rafmacalaba/data-use-mentions-extended huggingface dataset:
Table B.3: Comparison of One-Step and Two-Step RD Estimations

|||A. One-Step|B. Two-Step|
|---|---|---|---|
|||Estimation|Estimation|
||I. Number of Ca|rd Transactio|ns|
||80 Weeks BW|0.52|0.50|
|Li||(0.043)|(0.044)|
|near|Optimal BW|0.44|0.42|
|||(0.071)|(0.081)|
||80 Weeks BW|0.50|0.48|
|dti||(0.062)|(0.063)|
|Quarac|Optimal BW|0.33|0.38|
|||(0.083)|(0.084)|
||II. Volume of Ca|rd Transacti|ons|
||80 Weeks BW|0.28|0.28|
|Li||(0.023)|(0.024)|
|near|Optimal BW|0.24|0.27|
|||(0.039)|(0.039)|
||80 Weeks BW|0.29|0.30|
|di||(0.030)|(0.030)|
|Quaratc|Optimal BW|0.29|0.32|
|||(0.047)|(0.047)|

Notes: Column A displays our main (benchmark) RD estimates obtained from equation 1. Column B displays estimates from a two-step procedure. We first estimate equation 1 on the full 2010-2016 data to estimate the month-of-year fixed effects with the highest possible degree of precision. We then recover the de-seasonalized ˆ outcomes _log_ ( _Z_<sup> ̃</sup> _t_ ) = _log_ ( _Zt,
# FIGURE 12 Global value chain participation and pass-through

A. Global value chain participation <!-- Start of picture text --> Percent<br>30 Weighted average Median<br>28<br>26<br>24<br>22<br>C. Global value chain participation and pass-<br>through from monetary policy shocks in EMDEs<br>50<br>40<br>30<br>20<br>10<br>0<br>0 0.2 0.4 0.6<br>Pass-through, ratio<br>percent<br>Global value chain participation,<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> C. Global value chain participation and passthrough from monetary policy shocks in EMDEs B. Global value chain participation and passthrough from monetary policy shocks <!-- Start of picture text --> Ratio<br>0.6 Interquartile range Median<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>Low High Low High<br>Advanced economies EMDEs<br><!-- End of picture text --> D. Global value chain participation and average exchange rate pass-through in EMDEs <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>-0.1 0 0.1 0.2<br>Pass-through, ratio<br>percent<br>Global value chain participation,<br><!-- End of picture text --> Source: OECD; World Bank.

Note: Global value chain data are from the OECD-WTO TiVA database. The selected indicator is
food consumption are large and that they are majorly driven by climate variability.

The remainder of the paper is organized as follows. Section 2 provides definitions of our outcome of interest and the rainy season quality. Section 3 explains our methodology with an overview of the food insecurity prediction model based on a cat model framework, a description of the approach used to construct RSQ indices, and the empirical strategy for the estimation of vulnerability functions. Results are presented in section 4 and section 5 concludes.

# **2 Defining and measuring food security and rainy season quality**

## **2.1 Food security**

Food security is defined as a situation in which “all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.”<sup>4</sup> The concept of food security at the household-level rests on four pillars: (i) the (physical) _availability_ of food that depends on local food production, food stocks and net trade, (ii) the economic and physical _access_ to food, (iii) the _utilization_ of food that reflects the nutritional status of individual through feeding practices, food preparation, diet diversity and intra-household distribution of food and (iv) _stability_ that requires that the first three components be stable over time.

In this paper, we measure food security at the household-level with the Food Consumption Score (FCS). It is calculated as a weighted sum of reported consumption frequencies of various food groups over a period of seven days. The 8 food groups considered are assigned weights according to their nutritional importance. The highest weights are placed on food groups providing proteins such as meat (4), dairy (4) and pulses (3); the complete list of food groups and their associated weights is provided in Table A.1 in the appendix. Consumption frequencies for each food group can vary from 0 to 7 days and FCS values
br>Premium|RV-98 Cohort<br>Risk-free rate<br>UF1,000<br>Premium|RV-98 cohort<br>Risk-free rate<br>UF4,000<br>Premium|RV-98 Cohort<br>Corp. bond rate<br>UF1,000<br>Premium|
|Male, 65|0.979|0.981|1.012|1.013|0.905|
|Male, 55|-|0.941|0.976|0.999|0.879|
|Female, 60|0.963|0.925|0.958|0.992|0.845|
|Female, 55|-|0.899|0.929|0.977|0.810|
|Joint|1.000|0.977|1.008|1.025|0.883|

Source: James, Iglesias and Martinez (2005)

**Table 19** **<u>Money’s Worth Ratios for Chile Estimated by this Report, 1999 and 2003</u>**

||March 1999|Mar|ch|2003|
|---|---|---|---|---|
||RV-04 Cohort<br>Risk-free rate<br>Average|RV-04 Cohort<br>Risk-free rate<br>Average||RV-04 Cohort<br>Corporate bond rate<br>Average|
|Male, 65|0.996|1.069||0.955|
|Male, 55|0.981|1.049||0.897|
|Female, 60|1.021|1.077||0.971|
|Female, 55|0.994|1.049||0.905|
|Joint|0.998|1.050||0.892|

Source: Table
POLICY RESEARCH WQORKING PAPER 2102

# Summary findings

Maloney uses cross-country data from Latin America and OECD countries to test the predictions of a simple efficiency wage model (Krebs and Maloney 1998) about the share of the workforce in self-employment and the rate of labor turnover across the process of development and demographic transition.

The model is supported, with numerous demographic, economic, and labor market institutions appearing as important determinants of both self-employment and turnover. Social security taxes on firms and barriers to firing workers appear to reduce the size of the formal sector, and barriers to firing do appear to reduce turnover. But the level of formal sector productivity, real interest rates, and education levels generally have a greater impact.

turnover as indicators of distortion or rigidity without first adjusting for these factors. Somewhat speculatively, Maloney offers adjusted measures that suggest that Latin American labor markets are not especially distorted and are about average in flexibility, with important exceptions.

Central to the theoretical framework is the view that self-employment is a desirable destination for many salaried workers rather than the disadvantaged sector of a labor market segmented by union- or governmentinduced rigidities. To prevent the loss of investment in training to the informal sector, firms will pay abovemarket-clearing "efficiency wages," in the process creating unemployment or segmentation that may cut across lines of formality.

A central lesson is that it is misleading to use the size of the informal self-employed sector and the rate of labor This paper -- a product of the Poverty Reduction and Economic Management Unit, Latin America and the Caribbean Region is part of a larger effort in the region to understand the functioning of developing country labor markets and the impact of labor legislation. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Tania Gomez, room 18-102, telephone 202-473-2127, fax 202-522-0054, Internet address tgomez qworldbank.org. Policy Research Working Papers are also posted on the Web at http://ww-w.worldbank.org/html/ dec/
br>0.13|
|Electricity*|0.66<br>0.98|0.66<br>0.97|
|Refrigerator*|0.05<br>0.66|0.04<br>0.56|
|Television*|0.35<br>0.83|0.36<br>0.82|
|**HH demographics**|||
|Household size|6.86<br>2.47|7.34<br>3.84|
|Num children 0-7 yrs|2.11<br>0|2.26<br>1.08|
|Num children 8-17 yrs|2.15<br>0|2.38<br>0.66|
|Num children 18-34 yrs|1.35<br>0.73|1.46<br>1.62|
|Num children 35-54 yrs|0.967<br>0.86|1.01<br>0.39|
|Num children 55-69 yrs|0.194<br>0.67|0.20<br>0.04|
|Num adults 70+yrs|0.070<br>0.17|0.07<br>0.02|
|**Localities characteristics**|||
|Marginalityindex(1995)|0.58|0.58|
|Observations|4,494|2,265|

* Dummy variables

This table shows descriptive statistics of household and individual level variables that we will use to build our main dependent variables, as well as controls that we will use in our main specifications. Data comes from: (i) 1997 ENCASEH, which corresponds to the baseline data for the Progresa experiment and (ii) 2017-18 ENCEL, which is the latest round of the experiment’s panel.

11It is important to indicate that these
**Figure  16:  Outward foreign investment of Korea’s textiles, clothing and footwear industries**

<!-- Start of picture text --> Thousand US$,<br>constant 2005 US$<br>600,000<br>500,000<br>400,000<br>300,000<br>200,000<br>100,000<br>0<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08<br>asia_textiles m_america_textiles others_textiles<br>asia_clothing m_america_clothing others_clothing<br>asia_footwear m_america_footwear others_footwear<br><!-- End of picture text --> Note: Data for 1980 is a cumulative figure from 1968 to 1980. Data is recalculated on the basis of constant 2005 US$. Source: Korea Eximbank

# **III. The Opportunities and Challenges of China’s Rise**

40. Industrial upgrading to higher value-added commodities is particularly important in view of the opportunities and challenges to Latin America‘s economies due to the rise of China. The first decade of the twenty-first century will be considered a strategic inflection point in the global economic landscape. For the first time in almost two hundred years emerging economies will catch up with and race ahead of the developed ones, in terms of gross domestic product—a trend that has received added impetus from the financial crisis. China‘s meteoric rise is the hallmark of this transformation.<sup>16</sup> 41. Over the next few decades, the world will be subject to very significant demographic changes. While Latin America will grow robustly in terms of population, it will be outpaced by other countries in the developing world. Between the years 2010 and 2030, the population of Latin America is expected to increase by approximately 100 million people, but over the same period the population of India is expected to increase by almost three times that amount. China‘s population growth is expected to decelerate
Figure 12: Help Received <!-- Start of picture text --> Quintiles of Consumption Per Capita<br>(a) 2009 (b) 2014/15<br>70 60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0 0<br>Poorest Poor Middle Rich Richest Poorest Poor Middle Rich Richest<br>Food Aid Received Gov't Benefits Food Aid Received Gov't Benefits<br>Other NGO Non Hh. Members Other NGO Non Hh. Members<br>Other Groups Other Groups Zakat Center<br>Household Livelihoods<br>(c) 2009 (d) 2014/15<br>100 90<br>90 80<br>80 70<br>70 60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20 20<br>10 10<br>0 0<br>Food Aid Received Gov't Benefits Food Aid Received Gov't Benefits<br>Other NGO Non Hh. Members Other NGO Non Hh. Members<br>Other Groups Other Groups Zakat Center<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2009 NBHS and 2014/15 NHBPS data.

# 4.2 Resilience of Sudanese Households to Shocks

Drawing insights from the previous section on coping strategies, this paper examines the resilience of Sudanese households to shocks. For this analysis, resilience to shocks is examined through the lens of 43
Policy Research Working Paper 8945

# **Abstract**

Using firm-level survey data for a large cross section of countries, the paper assesses the gap in labor productivity between formal and informal firms in developing countries for which comparable data are available. It also investigates the impact of competition from informal firms on the labor productivity of formal firms. The results show that on average, the labor productivity of informal firms is about one-fourth that of formal firms. Moreover, the labor productivity of formal firms that face competition from informal firms is about 75 percent of the average labor productivity of formal firms that do not experience informal competition. This suggests that competition from the informal sector can erode formal firms’ market share and the resources available to boost productivity where formal firms shoulder the additional cost of regulatory compliance. These findings are robust to a range of firm and country characteristics as well as checks for endogeneity concerns.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Development Economics Global Indicators Group. 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 authors may be contacted at mamin@worldbank.org, fohnsorge@worldbank.org, and cokou@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
between Indian and African children. These results suggest that open defecation is a policy priority of first-order importance.

This paper makes several contribution to the literature. First, to my knowledge, it offers the first documentation of a quantitatively important cross-country gradient between sanitation and child human capital. Although the association between income and health has been widely studied within and across developing countries, the importance of sanitation has received much less attention. Moreover, I show that sanitation predicts child height even conditional on income. Controlling for GDP, the difference between Nigeria’s 26 percent open defecation rate and India’s 55 percent is associated with an increase in child height approximately equivalent to quadrupling GDP per capita.

Second, this paper documents an interaction between sanitation and population density, consistent with a mechanism in which open defecation harms human capital through exposure to environmental germs. The number of people defecating openly per square kilometer linearly explains 65 percent of international variation in child height. This finding clarifies the policy case for sanitation as a public good. Third, it contributes to a resolution of the puzzle of the “Asian enigma” of Indian stunting, which has received much recent attention from economists. Finally, the conclusions offer a reminder that height, often refereed to as an indicator of “malnutrition,” broadly reflects early-life _net_ nutrition, including losses due to disease.

Three sections of the paper contribute complementary analyses of the relationship between height and open defecation, each focusing on a different dimension of heterogeneity. Section 2 studies country-year average sanitation and child heights; here, each observation is a collapsed DHS survey. Open defecation is particularly harmful to children’s health where population density is high, creating a special risk of stunting in India. Section 3 compares children within one country, introducing district fixed effects to repeated crosssection data constructed out of two rounds of India’s National Family and Health Survey, in order to study differences within districts over time. Section 4 considers whether the 3
highly collinear variables, (2) estimating a separate regression for each region, (3) dropping high-leverage observations, and (4) avoiding overfitting by limiting the set of right-hand-side variables using a Lasso regression.<sup>9</sup> The model parameters and error terms allow the consumption and probability of falling into poverty ( �)ı of every household in the 2013 census to be predicted using equation (2). The predicted probability decreases with the vv predicted household consumption and increases on the variance of the errors.<sup>10</sup> As in Günther and Harttgen (2009), households exhibiting a probability of becoming poor above 50 percent (at least once) in the next two years are considered vulnerable, and the group of vulnerable households includes those households predicted to be poor (poverty-induced vulnerability) and those households that are nonpoor, but are at risk of poverty (risk-induced vulnerability).

> 9 Leverage measures the influence on the fitted values of a given observation. Following Corral Rodas et al. 2022, the analysis here controls for high-leverage observations by eliminating observations with leverage above . Because this incurs a loss of information, the predicted poverty rates in the new sample are checked to ensure they are similar to the original data. The census and survey questionnaires had 43 variables in common. The lasso (2kk+ 2)/ll selection process yields a selected set of covariates, although some of the included covariates may be nonsignificant. Reducing the number of variables avoids situations where the number of right-hand-side variables is large relative to the number of observations in a region, especially in regions with smaller populations.

> 10 � See Corral Rodas, Molina, and Nguyen (2021) for the estimation of . cc

> ηη 8

8
poorer, this is not typically the case in cross-sectional studies using the subjective approach, which tends to suggest greater economies of scale in consumption than normally assumed, at least in developing countries. For example, in using the ELQ to test the welfare consistency of prevailing objective

> 18 Clark et al. (2008) review the literature on the income effect on subjective welfare.

> 19 I focus on application to developing countries. The applications to date include Pradhan and Ravallion (2000) using data for Jamaica and Nepal, Ferrer-i-Carbonell and Van Praag (2001), for Russia, Taddesse and Shimeles (2005) for Ethiopia, Gustafsson (2004) for urban China, Lokshin et al. (2006) for Madagascar, Bishop and Luo (2006) for urban China and Carletto and Zezza (2006) for Albania.

> 20 An exception to this finding is reported for the US by de Vos and Garner (1991), where the SSPL is well above the prevailing (absolute) line, though the US line has not been updated in real terms since the 1960s; a more current absolute line for the US would probably be closer to the SSPL.

11
ruraland regional-level. We include 5,250 household observations after cleaning and removing nonagricultural households.

For the analysis, we combine data from the six countries and all waves to generate a single cross-country panel data set containing 33,738 household observations. For estimation, we include two measures of agricultural production: yield (kg/ha) of the primary cereal crop and the value (2010 USD/ha) of all seasonal crop production on the farm. As covariates in our regressions we include a set of inputs: labor, fertilizer, seed, pesticide, herbicide, and irrigation. Exact definitions of output and input variables are in Table 3. All cleaning code for each of the 17 data sets is available on Github.

# **2.3 Data Integration**

Methods of data integration are often overlooked in the process of merging spatial data, in particular weather data, with household surveys. Publicly available data sets obfuscate the exact GPS coordinates of unit-records to ensure privacy. If underlying data sets are fairly smooth and areas of interest are small relative to the resolution of spatial data, then the effect of integration method could be negligible. However, this is not known. As the spatial resolution of remote sensing data improves, these obfuscation methods are likely to matter more. Therefore, we include this as a parameter in our analysis, as well as the choice of spatial feature representation or abstraction.

11
# _Identification_

For some countries, the CET equation (5) might have identification problems as export supply could co-depend on global demand, expressed as an Armington import demand condition similar to equation 4: The demand for Et as a ratio to the global domestic good Dtw is a function of their relative price PPtdwte ~~;~~ β<sup>w</sup> is Previous literature often uses just the aggregation of industrial countries for Dtw and Ptdw with the bilateral trade flows as weights. However, the trade weights are shifting significantly over time, difficult to derive, or unavailable consistently for each country's entire 1970-2018 period. For this reason, we use the global aggregation of national accounts already available in the WDI database to derive Dtw and Ptdw, which are consistent with the specification of the 1-2-3 model. Since the global GDP and its components are also expressed in current and constant U.S. dollars, the global variables are consistent with the country variables.

However, for the 1-2-3 model, we are interested in the CET elasticity Ω from equation 5 and not the CES elasticity σ<sup>w</sup> linked to global demand in equation 8. Whenever the identification issue arises (usually if there is an incorrect sign for the CET coefficient), we consider including the variables of equation 8 as additional cointegrating or exogenous variables in the long-term cointegration equation or part of the error correction of the VEC.

Equations 5 and 8 could also be solved simultaneously, yielding equation 9 as another option for estimating Ω. We apply a time series technique like VEC to it.

# _Fluctuations and Breaks_

For many developing countries, the variables are often characterized by fluctuations rather than a single break, mainly due to policy reversals, crises, conflicts, or exogenous shocks from the weather (e.g., drought, hurricanes, etc.). Figure 1 shows the frequent fluctuations of relevant variables in Benin in contrast to the smoother movements in the United States. In these situations,
of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. The data for the countrylevel analysis and the subnational-level analysis comes from our newly constructed database. The equality test p-values show the t-test between the panel FE results vs. the long differences results, and the country analysis results vs. the subnational analysis results. *** p<0.01, ** p<0.05, * p<0.1.

32
)|(0.259)|(0.031)|(0.004)|(0.005)|(0.055)|
|Spillover students|0.023|0.278|0.043|−0.001|0.007|0.048|
||(0.031)|(0.270)|(0.031)|(0.004)|(0.006)|(0.048)|
|Control for baseline value of dep. var.|No|No|Yes|Yes|Yes|No|
|Mean of dep. var. control group|0.26|1.57|0.37|0.01|0.02|0.18|
|Observations|4,364|3,857|3,881|3,774|4,252|3,358|

_Source_ : Authors’ analysis from student surveys.

_Note:_ Dependent variables not standardized. All outcome variables are assessed six months after treatment. Standard errors clustered at the school level in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Column (1) includes students attrited for written survey but later tracked over the phone. Table does not control for baseline value of the dependent variable, because outcome was not measured at baseline (except for column (3)).<sup>§</sup> 3,358 students of the full sample agreed to be contacted for this part of the study. Specification controls for whether individual had a cellphone.