rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 6: Poverty Rate by Agricultural/Nonagricultural Household and Livelihood Type, 2009 and 2014/15

- (a) Poverty Rate for Agricultural/Nonagricultural Households

<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>Urban Rural Urban Rural<br>2009 2014/15<br>Agriculture Non-Agriculture Agriculture Non-Agriculture Agriculture Non-Agriculture Agriculture Non-Agriculture<br><!-- End of picture text -->

- (b) Poverty Rate by Main Livelihood Type in Rural Sudan

<!-- Start of picture text --> 2009 2014/15<br>60%<br>40%<br>20%<br>0%<br>Crop farming Animal Wages and Owned Property Transfers* Other<br>husbandry salaries business income<br>enterprise<br>Poverty rate<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15 and NBHS 2009.

_Note_ : *Transfers include main livelihoods from remittances, pensions, aid, and transfers from family members.

Reducing poverty, particularly for agricultural households, requires reforms that will boost incomes from agricultural activities. This paper aims to examine the relationship between agriculture and household welfare. With the significant loss of government revenue following the secession of South Sudan, agricultural growth has the potential to improve the welfare of Sudanese households. This entails not only increasing the agricultural productivity of Sudanese farmers but also improving access to markets, thereby allowing farmers to cheaply obtain productivity-enhancing inputs and get better prices for their marketable agricultural surplus.

Compared to other African countries that produce the same main crops as Sudan produces, Sudan has lagged in productivity growth (Figure 7). For instance, sorghum, Sudan’s most commonly produced crop, has seen yields increase from below 500 kg per ha in 1995 to almost 700 kg per ha in 2017. However,
# 1. INTRODUCTION

Longitudinal surveys, based on repeated observations of the same statistical units over time, are an invaluable source for analyzing the current state and changes in human populations over time. The Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMSISA) initiative demonstrates the value of longitudinal surveys by disseminating household panel data on issues such as health, agriculture, access to basic services, nutrition, poverty status, and much more, for countries across Sub-Saharan Africa. The European Union’s Statistics on Income and Living Conditions (EU SILC) and the US Bureau of Statistics’ Survey of Income and Program Participation (SIPP) are other key examples of the value of longitudinal studies.

In panel surveys, accuracy depends on many of the same factors as in cross-sectional surveys. However, maintaining the accuracy of estimates from a panel sample becomes more difficult as the length of the panel survey increases. One key concern is the panel attrition of respondents who drop out of the survey, which can become increasingly troublesome for long-running panels. Additionally, movers – that is, people who have moved away from where they were interviewed in the first observation of the longitudinal study – represent a dynamic subpopulation and are complex and costly to interview. Finally, new entrants to the population, such as immigrants and newborns, are particularly difficult to capture in traditional panel surveys.

The correct design, implementation, and use of a panel survey considers several methods to address these problems at different stages of the statistical process, namely the sample design, data collection, and estimation stages. To the extent possible, sample design should minimize the loss of representativeness of panel data due to attrition, leavers, and new entrants. Practical approaches to this include the use of refreshed samples (i.e., rotating panels or split panels) and protocols for incorporating individuals not previously involved in the study to capture population dynamics. Based on tracking rules to follow the movers, the data collection stage should be designed to retrieve essential information on individuals or
# **3. Nutritional Status of Children in Kagera**

The nutritional status of a child is usually assessed with three indicators: stunting (low height for age), wasting (low weight for height) and underweight (low weight for age). All indicators are expressed in “z-scores” which are derived by comparing the child’s height and weight with that of a “reference” group of well nourished children defined by the US National Center for Health Statistics (NCHS) (World Health Organization (WHO) 1995). More specifically the stunting z-score is the difference (expressed in standard deviations (SD)) of a child’s height for age from the median height of children of the same age and sex in the reference population. The z-score is thus the conversion of the child’s height into a standardized unit. Relatively short children have negative z score; tall children a positive z score. The commonly used cut-off point to identify severely malnourished children is a measurement of 2 SD below the median of the “reference” group. Thus, a child is “stunted” if his/her z-score- is below or equal to –2.

Table 1 reports the descriptive statistics related to the sample. On average, 28 percent of children aged 0-5 are moderately underweight (i.e. their weight-for-age is more than two standard deviations below the median of the international “reference” population). Nearly 40 percent of the children are stunted but only 6 percent are malnourished according to the weight for height indicator. This is a general pattern; low height for age or low weight for age reflect long term cumulative deprivation while low weight for height indicates an acute crisis (Alderman, 2000). The former two measures being cumulative generally reveal more children outside of normal ranges than does the latter, which typically only shows high values during periods of acute food shortage. The WHO recommends stunting as a reliable measure of overall social deprivation (WHO, 1986), whereas the MDG
# **II. Academic Literature and Previous Studies**

## **_The micro-determinants of FLFP_**

At the microeconomic level, Killingsworth and Heckman (1986) review the empirical and theoretical literature on the determinants of female labor supply and its interactions with fertility, marriage, and divorce decisions in the United States, Canada, Great Britain, and Germany. They established three stylized facts of FLFP observed during the 20th century: (a) FLFP only decreases after age 20; (b) single women are always more likely to work than married women, but married women have experienced a sustained FLFP increase over the past 100 years; and (c) in 1980, women were over-represented in occupations such as whitecollar clerical and services jobs and under-represented in blue-collar jobs such as crafts, operatives, and non-farm labor.

Using data from the 1960 census, their Ordinary Least Squares (OLS) results indicate that higher FLFP is associated with higher educational attainment, being single, and having a source of “other income.”<sup>3</sup> As we do in this paper, they acknowledge that their results arise from an observed equilibrium between supply and demand for women’s skills and consequently, cannot be taken as causal relationships. However, the observed characteristics of labor markets in these 20<sup>th</sup> century Western countries may differ greatly from those in Morocco—especially for female inclusion, because of the different conditions in terms of development.

> 3 Income other than own earnings, including transfer income.

4
in long-run GDP per capita from closing the gender gap in employment rates_ . For example, if GEGI=19% (the cross-country average), it would imply that long-run GDP (or GDPPC) would be almost 20% higher if the employment gender gap was closed by increasing female employment. See Appendix 1 for a derivation.

The largest increase in GDP would occur in countries with the largest gender gaps. For example, in economy where no women were employed, GEGI=100%, so GDP would increase by 100% moving to equal employment rates (GDP doubles). In contrast, when the GEGI=0, gender parity has already been achieved so there are no untapped gains due to employment gaps.<sup>5</sup> The GEGI has this interpretation because the size of the increase in long-run GDP is proportional to the change in the number of workers in the long-run in simple neoclassical growth models. This is because the capitalto-output ratio is constant in the long run (one of Kaldor’s stylized facts).<sup>6</sup> This means that moving from no female employment to achieving gender parity in employment would double the number of workers, and so long-run GDP would also double. It is important to note that this calculation assumes an _exogenous_ change in female employment, keeping everything else equal (productivity, human capital, etc.). In contrast, looking at a crosscountry relationship in the data between female employment rates and GDP is uninformative about the causal relationship, because female labor supply varies _endogenously_ . For example, an extended drought which lowers productivity and GDP could cause more women to work out of hardship, but this does not imply that higher female labor reduces GDP—that change in female labor supply is endogenous, not exogenous.<sup>7</sup> The GEGI calculation also assumes that human capital is the same across males and females—an accurate assumption based on the World Bank Human Capital Index (see Appendix Figure 3)
. Source: Own elaboration from ENIGH 2016 and ENCEL 2017.

When comparing different ethnic backgrounds, the educational and labor characteristics of both subgroups were similar, with the exception being that, although possessing occupations of similar status, indigenous individuals seem to receive lower labor income and a lesser proportion have access to health services, which also indicates an inequality in remunerations. It is possible that, in addition to discrimination due to their ethnic background, the disadvantages of indigenous individuals are associated with their context. Indigenous individuals not only come disproportionately from localities of very high marginalization (89.5% in comparison to 52.8% of non-indigenous), but also their current localities of residence are, in greater proportion, of very high marginalization (23.9% in comparison to 3.1% of non-indigenous).<sup>31</sup>

> 31 The marginalization of the localities of origin data correspond to those from CONAPO in 1997 and the localities of current residence to those from 2010.

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*#*|0.10|0.15|<br>-.0544**|0.09|0.12|-.025*#*|0.15|-.058***|
|Nepew|041|0A2|-0.01|0.41|0.39|0.02|0.41|0.45|-0.04|0.39|0.021|
|Tanzania(LSMS)|0.28|030"|-.025|0.28|0.39|-.111***|0.28|0.27"|004|0.39|-.1]###|
|<br>Uganda<br>|0.66<br>|0.72<br>|-.058***<br> <br>|0.67<br><br>|0.79<br>|-.126***<br>|0.66<br>|0.70<br>|-0.04<br>|0.79|-.13*#*|
|Notes:- stands for n<br>Source:Authors’calc|otavailbk. *<br>ulationsbase|,**,and*<br>donthedat|**indic**a**<br>th t <br>asetsmTable|te<br> the difference <br>2.1|1s statisticall|y sipnificantat|the 10%,5%and|1%levels re|spectively|||
002)|
|Test: x * POST + x + POST=0|0.91|32.12|327.33|139.68||
||0.34|0.00|0.00|0.00||
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Evaluation cycle FE|Yes|Yes|Yes|Yes|Yes|
|Observations|209150|209150|209150|209150|209150|
|R‐squared|0.011|0.011|0.011|0.017|0.016|

Note: Non‐compliance=1. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance.

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

_Fifth, does transparency shift factory efforts to alternative areas of compliance?_ One concern is whether the Public Disclosure Program incentivizing firms to shift efforts towards the disclosure questions at the expensive of other compliance areas. We test this by estimating equation (3) for the disclosure questions only, presented in Tables 12 and 13. The results do not show evidence that firms are improving more on the disclosure questions than the non‐disclosure questions in the post‐disclosure period. In Table 12 (between firm), the estimated coefficient of POST is nearly identical for disclosure questions and all questions (though not statistically significant in the latter). In Table 13 (within firm), there is some 28
# Wage Rigidity and Disinflation in Emerging Countries<sup>∗</sup>

## Julián Messina

of the Chief Economist for Latin America at the World Bank and IZA Anna Sanz-de-Galdeano Universitat Autònoma de Barcelona October 2011 Abstract This paper examines the consequences of rapid disinflation for downward wage rigidities in two emerging countries, Brazil and Uruguay, relying on high quality matched employer-employee administrative data. Downward nominal wage rigidities are more important in Uruguay, while wage indexation is dominant in Brazil. Two regime changes are observed during the sample period, 1995-2004: (i) in Uruguay wage indexation declines, while workers’ resistance to nominal wage cuts becomes more pronounced; and (ii) in Brazil, the introduction of inflation targeting by the Central Bank in 1999 shifts the focal point of wage negotiations from changes in the minimum wage to expected inflation. These regime changes cast doubts on the notion that wage rigidity is structural in the sense of Lucas (1976).

Keywords: downward wage rigidity, indexation, matched employer-employee data, emerging economies JEL Classification: J30, E24.

> ∗The opinions expressed in this article do not necessarily reflect the views of the World Bank. We are grateful to Francesco Devicienti, Chico Ferreira, Stefano Gnocchi, Lorenz Goette, Gustavo Gonzaga, Steinar Holden, Andres Neumeyer, Evi Pappa, Rodrigo Soares, Marcelo Soto, Uwe Sunde, Augusto de la Torre and seminar participants at the World Bank, PUC-Rio, Univ. de la Plata, Univ. di Tella and IAE-UAB for helpful comments on earlier drafts. We are specially grateful to Alvaro Forteza and Ianina Rossi for their help with the data from Uruguay. Anna Sanz-de-Galdeano is also an IZA and MOVE Research Fellow and is affiliated with the Barcelona Graduate School of Economics (Barcelona GSE). She acknowledges financial support from the Government of Catalonia (Contract no. 2009SGR189), the XREPP, and the Barcelona GSE Research Network. The authors would like to thank the Government of Spain, which partially funded this work through the SFLAC TF.

1
# **Declaration of interests**

We declare no competing interests.

# **Acknowledgments**

We acknowledge the kind support provided by the World Bank under a project “Bangladesh WASH (Water, Sanitation and Hygiene) and Poverty Diagnostic” for this work. Support from the Swedish International Development Cooperation Agency and the Japan Trust Fund is acknowledged. We acknowledge the Bangladesh Climate Change Trust (BCCT) for funding a regional-scale hydrological campaign in coastal region that was carried out by the Bangladesh Water Development Board (BWDB). Finally, we acknowledge the Director General of Health Services (DGHS) of Bangladesh for arsenic-affected population exposure dataset and the Bangladesh Agricultural Development Corporation (BADC) for irrigation dataset.

14
language instruction per academic week|29.0|
|Source: World Bank (1996a) Annex F||

What is somewhat puzzling is that class size reductions are even less effective than teacher salaries. However both of these results are consistent with recent estimates by Kingdon (1996) using sample survey data for students from 30 schools in urban Uttar Pradesh, who finds that if the cost effectiveness of teacher salary is normalized to 1, the cost 18
six visits for those under age 2 and one visit for those over age 60; 2–5 year olds needed six visits at midline but two visits at endline. We see that treatment increased compliance with program conditions among both 0–5 year olds and those over aged 60 at midline. By endline, however, treatment increase compliance with program conditions only among those over aged 60, and not among children aged 0–5.

Several caveats and observations are warranted. First, households in control villages that by endline anticipated receiving the program within a few months<sup>18</sup> may have increased

> 16At endline, those in treatment villages were still receiving the CCT program and expected it to continue. Those in control villages anticipated being enrolled within the next few months (by late 2012).

> 17Results hold whether we use heteroskedasticity robust or bootstrapped standard errors.

> 18The endline survey was carried out during August–October 2012, and control villages were told at baseline to anticipate receiving the program in late 2012.

13
of rights to be registered or pledged land to be foreclosed in case of default without incurring prohibitive transaction costs. In situations where unaffordable registration caused limited coverage with registered rights (and thus demand for titling) in the first place, land titling will be sustainable or improve land and credit market operations only if preceded or accompanied by legal, regulatory, and institutional reforms (‘policy’ reforms) to reduce such costs. The volume of registered land transactions can indicate whether such reforms are effective and whether titling programs achieved their social and economic objectives sustainably.

To illustrate how these principles can inform empirical analysis, we use the Lesotho Land Administration Reform Project (LARP) in Maseru in 2010–13, supported by the Millennium Challenge Corporation. The goal of LARP was to improve registry operation through policy reforms and increase coverage of registered land rights through systematic first registration of land rights. We use registry data from 1981–2019,

> 1 _Proper design_ means that accepted rules on how to prioritize between competing claims are in place, and that potential claimants are sufficiently aware that they can protect themselves from abuse by powerful or well-connected individuals.

2
present value of total costs of the program (including costs to both the implementer and the beneficiary) by the number of participants to determine the program cost per beneficiary. And we divide the amount of EYOS by the cost per individual, then multiply by 100 to express the outcome per a US$100 investment.

Benefit‐cost ratios or return to investment (ROI) ratios are calculated as the NPV of lifetime increased wage income divided by the NPV of the program costs per individual. All conversions and calculations follow the methodology described previously in this section.

# 3. Data

The primary data used in this paper are the World Bank’s STEP Skills Measurement Program. The STEP household surveys measure skills of the adult population in low‐ and middle‐income countries and provide comparable international scores of reading proficiency on the scale of OECD’s assessment of adult skills, the PIAAC (World Bank 2016a). We use the STEP survey data collected between March 2012 and July 2014 in five countries with available literacy assessment data: Bolivia, Colombia, Ghana, Kenya, and Vietnam.<sup>9</sup>

> 8 Wages are highly likely to increase over time and assuming no wage growth is a conservative approach. Nevertheless, the net present value of increased learning from interventions is considerable.

> 9 Data are also available for Armenia, Ukraine and Georgia but not included for the reason that over 50 percent of the population in these countries have more than 12 years of formal education, which dramatically reduces the sample size for the analysis in this paper.

7
has an impact on the women who were targeted by the program. Moreover, it is important to know whether the differences in the types of facilitators actually translated into differences in outcomes. To answer this question, we examine two types of data: block-level outcomes reported by the project, and results of a small survey of SHG women in the two types of areas. Given that the two areas were for the most part identical prior to the rollout of the RRLP, and that the program was rolled out in the two areas with almost the same intensity, we can, to some extent, interpret these estimates as causally related to the mobilization effort.<sup>28</sup> We present two sets of results for the block-level outcomes. The first is aggregate data taken from the website of the RRLP program. (The raw data are reported in the Appendix.) Analysis of these data suggests that Intensive and Resource blocks performed very similarly along a number of different metrics: both types of facilitators were able to set up approximately 4-5 SHGs per

> 28 Official data are collected by a strong team led by a statistician, Mr. Hardeep Chopra, who has no vested interest in either block. He was brought into the project right at the time that the Resource and Intensive blocks were being decided in 2012.

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**Figure 2. ICT use and F2F intensity across countries**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 1<br>PER<br>0.8<br>MEX LKA<br>TUR 0.4<br>RUS ECU 0.6<br>GRC LAO<br>LTU KAZ<br>ITA 0.2 CHL GHA<br>POL 0.4<br>SVK ESP JPN<br>HUN FRA 0 IRL 0.2 SLV<br>-0.5 -0.4 -0.3 -0.2 CZE-0.1 DEU0 ISR0.1 0.2 0.3 0.4 0.5 BOL COL<br>AUT KEN<br>EST 0<br>KOR-0.2 BELSVN -0.4 -0.3 -0.2 -0.1 VNM 0 UKR 0.1 0.2 0.3<br>CAN USA<br>NLD SWE DNKNORGBR FIN NZL -0.2 KOSGEO ARM<br>-0.4<br>SGP PHL -0.4<br>SRB<br>MKD<br>-0.6 -0.6<br>F2F index<br>F2F index<br>Low ICT use index Low ICT use index<br><!-- End of picture text --> Note: The vertical axis measures the Low ICT use index (a higher value means lower ICT use at work), while the horizontal axis measures F2F contact index (a higher value means more intense F2F contact). Both indexes in standard deviations from the mean for all PIAAC/STEP countries.

9
WPS3747 were taken from version 6 of the GTAP data base. These have recently been econometrically estimated for at the disaggregated GTAP commodity level (Hertel _et al.,_ 2004).

There are eight labor categories in the Indonesia CGE model: Urban Male Unskilled, Urban Male Skilled, Urban Female Unskilled, Urban Female Skilled, Rural Male Unskilled, Rural Male Skilled, Rural Female Unskilled, and Rural Female Skilled. The distinction between rural and urban labor implies that workers are not allowed to shift between rural and urban production sectors. Male and female, as well as skilled and unskilled labor, are assumed to be imperfect substitutes in the production activity of urban or rural sectors. Alternative specifications of the functioning of the labor markets can be implemented in the model. In the reference simulations, wages are assumed to adjust so as to clear all labor markets. This is consistent with the full employment assumption used in the global model. Two alternative specifications will also be examined.

Land appears as a factor of production in the agricultural sectors. Only one type of land is considered in the model, while capital markets are segmented into six categories: owner occupied housing, other unincorporated rural capital, other unincorporated urban capital, domestic private incorporated capital, public capital, and foreign capital. Given the medium-term perspective of the model, it is assumed that land is activity specific, while other types of capital are competitively allocated among the different sectors so that its marginal value product is equalized across activities.

Equilibrium in a CGE model is defined by a set of constraints that need to be satisfied by the economic system but are not considered directly in the decisions of micro agents. Aside from the supply-demand balances in product and factor markets, three macroeconomic balances are specified in the Indonesia CGE model: (i) the fiscal balance, with government savings equal to 6