rafmacalaba/datause-encoder-data huggingface dataset:
# Contents

|**1 The economic literature on unreliable power**|**2**|
|---|---|
|**2 The data on self-generated electricity**|**3**|
|**3 The extent of own generation in Africa**|**7**|
|**4 What drives firms to generate their own power?**|**12**|
|**5 The costs and benefits of own generation**|**16**|
|Costs|16|
|Benefits|20|
|Costs vs. benefits|21|
|**6 Conclusions and policy implications**|**25**|
|**References**|**27**|
|**Appendix 1 Electrical generating capacity in Sub-Saharan Africa**|**29**|
|**Appendix 2 Methodologies for estimating average costs of self-generated electricity**|**38**|
|Fuel approach|38|
|Electricity approach|38|
|**About AICD**|**40**|

## **The authors**

Vivien Foster (vfoster@worldbank.org) is a senior economist in the Africa Region at the World Bank and the staff director of the Africa Infrastructure Country Diagnostic. Jevgenijs Steinbuks is a visiting instructor in economics at Miami University (Ohio, USA).

iii
These trends had bearings on the wage differential. Table 12 presents conditional wage differentials between the public and formal private sectors in 1993-94 and 1999-00 estimated by three methods described above. Shaded cells indicate the increase in the differential between these two data points, showing that the total overall differential increased according to all three methods. In terms of patterns of change across different groups of workers, the public sector wage advantage increased for rural male and female workers according to all three sets of estimates. In urban areas the trend is more ambiguous. For male workers, OLS and PSM estimates show an increased differential, while the SBA method shows a slight decline in the differential. For female workers, the wage advantage of public sector has declined slightly according to OLS and SBC estimation, but increased according to PSM method.

# **7. Summary and Policy Options**

Examination of the wage structure in India shows that the differences in wages between workers in the public, and private-formal and private-informal sectors are positive and high. In 1999-00, the average real wage in the public sector was about 2.1 times that in the organized private sector. The difference in real wages between the public and privateinformal sector is even larger at 3.8 times.<sup>1314</sup> The public sector tends to employ workers with more human capital, and since the labor market places a premium on more education and experience, adjusting for these differences in characteristics between public and private sector workers reduces the size of the wage differential. To obtain robust estimates of the adjusted wage differential, we employed three methods, each relying on a different set of assumptions about the process of job selection and wage determination. The public sector wage premium remains, ranging from 62% to 102%, over private-formal, and between 164% and 259% over private-informal sectors, depending on the choice of the methodology.

> 13 Note that the alternative employment outside of the public sector for rank-and-file public sector workers, may be in self-employment or casual work. 14 Results pertaining to the differences between wages of public and private-informal sector workers are presented in the longer version of the paper (which was prepared as a background paper for “ _India – Sustaining Reform, Reducing Poverty”_ , a World Bank Development Policy Review released in July 2003). The longer version is available from the authors upon request.

24
53 Figure A2.6: Main source of energy for cooking across all households in Peru <!-- Start of picture text --> LPG 55<br>Firewood 29<br>Charcoal/coal 2.8<br>Kerosene 1.5<br>Electricity* 0.9<br>Natural gas 0.1<br>Other/not reported 10<br>0 10 20 30 40 50 60<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.

In addition, the survey asked as a series of questions on LPG use:

1. Did you use LPG last month? 11,747 respondents (corresponding to 4.22 million households) replied yes.

2. Did you use any LPG for cooking last month? 11,747 replied yes.

3. How much did you spend on LPG last month? 11,555 (4.15 million households) reported positive expenditures.

4. What is your primary cooking fuel? 10,095 (3.73 million households) answered that LPG was the primary cooking fuel.

These answers indicate that 1.6 percent of LPG users had not purchased LPG in the previous month, and that 88 percent of households using LPG used it as the primary cooking fuel. Only a handful of households used natural gas.

The national average retail price of LPG for 2009 of 3.242 nuevos soles per kilogram obtained from ECLAC (2010) was used to calculate estimates of quantities purchased by households based on their expenditure. LPG is sold to residential consumers in 10-kg cylinders in Peru. The rate of selection of LPG in urban areas was similar to the very high rate observed in Brazil, but was markedly lower in rural areas, especially at the lower quintiles (Table A2.23). The quantities purchased per household per month were slightly higher than in Brazil, while the per capita amounts were slightly lower.
vectors, which are influenced by hygienic practices); (3) nutrient deficiency (calories, protein, micronutrients, affected by feeding practices); (4) injuries (accidents and intentional injury, which are affected by care); (5) personal illness control (preventive measures and curative treatment, affected by health care practices). All social and economic determinants must operate through the proximate determinants to affect child health and survival.

In the basic microeconomic model of the family, the overall wellbeing of the household depends on the consumption of goods, services and leisure, and on the health of family members. Households maximize their utility function under several constraints: a time constraint for each household member, a budget constraint for the entire household, and a health production function. The health production function relates the health of child to his F-ast health status and the proximate determinants, as outlined in the Mosley-Chen framework. It is a complex relationship that cannot be captured easily by regression analysis based on cross-sectional survey data. Only a series of measurements over time can provide sufficient information to allow reliable judgements on the relative importance of different proximate determinants on growth attainment at the individual level. Health production functions are therefore best estimated with longitudinal data.<sup>7</sup> The estimation of health production functions is further complicated by the fact that proximate determinants are subject to individual/household choice. Correct estimation of their causal effect relies on controls for simultaneity bias that require information not available in most demographic and health surveys.

# **5.2 Reduced-Form Estimation**

The exogenous variables in the niicroeconomic model are the same as the socioeconomic variables in the Mosley-Chen franework, and the choice variables that appear as covariates in the health production function are the same as the Mosley-Chen proximate determinants (Schultz 1984). The economic model of the family can be solved to yield a reduced-form equation for health outcomes in which child anthropometry depends only on exogenous individual, household and community characteristics:

# z; = h(Ci, Ch, C., Si),

(Equation 1) where z; is the height-for-age z-score for child i; C, are individual characteristics of the child; Ch are household characteristics; C, are community characteristics; and si is an individual specific random disturbance associated with the anthropometric outcome of child i.

> 7 See Cebu Study Team (1991 and 1992) for a description of the conceptual and methodological issues involved in the integration of socioeconomic, behavioral and biomedical variables for studying child health with longitudinal data.

7
Other factors such as remittance costs and migrants’ vintage also play a role in influencing remittance flows. In a survey of Tongan migrants in New Zealand, Gibson, McKenzie, and Rohorua (2006) find that remittances sent would rise by 0.22 percent if costs fell by 1 percent. Other studies have found that remittances are influenced by interest rate differentials, exchange rate premia (the difference between the official and black market exchange rates), and the duration of migration (Glytsos 1997; El-Sakka and McNabb 1999). Freund and Spatafora (2008) report that recorded remittances depend negatively on transfer costs and the parallel market premium, as migrants may prefer to send money through informal channels when transfer costs are high or when the official exchange rate is unattractive. Some authors argue that the skill composition of migrants matter for remittances, but the evidence of this phenomenon is mixed. While Niimi and Ozden (2006), Faini (2007) and Adams (2009) using cross-country data find that countries that have a larger proportion of high-skilled migrants receive less remittances— perhaps because these migrants are also more likely to settle in the host countries and reunite with their families—studies based on micro-level survey data find the opposite result. Bollard, et al. (2009) using survey data in 11 OECD destination countries find a positive relationship between education levels of migrants and the amounts remitted. Clemens (2009) finds that Nigerian migrant doctors in the United States sent more than $5,000 a year in remittances.

# 4. A simple model for forecasting remittances

While remittances are influenced by all of the above factors, their use in a forecasting exercise is constrained by the lack of reliable forecasts of the future evolution of these explanatory variables. The data on remittance costs are not easy to model, although we know that remittance costs are falling and causing remittance flows to increase. The migrants’ vintage, or the number of years lived in the destination country, is also a plausible determinant of remittance flows to the origin countries (Merkle and Zimmermann 1992; Glytsos 1997). New migrants may send more remittances as a percentage of their income, since they have better ties back home. However, there is anecdotal evidence that new migrants often have financial obligations (such as repaying 5
Duranton, G., E. Ghani, A. Grover Goswami and W. Kerr. (2015). The misallocation of land and other factors of production in India. World Bank Working Paper 7221.

Eslava, M., J. Haltiwanger, A. Kugler and M. Kugler. (2006). Plant turnover and structural reforms in Colombia. IMF Staff Papers, 53: 58-75.

Evans, D.S. and B. Jovanovic. (1989). An estimated model of entrepreneurial choice under liquidity constraints. Journal of Political Economy, 97(4): 808-827.

Fisman, R. and I. Love. (2003). Trade credit, financial intermediary development and industry growth. Journal of Finance 58(1), 353-374.

Foster, L, J. Haltiwanger and C. Syverson. (2008). Reallocation, firm turnover, and efficiency: Selection on productivity or profitability? American Economic Review 98(1): 394-425.

Fox, J.T. and V. Smeets. (2011). Does input quality drive measured differences in firm productivity? International Economic Review 52(4): 961-989.

Ghani, E., A.G. Goswami and W.R. Kerr. (2012). Is India's manufacturing sector moving away from cities? NBER Working Paper 17992.

Ghani, E., A.G. Goswami and W.R. Kerr. (2014). Spatial dynamics and energy usage in India. World Bank Working Paper 7055.

Ghani, E., W.R. Kerr and A. Segura. (2015). Informal tradables and the employment growth of Indian manufacturing. World Bank Working Paper 7206.

González, R.L., J.A. Lopez and J. Saurina. (2007). Determinants of access to external finance: evidence from Spanish firms. Federal Reserve Bank of San Francisco Working Paper 2007-22.

Greenaway, D., A. Guariglia, and R. Kneller. (2007). Financial factors and exporting decisions. Journal of International Economics, 73(2): 377-395.

Guariglia, A. (2000). Inventory investment and capital market imperfections: A generalization of the linear quadratic inventory model. Oxford Bulletin of Economics and Statistics, 62(2): 223242.

Harding, J. and S.S. Rosenthal. (2013). Homeowner-entrepreneurs, housing capital gains and self-employment. University of Connecticut Working Paper.

Harhoff, D. and T. Korting. (1998). Lending relationships in Germany - Empirical evidence from survey data. Centre for European Economic Research Paper 98-21.

Harrison, A. and M. McMillan. (2003). Does direct foreign investment affect domestic firm credit constraints? Journal of International Economics, 61(1): 73-100.

47
<!-- Start of picture text --> Number of episodes<br>100 = Climate disaster<br>80 == BiologicalGeophysicaldisasterdisaster<br>60<br>40<br>20<br>0<br>1960-1979 1980-1999 2000-2018<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes TM 1960-1979<br>10 = 1980-1999<br>8 = 2000-2018<br>6<br>4<br>2<br>0<br>Climate Biological Geophysical<br><!-- End of picture text --> <!-- Start of picture text --> Percent of total<br>400 mEAP mECA @LAC &MNA @&SAR &SSA<br>75<br>50<br>25<br>0<br>1962 1970 1978 1986 1994 2002 2010 2018<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes<br>1.2 m Advanced economies<br>1.0 =m EMDEs<br>a LICs<br>oe<br>,<br>0.6<br>0.4<br>0.2<br>0.0<br>1960-1979 1980-1999 2000-2018<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes TM 1960-1979<br>1.5 =TM 1980-1999<br>= 2000-2018<br>1.0<br>0.5<br>0.0<br>Climate Biological Geophysical<br><!-- End of picture text --> <!-- Start of picture text --> Number of episodes = 1960-1979<br>40 = 1980-1999<br>= 2000-2018<br>30<br>20<br>10<br>0<br>EAP ECA LAC MNA_ SAR _ SSA<br><!-- End of picture text -->
Policy Research Working Paper 10548

# **Abstract**

This study demonstrates systematic bias against women in public perceptions of the fairness of wages. In nationally representative survey experiments across more than 70,000 individual vignettes posed to 4,500 respondents in three Central Asian countries, respondents were 13 percent more likely to say wages were “too high” when the randomly assigned person described in the vignette (subject) was a woman, and 34 percent more likely to say they were “too low” when the subject was a man. The pattern of bias favoring higher wages for men is statistically significant at conventional levels in all three countries, among both male and female respondents, and in each of the eight occupations studied. The results also demonstrate the presence of significant bias in favor or older workers, specifically for white-collar occupations, and the absence of this relationship for the blue-collar occupations included in the experiment. The findings reinforce the importance of bias as a contributing factor to the gender pay gap, and the value of equal pay regulations to prevent gender discrimination in wage setting.

This paper is a product of the Poverty and Equity 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://www.worldbank.org/prwp. The author may be contacted at wseitz@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
Table A.6: Robustness: alternative measure of TFPR

|A. Dep. variable: log TFPR (ACF)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Post _×_ Exports share (log)|0.041**<br>(0.018)|||||||
|Post _×_ Output share (log)||0.022<br>(0.025)||||||
|Post _×_ initial capital stock (log)|||0.022<br>(0.020)|||||
|Post _×_ initial capital per worker (log)||||0.024<br>(0.054)||||
|Post _×_ initial average wages (log)|||||0.041**<br>(0.019)|||
|Post _×_ RCA relative to Poland||||||0.080*<br>(0.040)||
|Post _×_ RCA relative to the world|||||||0.116**<br>(0.051)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|N (obs.)|134174|134174|134174|134174|134174|134174|134174|
|N (frms)|18355|18355|18355|18355|18355|18355|18355|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. RCA is a standard measure of comparative advantages of Germany versus Poland and the world. All measures of German influence refer to the period 1994-2004. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level. TFPR is estimated using the method proposed in Ackerberg et al. (2015).

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<!-- Start of picture text --> Na<br>02 4 6<br>Captivate Index<br>“ Control Group SC Group<br>® Coaching Group ——— Regression line<br><!-- End of picture text -->
# List of Figures

|1<br>The nal good sector . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>19|
|---|---|
|2<br>The labor market . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>23|
|3<br>Unemployment<br>. . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>24|
|4<br>Wage inequality . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>37|
|5<br>Goods markets<br>. . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>38|
|6<br>Labor markets . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>39|
|7<br>Unemployment<br>. . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>40|
|st of Tables<br>1<br>Calibration<br>. . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . . . .<br>16|

# List of Tables

3
# Interviewer Effects in Subjective Survey Questions: Evidence from Timor-Leste

# Kristen Himelein<sup>1</sup>

## _World Bank_

_1818 H Street NW, Washington DC, 20433, USA_

**Key words:** survey methodology, interviewer effects, subjective data, justice surveys, Timor-Leste

> 1 Kristen Himelein is a Senior Economist / Statistician in the World Bank’s Poverty Global Practice, and can be reached at khimelein@worldbank.org.  The author would like to thank Kinnon Scott, Steve Heeringa, Brady West, Melissa Plegue, the participants in the 2013 European Survey Research Association conference in Ljubljana, and two anonymous referees for very useful comments.  This analysis was supported by Research Support Budget program in the Development Economics Research Group of the World Bank.  All views are those of the author and do not reflect the views of the World Bank or its member countries.
informal sector workers were more likely to report receiving government assistance than public or formal sector workers. In Morocco, by contrast, public formal sector workers were significantly more likely to be receiving assistance (Krafft et al., 2021).

The World Bank, together with NSOs, collected HFPS data to monitor the impact of the pandemic on households in several African countries. These data can be used to evaluate the potential and targeting of social protection programs. Duchoslav and Hirvonen (2021) use these data to evaluate targeting of social protection and social assistance transfers in Ethiopia, Malawi, and Nigeria based on pre-COVID wealth quintiles. The targeting in Ethiopia which was primarily based on an expansion of the existing Productive Safety Net Program (PSNP) was progressive and covered about 27% of the poorest households in rural areas but was much less effective in urban areas. In Nigeria, there was a major expansion of cash transfers, but survey data showed only mildly effective targeting of government aid in urban areas, and a regressive distribution of probability of receiving aid in rural areas (Duchoslav and Hirvonen, 2021). In Malawi, major government plans to expand social protection did not materialize, so reported aid consisted of mostly uncoordinated privately run initiatives, which were poorly coordinated.

The targeting efficiency of the social protection response in South Africa has also been found to be generally pro-poor. South Africa benefited from a pre-existing means-tested comprehensive social protection system for which payment sizes were increased after the outbreak of the pandemic. The poorest two deciles of households, however, while being most affected by the lockdowns in terms of lost income, were less likely than households higher in the income distribution to have benefited from the expanded social protection as of April 2020, due to delays in enrollment and confusion about eligibility (Kohler Bhorat, 2020). Figure 11 compares the targeting performance reported in phone surveys in Morocco, Tunisia, Egypt, Ethiopia, Nigeria, and South Africa. Notably, in spite of the limitations mentioned above, the countries with the largest existing social national protection systems were able to expand quickly while maintaining overall pro-poor targeting.

27