rafmacalaba/datause-encoder-data huggingface dataset:
-4-

The paper's empirical results are presented in Sections 3 and 4. Section 4 discusses our data from India's 38th Round National Sample Survey (1983) and what it shows about regional disparities, and the regional profiles of poverty. Section 4 then presents our simulations of the effects on aggregate poverty of existing regional disparities, and the effects of regional redistribution in India, drawing on the theoretical results of Section 2. The final section summarizes our conslusions.

# 2. Regional Disparities and National Poverty

This section presents the theoretical results that we will need to answer the three questions posed in the Introduction. We first consider the contribution of regional disparities to aggregate poverty, and how that may be measured. We then ask the comparative static question: will small reductions in those disparities while preserving the national mean decrease aggregate poverty? Finally, we will consider the question of how much impact on poverty can be expected from reducing or eliminating regional disparities.

The national poverty level can be thought of as a function of three factors: regional disparities in average living standards, intraregional inequalities, and the national mean living standard. The contribution of regional disparities alone can be quantified by estimating the aggregate poverty level that what would o,cain if mean income levels were fully equalized across regions while holding the other two factors constant, i.e., maintaining the same overall national mean and the intraregional distributions. We shall term this "the partial contribution of
Bank, in partnership with the Government of Indonesia, initiated the “Pilot Program on Financial Literacy Education for Migrant workers and their Families” with the aim of identifying effective ways of improving the financial literacy of migrant worker households.

# **2.2 The Study Sample and Randomization Details**

The first step in designing this pilot was a diagnostic study (World Bank, 2010a) which gathered information on where there was a high concentration of migrant worker households and PPTKIS. Based on this assessment, East Java province was identified as an area with high numbers of both migrants and PPTKIS, and within East Java, the Greater Malang and neighboring Blitar districts as an area where there were sufficient PPTKIS to partner with. We then collaborated with Malang’s Manpower and Transmigration Office and 11 PPTKIS based in Greater Malang to obtain a sample of migrant workers and their families for this pilot.

Recruitment was on a rolling basis, with the project team periodically contacting the 11 PPTKISs to obtain lists of workers originating in the Greater Malang and Blitar districts who were recruited by these companies to work abroad. We set a target sample size of 400 households, and continued to collect workers in batches from these recruiting agencies until this target had been met.

Almost all (96%) of the migrants are female, with median age of 29. They are typically the daughter (41%), spouse (30%), or sibling (10%) of the household head. Education levels vary, with 26 percent having completed at most primary schooling, 45 percent secondary schooling, 28 percent senior high school, and only 1 percent a higher degree. The main occupation for migrant work abroad is as a housemaid (80%), with Hong Kong SAR, China, Taiwan, and Malaysia the main destination countries.

As batches of worker names were received from the PPTKIS, they were entered by project staff onto an Excel worksheet in the order listed by the PPTKIS, and a random number generator used to assign individuals to treatment status. Since batches of workers were often not of size divisible by four, and were of varying numbers, and that the only information available on the workers was basic data supplied by the PPTKIS, we did not stratify the randomization. The sample of 400 migrant workers was randomly assigned into one of the following groups:

- Treatment A: Financial literacy training is provided to the migrant worker only

- Treatment B: Financial literacy training is provided to the migrant worker’s household member only

6
Outside of Sub-Saharan Africa, mortality rates are already lower and existing infrastructure coverage is higher, which results in a higher expected cost of around US$ 3000 per life-year saved. However, given the higher average income levels in these developing countries, the ratio of the cost per life-year saved relative to income per capita is similar, and ranges between 0.65 and 0.82 across regions. Given that the WHO guidelines classify any intervention costing less than the annual GDP per capita per life-year saved as “highly cost effective”<sup>6</sup> , further investment in water and sanitation appears highly desirable from a policy perspective.

The estimates presented in this paper also underline the magnitude of the potential humanitarian impact of further investments in water and sanitation. Our estimates imply that in our sample alone – which accounts for approximately 60% of the 102.5 million children born per year in the developing world<sup>7</sup> (World Population Prospects, United Nations Population Division) - providing full access to water and sanitation infrastructure to all children has the potential to save about 1.3 million child lives per year. Assuming comparable health improvements in the rest of the developing world, this means that approximately 2.2 million lives could be saved across all developing countries each year if access to the firstbest water and sanitation infrastructure could be provided to all households in the developing world.

The rest of this paper is structured as follows: in Section 2 and 3, we discuss the data and methodology used, providing a detailed description of the construction of the main variables of interest. We present the estimation results in Section 4, and compare the predicted reductions in child mortality rates with the current gap between child mortality and the MDG objective for each country. We also show estimates of total investment needed per life-year saved. We conclude with a discussion of our estimates in relation to the existing literature and a short summary in Section 5.

> 6 www.who.int/choice/costs/CER_thresholds/en/index.html 7 Excluding China.

5
available in terms of the volume or number of loans or deposit accounts, but not in terms of the number of individuals who own them and thus does not give a clear picture of what percentage of households use formal financial services.<sup>7</sup> The Financial Access Survey focuses on measuring the usage of a set of basic financial products from various types of formal intermediaries. We confirm in Section 3 that the measures we collect correlate strongly with the few available data points from household surveys that measure the number of individuals using similar products.

To achieve the goal of measuring usage of financial services in the world, access to finance questionnaires were sent to 144 countries around the world: 13 countries in East Asia and Pacific, 27 countries in Europe and Central Asia, 23 countries in high income OECD region, 21 countries in Latin America and Caribbean, 14 countries in Middle East and North Africa, 6 countries in South Asia and 40 countries in Sub-Saharan Africa. For practical reasons, most of the small islands and countries at war were excluded from the sample. The survey was directed to the central bank governor’s office or head office of the financial regulator for approval and assignment to a contact person, often in the statistics department, who would be responsible for gathering the appropriate information. A response was received from 139 countries representing 90% of the world’s population and 97% of world GDP. For more detail on the coverage of individual data elements, see Section 4.

# **_2.1 Description of Survey_**

Data collection was implemented through a regulators’ questionnaire. The questionnaire consists of two main parts: statistical questions and regulatory questions. The statistical part of the survey collects data on the numbers and volumes of deposit accounts, loans, banking infrastructure (e.g. branches and ATMs) and other measures the usage of financial services usage. Table 1 has a list

> continuously substituting between a variety of formal, semi-formal, and informal financial products based on availability, product features, pricing, and other non-price barriers.

> 7 This implies a certain rate of double counting since individuals can – and regularly do – own more than one deposit account for instance.

6
_Brazilian spatial labor dynamics Page 34_

# Years of study growth - 1991/2000

<!-- Start of picture text --> Years of study growth - 91/00<br>-3.739 - -0.5<br>-0.5 - 0<br>0 - 0.3<br>0.3 - 1.245<br>N<br>W E<br>1000 0 1000 2000 Miles<br>S<br><!-- End of picture text -->

**Map 4**

Years of Study. Brazil. 2000.

<!-- Start of picture text --> Anos de estudo - 00<br>1 - 4<br>4 - 6<br>6 - 8<br>8 - 10.493<br>N<br>W E<br>1000 0 1000 2000 Miles S<br><!-- End of picture text --> <!-- Start of picture text --> Map 5<br><!-- End of picture text -->
**Table 11.G. Model comparison: Diebold and Mariano (1995) test for zinc prices**

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|...|||***|||
|Futures|3|*|...||***|†|*|
|Bivariate correlations|3|*||...|***|†|*|
|BVAR|3||||...|||
|OEM|3||||**|...||
|Machine learning|3||||***||...|
|Consensus|6|...|||**|||
|Futures|6|*|...||***||*|
|Bivariate correlations|6|||...||||
|BVAR|6|||||||
||||||...<br>|||
|OEM|6||||**|...||
|Machine learning|6||||***||...|
|Consensus|9|...|||***|||
|Futures<br>|9<br>|**|...||***||**|
|Bivariate correlations|9|||...||||
|BVAR|9||||...|||
|OEM|9|**|||**|...||
|Machine learning|9||||***||...|
|Consensus|12|...|||***||†|
|Futures|12|***|...|†|***|*|***|
|Bivariate correlations|12|||...||||
|BVAR|12||||...|||
|OEM|12|||||...||
|Machine learning|12||||**||...|
|Consensus|15|...|...|...|**||**|
|Futures|15|||||||
|Bivariate correlations|15|...|...<br>...|...<br>...|...|...|...|
|BVAR|15||...|...|...|||
|OEM|15||...|...|*|...|**|
|Machine learning|15||...|...|||...|
|Consensus|18|...|...|...|**||***|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18||...|...||||
|BVAR|18||...|...|...|||
|OEM|18||...|...|**|...|***|
|Machine learning|18||...|...|||...|
|Consensus|21|...|...|...|***||***|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21||...|...||||
|<br>BVAR|21||...|...|...|||
|OEM|21|***|...|...|***|...||
|Machine learning|21||...|...|||...|
|Consensus|24|...|...|...|*||***|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24||...|...||||
|BVAR|24||...|...|...|||
|OEM|24||...|...||...||
|Machine learning|24||...|...|||...|

_Source_ : World Bank.

_Note_ : Diebold Mariano statistic tests whether the forecasts in the model indicated in the row is more accurate than the forecast of the model indicated in the column. *** indicates statistically significantly more accurate model in the row at the 0.1 percent significance level, ** at the 1 percent level, * at the 5 percent level, and † at the 10 percent level.

58
has not been accompanied by a similar fall in total agricultural employment, implying that many workers who lost their jobs in agricultural firms moved to individual farming.<sup>19</sup> To sum up, the two most characteristic features of job turnover by sector of activity are (a) relatively low job turnover in the business sector in contrast to high job turnover in the non-business sector, and (b) large-scale reallocation of jobs away from the formal, enterprise-based agriculture to individual, likely informal, farming.

# **_Industry_**

Job turnover tends to vary substantially by industry. There are high job creation, high job turnover industries, which provide employment opportunities and improve chance to escape unemployment, even if high job creation accompanied by high job destruction (a typical example is construction industry). On the other extreme, there are low job destruction, low job turnover industries, which offer stable, secure jobs, but at the same time hire few new workers and thus provide little opportunity for the job seekers (a typical example is civil service). Then there are expanding industries, where job creation exceeds job destruction, and which provide good prospects for long-term employment. Finally, there are declining industries, where employment is falling and thus jobs are at risk and hiring is limited.

Industries where the job creation rate is high in Moldova include manufacturing of office machinery and paper, wholesale trade, financial intermediation, real estate and the so-called “other business activities”, and somewhat surprisingly, education and health care.<sup>2021</sup> Most of these high job turnover industries create more jobs than destroy and thus improve employment opportunities. However, with the exception of trade, these industries account for a small share of total employment, which limits their overall impact. Nonetheless, it is important to realize that although the labor market at large is depressed, there are emerging oases of employment growth, activities where Moldova

> 19 According to the LFS data, total agricultural employment increased considerably in the late 1990s, from 683 thousand workers in 1997 to 764 in 2001 (DSS, 2002).

> 20 Note: Data refer to the employment change within one year only, and as such are not necessarily representative of a long-term trend.

> 21 “Other business activities” include legal services, accounting, business counseling, marketing, personnel recruitment, etc.  These activates, associated with the development of a market economy, are expanding in virtually all transition economies of CEE.

23
(1996-2015), common factors account for 60 percent of the variance of CIF and COD, with global factors contributing close to two-thirds of that total.<sup>26</sup> Emerging countries exhibit similar ‡uctuations, but on a reduced scale. In their case, the variance contribution of common factors remains consistently smaller than in advanced countries, for both CIF and COD. Their variance share remains relatively ‡at until the mid2000s, and then rises until the global crisis –especially in the case of out‡ows –re‡ecting an increasing role of global factors. At the peak, common factors account for just under 50 percent of the overall variance of both in‡ows and out‡ows. Like in advanced countries, the contribution of the common factors declines post-crisis –although in this case there is no noticeable change in the relative shares of global and group factors. In the ...nal window of the sample, the global and group factors combined account for some 40 percent of the overall variance of both CIF and COD. Unlike in advanced countries, where global factors play by far the biggest role for both in‡ows and out‡ows, among emerging countries the relative roles of global and group factors vary depending on the direction of the ‡ows. The former factors outweigh the latter for out‡ows, but the opposite happens with in‡ows.

# 4.5 What shapes the role of the global factors?

The global crisis has prompted renewed interest in the factors that shape countries’exposure to common shocks through their capital ‡ows. As Figures 5 and 6 show, exposure varies a lot across countries. This raises the question of what drives such heterogeneity. In this regard, structural and policy features such as capital account openness and ...nancial development have received considerable attention. In addition, the ability of ‡exible exchange rates to provide insulation from the global ...nancial cycle has attracted an active debate following Rey’s (2013) in‡uential work.

As a ...rst illustration of these issues, we present variance decompositions of capital ‡ows over time, analogous to those shown in Figure 7, but grouping countries according to particular features of their structural and policy framework. Figure 8 does this distinguishing between countries exhibiting high and low degrees of ...nancial openness. For each estimation window, countries are allocated to either group depending on whether their average ...nancial openness (as measured by the Chinn-Ito index) over the window in question is above or below the overall sample median. The pattern of rise and subsequent fall of the variance contribution of common factors found in Figure 7 is apparent in the group of more ...nancially

> 26The rise and fall in the contribution of common factors before and after the global crisis are consistent with the changing composition of ‡ows. The reason is that the pre-crisis capital ‡ow boom, as well as the post-crisis collapse, were led by portfolio and, especially, bank ‡ows, which are commonly found to be the ‡ow type most responsive to global variables.

20
Let us denote household income by _y_ and its cumulative distribution function by _F_ ( _y_ ). Let _τ_ denote the income threshold above which we will refer to incomes as “top incomes”, and let _λ_ measure the share of the population enjoying a top income, i.e. _λ_ = _Pr_ [ _Y > τ_ ] = 1 _− F_ ( _τ_ ). It is assumed that DB-1 permits a consistent estimator for _F_ 1( _y_ ) = _Pr_ [ _Y ≤ y|Y ≤ τ_ ], and that DB-2 permits a consistent estimator for _F_ 2( _y_ ) = _Pr_ [ _Y ≤ y|Y > τ_ ]. By the same token it is assumed that DB-1 does not permit a consistent estimator for _F_ 2( _y_ ), while DB-2 does not permit a consistent estimator for _F_ 1( _y_ ). Suppose also that an estimate of _λ_ is available.<sup>5</sup> Given estimates of _F_ 1( _y_ ), _F_ 2( _y_ ) and _λ_ , an estimator for the complete income distribution function _F_ ( _y_ ) can be obtained as follows:

Given _F_ ( _y_ ), any measure of income inequality can readily be computed. Alternatively, one may appeal to the sub-group decomposition of one’s inequality measure of choice, which would by-pass the need for evaluating the income distribution for the population ( _F_ ( _y_ )). We have two sub-groups, those with income below _τ_ (sub-group 1) and those with income above _τ_ (sub-group 2). Let _Pk_ denote the population share of sub-group _k_ , and let _Sk_ denote their corresponding income shares, i.e. _Sk_ = _Pkμk/μ_ , where _μk_ and _μ_ measure average income in sub-group _k_ and the total population, respectively. Note that _P_ 1 = 1 _− λ_ and _P_ 2 = _λ_ . Let us also define _S_ 1 = 1 _− s_ and by extension _S_ 2 = _s_ . It can be verified that income inequality as measured by the Gini coefficient satisfies the following decomposition (see e.g. Alvaredo, 2011): where _Ginik_ measures the Gini coefficient for population sub-group _k_ . A similar decomposition can be obtained for the mean-log-deviation _MLD_ (see e.g. Shorrocks, 1980):

> 5It is generally assumed that DB-2 contains the total number of units (i.e. households or tax units) whose income is above _τ_ . Combined with the total population this yields an estimator for _λ_ .

5
<!-- Start of picture text --> Probability of Stunting by Wealth Quintiles and E.Coli Contamination at Household<br>Specification (1.1e)<br>d<br>ap ©<br><<br>LB<br>iS<br>Bo<br>a<br>—<br>°<br>Eo<br>5<br>©<br>64~~“<br>a<br>oO<br>0) 5 1 1.5 2 2.5 3 3.5 4 4.5 5<br>Log of Child Age<br>-————+ Low Contamination; Poorest Quintile 1-4. Low Contamination; Richest Quintile<br>+1 Some Contamination; Poorest Quintile -————1_ Some Contamination; Richest Quintile<br><!-- End of picture text --> <!-- Start of picture text --> Probability of Stunting by Wealth Quintiles and E.Coli Contamination at Household<br>Specification (1.2e)<br>a<br>ow ©<br>c<br>Bb<br>c<br>FrBo2<br>—_<br>°<br>Es<br>5<br>©<br>64hk<br>a<br>oO<br>0) 5 1 1.5 2 2.5 3 3.5 4 4.5 5<br>Log of Child Age<br>/—-_ Low/Med Contamination; Poorest Quintile -—__ Low/Med Contamination; Richest Quintile<br>-——-_ High/V. High Contamination; Poorest Quintile -————J_ High/V. High Contamination; Richest Quintile<br><!-- End of picture text -->
Table 4. Estimates of the effect of the crisis on potential household-level mechanisms in migrant households

|||<br>||Logarithm of<br>|<br>||Children < 6<br>||||<br>Home<br>improvement<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Receives|<br>Logarithm of|Logarithm of|per capita|Logarithm of<br>|Expenditure|years<br>old|Have||Chimney to|<br>(walls,|
||remittances<br>= 1|<br>total<br>expenditures|per capita<br>expenditures|non-food<br>expenditures|per capita food<br>expenditures|share on<br>protein|food secure<br>= 1|improved<br>toilet = 1|Have piped<br>water = 1|let out<br>smoke = 1|<br>ceiling, floor)<br>= 1|
|_Panel A: Households with chil_|_d in cross-sectio_|_nal cohort ana_|_lysis_|||||||||
|Migrant household = 1|0.76***|0.37***|0.22***|0.40***|0.04|0.02*|0.11**|0.17***|0.02|0.09*|0.02|
||(0.04)|(0.08)|(0.08)|(0.10)|(0.08)|(0.01)|(0.05)|(0.05)|(0.05)|(0.05)|(0.04)|
|Year 2012 = 1|-0.06***|-0.15**|-0.14*|-0.30***|-0.10|-0.05***|-0.15***|0.07**|-0.05|0.10**|-0.05**|
||(0.02)|(0.07)|(0.07)|(0.09)|(0.09)|(0.01)|(0.04)|(0.03)|(0.04)|(0.04)|(0.02)|
|DD|-0.37***|-0.21*|-0.09|-0.11|0.02|0.00|-0.03|-0.11|-0.06|-0.06|0.05|
||(0.07)|(0.11)|(0.13)|(0.17)|(0.11)|(0.02)|(0.09)|(0.08)|(0.06)|(0.08)|(0.05)|
|Constant|0.08***|10.14***|8.24***|7.25***|7.65***|0.13***|0.36***|0.20***|0.81***|0.53***|0.11***|
||(0.02)|(0.05)|(0.05)|(0.06)|(0.05)|(0.01)|(0.04)|(0.04)|(0.04)|(0.04)|(0.02)|
||963|963|963|963|962|961|963|963|963|963|963|
|_Panel B: All households_||||||||||||
|Migrant household = 1|0.72***|0.30***|0.35***|0.44***|0.23***|0.02***|0.20***|-0.03|0.02|0.05**|0.05**|
||(0.02)|(0.05)|(0.06)|(0.08)|(0.04)|(0.01)|(0.03)|(0.03)|(0.03)|(0.02)|(0.02)|
|Year 2012 = 1|-0.08***|-0.15**|-0.15**|-0.38***|-0.07|-0.06***|-0.06**|-0.08***|-0.07**|-0.03*|-0.03*|
||(0.01)|(0.06)|(0.06)|(0.07)|(0.07)|(0.01)|(0.03)|(0.02)|(0.03)|(0.01)|(0.01)|
|DD|-0.21***|-0.08|-0.14*|-0.08|-0.08|0.00|-0.11***|0.05|-0.01|0.02|0.02|
||(0.04)|(0.06)|(0.08)|(0.10)|(0.07)|(0.01)|(0.04)|(0.04)|(0.04)|(0.03)|(0.03)|
|Constant|0.10***|10.10***|8.39***|7.48***|7.71***|0.14***|0.55***|0.20***|0.81***|0.09***|0.09***|
||(0.01)|(0.04)|(0.05)|(0.07)|(0.04)|(0.01)|(0.03)|(0.04)|(0.03)|(0.01)|(0.01)|
||2,530|2529|2529|2529|2519|2422|2,530|2,530|2,530|2,530|2,530|

Notes: Panel A includes sample of all households with one or more children under three years old in 2008 or 2012. Panel B includes all households. Standard errors estimated allowing for clustering at the sample cluster level in parentheses . *** indicates significantly different at p<0.01, ** at p<0.05, and * at p<0.10.
<!-- Start of picture text --> 0.20%<br>0.00%<br>-0.20%<br>-0.40%<br>-0.60%<br>-0.80%<br>-1.00%<br>-1.20%<br>-1.40%<br>Actual BCA Reduced-Form Model Portfolio Growth Model<br>Percentage of U.S. GNP<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text -->

**Figure I. Bilateral Current Account: U.S. and Japan**

This figure shows the annual bilateral current account balance between the U.S. and Japan, from 1960 to 2005. It also shows the fitted values of a portfolio growth only model, representing an adaptation of Kraay and Ventura's (2000) model, and the estimates of our reducedform model in equation (5) with both portfolio growth and portfolio rebalancing components. Values are shown as a percentage of U.S. GNP.

55
# Figure 3. World trade and financial integration

<!-- Start of picture text --> A. Trade openness B. Financial openness<br>Percent of GDP Percent of GDP<br>60 400<br>300<br>40<br>200<br>20<br>100<br>0 0<br>1950s 1960s 1970s 1980s 1990s 2000s 2010s 1970s 1980s 1990s 2000s 2010s<br>Sources: Feenstra, Inklaar, and Timmer (2015); International Monetary Fund; Lane and Milesi-Ferretti (2018);<br>World Bank.<br><!-- End of picture text --> Note: Trade openness is the ratio of world exports and imports to world GDP. Financial openness is the sum of foreign assets and liabilities in percent of GDP across all countries. Each bar corresponds to the average in the decade indicated. The 2010s period refers to 2010-19, with estimates for 2019 in trade openness. For financial openness, data for 2019 are based on the numbers for the first two quarters.

32