rafmacalaba/datause-encoder-data huggingface dataset:
The conceptual underpinning of the choice of macro and micro factors lies in their effect on the demand and supply of labor. Country-level income may proxy for opportunities available in the labor market, while institutional factors may account for the likelihood of discrimination against certain groups. Population demographics provide simple proxies for labor supply while human capital controls account for the quality of labor.

We use firm level and country level data in ways that is innovative yet consistent with the broad framework discussed above. We control for business climate as experienced by the firms in addition to the macro-indicators. We also control for the presence of women in firmownership and in the country’s parliament since such a presence could motivate other women to seek employment or lower discrimination; it may also signal greater preference among women for work over household activity accounting for the preference based model above. We consider firm size, foreign exposure of firms via trade and foreign owners. Foreign owners may import the institutions in their home country which may be more women friendly; exporting markets are typically more competitive creating incentive to hire women who tend to have lower wages; larger firms may be more conscious of their image implying less discrimination and hence more women workers. Large firms are more likely to be monopsonists in the labor market, potentially accounting for the monopsonist model of women’s employment discussed above.

While the above potential mechanisms are of great interest, we note that the theoretical and the empirical literature connecting these relationships are nascent. Hence, it is beyond the scope of this study to develop these hypotheses further in the theoretical sense. We aim to provide robust empirical evidence hoping it would encourage more research in the area.

# **3. Data Description and Methodology**

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The Nitrogen Legacy: The Long‐Term Effects of Water Pollution on Human Capital<sup>1</sup> Esha Zaveri, Jason Russ, Sébastien Desbureaux, Richard Damania, Aude‐Sophie Rodella, Giovanna Ribeiro

**Keywords:** Water Pollution, Health, Nitrogen **JEL Codes:** O13, O15, Q15, Q25, Q54

> 1 This research was part of the World Bank report titled Quality Unknown: The Invisible Crisis of Water Quality. We are grateful to Kelly Baker and seminar and conference participants at the International Food and Policy Research Institute (Delhi), Center for Policy Research (Delhi), Indian Statistical Institute (Delhi), the annual meetings of the Association of Environmental and Resource Economists (AERE) (2019), and the Workshop on Environmental Economics and Data Science (TWEEDS) (2019)  for helpful feedback. Ali Sharman provided excellent research assistance. All errors are our own
**Figure 2:** _Average HIPC Debt Service Payments as a Share of Exports and GNI_ . Average debt service costs are normalized at Decision Point (grey line) and Completion Point (black line). The vertical lines mark the years of Decision Point and Completion Point, respectively. Source: World Bank (2015).

<!-- Start of picture text --> Years from Completion Point<br>-40 -35 -30 -25 -20 -15 -10 -5 0 5<br>-30 -25 -20 -15 -10 -5 0 5 10 15<br>Years from Decision Point<br>Debt Service (% of Exports) (Average, Normalized DP)<br>Debt Service (% of Exports) (Average, Normalized CP)<br>(a) Debt Service (% of Exports).<br>Years from Completion Point<br>-40 -35 -30 -25 -20 -15 -10 -5 0 5<br>-30 -25 -20 -15 -10 -5 0 5 10 15<br>Years from Decision Point<br>Debt Service (% of GNI) (Average, Normalized DP)<br>Debt Service (% of GNI) (Average, Normalized CP)<br>40<br>30<br>20<br>10<br>0<br>Average Debt Service (% of Exports) in HIPCs<br>15<br>10<br>5<br>0<br>Average Debt Service (% of GNI) in HIPCs<br><!-- End of picture text --> (b) _Debt Service (% of GNI)._ 22
# (a) Developing countries

<!-- Start of picture text --> 90<br>70<br>50<br>30<br>10<br>-10 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-09<br>-30<br>-50<br>Import-competing Exportables Total<br><!-- End of picture text -->

- (b) High-income countries plus Europe’s transition economies

<!-- Start of picture text --> 90<br>70<br>50<br>30<br>10<br>-10 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-10<br>-30<br>-50<br>Import-competing Exportables Total<br><!-- End of picture text --> Figure 10. NRAs to exportable, import-competing, and all covered agricultural products in (a) developing and (b) high-income and European transition economies, 1955–2010 (%). Fiveyear weighted averages for covered products only. The total also includes nontradables. The straight line in the upper segment of each graph represents an ordinary-least-squares regression based on annual NRA estimates.

Source: Anderson 2009, Chap. 1, updated using Anderson and Nelgen 2012b.

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are not actually made by them. This claim is echoed by Abraham and Platteau (2001) who argue that CDD processes substantially privilege local elites over the less advantaged which leads to a "serious risk that development efforts are hijacked by unaccountable members of the elite." Abraham and Platteau focus on the structures of power and information within communities, arguing that the socio-cultural, political and economic context of the community within which the CDD project is being implemented is extremely important in determining its success or failure.

Community Driven Development and Social Investment Funds are, clearly, contentious topics with their advocates and detractors. While the evidence seems to indicate that Social Funds reach the poor and are relatively successful at improving service delivery, the assumptions under which they operate have still not been put to test. We do not know if SIFs are participatory: Do projects generated by SIEFs meet the expressed preferences of the members of the community? Does the involvement of the community improve the community's capacity for collective action? Does the possibility of greater community involvement and ownership result in more sustainable facilities? These questions are best understood within the political, social and cultural life of communities within which CDD processes are deeply imbedded.

The Jamaican case-study analyzed in this paper will attempt to provide some answers to these questions, while providing an example of the use of mixed-method techniques to conduct an ex-post impact evaluation. The paper is organized as follows: In section 2 we will provide a brief history of Jamaica and its involvement in community led development, this will be followed in Section 3 with a an overview of the qualitative evidence from the five sets of communities. The findings from the qualitative evidence will then be tested for their generalizability with survey data, Section 4 will outline the methods used to analyze the survey data and the sampling methodology, section 5 will present the quantitative results, and section 6 will briefly review the qualitative and quantitative evidence to provide an integrated perspective and conclude the paper by summarizing the policy implications of the evidence. We should emphasize the paper should not be viewed as an evaluation of the Jamaica Social Investment Fund in its entirety. As a case study it benefits from the ability to examine five projects in some depth, but it also suffers from having a limited sample that lacks the scope to give a broad sense of JSIF's full portfolio of projects.

# The Context: History. Political Culture and CDD in Jamaica

It is difficult to understand the role of community driven development in Jamaica, and JSIF in particular, without some sense of Jamaica's political history and culture. Modem Jamaican history is dominated by two cousins Norman Manley and Alexander Bustamante. Manley founded the People's National Party (PNP) in 1938 in an organized effort to end British rule. The PNP was closely associated
debtors to borrow longer term to reduce refinancing risks (Graham and Harvey, 2001; Brunnermeier, 2009; Krishnamurthy, 2010; Almeida et al., 2011; He and Xiong, 2012; Broner et al., 2013; Diamond and He, 2014; Harford et al., 2014). The existing literature tends to draw its conclusions from analyses of individual debt markets, overlooking the effects of changes in debt issuance composition.<sup>2</sup> Our results highlight that the overall debt maturity, at both the firm and aggregate levels, depends not only on how maturity evolves in individual markets, but also on the relative importance of each market. Consistent with the literature, we show that the maturity of debt at issuance tended to decline in individual markets during crises. But we also show that switches between debt markets typically lengthened the overall debt maturity, driven by the fact that firms issued bonds at longer maturities than syndicated loans. This finding is consistent with the notion in the literature that banks offer more financial flexibility to firms in terms of renegotiation. Thus, when moving from bank credit to bonds, firms might try to lengthen maturities to avoid the higher refinancing risks of bonds.

Fourth, this paper contributes to research on the determinants of foreign currency debt, where the arguments are similar to those on debt maturity, with both demand and supply factors playing a role. Firms try to limit the risks associated with exchange rate movements by matching the currency denomination of their earnings with that of their debt obligations (Graham and Harvey, 2001; Servaes and Tufano, 2006). In the extreme case of market segmentation, emerging market borrowers have limited availability of local currency financing in international markets, also known as “original sin” (Eichengreen and Hausmann, 1999; Hausmann and Panizza 2011; Hale et al., 2016). During crises, currency risks tend to increase, as local currencies tend to depreciate. Borrowers might want to reduce

> 2 Other papers document a shortening of the maturity structure in firms’ balance sheets, which consider short-term debt (under one year in maturity) and long-term debt (over one year) (Gonzalez, 2015; Demirgüç-Kunt et al., 2020). However, these analyses cannot distinguish between firms not issuing during crises (hence, shortening the maturity structure of the existing debt) or the new debt issued during crises being shorter term. In contrast, issuance data allow us to study the behavior of the maturity of new debt.

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A-2 shows that this is indeed the case. Price levels fell significantly in sectors where foreign firms exerted a significant downward pressure via backward linkages. Since industry-level fixed effects are included in the estimation, the results can be interpreted to suggest that one important vehicle through which foreign firms played a key role was by exerting downward pressure on prices of domestic suppliers. The evidence on the competition effect induced by foreign firms on prices of input suppliers reported in Table A-2 is also useful in another respect. It illustrates the importance of using sector-specific price deflators (or prices) when identifying the spillovers from foreign investment, and explains why previous work on China failed to identify backward spillovers.

In Table A-1, we compare the coefficient estimates using OLS with firm fixed effects and the OP approach. OP, as well as LP predict, after implementing these two-stage procedures, that the coefficient on L should decrease, the coefficient on intermediate inputs should decrease and the coefficient on capital should increase. The results are generally consistent with these predictions across ownership classes. The coefficient on capital inputs is higher using OP across all specifications. We also generally find that the coefficient on the labor shares and material shares are lower with OP. What is unusual across all specifications is that the labor share is very low, compared to estimates for other countries, while the coefficient for input costs is very high. As a robustness check, we performed two tests. First, we calculated the share of labor expenditures in total output—the labor share in output according to the data. Under certain plausible restrictions Cobb-Douglas production function, perfect competition) the coefficients on the factor inputs in our estimating equations should equal the factor shares. Imposing these restrictions, the estimate of labor‘s share over the sample period is around 10 percent (reported in column (5) of Table A-3), which is similar to the underlying OLS fixed effect estimates reported in Table A-1. Second, we compare the implied average wages from our sample (calculated by dividing total wages by the number of employees with average wages reported in the Chinese Statistical Yearbook for 1998 through 2007. The results are listed in Appendix Table A-3. From Table A-3, we (i.e., 19
_Africa’s Macroeconomic Story_

**Figure 15: Cyclical-Adjusted Budget Balances in Africa, 1990-2012**

_Percent of GDP_ <!-- Start of picture text --> 0<br>-2<br>-4<br>-6<br>-8<br>1985 1990 1995 2000 2005 2010<br>Median Lower quartile<br><!-- End of picture text --> _Source:_ April 2013 IMF _World Economic Outlook_ database. _Notes:_ Estimates for 2012; projections for 2013.

**Figure 16: Countries with large Cyclically-Adjusted Budget Surpluses in 2007**

_Percent of GDP_ <!-- Start of picture text --> Oil exporters<br>12<br>8<br>4<br>0<br>-4<br>2007 2012<br><!-- End of picture text --> _Source:_ April 2013 IMF _World Economic Outlook_ database.

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<!-- Start of picture text --> 15<br>7<br>12<br><!-- End of picture text --> a Countries without @ legal framework on agent banking = Countries with a legal framework on agent banking = Countries allow individuals as well as business to act as banking agents Countries do not allow individuals as well as business to act as banking agents <!-- Start of picture text --> so 56 37<br>i9<br>Countries without a legal legal framework on on dectronic money<br>Countries with a legal framework framework on electronic money money<br>Countries allow non-financial institutions to institutions to to issue eletronic money money<br>Countries do not allow non-financial institutions to issue eletronic money<br><!-- End of picture text --> a Countries without a legal legal framework on on dectronic money

- = Countries with a legal framework framework on electronic money money

- = Countries allow non-financial institutions to institutions to to issue eletronic money money

> 100 ~~«|_|~~ GPmax;-—GPmin; ~~GPjj;-GPmin;~~
The sectoral pattern of growth matters to both the level of growth and its distributional impacts. The fact that a rising share of GDP in non-farm sectors is associated with higher GDP per capita, yet agricultural growth is especially pro-poor in many countries, raises the possibility of a trade off in poverty reduction between the growth impacts of structural transformation and its distributional impacts. Agricultural growth often has better distributional impacts, but nonagricultural growth rates tend to be higher. The extent of this trade-off is unclear, however, and will depend crucially on the sources of growth in each sector, and on country initial conditions and how they evolve in the course of economic development. More research is needed on the poverty impacts of structural change.

This agenda also has important implications for data collection, so as to properly test hypotheses and lay a firmer foundation for policy making. Here there is a rich and diverse crosscountry experience that needs to be properly documented, drawing on both national accounts data and the wealth of household and enterprise surveys that have been done in developing countries, as well as implementing new surveys that are tailored to the most important knowledge gaps.

Structural transformation is not, of course, an end in itself. At the heart of this empirical research agenda is the task of properly documenting the successes and failures in attaining longterm progress against poverty and the role played by structural transformation. Newly available data on trends in poverty and inequality and improved methodologies offer an opportunity to reexamine how countries have fared over time. They have shown, for example, that China and India have experienced rising inequality, which has attenuated the gains to the poor from more rapid economic growth while in Brazil, inequality has fallen in recent years (due in large measure to more effective social policies), which has facilitated a high proportionate rate of progress against poverty despite lower growth rates.<sup>11</sup> A systematic effort to compile up-to-date “stylized facts” on such trends is a prerequisite to an analysis of the underlying drivers of improvements in well-being.

In thinking about data and methods it is acknowledged that examining growth and distributional change on the basis of cross-country comparisons is useful, but also that it runs the

> 11 See Martin Ravallion ibid.

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# **1. Introduction**

What causes civil war? To-date, differences in income have received the most attention as the answer. The idea that poverty increases the risk of conflict is stated in the United Nations’ Millennium Development Goals: “Poor and hungry societies are much more likely than highincome societies to fall into conflict over scarce vital resources, such as watering holes and arable land. Poverty increases the risks of conflict through multiple paths.”<sup>2</sup> However, the link between poverty and civil wars is largely untested: only two empirical studies provide some evidence. Fearon and Laitin (2003) find that lower income per capita increases the likelihood of civil war, alongside other correlates like large populations and mountainous geography. They argue that income per capita is a proxy for the “state's overall financial, administrative, police and military capabilities.” If the government is weak, rebels can expect success. Collier and Hoeffler (2004) find that income variables, which could proxy some grievances but are perhaps more related to the viability of rebellion, provide considerable explanatory power. Neither study deals with the possibly endogeneity of war to economic stagnation. This is done in Miguel et al. (2004) in a study of 41 African countries. They use rainfall as an instrument for economic growth and find that economic stagnation is strongly related to civil conflict, i.e., civil war is related to changes in income, not the level of income.

There is a larger literature that studies the relationship between political institutions and civil war. Sambanis (2001), Hegre et al. (2001), and Reynal-Querol (2002a,b) find that partial democracies are more prone to civil wars than full democracies and autocracies. Reynal-Querol (2005) tests whether the type of democracy, rather than democracy _per se,_ can explain why some countries have civil war and others do not. She finds a negative relationship between the

> 2 United Nations (2005), p. 6.

2
to the arrival of new information: _ρT_ , _σT_ and _θ_ are chosen such that _MSFE_ ( _θ, τt_ ) _− MSFE_ ( _θ_ ), the MSFE reduction resulting from the arrival of new information in capital controls, matches the magnitude measured later in the empirical section, where I complete a nowcast of Brazilian real GDP and use capital control announcements to help improve the nowcast. Given _σT_ , _φ_ 0 and _φ_ 1 are then set such that the agent does not have perfect information regarding next period tradable output after learning from policy.

# **4.2 Quantitative Results**

In this section, I compare and contrast the long-run and financial crisis moments of the three different equilibria: the decentralized equilibrium (denoted **D** ); the equilibrium with only policy intervention (denoted **P** ); the equilibrium with both policy intervention and learning from policy(denoted **PL** ). When people learns from capital controls, these policy actions may entail unintended consequences such as higher debt levels and more severe financial crisis in the sense of larger consumption drops and currency depreciation. When the reduction of precautionary savings is strong enough, capital controls may even lead to welfare losses.

The first and second columns in Table 2 show that the effects of the tax rule are quantitatively similar to these of the Ramsey-optimal tax: financial crisis happens much less frequently, the economy accumulates less debt and the current account is less volatile. Capital controls also reduce the severity of a financial crisis: the magnitudes of consumption drops, real exchange rate depreciations and capital outflows are mitigated by capital controls. The tax rule is associated with a welfare gain of about 0.14% relative to the decentralized equilibrium. These results are consistent with findings in Bianchi (2011) about the effects of an optimal tax. In sum, the assumption of a simple tax rule does not make a material difference in the study of capital controls, allowing me to use the simple rule to facilitate learning from policy.

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# IV. **Distributional Implications of Higher Energy Costs**

While the energy price shocks can be expected to have broad macroeconomic implications, the distributional impact of these higher prices will differ across households and location (i.e., large cities, small towns and rural areas).<sup>10</sup> In the first instance, the direct impact of higher energy costs on individual households will depend on _whether that household is a consumer_ of gas and/or other energy products. In addition, the distributional impact also depends on how the _intensity of energy use_ or consumption level varies across household quintiles and geographic location. Finally, the impact depends on the _relative share of gas and energy products in total household expenditure_ . Households who spend larger shares of their budgets on energy are expected to bear a greater burden of the price increase.

# _Who are connected?_

Almost all households are connected to electricity while the connection rates for central heating and central gas and the consumption incidence of liquefied petroleum gas (LPGs) vary by location. As shown in Table 3, while over 70 percent of households in large cities (Chisinau and Balti) and small towns are connected to central gas services, only 11 percent of rural house are connected.<sup>11</sup> On the other hand, most rural households consume gas cylinders or liquefied petroleum gas (LPG) while few households in large cities consume them. About a quarter of all households and over three-quarters of households in small towns and rural areas, respectively, consume LPG. As for electricity, virtually all households are connected. Central heating, on the other hand, is mainly an urban consumption item.

> 10 Energy expenditures are extracted from Moldova Household Budget Survey 2004, a survey conducted every year since 1997. By design, about 660 households are sampled to be interviewed each month. As it is virtually impossible to achieve a 100 percent response rate, in 2004 the total number of observations in the final dataset is 6121. Collection rates are relatively high across energy products (or have been steadily increasing, where collection rates have been historically low, such as in heating) and this increases confidence in the general reliability of HBS data. The consumption aggregate created by the World Bank’s poverty assessment team is used as a proxy of household welfare, with a single modification. The poverty assessment team excluded three consumption categories (utility, housing, and durables) from final consumption aggregate (see also World Bank, 2004). For the purposes of this analysis, however, utility expenditures are included and the new _per capita_ consumption quintiles are based on this adjusted aggregate.

> 11 There is an additional geographic dimension to connection rates and consumption: The few rural households connected to central gas tend to be concentrated in a few specific localities, due to the Moldovan Village Program and the multi-year program aimed at gradually connecting all villages to the gas network.

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