rafmacalaba/datause-encoder-data huggingface dataset:
inequalities significantly declined (or a significant increase in RRs) include modern contraceptive use and SBA. The poorest to the richest RRs of modern contraceptive use by married women increased from 0.12 to 0.54 [p<0.05]. For SBA, both the poor to the rich RR and the poorest to the richest RRs increased significantly, respectively from 0.13 to 0.21 [p<0.05] and 0.03 to 0.08 [p<0.05]. However, the improvement in relative gap by wealth group is not significant for ANC visits.

**Table 4b. Trends in relative gaps MNCH inequalities by education and place of residence, 2000-2014**

||Ratio Pri<br>below/Se|mary &<br>condary+||Ratio: Ru|ral/Urba|n|
|---|---|---|---|---|---|---|
||Earlier<br>Survey|Latest<br>Survey|P-value|Earlier<br>Survey|Latest<br>Survey|P-value|
|Stunting|1.60|2.30|0.519|1.25|1.58|**0.102**|
|Wasting|1.80|1.29|0.279|1.68|0.98|0.432|
|Underweight|1.75|4.71|**0.001**|1.53|1.48|**0.036**|
|NMR|1.76|2.00|NA|1.10|0.74|NA|
|IMR|1.46|1.69|NA|1.19|1.27|NA|
|U5MR|1.81|2.27|NA|1.29|1.07|NA|
|Measles|||||||
|Vaccination|0.41|0.61|**0.012**|0.36|0.66|**0.008**|
|Full Immunization|0.27|0.40|0.363|0.26|0.43|0.822|
|Contraceptive|0.15|0.65|0.137|0.11|0.68|**0.036**|
|ANC4+|0.16|0.30|0.225|0.14|0.27|0.118|
|SBA|0.08|0.16|0.290|0.07|0.16|**0.029**|

Source: Authors’ compilation from the DHS (2000, 2005, 2011 & 2014) data. Note: The p-values are for the F-test of the equality of rate ratios. The first part of the table is based on: _Is [(primary and none, in the latest survey) / (secondary and above, in the latest survey)] equal to [(primary and none in the Earlier Survey) / (secondary and above, in the Earlier Survey)]?_ Similarly, in the second part of the table the quality of RR tests are: _Is [(rural, in the Latest Survey) / (urban, in the Latest Survey)] equal to [(rural, in the Earlier Survey) / (urban, in the Earlier Survey)]?_ Test is not applicable for NMR, IMR and U5MR Table 4b presents the education and spatial dimensions of relative gaps in health.

The results, in general, indicate that there has been narrowing relative gap by education and place of residence for all services and nutrition and mortality indicators. Nine out of ten indicators show a narrowing relative gap between rural and urban areas. The 23
- Akin, J., D. Guilkey, and H. Denton. 1995. "Quality of Services and Demand for Health Care in Nigeria: A Multinomial Probit Estimation." _Social Sciences and Medicine_ 40(11): 1527-37.

- Akin, J. and P. Hutchinson. 1999. "Health-Care Facility Choice and the Phenomenon of Bypassing." _Health Policy and Planning_ 14(2): 135-51.

- Akin, J., S, C. Griffin, D. K. Guilkey, and B. M. Popkin. 1984. _The Demandfor Primary Health Care in Developing Countries._ Totowa, N.J.: Littlefield, Adams.

- Alderman, H., and V. Lavy. 1996. "Household Responses to Public Health Services: Cost and Quality Tradeoffs." _World Bank Research Observer_ 11(1): 3-22.

- Anderson, D. L. 1980. "A Statistical Cost Function Study of Public General Hospitals in Kenya." _Journal of Developing Areas_ 14(2): 223-35.

- Angeles, G., J. F. Stewart, R. Gaete, D. Mancini, A. Trujillo, and C. I. Fowler. 1999. "Health Care Decentralization in Paraguay: Evaluation of Impact on Cost, Efficiency, Basic Quality, and Equity. Baseline Report." MEASURE Evaluation Technical Report Series No. 4. Carolina Population Center, University of North Carolina at Chapel Hill.

- Arrow, K. J. 1963. "Uncertainty and the Welfare Economics of Medical Care." _American Economic Review._

- Atkinson, S. J. 1993. "Anthropology in Research on the Quality of Health Services." _Caderno de Sauide Publica, Rio de Janeiro_ 9(3): 283-99.

- Barnum, H. N. and J. Kutzin. 1993. _Public Hospitals in Developing Countries._ Baltimore: Johns Hopkins University Press.

- Barnum, H. N., J. Kutzin, and H. Saxenian. 1995. "Incentives and Provider Payment _Methods." International Journal of Health Planning and Management 10:_ 23-45.

- Baumol, W. J., J. C. Panzar, and R. D. Willig. 1982. _Contestable markets and the theory of industry structure._ New York: Harcourt Brace

- Beegle, K. 1995. "The Quality and Availability of Family Planning Services and Contraceptive Use in Tanzania." World Bank, Washington, D.C.

- Benefo, K. and P. T. Schulz. 1994. "Determinants of Fertility and Child Mortality in Cote d'Ivoire and Ghana." Living Standards Measurement Study Working Paper 103. World Bank.

- Bennet, S. and L. Franco. 1999. "Public Sector Health Worker Motivation and Health Sector Reform: A Conceptual Framework." Major Applier Research 5, Technical Paper 1. Partnerships for Health Reform.

- Bennet, S., D. Gzirishvili, and R. Kanfer. 2000. "An In-depth Analysis of the Determinants and Consequences of Worker Motivation in Two Hospitals in Tbilisi, Georgia." Major Applier Research 5, Working Paper 9. Partnerships for Health Reform.

- Bertrand, J. T., T. Sullivan, and J. Rice. 2001. "Quick investigation of quality: measuring quality of care in family planning." Caroline Population Centre, Chapel Hill, NC.

- Bessinger, R. and Bertrand. 2001. "Monitoring Quality of Care in Family Planning Programs: A Comparison of Observations and Client Exit Interviews." _International Family Planning Perspectives_ 27(2): 63-70.

40
Regarding institutional explanatory variables, the results are mixed. Those<sup>that</sup> attempt to control for institutional quality in the recipient country have little explanatory power while those that control for institutional features related to the Bank -- the type of intervention, the nature of the reform -- are far more successful. Although the BERI and ICRG variables have substantial explanatory power in growth regressions, the same is not true here.<sup>3</sup> ' There are a number of possible explanations. The most plausible is that DBPC/GDP and the indices are all measures of the security of property rights and thus are highly correlated.<sup>3 8</sup> Clague et al, found a similar connection between contract<sup>intensive</sup> money -- the ratio of _non-currency_ money to the total money supply<sup>-</sup> and the security of property rights.<sup>3 9</sup> Another explanation is that, for many of the countries in the sample, BERI data (and, to a lesser extent, ICRG data) were not available in the year of the intervention, which further limits an already small sample, and may make<sup>estimated</sup> coefficients somewhat less reliable. Yet another is that growth regressions typically include both industrialized and developing countries which often vary widely with respect to these indicators. Here, because we are restricted to Bank interventions (which tend to occur in countries with relatively low institutional capacity), we lose variation and, 37 This is true regardless of whether the BERI and ICRG variables enter the specification individually or as part of indices. The ICRG results appear in Table 3.1, models (5) and (6). Also, some parts of the indices (such as the BERI measure<sup>of</sup> expropriation risk) are, not surprisingly, highly co-linear with our initial conditions variables, which further muddles the specification. For example, in this small sample<sup>of</sup> developing countries, it appears that those that pose little expropriation risk to foreign investors also have relatively stable macroeconomic situations, at least as measured by inflation.

**38** Indeed, the BERI index and initial DBPC/GDP are correlated at the .549 level. however the ICRG-DBPC/GDP correlation is only .142. 39 Clague et al. (1994), pp. 16-18.

31
of primary education. Bahia, as other states of Brazil, has seized the opportunity effectively. At both the national and state level, governments have raised education’s share of the public budget and introduced policy changes aimed at improving access to primary education, increasing enrollment in secondary school, and diminishing regional disparities. Although progress has been made in increasing educational attainment over time, gaps remain for the poor and blacks and mulattos.

In Bahia, school enrollment for the 7 to 14 age group is high and in line with the Northeast and Brazil as a whole (Table 6.4). Enrollment of children from poor households is slightly lower, 93.5 percent in 1999. In older age groups Bahia is ahead. Enrollment for ages 15 to 17 in Bahia is 79 percent, above regional and national averages. While primary enrollment rates have sharply increased in the last decade, completion is still a challenge.

|**Table**|**6.4: Scho**|**ol Enrollmen**|**t, 1999**|
|---|---|---|---|
|**Age **|**Brazil**|**Northeast**|**Bahia**|
|**5–6**|70.9|76.9|74.5|
|**7–14**|95.7|94.1|95.0|
|**15–17**|78.5|76.7|79.2|
|**18–19**|51.9|52.8|58.1|
|**20–24**|25.5|26.3|28.1|

_Source: Síntese de Indicadores Sociais_ <u>, 2000, IBGE.</u> School attendance of poor students increased but they still lag behind. In Bahia, as in the rest of Brazil, children from richer households have on average a higher school attendance, are less likely to repeat a school year, and have more completed years of schooling than children from poor households. Data reveal a strong correlation between poverty and educational attainment in the Northeast. The level of education of the extremely poor is the lowest, and it is also increasing more slowly than average. Average years of effective education of the total active population in Brazil increased by almost 2 years, from 4.5 years in 1981 to 6.4 years in 1999. In 1999, average years of effective education of the total population in Bahia increased by 1.5 years, from 3.2 years in 1981 to 4.7 years in 1999 (Table 6.5). For the indigent poor—with per-capita family income below the indigence poverty line—it increased slightly less, from 1.9 years in 1981 to only 3.1 years in 1999. This shows that school attainment of the poor is increasing fast, but the gap between the poor and non-poor in school attainment is not closing.

31
time-profile of impacts on, for example, inequalities between leading and lagging regions. Investments could initially increase inequality in real wages between regions, but labor mobility will then eventually arbitrage away such (cost-of-living adjusted) differences. Simulating the long-run equilibrium does, however, introduce an extra layer of complexity, not least because of the crudeness of the modeling of migration decisions in NEG theory to date. It is for this reason, as stated before, that we focus only on short-run impacts in our application to China.

Initial transport impact assessments built on elements of NEG theory took the form of Spatial Computable General Equilibrium (SCGE) models. For instance, the EU's _CGEurope_ model has been developed to assess the impacts of proposed Trans-European Transport Network (TEN-T) projects to better connect lagging European regions with leading ones.<sup>10</sup> Further examples include the RAEM model for the Netherlands for _ex ante_ assessment of proposed magnetic levitation rail projects (Oosterhaven and Elhorst, 2008). Our approach instead builds more directly on the academic empirical NEG literature which estimates the so-called NEG wage equation – the predicted relationship between nominal wages and a measure of a region's market access (Au and Henderson, 2006; Bosker _et al._ , 2010; Brakman _et al_ , 2006; Hering and Poncet, 2010a, 2010b). Other closely related work uses a full structural NEG model to simulate the short-run spatial economic impacts of a generalized reduction in transport costs. This reduction is modeled as a change in a scalar parameter which enters into a transport cost function based on straight-line distance (Brakman _et al._ , 2006; Fingleton, 2005b, 2007).

# **3. Theory and Strategy for Estimating Impacts**

# _3.1. The Model_<sup>_11_</sup>

The theoretical model which underpins our approach is a development of the basic two sector NEG model (Krugman, 1991a, 1991b) that accommodates explicitly defined transport costs and the extension of Dixit-Stiglitz "love of variety" preferences (and, therefore, product differentiation) to both sectors, as set out by Fujita _et al_ . (1999, chp. 7) and Fingleton (2005b,

> 10 These include its IASON (Integrated Appraisal of Spatial Effects of Transport Investments and Policies) project, its EPSON (European Spatial Planning Observation Network) Territorial Impact of EU Transport and TEN Policies project, and its ASSESS report.  For an overview of the theory underlying this model see Bröcker (2002).

> 11 Given that the basic structure of NEG models is now very well-known (see, for example, the detailed textbook overviews provided by Fujita _et al_ ., 1999, and Brakman _et al._ , 2009), we do not provide a detailed technical overview of our model in this section.

9
<!-- Start of picture text --> NMV per ha, USD (2024) Plot size, ha Soil density 0-60cm, cgicm3<br>3<br>30<br>1.0<br>2 20<br>1 05 10<br>0) 0.0 0<br>2sc 5 00 00 01 Soil 10nitrogen10.00-60em,100.01calkg000.00 doa00 dacoa0'000.0 00 00 Soil organicot content,1'0 tha100 1000 100.0 110.0 Soil pH120.0 0-60cm 130.0 140.0 TransactedNot transactedparcelsparcels<br>6<br>4<br>20<br>44<br>2<br>10<br>2<br>1<br>0 0 0<br>200.0 300.0 400.0 500.0 700.0 1000.0 40.0 50.0 60.0 70.0 80.0 100.0 50.0 55.0 60.0 65.0 70.0 75.0 80.0<br><!-- End of picture text --> <!-- Start of picture text --> Dist. city, km Dist. elevator, km 1,00 Dist. motorway, km<br>15 15<br>0.75<br>1.0 10<br>0.50<br>m8 05 0.25<br>0.0 0.0 0.00<br>[tone00 OO OO OO Of 10 10.0Sd100.0 0.0 0.0 Dist0.1rina on1.0 10.0 100.0 0.0 0.0 Dit0.4 ray1.0 10.0 100.0<br>1.00<br>0.9<br>2 0.75<br>2 06 Not transacted parcels<br>2 0.50<br>o Transacted parcels<br>U4<br>0.25 os<br>o 0.00 0.0<br>1.0 3.0 10.0 30.0 100.0 300.0 10000 0.0 0.0 0.0 0.4 1.0 10.0 100.0 0.0 0.0 0.4 1.0 10.0 100.0<br>Dist. secondary road, km Dist. tertiro a d,rykm<br>og 0.9<br>06 06<br>03 03<br>0.0 0.0<br>0.0 0.0 0.0 0.0 0.0 01 03 1,0 3.0 10.0 30.0 0.0 0.0 0.0 0.0 Oh 1.0 10.0<br><!-- End of picture text -->
# **2.1. Data Description**

We study four complementary types of investment: bank loans and deposits, portfolio investment in debt and equity, FDI, and international reserves. These investment types make up the bulk of the IIP for most countries in the world and are known as functional categories in the balance of payments (IMF 2009). They represent (together with financial derivatives, not covered here) countries’ external assets in the External Wealth of Nations database (Lane and Milesi-Ferretti 2001, 2007). Instead of using aggregate international investment at the country level, we work with the underlying bilateral data. That is, for each country in the sample, we analyze the value of investments held by that country vis-à-vis each individual counterparty country.

Our data sets comprise bilateral year-end stocks of cross-border loans and deposits, portfolio investment, and FDI. The appendix provides detailed explanations of how we construct the data for each investment type. Because no bilateral data are available for international reserves, we construct a bilateral data set at the country-to-region level, that is, the international reserves each country holds in assets from different regions. We combine data on the level of international reserves from the IMF’s IFS with data on the currency denomination of reserves from the IMF’s COFER. Reserve assets denominated in a given currency typically consist of government debt owed by the sovereign issuing that currency. We thus assign as destination of reserve investments the region of the country in whose currency reserves are denominated. For example, we assume that euro denominated reserves are issued by the North.

For loans and deposits and FDI, we take advantage of “mirror data” to maximize sample coverage. For many country pairs, we have assets reported by the source country as well as liabilities reported by the destination country. Thus, whenever a source country A does not report its asset holdings in a country B, we use the liabilities reported by country B as the investment for the country pair A→B. Using this method, the only case when coverage remains incomplete is for bilateral 7
|Road (1=Yes; 0=No)|2,022<br>(2.96)**|722<br>(0.55)|3,313<br>(3.66)**|
|---|---|---|---|
|Distance to township government|21.9|83.4|-35.8|
||(0.77)|(1.19)|(0.93)|
|Share of irrigation in village|4.6<br>(1.11)|||
|If participate production association|1,713|2,940.6|-2,168.4|
|(1=Yes; 0=No)|(2.50)*|(2.57)*|(1.27)|
|Share of labor without education|4.901|24.6|-9.3|
||(0.71)|(1.71)|(0.90)|
|Log of cultivated land area per<br>household|-5,189|-4,942|-3,934|
||(29.46)**|(13.72)**|(14.53)**|
|Elevation|-1.956|-0.920|-3.493|
||(4.56)**|(1.41)|(2.46)*|
|Constant|26,242|-4,167|70,431|
||(3.28)**|(0.19)|(5.22)**|
|Observations|8405|2750|2119|
|Adjusted R-squared|0.21|0.17|0.26|
|F-test|89.23|||

* denotes significant at 5%, ** denotes significant at 1% level

25
For each country, we first deflate the official poverty lines to the annual average of monthly consumer price indices of 2011 and then convert them to PPP using the 2011 ICP update.<sup>26</sup> Table 2 presents population-weighted averages of extreme and moderate urban official poverty lines for all countries in LAC in 2011, and Figure 2 shows the density function of all subnational official poverty lines. Extreme poverty lines fall within a limited range between $2 and $4.4 per person per day, while moderate poverty lines are more disperse, ranging from $4 to $12.2 per person per day. This implies that the moderate poverty lines of some countries are lower in PPP terms than the extreme poverty line of other countries. For instance, the population-weighted average extreme poverty line of Paraguay ($4.4 a day) is slightly higher than the population-weighted average moderate poverty lines of Nicaragua, Ecuador, and Brazil.

The relatively large dispersion of the moderate poverty lines is also evident in Figure 3, which presents the cumulative distribution function (CDF) of the total number of official extreme and moderate poverty lines at the subnational level. The figure shows that the rank of the subnational regions based on the extreme official poverty lines is different from the rank based on moderate official poverty lines, suggesting a large variation of the Orshansky coefficients in LAC. Additionally, the slope of the CDF based on extreme poverty lines is steeper than the one based on moderate poverty lines, which suggest that there is more heterogeneity in the normative cost of living when non-food items are added to the overall household consumption bundle.

Additional key messages emerge from Figure 3. First, there is a substantial overlap between subnational poverty lines – both extreme and moderate – across countries in LAC. Taking Brazil as an example, Leste-Urban Area has the lowest moderate poverty line ($3.5 per person per day) in LAC, which is considerably lower than the moderate poverty line of Region Pampeana in Argentina ($4.8 per person per day). By contrast, the metropolitan Area of Porto Alegre in Brazil has a moderate poverty line of $5.5 per person per day, which is above the median moderate poverty line in LAC ($5.3 per person per day) and considerably higher than moderate poverty lines in subnational areas of Argentina. Second, there are highly populated subnational regions that contribute substantially to the overall population in LAC. For instance, Mexico and Brazil, which are characterized by larger circles in the figure, represent roughly 60 percent of LAC’s population. Third, there is large heterogeneity in terms of the number of subnational poverty lines by countries.

> 26  The annual average was chosen because the PPP figures reflect prices for the entire calendar year.

11
8 remit for altruistic purposes in order to help their poor relations at home. Another (more likely) interpretation is that countries with high levels of poverty – like those in SubSaharan Africa -- are not producing many international migrants (and thereby receiving international remittances) in the first place. More empirical work is needed to determine which of these interpretations is correct.

# 3.  International Remittances, Welfare and Poverty

Now that the economic determinants of international remittances have been identified at the macro-level in the developing world, it becomes important to identify the impact of these resource flows on welfare and poverty in specific developing countries. Household surveys represent the best source of information for doing this, because these surveys collect disaggregated data on the level of income received by households with and without remittances.

It should, however, be noted that any effort to use household data to examine the impact of international remittances on welfare and poverty involves several important methodological issues. On the one hand, it is possible to treat international remittances as a simple exogenous transfer of income by migrants. When treated as an exogenous transfer, the economic question becomes: How do international remittances, in total or at the margin, affect the observed level of welfare and poverty in a specific developing country? On the other hand, it is also possible to treat international remittances as a potential substitute for domestic (home) earnings. When treated as a potential substitute for home earnings, the economic question becomes: How does the observed level of welfare and poverty in a country compare to a counterfactual scenario without migration and remittances but including an imputation for the home earnings of migrants had those people stayed and worked at home? This latter treatment uses econometric techniques to compare the level of welfare and poverty in a country with and without international remittances.

The rest of this section will draw upon recent papers to illustrate each of these methodological approaches. The first three papers treat international remittances as a simple exogenous transfer of income, and simulate changes in the level of poverty based
potential cocaine production). Another source of bias has to do with the assumptions on yields and technical coefficients of transformation that UNODC and ONDCP use in order to come up with an estimate of potential cocaine production. The possible biases could be the result of a higher density of coca crops, more efficient planting techniques, the use of fertilizers, and the development of genetically modified coca plants with much higher yields. For instance, Colombian authorities have recently argued that coca yields have increased as a result of the introduction of a new, genetically modified, coca variety, which is supposedly much taller, of a much higher quality and higher percentage of hydrochloride (more cocaine and cocaine of higher purity can be extracted from each leaf), and which is glyphosate resistant (McDermott, 2004).<sup>31</sup> The implementation of more efficient planting techniques and the introduction of new fertilizers and new chemicals in the manufacturing process would also result in more cocaine being produced from fewer coca fields. For instance, after the successful operations to stop the diversion of potassium permanganate (a precursor used in the manufacture of cocaine) in Colombia during the end of the nineties, drug producers adapted and started using an alternative chemical (sodium hypochlorite), which may have resulted in higher rates of extraction and yields (UNODC, 2005b).

Although these possible sources of bias in the estimation of coca cultivation and potential cocaine production are hard to verify, the current efforts undertaken by UNODC to conduct field studies in each of the producer countries, in order to have better estimates of coca yields and, in general, of the technical coefficients of the cocaine production process, are headed in the right direction. Because profit margins are extremely high in the cocaine market, cocaine producers respond and

> 31 TNI (2004) quickly responded to this information by questioning its scientific validity.

23