rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8395

# **Abstract**

Evidence on the effectiveness of workfare as an anti-poverty program in developing countries is weak compared with the relatively well-established role of public works during economic crisis as a social safety net. This paper contributes to evidence building by examining the impact of a large-scale workfare program in Bangladesh, the Employment Generation Program for the Poorest. Taking advantage of the program’s distinguishable feature of direct wage transfer to a person’s bank account, the paper uses accessibility to local banks as an instrumental variable to identify the program’s impacts on rural social assistance beneficiaries. Based on locality-by-time fixed effects models over two rounds of locality panel data, the analysis finds that the Employment Generation Program for the Poorest has contributed to increasing overall household consumption and reducing outstanding loans. In particular, expenditures on quality food and health care have significantly increased, which likely helps individuals continue to engage in income-generating activities in the labor market. However, the implementation costs and poor quality of public assets built through work projects could potentially undermine the program’s efficiency. Moreover, further evidence is required on the impacts of work experience through workfare on subsequent labor market outcomes and the value of public assets, to assess the program’s effectiveness compared with administratively simpler alternative instruments such as unconditional cash transfers.

This paper is a product of the Social Protection and Labor Global Practice. 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 ycho1@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
# **The Savings Collapse during the Transition in Eastern Europe**

Cevdet Denizer<sup>1</sup> World Bank Holger C. Wolf George Washington University and NBER l This paper is part of a World Bank research project entitled "Saving Across the World". We thank Eduardo Borenzstein, Norman Loayza, Ratna Sahay, Klaus Schmidt-Hebbel and Luis Serven for many useful suggestions. Nihal Bayraktar and Nikolay Guerguiev provided extremely able research assistance. The usual disclaimer applies. Correspondence address: Cevdet Denizer: World Bank, 1818 H Street, Washington, DC, 20431. email: cdenizer@worldbank.org. Holger Wolf. Email: holger.wolf@mailexcite.com
The results have several policy implications. Severe salinity needs to be addressed if the recent gains in infant and neonatal mortality are to be sustained. Policies need to be developed to prevent intrusion of saline water into drinking groundwater, particularly in the coastal areas of Bangladesh. Adoption of technology such as desalination could be considered. However, rainwater does not contain salubrious minerals. Hence consumption of groundwater over rainwater should be encouraged. Over the long run, the causes of increasing salinity such as climate change need to be addressed. Finally, our analysis has several limitations. We used the FAO salinity definitions for irrigation, as there are no corresponding definitions of salinity in WHO regulations. There is a need to develop such regulations for drinking water. We also do not have the information on the exact drinking water salinity for the household survey under BDHS, as this parameter was not collected as part of the survey. Further, due to lack of data as a proxy variable for drinking water salinity, we used groundwater EC values from an interpolated map at the national scale which included groundwater EC data from coastal districts that were installed and sampled by the Bangladesh Water Development Board. As is evident from the literature, the EC of groundwater may have seasonal variations and concentrations of other chemicals which result in seasonal and temporal variations in the host groundwater. The interpolated EC from point data is smooth in spatial distribution because of the geospatial interpolation method (IDW) applied for mapping. The interpolated EC values may be more or less than the actual EC values of the tubewell water used by the participants in the BDHS clusters. Households in rural Bangladesh usually have separate tubewells for drinking water supply, suggesting that well water chemical concentrations are likely different across the households recorded in BDHS clusters. Finally, this study relies on self-reported outcomes and covariates that can introduce recall bias in the analyses. Hence, we were unable to control for important factors determining early neonatal death such as low birth weight, premature births, birth asphyxia, meningitis, and sepsis. Further research addressing these issues is needed in the future.

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followed by the end of the multi-fiber agreement (MFA), meant greater competition in the European market overall and especially in textiles and apparel (Lopez-Acevedo & Robertson, 2012).

# **_Labor Market Outcomes_**

Growth of Morocco’s working age population<sup>5</sup> has outpaced job creation. The rural working-age population increased 14.2 percent from 2000 to 2018. The urban working age population, however, increased 49.9 percent. The same trend emerged for both genders (Figure 2.6). Rural employment increased 1.9 percent and urban employment grew 45.8 percent, mainly driven by jobs for men (Figure 2.7). More drastically, while the working-age women population increased 3.4 million, just 117,800 new jobs were created. The gap between male and female employment grew dramatically.

**Figure 2.6: Working age population has increased, especially in urban areas** . Millions of persons

<!-- Start of picture text --> Rural - Female 10<br>Urban - Female<br>Rural - Male<br>Urban - Male 8<br>6<br>4<br>Source: Estimates based on the Enquête<br>Nationale sur l’Emploi (ENE; Labor Force<br>Survey)<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

**Figure 2.7: Employment growth has not kept the same growth pace** Millions of workers

<!-- Start of picture text --> Rural - Female<br>6<br>Urban - Female<br>Rural - Male<br>Urban - Male 4<br>2<br>0<br>Source: Estimates based on the Enquête<br>Nationale sur l’Emploi (ENE; Labor Force<br>Survey)<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> At the same time, the labor market has failed to create new opportunities for the large number of women and youth entering the workforce. Women constitute the majority of the inactive population. Female unemployment is high, which hints that inactivity might be due to women being discouraged from working. In 2018, female unemployment reached 14 percent, while male unemployment was 8.37 percent. For the youth aged 15-24, both inactivity and unemployment are high in comparison with other age groups. The “not in education, employment or training” (NEET) rate has remained among the highest in MENA, and those who were NEET in 2010 were still NEET even after 10 years (Alfani et al. 2020).

Unemployment slightly decreased, especially among men, before the COVID-19 pandemic.<sup>6</sup> Reaching an all-time low in 2011 of 8.91 percent, the unemployment rate declined from 13.4 percent in 2000 to 9.75 percent in 2018 (Figure 2.8). This decrease was driven mainly

> 5 Defined as the population aged 15-99 years old.

> 6 Data from the Labor Force Survey are available from 2000 to 2018.

6 | P a g e
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|---|
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|Buenos Aires Urban Transport Project<br>Shomik RajMehndiratta|
|HIV/AIDS and STD Prevention and Management Project<br>Samuel S. Lieberman|
|HIV/AIDS and STD Prevention and Management Project<br>Samuel S. Lieberman|
|HIV/AIDS and STD Prevention and Management Project<br>Samuel S. Lieberman|
|HIV/AIDS and STD Prevention and Management Project<br>Samuel S. Lieberman|
|HIV/AIDS and STD Prevention and Management Project<br>Samuel S. Lieberman|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|West Java Basic Education Project<br>Mae Chu Chang|
|Health Sector Reform Project<br>Jean-Jacques Frere|
|Health Sector Reform Project<br>Jean-Jacques Frere|
|Health Sector Reform Project<br>Jean-Jacques Frere|
|Health Sector Reform Project<br>Jean-Jacques Frere|
|Health Sector Reform Project<br>Jean-Jacques Frere|
|Health Sector Reform Project<br>Jean-Jacques Frere|
|Health Sector Reform Project<br>Jean-Jacques Frere|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|Second Technical Assistance Project for the Public and Private Provi Stephen R. Dice|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
|BiodiversityConservation Project(GEF)<br>Robert Ragland Davis|
methods is presented in Section 2. In this section we also offer a methodological contribution by deriving standard error estimates for the remote-sensing based poverty map. Section 3 describes the different data used in this study. Our empirical findings are presented in Section 4. Finally, Section 5 concludes.

# **2 Methodologies**

## **2.1 Population census-based small area estimation of poverty**

Elbers et al. (2003; ELL) pioneered the use of unit record population census data combined with household consumption expenditure survey data for the small area estimation of poverty and inequality. ELL assume the following data generating process (DGP): where _y_ is log per capita income or expenditure, _x_ is a vector with independent variables, and where _v_ and _ε_ are zero expectation error terms that are independent of each other with variances denoted by _σv_<sup>2and</sup><sup>_σ_</sup> _ε,ah_<sup>2, respectively.The subscripts indicate household</sup> level _h_ residing in small area _a_ . The predictors _x_ may consist of variables observed at different levels of aggregation, i.e. household level employment status and local area employment rates may both serve as good predictors of household expenditure.

The model permits heteroskedasticity in the household errors _εah_ to allow for instances where the predictability of expenditures varies between demographics (i.e. elder versus young), employment status (i.e. unemployed versus employed), and sectors (i.e. blue collar versus white collar). Specifically, the variance _σε,ah_<sup>2isassumedtobeafunc-</sup> tion of household and area characteristics, denoted by _zah_ . A logistic transformation of the squared error will serve as the dependent variable (as the distribution of the untransformed squared error tends to be heavily skewed): where the vector _zah_ includes the constant term. An estimate of _σε,ah_<sup>2=</sup><sup>_E_[</sup><sup>_ε_2</sup> _ah_<sup>_|zah_]is</sup> obtained by applying the inverse of the logistic transformation (see Elbers et al., 2003). Estimates of the household level variances are re-scaled such that the sample mean matches the estimate of the unconditional variance _σ_<sup>2i.e.</sup><sup>_E_[</sup><sup>_σ_2</sup> _ε_<sup>,</sup> _ε,ah_<sup>] =</sup><sup>_σ_</sup> _ε_<sup>2.</sup>

For households sampled in area _a_ , the residuals _eah_ = _yah − x_<sup>_T_</sup> _ah_<sup>_β_=</sup><sup>_va_+</sup><sup>_εah_are</sup> informative of the latent area error _va_ . Assuming both errors are normally distributed, 6
II. INEQUALITY DURING PAST INDUSTRIAL REVOLUTIONS Before we can talk about poverty, we need to understand what history tqlls us about inequality. It's important to understand first the i lustrial revolutionary forces driving inequality because they illustarte best the determinants of poverty eradication, namely the demand for and supply of unskilled labor. Having done so, we shall be better equipped to return to the poverty theme in Section III.

# The Kuznets Curve: Contemporary Cross-Sections Once More

More than three decades ago, Simon Kuznets (1955) noted that income inequality seemed to have declined in the industrialized nations across the mid-2Oth century, and ventured the guess that it bad risen earlier just as the critics of capitalism had asserted. While Kuznets was drawing on limited historical evidence, others rose to his challenge by pursuing the more abundant cross-sectional evidence. Felix Paukert (1973), Rollis Chenery and his World Bank team (1974), Nontek Ahluwalia (1976; 1980), and Edmar Bacha (1979), all thought they saw contemporary cross-section evidence supporting the Kuznets Curve, namely that income inequality first rose and then declined with development.

The Kuznets Curve is illustrated in Figure 1, based on Ahluwalia's World Bank sample. A quadratic fits this 60-country sample fairly well, where the inequality statistic is simply the income share of the top 20%. The underlying data can and have been criticized. Indeed, some have argued that the data is much too fragile to resolve the debate. But whatever your position on the issue of data quality, two morals leap out from Figure 1. First, the more robust portion of the Kuznets Curve lies to the right: income inequality falls

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**Table 5  Change in Asset Allocation of Life Companies and Pension Funds**

||Domestic<br>Bonds|Foreign<br>Bonds|Domestic<br>Equities|Foreign<br>Equities|Investment<br>Trusts|Other<br>Assets|
|---|---|---|---|---|---|---|
|1998|54.4|1.3|13.4|12.3|1.9|16.7|
|1999|46.6|2.1|12.6|18.3|3.5|17.0|
|2000|44.7|4.8|11.3|18.5|4.6|16.1|
|2001|45.1|10.7|8.6|12.5|6.3|16.8|
|2002|53.0|13.6|4.6|6.9|6.2|15.7|
|2003|51.3|12.4|4.4|8.0|8.1|15.8|
|2004|43.2|14.6|4.6|8.3|12.3|17.1|

Source: DFSA For life insurance companies and multi-employer pension funds, the picture is not as sharp but is still quite clear. Holdings of domestic bonds fluctuated from year to year but there was a large increase in holdings of foreign bonds. Holdings of both domestic and foreign equities fell sharply and rose very slightly after the recovery of equity markets in 2003 and 2004, implying a quantity effect was in place, and not just a price effect. However, L&P institutions invested less heavily in equities before 2000 and the impact is smaller. A switch away from equities is also discernible.<sup>6</sup> The evolution of the asset composition of pension institutions is also shown in the following graph. The supervisory authorities intervened to force remedial action in institutions that were in a red light situation, but refrained from influencing management decisions and forcing asset sales in all other institutions.

> 6 Investment trusts invest in both equities and bonds. Other assets include buildings, subsidiaries and loans.

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matching are fairly robust relative to changes in the variables included in the propensity score model, to differences in the source of data used and to variation in the number of neighbors used.<sup>10</sup> The estimated impact for adult women and young males on earnings is around US $20$25 / month. Rate of return estimates, obtained under alternative assumptions on the discount rate, show that the program would have a positive return only if the benefits are fairly longlasting (9 years or more).

_ProJoven program in Peru_ : Ñopo, Robles and Saavedra (2007) analyze the impact of a Peruvian youth labor training program, called _ProJoven_ , on female and male youth living in urban areas. The program provided classroom training and internships lasting about three months for youths from poor families. Trainees received stipends during their training period, with mothers of young children receiving a double stipend. More than 20,000 youth participated in the program. An interesting feature of the program was that one of its explicit goals was to train female youth for traditionally male occupations so as to reduce gender segregation in the labor force.

Ñopo et al.‟s evaluation of the program is based on a sample of beneficiaries and a sample of matched controls selected on the basis of gender, age, geographic proximity, poverty status, income, schooling, number of children and employment status. These individuals were administered a baseline survey and three follow-up surveys at 3, 6 and 18 months. The impact estimates are derived from a somewhat unusual two-stage matching procedure that first selects for each treated individual three matched controls on the basis of similarity in hourly wages. The motivation for this two-stage procedure is to closely align the treatment and comparison groups in terms of pre-program earnings so as to account for the so-called “Ashenfelter Dip” problem, namely, that program participants often exhibit a pre-program dip in their earnings that is not seen in comparison group data. The two-stage matching procedure generates a similar preprogram earnings dip pattern in both the treatment and comparison groups, although aligning the groups in terms of preprogram earnings would not necessarily guarantee that post-program comparisons are valid.

> 10 Recent research has shown that bootstrapping does not lead to valid inference about standard errors for nearest neighbor matching estimators, so the standard errors reported in this study would not be valid. However, correcting them using the alternative standard error estimators suggested in Abadie and Imbens (2005) or using kernel smoothed estimators for which bootstrapping is valid probably would make little difference.

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# **References:**

Alderman, Harold (1993) “Obtaining Useful Data on Household Incomes from Surveys” pp. 193-201 in Joachim von Braun and Detlev Puetz (eds.) _Data Needs for Food Policy in Developing Countries: New Directions for Household Surveys_ Washington, DC: International Food Policy Research Institute.

Banerjee, Abhijit and Esther Duflo (2007) “The Economic Lives of the Poor”, _Journal of Economic Perspectives_ 21(1): 141-167.

Daniels, Lisa (2001) “Testing Alternative Measures of Microenterprise Profits and Net Worth”, _Journal of International Development_ 13: 599-614.

Deaton, Angus and Margaret Grosh (2000). “Consumption” pp 91-133 in M. Grosh and P. Glewwe (eds.) _Designing household questionnaires for developing countries: lessons from fifteen years of the living standard measurement study, Volume 1_ . Washington, DC, World Bank.

de Mel, Suresh, David McKenzie and Christopher Woodruff (2007) “Returns to Capital in Microenterprises: Evidence from a Field Experiment”, Mimeo. World Bank and UCSD.

Liedholm, Carl (1991) “Data Collection Strategies for Small-scale Industry Surveys”, _GEMINI Working Paper No. 11_ , Bethesda, MD: Development Alternatives Inc. Samphantharak, Krislert and Robert M. Townsend (2006) “Households as Corporate Firms: Constructing Financial Statements from Integrated Household Surveys”, Mimeo. UCSD and University of Chicago.

Vijverberg, Wim P.M. (1991) “Measuring Income from Family Enterprises with Household Surveys”, _LSMS Working Paper no. 84._ Vijverberg, Wim P.M and Donald C. Mead (2000) “Household Enterprises”, pp. 105-137 in Margaret Grosh and Paul Glewwe (eds.) _Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 years of the Living Standards Measurement Survey_ , Volume 2. The World Bank: Washington, D.C.

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# **Table 4. Average Growth Rate of Share of Private Consumption and Total Investment in GDP During Disinflation.**

|Sample|Private Consumption|Investment|
|---|---|---|
|Latin America and Israel: 1960-90|1.8 %|-0.24 %|
|Latin America and Israel: 1970-90|2.22 %|-3.9 %|
|Latin America in the 1960s|**_0.5_**_%_|2.9 %|
|Latin America during the Tablitas|1.2 %|-0.11 %|
|Latin America in the 1980s|1.6 %|0.32 %|
|Israel|3.7 %|-4.1**%**|
|Denmark: 1983-1986|0.7 %|9.4%|

_Note:_ For Latin America, Investment stands for total investment. For Denmark, we used Private non residential capital formation.

_Sources:_ For Latin America and Israel, Kiguel and Liviatan 1992; for Denmark, OECD National Income Accounts.

the whole sample. These results are consistent with the idea that beliefs on future reversals of policy were grounded on previous failed stabilizations. This effect is expected to have become very significant since the 1970's.