie (2015) measure which is the first principal component of Raven test and digit span, and the Fafchamps and Woodruff (2017) measure which is the first principal component of Raven, digit span, and education, are significant predictors. Examining the sub-components, the Raven test score is the most important contributor here. We then also see some role for business sector in predicting some outcomes: IT firms tend to hire fewer workers, while retail firms have higher sales.

11
(intermediate and high GVC participation) are typically larger and are, on average, more productive, as measured by total factor productivity.

Table 2: Summary Statistics

|Variable|Average|Unit|
|---|---|---|
|Output|2.157|in million Indian Rupees (logged and deflated)|
|Wage|0.544|in million Indian Rupees (logged and deflated)|
|Productivity|0.002|total factor productivity|
|Service Intensity|15.230|relative to total expenditures|
|IT related service intensity|1.360|relative to total expenditures|
|Complex service inputs|6.590|relative to total expenditures|

Table 3: Average Output and productivity by GVC mode

|GVC Mode|Output|Productvity|
|---|---|---|
|No|1.187|-0.149|
|Very Low|2.277|0.040|
|Low|2.585|0.059|
|Intermediate|3.087|0.094|
|High|3.019|0.189|

Note: Calculation based on all years. Output is in million Indian Rupees (logged and deflated). Productivity is measured by TFP constructed following the methodology of <u>Ackerberg, Caves and Frazer (2015).</u> Firms that are more deeply integrated into GVCs contribute a disproportionate share of the output and exports. Double and triple mode firms (intermediate and high levels of GVC integration) constitute less than 10 percent of the total manufacturing firms in numbers but produce over 22 percent of the total manufacturing output and 50 percent of exports (Figure 2). Triple mode firms by themselves account for about 6 percent of total output and for nearly 15 percent of total exports. The majority of the output share, nearly half of the total output, is produced by firms who are classified as low or single mode of GVC integration. Similar patterns are observed when we look at the shares within specific industries. We show the distribution of firms, output and exports for two major manufacturing industries - Apparel and Textiles and Electronics in Figure 3 and Figure 4, respectively.<sup>8</sup> The kernel-density functions of productivity with respect to different international trading activities and different modes of GVC participation are presented in Figure 5 and Figure 6, respectively. Since the TFP distribution is skewed, it is preferable to look at the full distribution of TFP levels rather than only average values. The kernel density shows the shares of firms (density) that reach each productivity level. Thus, it shows the probability of choosing a firm with a certain productivity level when firms are randomly drawn from each category of GVC activities.

Firms that both export and import are more productive than firms that are only exporters, only importers and firms serving the domestic market only. Further, the distribution of the estimated productivity confirms that firms that are more deeply integrated in GVCs are, on average, more productive. The superior performance of deeply integrated firms (dual and triple mode) in GVCs is particularly visible in

> 8 Apparel and Textiles comprise the sector categories 13, 14 and 15 (according to 2-digit NIC classification); Electronics comprise the sector categories 26 and 27 (according to 2-digit NIC classification).

7
Hertz Tom, Tamara Jayasundera, Patrizio Piraino, Sibel Selcuk, Nicole Smith and Alina Veraschagina. 2007. ―The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends‖. _The B.E. Journal of Economic Analysis and Policy_ (Advances), 7(2), Article 10.

<mark>Kotwal, A, B. Ramaswami and W. Wadhwa (2011). "Economic Liberalization and Indian Economic Growth: What's the Evidence?,"</mark> _<mark>Journal of Economic Literature</mark>_ <mark>, American Economic Association, vol. 49(4), pages 1152-99, December.</mark> Jalan, J. and R. Murgai. 2008. ―Intergenerational Mobility in Education in India‖, Manuscript, World Bank, Delhi.

Jalan, J and M. Ravallion (1999), <mark>"China's Lagging Poor Areas,"</mark> _<mark>American Economic Review,</mark>_ <mark>vol. 89(2).</mark> <mark>Jushong, Z and R. Kanbur (2012), ―Confronting Rising Inequality in Asia‖, Chapter in Asian Development Outlook 2012, ADB.</mark> <mark>Kingdon, G (2007). "The progress of school education in India,"</mark> _<mark>Oxford Review of Economic Policy</mark>_ <mark>, vol. 23(2), pages 168-195, Summer.</mark> Kochhar, K., U. Kumar, R. Rajan, A. Subramanian and I. Tokatlidis. 2006. ―India‘s pattern of development: What happened, what follows?" _Journal of Monetary Economics,_ 53 (5): 981– 1019.

<mark>Lillard, L, and R. J. Willis, 1994. "Intergenerational Educational Mobility: Effects of Family and State in Malaysia,"</mark> _<u><mark>Journal of Human Resources</mark></u>_ <mark>, vol. 29(4), pages 1126-1166.</mark> Lindahl, L. 2008. ―A Comparison of Family and Neighborhood Effects on Grades, Test Scores, Educational Attainment and Income—Evidence from Sweden‖. Essay III in L. Lindahl, _Family Background and Individual Achievement,_ dissertation no.75, Swedish Institute for Social Research.

- Lindahl L (forthcoming) ―Does the Childhood Environment Matter for School Performance, Education and Income? - Evidence from a Stockholm Cohort.‖ _Journal of Economic Inequality._

- Manski, C (2011), ―Genes, Eyeglasses, and Social Policy‖, _Journal of Economic Perspectives_ , Fall 2011.

- Mazmuder, B (2012), ―Is intergenerational economic mobility lower now than in the past?‖, _Chicago Fed Letter_ , April 2012.

34
Galal & Nauriyal 15 Our judgement is that only Chile and Jamaica were able to assure neutrality of the enforcing agencies. In Chile, neutrality is derived from relying on multiple agencies to resolve conflicts, and on the reputation for independence of the court system. ' In Jamaica, Spiller and Sampson (1993) argue for neutrality on the grounds of court independence, with the ultimate appeal to the Commonwealth Council in London serving as a deterring factor against government opportunistic behavior. In all other cases, the regulatory agencies are extensions of the bureaucracy, with the concerned minister having the final say when conflicts arise. The minister may of course attempt to balance the interests of the producers and consumers, but there are no guarantees of such behavior.

All but two countries in the sample have empowered their regulatory agencies with the authority to request the necessary information from the firms and to enforce the regulation. The first exception is the Philippines, where the presence of two agencies with vaguely defined mandates may have undermined their power. Malaysia is another exception in that the company is still publicly owned in large measure, which places the power of enforcement with the bureaucracy." Finally, it appears that the regulatory agencies are generally at a disadvantage compared with regulated firms, in large measure because they are unable to attract and retain skilled employees due to low civil service compensations. However, Chile, Mexico and Argentina were able to reduce the skill gap by relying on consultants to prepare or review the proposals, for example, for tariff revisions. On the other hand, CONATEL in Venezuela was more than burdened by the need to review tariffs quarterly. As a result, the tariff increases promised to the firm have been delayed from taking effect in 1993.

Finally, our sample countries attempted, with varying degrees of success, to _insulate their regulation_ from arbitrary changes arising from political turnovers. Once again, Chile and Jamaica seem to have succeeded the most. Chile resolved this problem by enacting its regulation in a detailed law, which includes specific provisions for tariff formulation and

> **10** Galal (1994) reaches the conclusion of court neutrality on the basis of a study of court rulings over the past 40 years. See also Shugart and Carey (1992) on the nature of goveming in Chile.

> " Although JTM (of Malaysia) is modeled after OFTEL in the U.K., and headed by a Director General, the minister still approves all tariffs and licensing decisions. Tariffs have not changed since 1985, although the company is allowed to adjust them for inflation under the price cap regulation.