rafmacalaba/datause-encoder-data huggingface dataset:
Table II.2 Descriptive statistics for women ages 25 to 49 years old who are household heads of their spouses in 2018

||p25|Mean|p75|N|
|---|---|---|---|---|
|<br>Panel A: Local|Labor Market||||
|Labor force participation|0.00|0.32|1.00|1,696,260|
|Unemployment rate|0.00|0.01|0.00|542,137|
|Employment rate|0.00|0.32|1.00|1,696,260|
|Panel B: Individual and H|ousehold Cara|cteriscs|||
|Age|31.00|36.55|42.00|1,696,260|
|Share with no education|0.00|0.25|0.00|1,696,260|
|Share with primary level|0.00|0.43|1.00|1,696,260|
|Share with secondary level|0.00|0.26|1.00|1,696,260|
|Share with university level|0.00|0.07|0.00|1,696,260|
|Indigenous|0.00|0.41|1.00|1,696,260|
|Married|1.00|0.88|1.00|1,696,260|
|Number of children 0-3|1.00|1.17|1.00|539,732|
|Number of children 4-6|1.00|1.12|1.00|580,115|
|Number of children 7-12|1.00|1.47|2.00|976,611|
|Number of people > 12|2.00|3.28|4.00|1,696,260|
|Presence of grandmother|0.00|0.05|0.00|1,696,260|
|Household head|0.00|0.22|0.00|1,696,260|
|Spouse works|1.00|0.89|1.00|1,395,027|
|Income stability|0.00|0.56|1.00|1,696,260|
|Someone in the family receives remittances|0.00|0.08|0.00|1,696,260|
|Household Home & WASH index|-0.21|0.04|0.51|1,696,260|
|House|1.00|0.96|1.00|1,696,260|
|Ownership of the dwelling|1.00|0.79|1.00|1,696,260|
|Resilience of Walls (bricks/concrete)|0.00|0.66|1.00|1,696,260|
|Resilience of Roof (concrete/cement)|1.00|0.98|1.00|1,696,260|
|Access to water (pipeline)|0.00|0.73|1.00|1,696,260|
|Toilet Safety|0.00|0.54|1.00|1,696,260|
|Urban|0.00|0.57|1.00|1,696,260|
|Panel C: Technolo|gy Access & Us|e|||
|Washing machine|0.00|0.20|0.00|1,696,260|
|Use of internet in the last 3 months|0.00|0.29|1.00|1,696,260|
|Panel D: So|cial Norms||||
|Female participatea in hhld decision making|1.00|0.77|1.00|1,696,260|

Sources: 2018 National Census of Guatemala. Notes: Table A1 in the paper provides the definition of each variable.

Table II.3 presents statistics for municipality and department level variables. Panel A reports statistics on policy-related variables, Panel B on social norms and Panel C on local labor market variables. Panel A shows that the number of public preprimary centers per 100 children aged 4 to 6 years old in each municipality is 1.5 in 2017. Preprimary education is mandatory in Guatemala and most preprimary centers are public (MINEDUC, 2021). However, net pre-primary attendance rate was only 52% in 2017 (MINEDUC, 2021). The availability of public education centers (free of charge) implies that fertility and work outside the home are not necessarily mutually exclusive for lower income families. The low net attendance rate may be reflecting low accessibility or low quality of public centers. The average per capita spending in education and health at the department level was US$ 201 and US$101 (2007 PPP), respectively. Guatemala is among the countries with 44
7 power parities (PPPs), and the process of constructing expenditure totals and subtotals is guided by the UN’s Classification of Individual Consumption According to Purpose (COICOP) classification system. In both exercises, the various components of consumption – including out-of-pocket health expenditures – are also distributed along with the consumption aggregate. Often, the original microdata (or parts of the microdata) are also made available to authorized users (typically World Bank staff), which allows for validation, where possible, of different methods to estimate out-of-pocket health expenditures, which can then be related to a standardized consumption aggregate.

Some country-specific surveys can appear in multiple (harmonized) multi-country collections. France’s HBS, for example, has been harmonized by both Eurostat and the LIS. Peru’s ENAHO survey has been harmonized by both the LIS and the World Bank’s SHES exercise. Romania’s HBS has been harmonized by Eurostat and the World Bank’s Europe and Central Asia harmonized collection (ECAPOV). These multiple adaptations provide additional opportunities for data-validation but also highlight the potential for obtaining different results depending on which adaptation one uses.

The studies to date summarized in Table 2 have mostly used standalone surveys. The exceptions are the studies by Wagstaff et al. (2018b; 2018c) – they use standalone surveys and surveys from several collections. Often, the authors sifted through alternative estimates (e.g. of catastrophic expenditures) for a particular country and year, using external checks to choose between them.

# <u>Measuring out-of-pocket spending</u>

Household surveys vary in how (and indeed whether) they inquire about out-ofpocket health expenditures. This poses challenges for comparisons across countries, and over time within the same country.

First, not all surveys are clear whether the spending reported is gross or net of any reimbursement by a health insurer, and even when it is clear, the amount reimbursed may not be known at the time of the survey, making it impossible to correct for overreporting. Second, surveys likely vary in their comprehensiveness. This is likely linked to the fact that surveys vary in the number of items they inquire about. Some surveys also have an open-ended number of health expenditure items. Exploiting the fact that the WHS asked about out-of-pocket expenditure in two ways, Lu et al. (2009) find that use of the single-item question leads to a smaller estimate of out-of-pocket expenditure than the survey’s multi-item question. Third, surveys vary in their recall periods, sometimes using recall periods for infrequent items – e.g. inpatient care – that are likely to be inappropriately short (e.g. 3 months) and recall periods for frequent items – e.g. medicines – that are likely to be inappropriately long (e.g. 12 months). On one hand, a household might find it difficult to remember spending that occurred a long time ago. On the other
to quantify. We would suggest a conservative approach and conclude that the changes we observe over time are not statistically significant. We return to some of these issues in the conclusion and suggest ways in which we might address them in future work.

This paper is structured in six main parts. First, we provide an overview of the literature on global inequality, the measurement of purchasing power parities (PPPs), the discrepancy between national accounts and household surveys, the underestimation of top incomes in household surveys and outline our approach to all these issues. Section 2 summarises our data construction and methodology, including the welfare concept used and the Pareto imputation to account for top incomes. Section 3 provides summary statistics on our database, particularly the coverage of global and regional GDP and population, and presents the main results regarding inequality in the global distribution of income. We test for the robustness to different measures of inequality and investigate the changing regional composition as well as global growth incidence curves. In Section 4, we provide an upper bound on the global Gini by accounting for the underestimation of top incomes. Section 5 moves from a cross-sectional focus to a panel analysis, taking account of the movement of individual country-deciles in the global distribution. Section 6 presents conclusions.

5
data set includes an estimated 400,000 km of roads. Each road segment is categorized into four classes according to road quality: national highways (about 30,000 km or 7.7 percent of total roads), state highways (90,000 / 22.5 percent), secondary connector roads (120,000 / 29.8 percent), and other roads (160,000 / 39.9 percent). The complete digital representation of India's transport network that is used for the accessibility index thus consists of a set of urban centers represented as nodes connected by lines that correspond to roads of different quality. Rather than distance, the weighting parameter used in the accessibility computation is an estimate of travel time. This makes it possible to incorporate road quality information by assigning different travel speed estimates to different types of roads. Based on information available in the Indian Infrastructure Handbook (CMIE, 1998) varying travel speeds ranging from 25 to 50 km/hour were used, depending on the type of road. The algorithm used to compute the accessibility measure is based on the familiar Dijkstra algorithm. We use this to compute the network travel time to urban centers for each of more than 100,000 points distributed across India. As the exact geographic location of each firm is not publicly available, we summarized the accessibility for each district by averaging the individual values for all points that fall into the district. The negative exponential function in Equation (7) is chosen as the most suitable functional form for the decay of interaction with increasing travel time.

# **1.3** Econometric SDeciflcation

> In this section, we present the econometric specification to test the effects of economic geography factors in explaining the location of economic activity. Our basic premise is that firms will locate in a particular location if profits exceed some critical level demanded by entrepreneurs. We estimate a cost function with a mix of micro level factory data and economic geography

15
those communities.

- In-migration by conflict victims (household survey). Security should be associated with higher rates of in-migration of such vulnerable households as they leave areas of higher risk and enter areas of lower risk.

- Infrastructural regeneration in terms of number of schools, health posts, and wells built either by the community itself or by NGOs (chief survey). A security bubble surrounding deployment bases is hypothesized to allow these projects to occur with greater frequency. A better security environment allows for these projects to take place without fear of attack or vandalism. It may also provide motivation in that communities and NGOs would feel more confident that community members will be able to remain and enjoy the benefits of rebuilt infrastructure.

With respect to downstream economic and social revitalization outcomes that the security bubble and direct assistance should improve, we have,

- Having a productive livelihood (household survey), measured in terms of the share of households having business, skilled labor, or professional occupations relative to unskilled, agricultural, or no occupation.

- Personal monthly income (household survey).

- Levels of alimentary consumption in terms of meals eaten in the previous day (household survey).

- Higher order consumption in terms of investment in household repairs (household survey).

- Participation in community organizations such as a rotating credit group ( _Susu_ ), farmers self-help group ( _Koo_ ), producers’ cooperative, or social club (household survey).

xv
**Table 2: Volatility of Key Variables**

|**Variable**|**1994Q1-2012Q4**|**1994Q1-1999Q4**|**2000Q1-2012Q4**|
|---|---|---|---|
|gdp|5.54|3.20|5.50|
|gdp agri|8.36|3.52|9.74|
|gdp non-agri|5.97|3.32|5.97|
|investment|18.19|13.50|17.65|
|int rate|7.25|5.41|6.82|
|terms of trade|15.10|11.61|15.70|
|gov cons|13.73|3.25|14.79|
|privcons|7.00|5.24|6.90|

_Source_ : Authors.

Among domestic factors influencing business cycle fluctuations, we consider domestic investment, trade balance and variables that capture the stance of policy in Paraguay. In terms of the latter, we focus on two key variables: domestic real interest rate and government consumption. Our interest in policy variables reflects the well-known fact that government policies in developing countries tend to exacerbate rather than smooth out business cycle fluctuations.

Recent work shows that real interest rates tend to be counter-cyclical in developing countries, while they tend to be pro-cyclical in developed economies (see, for instance, Neumeyer and Perri (2005), Uribe and Yue (2005)). One of the prominent explanations behind this fact is distortions in factor markets (for instance, the requirement that firms have to pay for part of the factors of production before production takes place, creating a need for working capital). This is also the case for Paraguay, where GDP and real interest rate are negatively correlated, with unconditional correlation equal to -0.17 (see Figure 1.2(b)). This correlation, however, is somewhat smaller (in absolute terms) than the corresponding number in the other Latin American countries, where it is equal to -0.63 in Argentina, -0.49 in Mexico, -0.38 in Brazil (see Neumeyer and Perri (2005)).

In the same spirit, a number of studies have shown that fiscal policy tends to be pro-cyclical in developing countries as well. The pro-cyclicality is defined as a positive response of government spending to an exogenous expansionary business cycle shock. Gavin and Perotti (1997) showed that this is the case in Latin America. Talvi and Végh (2005) then claimed that pro-cyclical fiscal policy is not only a Latin-American phenomenon, but instead characterizes the entire developing world. In a recent study, Ilzetzki and Vegh (2008) revisit the evidence using a sample of 49 countries while allowing for a reverse causality running from fiscal policy to GDP. They show that fiscal policy is indeed pro-cyclical in developing countries.

There are several explanations for this finding: one is that frictions in international credit markets prevent developing countries from borrowing in bad times ((Gavin and Perotti (1997), Caballero and Krishnamurthy (2004), Mendoza and Oviedo (2006), and others); the second is a political economy explanation that good times encourage fiscal profligacy ((Tornell and Lane (1998), Talvi and Végh (2005), and others); and the third is the delays in the implementation and execution of fiscal policies in developing economies.

We find that Paraguay follows the same practice. We measure fiscal policy in Paraguay by considering two key policy instruments: government consumption and government investment.
# **Estimating the Welfare Costs of Reforming the Iraq Public Distribution System:**

# **A Mixed Demand Approach**<sup>**1**</sup>

Nandini Krishnan<sup>2</sup> , Sergio Olivieri<sup>3</sup> and Racha Ramadan<sup>4</sup> Keywords: Public Distribution System, Iraq, quota, demand analysis, Mixed Demand Approach, food subsidy JEL: D12, D39, O12, O53

> 1 This paper has been benefited from our reviewers; Cesar Cancho and Gabriela Inchauste, and seminar participants at the World Bank, workshops and conversations with senior technical counterparts from Government of Iraq and Kurdistan Region in Erbil, Iraq, May, 2014.

> 2 Senior Economist, Poverty and Equity Global Practice, The World Bank Group, 1818 H St, NW – Washington DC, 20433 <u>nkrishnan@worldbank.org</u> 3 Senior Economist, Poverty and Equity Global Practice, The World Bank Group, 1818 H St, NW – Washington DC, 20433: solivieri@worldbank.org

> 4 Assistant Professor- Faculty of Economics and Political Science- Cairo University: <u>racha.ramadan@feps.edu.eg</u>
economic activity and the volume of traffic responded. An Indian official in 1846 cited the long gestation perod before profits could be expected as a particular impedimen **t** o railway investment'<sup>2</sup> Foreign mvestors could be discouraged as well by uncertainty about political stability. The hesitncy of foeig investors to place ther fnds in the United States lingered into the 1870s, well after the conclusion of the Amenican Civil War. The unfortunate name given to the 1857 rebellion in India ("the Mutiny') represented a persistent impedinto borrowing.'<sup>3</sup> Along with the economic prospects of the project, it was necessary to evaluate the promoter undertaing it Recently settled, sparnzly populated areas were prme locations for fly-by-night operators. Promoters could strike sweedteart deals with constructon companes, siphoning off the resource of the project and saddling it with an insupportable debt burden. This danger represented a lear impedimen **t** o investment.

# _Mewas of_ **_Reducing Risk_**

An obvious way of reducing these risks was to exploit the informational advantage possessed by local inrestors. Countries with precious industial and commercal development could possLbly fmance infrastructure investment through a limted partnesip of wealthy reidents who were well informed about local prospects. Early tumnpies, canals and railrads had modest capal requments compared with the long-disance rail lines that

**MsPhccn** (1980, pp. **180-1** 1). "3 **M dcPheco** (1955, p.lIC).

10
25 unreasonably refuse offers of assistance or make insufficient<sup>effort themselves will have their income</sup> support payments suspended for a period or canceled.

# E. Evaluation Studies of Retraining Prarams

Despfte the ambitious active labor market policies<sup>of Sweden and other Westem European</sup> nations, most available evaluation studies examine<sup>displaced worker programs in the U.S. and, to</sup> a lesser extent, in Canada. Haveman and Saks<sup>(1985) comment on this phenomenon suggesting that</sup> the stable training systems In place in Europe provide<sup>constant feedback on program effectiveness from</sup> employers and workers. In contrast, the narrowly focused<sup>and less stable training programs In the U.S.</sup> generally fail to provide detailed feedback to program<sup>managers, necessitating more formal statistical</sup> analyses of program effects. Canada, like the U.S., requires<sup>the formal evaluation of social programs (see</sup> Riddell 1991). One difference between the two nations<sup>Is the absence of demonstration projects In</sup> Canada.

This section begins with a discussion of evaluation evidence<sup>yielded by four major U.S.</sup> demonstration projects carried out during the 1980s. Then<sup>considered are available evaluation studies</sup> for Canada and other OECD nations examined in<sup>this essay.</sup>

# U.S. Demonstration Projects

Table 4 fumishes an overview of some of the pertinent<sup>features of the four demonstration</sup> projects.' One common element is the type of reemployment<sup>services provided. As the table Indicates,</sup> the demonstrations supplied a mix of services including job<sup>search assistance (JSA), classroom training</sup> (CT), and OJT. JSA services Include initial orientation<sup>and assessment sessions, a job search workshop,</sup> and job development and referral services. The New<sup>Jersey Ul Demonstration added to this mix a cash</sup> bonus for early reemployment.
We address the above concerns, using several different empirical techniques to examine the relationship between remittance flows and financial development. First, we conduct fixed and random effects estimations to account for unobserved country effects, ignoring other sources of biases. Second, we obtain estimates of the impact of remittances over the last decade to account for the fact that recent remittances data are likely to be more accurate relative to statistics from the beginning of the sample, when less attention was given to these kinds of flows. Third, we present estimations including time dummies to control for unobserved time effects or common country shocks. Fourth, to mitigate concerns about reverse causality we run regressions lagging all regressors and we conduct dynamic system Generalized Method of Moments (GMM) estimations à la Arellano and Bover (1995), using lagged regressors as instruments. Finally, we perform instrumental variables (IV) estimations to address the potential endogeneity of remittances arising from measurement error, omitted factors, and/or reverse causation in a more direct and complete manner. In particular, we use economic conditions in the remittance-source countries (i.e., the countries where migrants sending remittances reside) to instrument for remittance flows received by countries in our sample.

Our empirical analysis provides support for a robust positive impact of remittances on financial sector development, even after controlling for other factors that affect financial development and after correcting our estimates for different potential sources of bias. The results are invariant to whether we measure financial development by the ratio of deposits or credit to GDP. All in all, our findings confirm yet another channel through which remittances can a have a positive influence on recipient countries’ development.

The rest of the paper is organized as follows. Section I summarizes the main findings from the research on financial development and reviews the literature on the development impact 4
Europe, China, the United States, Argentina, and Japan are the main trading partners of Brazil, accounting for 60.9 percent of total sales in 2009–11 (table 2). The top five destinations accounted for 59.9 percent in 2006–8 and 66.9 percent in 1996–98. China has undoubtedly been the destination that has recently gained the most, up 14 percentage points from 1996–98, followed by India and St. Lucia, both up 1.4 percentage points each. Europe, the United States, and Argentina were the largest declines in shares of Brazilian exports, down 8.8, 4.2 and 3.5 percentage points, respectively.

**Table 2**

**MAIN EXPORT DESTINATIONS**

|**Destinations**|**1996–98**<br>**(%)**|**2006–8**<br>**(%)**|**2009– 11**<br>**(%)**|
|---|---|---|---|
|**European Union (27)**|26.1|24.4|21.9|
|**China**|2.1|7.4|16.1|
|**United States**|19.1|16.1|10.3|
|**Argentina**|12.6|9.0|9.1|
|**Japan**|5.6|3.0|3.5|
|**Chile**|2.2|2.7|2.1|
|**Venezuela, R. B. de**|1.3|2.8|2.0|
|**Russian Federation**|1.3|2.5|1.9|
|**Korea, Rep. of**|1.4|1.5|1.9|
|**Mexico**|1.7|2.7|1.7|
|**India**|0.3|0.6|1.7|
|**Saudi Arabia**|0.8|1.1|1.4|
|**St. Lucia**|0.0|1.0|1.4|
|**Paraguay**|2.6|1.1|1.2|
|**Canada**|1.1|1.3|1.2|
|**Colombia**|0.9|1.4|1.1|
|**Others**|20.8|21.5|21.5|

_Source_ : World Integrated Trade Solution.

# **Diversification of exports**

The recent global economic crisis has highlighted the importance of diversification (products, markets, and firms) to reduce risks associated with growth volatility. On the other hand, recently intensified globalization has contributed to a resurgence of specialization in diverse economies. Although predicted by trade theory, the degree of vertical specialization has surprised many with its intensity, which emerged from increasing fragmentation of trade in global supply chains. Consequently, economic diversification is now a high priority on the policy agenda for most developing countries.

Brazil is recognized as a global trader, endowed with a wide range of natural resources along with a welldiversified industrial structure. This translates into an impressive level of export diversification, not only in terms of number of destinations, but also of products. When compared with other countries (figure 3), 6