rafmacalaba/data-use-mentions-extended huggingface dataset:
argument goes, do race, disability, gender, caste, place of birth, etc. If that is so, can one compute acceptable summary indicators of this ex-ante set of potential outcomes? Initial attempts have been made, but the area is in its infancy, and more work is needed.<sup>14</sup> Since inequality of opportunity is closer to the concept of distribution that is relevant to the theoretical literature on inequality and growth than income inequality, a related question is whether turning to such an indicator might shed light on the inconclusive empirical cross-country literature on the subject. While most cross-section regressions of growth on initial income inequality (with controls) have returned negative and significant coefficients on inequality, most panel regressions of growth on lagged, time-varying income inequality (with controls) have returned positive and significant coefficients.<sup>15</sup> If we were able to measure inequality of opportunity directly for a number of countries, so that one no longer needed to rely on income inequality as a proxy, would the crosscountry result shed light on the ‘macroeconomic’ effect that corresponds to the microeconomic impacts identified through studies such as those discussed in Section 3? The question remains open.

> 14 See Bourguignon, Ferreira and Menéndez (2003), Cogneau and Gignoux (2005) and O’Neill, Sweetman and Van de Gaer (2000).

15 See Bénabou (1996) for a survey of the cross-section results, Forbes (2000) and Li and Zou (1998) for two influential panel studies, and Banerjee and Duflo (2003) for a possible interpretation of the differences.
5. **Average labor earnings:** Data is taken from national household income surveys with the exception of Croatia and the UK from the Eurostat and the ILO; Malaysia from the Povcal Net database; and Hungary, Poland, Slovenia and South Africa from the ILO.

# 6. **Complementary Data for the Panel Analysis**

- a. Trade Shock: The trade shock series are based on data from the IMF’s Direction of Trade database.

- b. Unemployment Rate: The Unemployment rate data have been collected in the IMF’s World Economic Outlook database.

|Argentina|_www.mecon.gov.ar/hacienda_<br>_www.indec.gov.ar_<br>Useful|Mexico<br>Links for Data Sources|_www.apartados.hacienda.gob.mx_<br>_www.inegi.org.mx_|
|---|---|---|---|
|Australia|_www.abs.gov.au_|Peru|_www.bcrp.gob.pe_|
|Belgium|_statbel.fgov.be/en_||_www.mef.gob.pe_|
|Brazil|_www.tesouro.fazenda.gov.br_||_www.inei.gob.pe_|
||_www.receita.fazenda.gov.br_|Slovakia|_www.statistics.sk_|
||_www.ipeadata.gov.br_|Slovenia|_www.stat.si_|
|Bulgaria|_www.nsi.bg_||_www.mf.gov.si_|
|Canada|_www.statcan.gc.ca_|South Africa|_www.reservebank.co.za_|
|Chile|_www.dipres.cl_|Thailand|_nso.go.th/en_|
||_www.bcentral.cl_|UK|_www.ifs.org.uk_|
|Colombia|_www.banrep.gov.co_<br>_www.dane.gov.co_
back into poverty on the other hand, what may be required is strengthening the coverage, reach and if needed also the generosity of social protection and safety net systems.

**Figure 4: Episodes of movements in and out of poverty (% of population)**

(a) Episode between 1995/96-2003/04 (b) Episode between 2003/04-2010/11 <!-- Start of picture text --> 40 40<br>36<br>35 33 35<br>29<br>30 30<br>26<br>24<br>25 25<br>20<br>20 17 20<br>14<br>15 15<br>10 10<br>5 5<br>0 0<br>Poor, poor Poor, non- Non-poor, Non-poor, Poor, poor Poor, non- Non-poor, Non-poor,<br>poor poor non-poor poor poor non-poor<br><!-- End of picture text --> _Source_ : World Bank staff calculations using data from three rounds of NLSS data for 1995/96, 2003/04 and 2010/11. The methodology used is called the synthetic panel methodology and is described in Annex 2.

What is the relative size of these groups in Nepal? We answer this question for two poverty reduction episodes between 1995/96 and 2003/04 using data from three rounds of the NLSS data. Recall that the headcount poverty rate using the comparable 2010/11 poverty line declined by 14 and 19 percentage points respectively during these two episodes. From what is presented in Figure 4, what we see is that if 26 percent of the population escaped poverty between 1995/1996 and 2010/11, 17 percent actually fell into poverty during the process. Likewise, between 2003/04 and 2011, 29 percent moved out of poverty while 14 percent fell back in. This essentially implies that that for every 2 Nepalis that got out of poverty in
.|17.43|
|**OIC**<br>**countries**<br>**average**|72.97%|8.11%|10.81%|13.51%|62.16%|48.65%|151.00|4,008,095.95|284,592.68|7.43|
|**Developing**<br>**countries**<br>**average**|47.52%|9.90%|26.73%|17.82%|48.51%|37.62%|141.19|9,150,287.27|168,779.94|7.44|
|**OIC-GCC**<br>**average**|79.41%|8.82%|8.82%|14.71%|61.76%|52.94%|155.33|3,837,600.94|138,565.15|7.43|
|**Low-income**<br>**countries**<br>**average **|58.62%|3.45%|20.69%|6.90%|72.41%|51.72%|66.43|1,018,377.27|72,356.47|5.58|
|**OECD**|30.00%|86.67%|53.33%|93.33%|33.33%|50.00%|274.00|56,203,661.64|2,080,697.75|24.47|

Source: CGAP financial access report data 2010 Note: The CGAP Financial Access Database covers questionnaires sent to 151 economies: 13 in East
The rate of non-compliance that resulted was 12%. In those cases, the household received zero CFLs.<sup>33</sup> All study households were visited again in the spring of 2014, one year following the intervention, for a follow-up survey. Of the original 1,000 study households, 101 addresses were identified as having new tenants in the year since the intervention. We interviewed all households currently living at the original addresses, as it was difficult to know exactly when residents moved or if the CFLs had moved with them.<sup>34</sup> After completion of the follow-up survey, survey respondents were again offered 150 KGS to compensate for their time.

# **4 Study sample**

## **4.1 Data**

We use several datasets, including electricity utility data, baseline and follow-up household survey data, and GIS spatial data, which are matched at the household-level. All of these data are then matched to heating degree day data from a nearby weather station. A description is provided in the Appendix.

**Electricity utility data:** We utilize electricity utility records identifying both transformers and households served by them, as well as the monthly household electricity bills. These data start in October 2010 and continue through September 2014. This provides observations 30 months prior to and 18 months following the intervention, held in March 2013. One important feature of this time period is that electricity prices remained constant at 0.02 USD per kWh.<sup>35</sup> The utility, however, was not collecting transformer-specific outage data at the time of the intervention.<sup>36</sup>

**Household survey data:** As indicated in the previous section, we conducted two rounds of household surveys, one immediately prior to the intervention (baseline) and one a year

> 33For intent-to-treat estimates, these households are considered treated. Compliance is discussed below.

> 34Survey enumerators made at least four attempts to
br>48.4%<br>45% 47.5%<br>40%<br>35%<br>30%<br>25% 22.1%<br>19.7%<br>20% 17.5%<br>15% 11.7% 10.6%<br>8.9%<br>10% 6.7%<br>5% 0.9%3.4% 2.8%<br>0%<br>1 2 3 4 5 6<br>Population distribution per income strata<br>Distribution of bank branches per neighborhood income strata<br>Distribution of population per income strata<br>Population %<br><!-- End of picture text --> _Source: Medina and Steiner (2002), based on Superintendencia Bancaria data_ 24. **Credit cooperatives have a higher presence in lower income areas than banks** . A separate analysis by Medina and Núñez<sup>38</sup> about bank representation in Bogotá compares branch presence of banks with financial credit cooperatives. The analysis shows that bank branches are concentrated in higher income areas. Medina’s analysis statistically 37 Includes Banco Popular, Banco Agrario, Banco Caja Social, Banco Colmena and Megabanco. 38Medina and Núñez, 2001.

42
crude proxy that does not account for the different unit or per-capita costs of, for example, providing a household with flush to sewer versus providing piped water. Further analyses could attempt to draw up actual costings to enable more refined comparisons.

# **A5 CONCLUSION**

Ongoing epidemics in cities across the developing world demonstrate the persistent threat of Cholera and its potentially devastating impact. International efforts led by the Global Task Force on Cholera Control (GTFCC) have been focused on rapid response through stepping up vaccine availability, while a long-term solution rests in the provision of clean water and safe sanitation infrastructure facilities. Basic improvements in infrastructure, alongside more specific time-limited interventions, can dramatically reduce risk but, in heavily resource constrained settings, must be effectively chosen and targeted in order to maximize their impact. This study has showcased how the growing availability of geospatial data on water and sanitation infrastructure, environmental characteristics and other risk factors can be coupled with spatial statistical analysis to provide granular and robust information to support more precise and impactful infrastructure interventions to improve public health.
Argentinean municipalities where water services were privatized, the incidence of child mortality from water-related diseases declined significantly (whereas the incidence of child mortality for other reasons remained stagnant). They therefore provide indirect evidence of improvements in water quality and access.<sup>6</sup> The empirical methodology that we followed is similar to that employed in Galiani, Gertler, and Schargrodosky (2005). Using panel data for the periods surrounding the episodes of privatization and nationalization, we identify differences in sanitation rates between regions that first privatized their water suppliers and later nationalized them and those that did not using a difference-in-difference estimator.<sup>7</sup> Our study has at least two important differences from Galiani, Gertler and Schargrodosky (2005). First, as explained above, we focus on the nationalization of water services, not only on their privatization. Second, our dependent variables are direct measures of the quality of service provided by water companies (access to and quality of water). Health outcomes are important, particularly child mortality, but they tend to be determined by many external factors other than water services. In a middle-income country such as Uruguay, child mortality is rarer than in low-income countries. Therefore, water quality may improve dramatically without any observable change in child mortality.<sup>8</sup> Direct measures of the quality of water services seem more appropriate in such a context.

Our results suggest that although the earlier privatization period had little impact in terms of access to the sanitation network, the nationalization of water services had a positive and statistically significant impact on access, particularly among households in the bottom 25 percent of the income distribution. Nationalization also seems to have led to improved water quality. Indeed, the impact of nationalization on the detection of abnormal levels in microbiological and inorganic water tests is consistently negative and has a relatively large coefficient.

It is important to note that although it may be tempting to conclude from our results that the public sector can provide water services as well
. Therefore, with rising retail inflation, real wages of low-skilled workers unambiguously decrease. The rise in the number of expensive varieties and varieties mostly consumed by skilled workers is consistent with the fact that the rise in income dominates the price effect for skilled workers, thereby leading to rising real wages. The introduction of MDPs therefore can lead to increased local inequality through its effect on local inflation. We also introduce a theoretical model that is consis-

> 19Since skill is not directly observed at an aggregate zip-code level, we use IRS SOI tax returns as a proxy for skill. We define households with taxable income of $50,000 or more as high-skilled households, and those with smaller incomes as low-skilled households. Finally, zip-codes with at least half of high-skilled households are categorized as high-skilled.

33
**–**

# **From Farms to Factories and Firms**

# **Structural Transformation and Labor Productivity Growth in Malaysia**

# **1. Introduction**

Since independence in 1957, Malaysia has undergone a profound development process that has radically improved nutrition, health care, and social service provision. During the last decades, Malaysia has also achieved an increasingly low mortality rate, a decline in fertility rates, and increases in life expectancy. As a result of generally favorable economic and demographic developments and in spite of more recent challenges like the COVID-19 crisis, Malaysia has also seen relatively high levels of economic growth. Among the 88 countries for which comparable data on GDP per capita in both 1960 and 2019 are available in the World Bank’s World Development Indicators, Malaysia recorded the seventh highest growth rate in the intervening years (in local currency units). These relatively high levels of economic growth have propelled it to becoming the uppermiddle income country at the cusp of high-income status that it is today (World Bank forthcoming).

During the last decades, Malaysia has also gone through a rapid and sustained structural transformation, defined by the reallocation of economic activity across sectors such as agriculture, manufacturing, and services. Within only two generations, the South-East Asian country moved from an agriculture-centric economy to one with an important manufacturing base and then one dominated by services firms. In 1957, agriculture accounted for 58 percent of employment (World Bank 2019a). Since then, its share of employment has declined to 10.6 percent in 2018, releasing labor first to the manufacturing sector and then increasingly to the services sector. Similarly, the share of agriculture in value-added was 32.6 percent in 1970 and has since declined to 7.4 percent in 2018. As Malaysia pursues further development toward becoming a high-income and developed nation but faces a slowdown in both structural change and productivity growth, questions have been raised whether the reallocation of production factors can be a driver of the country’s
One possible explanation is that during the period in which households were interviewed in 1995 (April-May), food expenditures were at a seasonal low and nonfood expenditures were at a seasonal high for West Forest export crop farmers. However, the 1985-1988 data do not support this hypothesis. Carrying out regressions similar to those presented in table 24, the regression coefficient for West Forest*cocoa and coffee farners*April-May dummy is positive but not significant with respect to food, and negative but not significant for nonfood expenditures. Using these coefficients to produce a seasonally corrected estimate of food expenditure would reduce food expenditures and increase nonfood expenditures, thus further reducing the food shares of West Forest export crop households in 1995.

A second finding that casts some doubt on the large decline in poverty in the West Forest is the apparent undersampling of cocoa-growing households in 1995 compared to 1993. We suspect that there may have been undersampling because the percentage of cocoa-growing households in the West Forest fell from 63.3 percent to 50.6 percent of the total regional population, a drop of 20 percent.<sup>30</sup> As a result, in the West Forest, mean cocoa revenues per capita showed no increase -- from 16200 to 16032 CFAF -- between 1993 and 1995, in contrast to the East Forest, they increased by 30 percent from 11942 CFAF to 15489 CFAF (table 35). For the subset of households that declared earning cocoa revenues, mean real cocoa revenues went up 17 percent in the East Forest and 24 percent in the West Forest (table 36). Thus the stagnation in cocoa revenues in the West Forest is due to a decline in the percentage of households declaring cocoa revenues, rather than to a decline in the revenues of the households that do grow cocoa.

**Table 35 Mean Export Crop Revenues Per Capita by Region** _(in 1985 CFAFper year)_

|_Region_|_Cocoa_|_Coffee_|_Cotton_|_Total_|
|---|---|--
A key component of our empirical strategy relies on the distance of the household from the active fault-line and the epicenter of the earthquake. We obtained the digital mapping of the Himalayan Frontal Thrust from publicly available geological maps. We then calculated the linear distance to the fault-line and the distance to the epicenter using each household’s GPS location and using the Haversine formula.<sup>9</sup> These spatial data were supplemented with data on the average and maximum slope of the Union Council (a group of 4-5 geographically contiguous villages) to account for the hilliness of the terrain. Finally, we also complement these data with statistical village level data provided in the 1998 village population census to examine correlations between population characteristics and the intensity of the earthquake. We use these data, together with retrospective information from the household survey in 2009 to investigate pre-existing correlations between socioeconomic characteristics and earthquake intensity.

Table 1 provides descriptive statistics for the variables used in the study. There are 4670 individuals in our sample, of whom 49.8 percent (2324) are male and 46.5 percent have obtained some formal education. The average household was 18.8 Km from the fault-line and 38.6 Km from the epicenter; as expected, the bulk of the sample lies in the mountainous regions of the two provinces surveyed.

Particularly relevant are answers to the questions on trust. We asked the following question from one male and one female respondent within each household, typically the head of household and his/her spouse and coded the responses in a binary fashion as “yes” or “no”: _Imagine you are walking down a street and dropped a Rs. 1000 note without noticing. __________ was walking behind you without you knowing and picked it up. What is the likelihood that they would return it to you?_ This departs from the usual question asked in the World Values Survey (WVS) on trust: “ _Generally speaking, would you say that most people can be trusted or that you can’
throughout the day prior to the survey day. We develop our own questions around selfworth rather than employing the more standard Rosenberg Self-Esteem Scale, which we found inappropriate given the Rohingya’s recent experiences. Specifically, we construct an index of self-worth from two questions designed to elicit respondents’ beliefs about how they contribute to their family and community. Finally, we adapt the Cantril Self-Anchoring Striving Scale (Cantril, 1965) to measure how stable respondents feel in their present lives and in the future.

We additionally examine the impacts of each treatment on physical health, cognitive function, economic decision making, time-use, and consumption. We capture respondents’ sense of physical health by asking how many days they have fallen sick in the past thirty days and cognitive function by employing a digit-span memory test and a series of basic arithmetic problems. We explore economic decision making along two dimensions: incentivized time preferences (Andreoni and Sprenger, 2012; Gin ́e et al., 2018) and incentivized risk preferences (Holt and Laury, 2002). We measure time-use through the number of hours in the previous day a respondent reports spending idle, as well as the amount of time spent on a variety of other common activities one might do in the camps (including bathing, market, chores, collection of rations, eating, child-rearing, sitting at tea stalls, praying, sleeping, visiting friends/relatives, playing games, playing sport, sitting idle). Finally, we ask respondents how much they consume, borrow and save over the past week.

We further consider changes in perceptions on gender and power in two ways. First, we generate a Household Power Index, composed of a set of questions on perceptions of gendered decision-making and intimate partner violence. The questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home
variable we have four proxies with different data sources coming from the Eurostat. Firstly, we use one indicator from the household survey European Union-Statistics on Income and Living Conditions (EU-SILC), stating the share of labor force not contributing to pension system (both private and public) adjusted for the unemployment rate.<sup>10</sup> Yet, this variable is available for 2007 only and offers a very rough picture of shadow employment. Also the reliability of the information is questionable.<sup>11</sup> Moreover, comparison of this variable with previously mentioned indicator of shadow production uncovers substantial differences between these two data sets. As described in Annex 1, ranking of countries changes substantially when ordered according to values of these variables.<sup>12</sup> Secondly, based on the Labor Force Survey (LFS) we use three proxies of shadow employment. Two of them indicate the share of labor force working in small firms with less than ten employees and the share of self-employed. Both these groups are supposed to be more exposed to shadow employment (Perry et al., 2007); however, the link need not be as straightforward and need not be of the same intensity in all the countries. Again, these variables are available for 2006-2007 only. The last proxy from the Eurostat consolidated LFS we use states "workers without a contract" as the share of labor force. For our country sample, the variable is available in longer time series since 2001. The shortcoming of this proxy is that Eurostat adds up all workers who are on temporary legal contracts and workers with no written contract, all together.<sup>13</sup> That means, this group covers both those who are indeed employed in the shadow economy, and those who are employed legally, but on a temporary

> 9 For details on the methodology used, see Schneider et al. (2010).

> 10 The adjustment for the unemployment rate makes this variable methodologically comparable to