rafmacalaba/datause-encoder-data huggingface dataset:
Anderson 2012). It is possible that such a set of interventions would lower the impacts of high prices on poverty by lowering prices in the countries where high prices have the greatest adverse impact on poverty. However, there is no guarantee that this would be the case. When, for instance, the EU used variable import levies to stabilize its domestic prices, this resulted in instability being exported to the rest of the world by some of the richest countries in the world. In the presence of this collective-action problem, only a policy that takes into account these interactions can reduce volatility without creating the beggar-thy-neighbor problem inherent in this type of policy response.

When Martin and Anderson (2012) investigated the implications of countries‘ responses to the price surges for wheat and rice in 2008, they found that almost half the increase in the world price of rice could be explained by countries‘ attempts to insulate themselves from the primary shocks causing the world price of rice to rise. While some low and middle income countries were relatively successful in insulating themselves against the increases in world prices, domestic prices in low income countries in Africa rose almost as much as world prices did, suggesting that price volatility in many African countries may have been greater given insulating policies than it would have been otherwise—see Figure 5.

**Figure 5. Domestic and international price changes for rice, 2006-10 %**

<!-- Start of picture text --> 250<br>Producer Price Asia<br>Producer Price Africa<br>Producer Price Latin America<br>200<br>International Reference Price<br>150<br>100<br>2006 2007 2008 2009 2010<br>50<br>0<br><!-- End of picture text --> _Source:_ Martin and Anderson (2012) 19
to a flat 11% in 1991, however different preferential trade agreements signed with countries like Colombia, Ecuador, Mexico, Venezuela, Canada, etc., have caused average tariff to stand even lower.

In this context of high growth, poverty has decreased sharply in Chile, while the official poverty head count ratio was 44.6% in 1987, by 1994 it had been reduced o 28.5%. However, important challenges await for Chile. The economy has to reduce present Current Account deficits, increase the value added of its exports. For the latter, it must create a stock of human capital able to manufacture more technology intensive exports.

## **References**

I. Economic Intelligence Unit, Country Profile: Chile, 1991-1992, 1994-1995, 1996-1997.

2. Ffrench-Davis, Ricardo, Patricio Leiva, Roberto Madrid, Liberalizacion Comercial y Crecimiento: La Experiencia de Chile, 1973-89, in "Pensamiento Iberoamericano", No. 21, 1992, pp. 33-55.

3. Ffrench-Davis, Ricardo, Oscar Munoz, Desarrollo Economico, Inestabilidad y Desequilibrios Politicos en Chile: 1950-1989, in "Coleccion Estudios CIEPLAN", No. 28, June 1990, pp. 121156.

# **Brazil**

## **History**

Over 1.5 million indigenous people, belonging to various tribes and speaking different languages, inhabited in Brazil when the Portuguese arrived in 1500. The Spanish colonial control lasted about three centuries. The Spanish authority provoked the anti-colonial movement when it tried to impose a strict tax system and a law prohibiting Brazilian manufacture. In 1822, Brazil gained independence in the form of monarchy. In 1824, the first constitution was promulgated, a constitutional monarchy was established with three branches, executive, legislative, and judicial. In the second half of the 19th century, the immigrants from other European countries, especially United Kingdom, helped the country creating a domestic market withi local commerce and industry. Coffee plantation was one of the major economic activities. Dom Pedro 11 was crowned in 1841, but in 1889 a federal republic was constituted and the royal family banned from the country.

## **Economic Miracle** (1960s-1970s)

The military leadership, who supported President Getulio Vargas (1930-45), saw the importance of industrial development. Motivated by this aspiration, the military officer corps seized power in the March 1964 and controlled the country for two decades. Import substitution industrialization (ISI) was the major strategy of economic development. This inward-looking strategy was supported by the advantage of rich natural resources, such as arable land, metalliferous ores (iron and aluminum), livestock, wood and hydroelectricity. The country earned the reputation of being 46
recent past also increases the probability of _work only_ for both men and women, but by a smaller amount (between 9.2 for women and 34 p.p. for men).

Figure 6. Proportional Change in the Probability of Being Nini to a One-Unit Change in the Corresponding Index of Past Decisions, Youth Aged 15-18 y.o., by Gender (a) Male (b) Female <!-- Start of picture text -->
1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2<br>Study Only Work Only Nini Study Only Work Only Nini<br>Index Index Index Index Index Index<br><!-- End of picture text -->

_Notes_ : The value of the bars represent the marginal effects on the probability of being Nini for the corresponding variables from the underlying estimation of multinomial logit models (one for each gender). The dependent variable in the multinomial models takes three states (Nini, Work Only, and Study Only). Only marginal effect for the outcome of Nini is presented in this graph. The models include controls for history of past decision indices, youth’s own education level, parental education level, household income quintiles, a set of demographic variables, and state economic condition variables. Complete results are presented in Appendix Table A1. Indices take values from 0 to 1. _Source_ : Authors’ calculations based on model estimation using data from the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

Having been enrolled in school for longer in the recent past, as reflected by the Study index, increases the probability of current _study only_ (for both men and women, but more for women), reduces the probability of current _work only_ (for both men and women, but more for men), and increases marginally (for men) or has no impact on the probability of current Nininess (Appendix Table A1 and Figure 6). In contrast, having been in _work only_ in the recent past in general has the opposite effect to having been in school only (just described). The only exception is that having been in _work only_ in the recent past increases the probability of currently being _Nini_ (Figure 6), more for men than for women (in 51.1 p.p. for men and 74.7 p.p. for women; in comparison with _work & study_ ).

Taken together, the previous estimates in Appendix Table A1 and Figure 6 indicate that youth tend to remain in school, but that those who leave are unlikely to go back. For those who have left school and have experienced being _Nini_ , the probability of going back to school is diminished by 89.7 p.p. in the case of men, and by 83.3 p.p. in the case of women, all relative 22
**GDP growth:** Growth rate of real per capita **GDP.** Summers and Heston dataset and World Development Indicators.

Trade openness: Residual of a regression of the log of the ratio ofr eal exports plus imports (in **1995 US$)** to real **GDP** (in **1995 US$)** on the logs of area and population, and dummies for oil exporting and for landlocked countries (see Loayza, Fajnzylber, and Calder6n 2002). World Development Indicators.

Financial Depth: Ratio of private credit **by** deposit banks and other financial institutions to **GDP** (Beck, Levine, and Demirgug-Kunt 2000).

Government consumption: Ratio of general government final consumption expenditure to **GDP.** World Development Indicators.

Institutional quality: Civil liberties **(0,1).** Higher scores indicate more liberties. Freedom House: Freedom in the World.

REER growth: Growth rate of real effective exchange rate. IMF International Financial Statistics.

REER volatility: Standard deviation of real effective exchange rate percentage changes. IMF International Financial Statistics.

**GDP** growth volatility: Standard deviation of growth rate of real per capita **GDP.** Summers and Heston dataset and World Development Indicators.

Terms of Trade volatility: Standard deviation of terms of trade percentage changes. World Development Indicators.

Wages: Average annual rate of wages and salaries in the manufacturings ector, expressed in constant **1995 US$.** United Nations: Industrial Development Organization.

Traded Value to **GDP:** Ratio of total shares traded on the stock market exchange to **GDP** (Beck, Levine, and Demirgug-Kunt 2000).

Official liberalization: Dummy variable that takes the value of one if the equity market is liberalized based on the chronology in Bekaert and Harvey (2000).

First sign: Dummy variable that takes the value of one if there is either an official liberalization or ADR and country fund introduction.

Investability: Ratio of capitalization of the IFC "investable" to the "global" stocks in a country as in Edison and Warnock (2001).

Balance of Payments Restrictions: Simple average of **(0,1)** scores for the following categories of balance of payments restrictions: multiple exchange rate practices,c urrentaccount restrictions, capital-account restrictions, and surrender of export proceeds. IMF Exchange Arrangements and Exchange Restrictions.

**34**
**Table 9.  Financial Statements of KVK Apex Federation**

|_Particulars_|_1998-99_|_1999-00_|_2000-01_|
|---|---|---|---|
|**Income**||||
|Interest on loans<br>|21,597|227,415|981,371|
|Service charges/Fees<br>|4,200|27,100<br>|31,550|
|Donations<br>|47,379|53,140<br>|79,200|
|Sundry Income<br>|4,952|18,514<br>|65,808|
|Total<br>|78,128|326,169|1,157,929|
|**Expenditure**||||
|Interest on loan<br>|-|212,448|804,470|
|Salary and benefits<br>|-|12,660<br>|95,852|
|Travel<br>|402<br>|5,082<br>|21,062|
|Office administration<br>|9,561<br>|9,344<br>|25,663|
|Printing and Stationery<br>|-|10,373<br>|29,849|
|Training<br>|9,561|50,802<br>|86,017|
|Audit fee<br>|2,598|-<br>|1,500|
|Sundry expenses<br>|756<br>|1,610<br>|21,094|
|Profits<br>|55,250|23,850<br>|72,422|
|Total<br>|78,128|326,169|1,157,929|
|**Equity and Liabilities**||||
|Reserves and Surplus<br>|440,731|450,270|479,240|
|Member Deposits<br>|364,035|872,635|973,470|
|Loans<br>|-|2,896,000|9,377,123|
|Specified Funds<br>|96,509|205,070<br>|565,595|
|Current liabilities<br>|1,000|-<br>|700|
|Total<br>|902,275|4,423,975|11,396,128|
|**Assets**||||
|Land and Fixtures<br>|253,623|375,176|1,167,926|
|Deposits<br>|-|575,000|575,000|
|Loans and Advances<br>|366,677|3,383,797|9,584,839|
|Cash<br>|281,975|90,002<br>|68,363|
|Total<br>|902,275|4,423,975|11,396,128|

Item heads in original statements reorganized and relabeled for easier comparison.

- 31 -
# Empirical Results

# Foreign direct investment

Before proceeding to the estimates, we evaluate the overall classification performance of the empirical model using the receiver operating characteristic (ROC) curve. The ROC curve plots the share of model's correct classification of positive FDI flows in total number of sectors with FDI flows (true positive rate) versus the percentage of incorrect signals of positive FDI flows out of all sectors without FDI flows (false positive signals), along varying probability cutoff levels. The relative quality of the model's empirical performance can be observed (and estimated) by looking at the area under the curve. If the model unveils associations with the FDI performance, the ROC curve should lie closer to the top left corner. The corresponding area under the ROC curve (AuROC) for an informative signal is then strictly above 0.5, which is the value for an uninformative signal.

Figure 4 shows very good empirical performance of the estimated model. The ROC curve is close to the top left corner and AuROC's value of 0.93 is close to the maximum attainable level. To understand better the model’s performance, we also construct sector level values of AuROC. Figure 5 shows the distribution of the sector level AuROC values. Even though the model is not estimated at the individual sector level, the model is highly informative about the sector’s empirical performance, as all AuROC values are strongly above 0.5. As expected, we observe some heterogeneity in sector level performance, nevertheless for majority of sectors the estimated value of AuROC is above 0.8.

_Figure 4. ROC curve: FDI data_ Note: The figure reports estimated receiver operating characteristic (ROC) curve. Total positive rate is the share of model's correct classification of positive FDI flows in total number of sectors with FDI flows. False positive rate is the share of model's incorrect signals of positive FDI flows in total number of sectors without FDI flows. The curve is plotted for varying values of threshold probability (threshold probability determines whether the observation is classified as a signal of positive or zero FDI flows). Estimation and classification are performed over the in-sample 2015-19 period. Estimated area under the ROC curve (AuROC) equals 0.927 and significantly differs from 0.5.

17
**Table 39. Governance drivers of political risk: Quality of laws**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.00696|0.0115|||0.00624|0.0213|||
||(0.0544)|(0.0533)|||(0.106)|(0.109)|||
|Export % of sales|-0.00109|6.79e-05|-0.00107|-2.41e-05|-0.00216|0.000323|-0.00215|7.57e-05|
||(0.000718)<br>0.00281**|(0.000747)<br>0.00230**|(0.000737)<br>0.00262**|(0.000760)<br>0.00209**|(0.00140)<br>0.00542**|(0.00153)<br>0.00493**|(0.00150)<br>0.00538**|(0.00162)<br>0.00463**|
|Share of sourcing via imports|*|*|*|*|*|*|*|*|
||(0.000773)|(0.000766)|(0.000798)|(0.000795)|(0.00151)|(0.00157)|(0.00162)|(0.00169)|
|High-income source country|-0.0396|0.00773|-0.00265|0.0325|-0.113|-0.0216|-0.0612|0.0150|
||(0.0824)|(0.0820)|(0.0828)|(0.0828)|(0.170)|(0.176)|(0.176)|(0.186)|
|>250 employees|-0.0432|-0.00849|-0.0335|-0.00873|-0.100|-0.0415|-0.0898|-0.0386|
||(0.0694)|(0.0703)|(0.0691)|(0.0710)|(0.138)|(0.148)|(0.143)|(0.156)|
|Cumulative inv. >US$10M|0.0855|-0.0145|0.0817|-0.0173|0.179|-0.0276|0.189|-0.0245|
||(0.0685)|(0.0684)|(0.0682)|(0.0676)|(0.137)|(0.144)|(0.141)|(0.147)|
|Years in country|0.000532|0.000970|0.000868|0.00151|0.000202|0.000403|0.000645|0.00136|
||(0.00180)|(0.00180)|(0.00179)|(0.00179)|(0.00356)|(0.00384)|(0.00375)|(0.00396)|
|Level of foreign ownership (%)|-0.00108|-0.000626|-0.000914|-0.000474|-0.00229|-0.00137|-0.00203|-0.00122|
||(0.00111)|(0.00122)|(0.00107)|(0.00115)|(0.00227)|(0.00260)|(0.00226)|(0.00255)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.014|0.074|0.048|0.105|||||

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Quality of laws and regulations]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

56
43

# RER & TOT UNCERTAINTY

<!-- Start of picture text --> 0.6 TA<br>0.5<br>AR<br>PE<br>0-0.4<br>z<br>< IN<br>U 0.3 C H M iR GT E<br>z - '<br>Lj0. D 0lA--25 - ~~~~~~HD -~BR<br>ui~~S 0. 2 ~~ PH~K<br>T U<br>0<br>0.05 0 1 0.15 0.2 0.25 0.3 0.35<br>TOT UNCERTAINTY<br><!-- End of picture text --> FIGURE E.11
8 Tables

|Table 1. Sample Statistics<br>~~a~~<br>~~5 Be~~<br>Price Ratio (CBI Price/US Price)<br>91.28<br>95.96<br>Tariff Difference (%) (post 1991)<br>Export Value (mn$)<br>10.02<br>32.34<br>Total US Imports (mn$)<br>193.91 | 496.76<br>~~Nar~~<br>~~ofcis 8~~<br>Number of Years<br>Number of 8-digit product categories<br>Number of 4-digit product categories<br>Number of Observations<br>7784|
|---|

22
an aggregate estimate of the number of total health workers demanded.

2. Project the future supply of physicians and nurses/midwives per 1,000 population for each country between 2013 and 2030, based on historical trends via a simple linear projection. Constant multipliers by income level are then applied to the combined supply densities of physicians and nurses/midwives to obtain supply density estimates of AOWs. Projected population size is then used to calculate the corresponding numbers of workers of each cadre that could be supplied for each future year.

3. The projected number of total health workers demanded in 2030 is then compared to the number supplied in 2030. A country is deemed to have a shortage of health workers in 2030 if the number of workers demanded exceeds the number supplied. The total number of health workers in countries that are predicted to have a shortage in 2030 are then summed by World Bank region and income level.

<!-- Start of picture text --> Figure A.1 Conceptual framework for projecting health worker labor supply and demand<br>5.00<br>4.50<br>4.00<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>2010 2015 2020 2025 2030<br>S: projected supply<br>D1: forecasted demand (senario with surplus)<br>D2: forecasted demand (scenario with shortage)<br>N: estimated need based on benchmark utilization<br>Health workers (per 1000 Population)<br><!-- End of picture text --> It is important to note that demand and supply are endogenously related in the labor market. However, we employ different methods to project each that seek to address potential endogeneity. Namely, we purposively employ a linear growth projection model for supply that is based solely on an exogenous time trend. Further, all parameter inputs into the demand model are lagged up to five years to ensure the direction of causality.

# **A2. Data sources**

The data sources used in this exercise are outlined in **Table A1.** All available data points for all countries/territories and years indicated were downloaded and merged together.

24
|P004172|1994|1999||13 Closed|Korea,Republic of|
|---|---|---|---|---|---|
|P004173|1995|1995||3 Closed|Korea,Republic of|
|P004173|1995|1996||6 Closed|Korea,Republic of|
|P004173|1995|1997||9 Closed|Korea,Republic of|
|P004173|1995|1998||11 Closed|Korea,Republic of|
|P004173|1995|1999||13 Closed|Korea,Republic of|
|P004173|1995|2000|6/15/2000|15 Closed|Korea,Republic of|
|P004173|1995|2001|6/12/2001|17 Closed|Korea,Republic of|
|P004173|1995|2002|6/25/2002|19 Closed|Korea,Republic of|
|P004173|1995|2003|6/30/2003|21 Closed|Korea,Republic of|
|P004173|1995|2004|12/24/2003|22 Closed|Korea,Republic of|
|P004174|1994|1994||1 Closed|Korea,Republic of|
|P004174|1994|1995||3 Closed|Korea,Republic of|
|P004174|1994|1996||6 Closed|Korea,Republic of|
|P004174|1994|1997||8 Closed|Korea,Republic of|
|P004174|1994|1998||10 Closed|Korea,Republic of|
|P004174|1994|1999||10 Closed|Korea,Republic of|
|P004175|1995|1995||1 Closed|Korea,Republic of|
|P004175|1995|1996||4 Closed|Korea,Republic of|
|P004175|1995|1997||5 Closed|Korea,Republic of|
|P004175|1995|1998||7 Closed|Korea,Republic of|
|P004175|1995|1999||9 Closed|Korea,Republic of|
|P004175|1995|2000|6/22/2000|11 Closed|Korea,Republic of|
|P004175|1995|2001|6/21/2001|13 Closed|Korea,Republic of|
|P004175|1995|2002|12/13/2001|14 Closed|Korea,Republic of|
|P004176|1994|2000||0 Closed|Lao People's Democra|
|P004176|1994|2001|3/9/2000|14 Closed|Lao People's Democra|
|P004176|1994|2015|11/16/2014|15 Closed|Lao People's Democra|
|P004177|1978|1985||0 Closed|Lao People's Democra|
|P004177|1978|1986||0 Closed|Lao People's Democra|
|P004178|1979|1985||0 Closed|Lao People's Democra|
|P004178|1979|1986||0 Closed|Lao People's Democra|
|P004178|1979|1987||0 Closed|Lao People's Democra|
|P004179|1980|1985||0 Closed|Lao People's Democra|
|P004179|1980|1986||0 Closed|Lao People's Democra|
|P004179|1980|1987||0 Closed|Lao People's Democra|
|P004179|1980|1988||0 Closed|Lao People's Democra|
|P004180|1982|1985||0 Closed|Lao People's Democra|
|P004180|1982|1986||0 Closed|Lao People's Democra|
|P004180|1982|1987||0 Closed|Lao People's Democra|
|P004180|1982|1988||0 Closed|Lao People's Democra|
|P004180|1982|1989||5 Closed|Lao People's Democra|
|P004181|1983|1985||0 Closed|Lao People's Democra|
|P004181|1983|1986||0 Closed|Lao People's Democra|
|P004181|1983|1987||0 Closed|Lao People's Democra|
|P004181|1983|1988||0 Closed|Lao People's Democra|
|P004181|1983|1989||8 Closed|Lao People's Democra|
Policy Research Working Paper 8846

# **Abstract**

The low take-up of cost-effective and highly subsidised preventive health technologies in low-income countries remains a puzzle. One under-studied reason is that the design of subsidy schemes is such that households remain financially constrained. This paper analyses whether, and how, micro-finance supports a large public health subsidy program in the developing world—the Swachh Bharat Mission—in achieving its aim of increasing uptake of individual household latrines. Exploiting a cluster randomised controlled experiment of a sanitation micro-finance program that coincided with the launch of the SBM program, and unique survey data matched to administrative data, findings reveal that the complementarity runs on two levels: First, micro-credit allows households officially ineligible for the subsidy to invest in sanitation by alleviating credit constraints. Second, micro-credit also helps subsidy eligible households to overcome short-term liquidity constraints induced by the remuneration-post-verification subsidy design to invest in sanitation. Subsidy eligible households living in areas experiencing large delays in subsidy disbursement, or high toilet costs, are more likely to take a sanitation loan, but less likely to use the loan to construct a toilet.

This paper is a product of the Water Global Practice. 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 bet_c@ifs.org.uk.

_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 governments they represent._ Produced by the Research Support Team