rafmacalaba/datause-encoder-data huggingface dataset:
In an influential paper, Bagwell and Staiger (1990) develop a model of a cooperative trade agreement between two large countries.<sup>1</sup> They show that, in a dynamic, repeated trade policy-setting game, a cooperative trade policy equilibrium characterized by relatively low trade taxes can be sustained by the threat of infinite reversion to a Nash equilibrium of high trade taxes. Governments optimally choose low cooperative tariffs so that they can reap the benefits of greater trade. This cooperative equilibrium is characterized by a positive correlation between unexpected increases in import volumes and import tariffs. That is, when the import volume rises in response to an output shock, the lowest import tariff that governments can sustain as the cooperative equilibrium in the infinitely repeated dynamic trade policy game must rise. Our paper provides the first empirical investigation of the intertemporal and cross-sectional predictions of the Bagwell and Staiger model.

Import tariffs in the Bagwell and Staiger model generate terms-of-trade gains and thus vary intertemporally and cross-sectionally according to observable characteristics. The model first predicts that increases in an import tariff are more likely when import volumes increase. Second, conditioning on a positive import surge, the gains from (and thus the likelihood of) a tariff increase are rising in the inverse of the sum of the export supply and import demand elasticities. Thus, in the cross-section, a tariff increase is more likely for an import surge of a given size if import demand and export supply are more inelastic. Third, the gains from (and thus the likelihood of) maintaining a cooperative equilibrium with low trade taxes are increasing in the mean and variance of the underlying free trade volume. Therefore, conditional on an import surge of a given magnitude, an increase in a tariff for a product is more likely, the smaller is the variance of imports of that product in the cross-section.

To analyze the model, we use data on increases in US import tariffs against 49 countries under the US’s antidumping and safeguard laws over the 1997-2006 period.<sup>2</sup> Although trade agreements like that embodied by

> 1Bagwell and Staiger (1990) helped influence a rich body of theory to understand the trade policy choices of countries that voluntarily submit to the rules of international trade agreements and their associated institutions (Bagwell and Staiger, 1999; Maggi, 1999; Ossa, 2011).

> 2We therefore examine whether antidumping and safeguard tariffs are consistent with the conditions under which trade volume shocks increase the incentive for a government to raise cooperative tariffs in order to continue participating in a self-enforcing trade agreement. Such an interpretation is consistent with Bagwell and Staiger (1990, p. 780, emphasis added), which states “[c]ountries can cooperatively utilize protection during periods of exceptionally high trade volume to mitigate the incentive of any country to

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slower than in other regions in Brazil, but on the other hand, the Northeast region suffered less in terms of increased poverty during the crises in 1998.

In the Northeast, as well as in the rest of Brazil, inflation is an important actor in the drama of income and poverty as the poor generally lose - more than the nonpoor during inflationary periods. One reason is that the poor cannot protect themselves against inflation as they lack access to the financial and banking systems. Hence, the macroeconomic stabilization plans that attack inflation and successfully drive it down commonly have a positive impact on the incomes of the poor. This is also the case for the poor in the Northeast of Brazil, Rio Grande do Norte as a whole, as well as the poor in its municipalities. Hence, spatial differences in the inflation rate among regions, because they are minuscule, cannot be the sole determinant of regional differences in poverty. But, inflation does impact the overall poverty trend. Amadeo and Neri (1997) study the relation between poverty, inflation, unemployment and the minimum wage. They find that inflation affects the headcount ratio by two percent and the result is not very different across Brazil. The authors also consider the minimum wage and find that it impacts negatively on poverty. That is, when the minimum wage is increased the number of people below the poverty line decreases, hence a reduction occurs in poverty. The impact varies across metropolitan areas, for example, in Recife it is –0.18, Salvador -0.65, and Brazil as a whole –0.43.

Demographic changes are linked with poverty. Barros _et al._ (1999) analyzes the impact of demographic changes on poverty in Brazil and conclude that these are important factors in reducing poverty disparities. The main findings by the authors are that the demographic transition benefited the least developed more than the more developed regions. And if the Northeast had the _national_ demographic composition, poverty would be three percent points lower (Barros _et al.,_ 1999).

Also, there has been a substantial increase in the coverage of social security in Brazil in the last decade, which had a substantial impact mainly on rural poverty. The 1988 Federal Constitution established the universal right to social security and instituted special eligibility conditions for rural workers under the _Regime Geral da Previdência Social_ (RGPS), Brazil’s public pension system for workers in the private sector. Recent analysis based on the 1996-97 _Pesquisa sobre Padrões de Vida_ (PPV) survey found that 3
district-level characteristics. Unlike the case for gender and social minorities, we see instead evidence of strong complementarities between educational attainment and district characteristics in terms of rural non-farm employment – particularly for salaried workers in the service sector. Thus, in districts with relatively high per capita bank branches or bank credit (Tables A9 and A10, Appendix A.1), rural workers with secondary education have an additional 6 percentage point higher likelihood of being employed in non-farm activities, driven almost exclusively by an additional 4 percentage point increase in the likelihood of being employed as a salaried worker (across both the manufacturing and service sectors).

# **3.3 Rural Non-Farm Employment and Household Consumption**

This section examines whether rural non-farm employment affects household consumption using the same specification outlined in equation (1). We have two outcomes of interest: logged household monthly per capita consumption, and a dummy which equals 1 if the household falls below the poverty line (BPL).<sup>9</sup> A key distinction from the specifications undertaken till now is that the unit of observation for the consumption regressions is the household, instead of the individual. To this extent, we aggregate the individual level employment data to create binary indicators for farm and non-farm employment types and sectors at the level of household. To account for mechanical increases in consumption due to price inflation, we deflate all consumption values to 1993 prices using the aggregate annual wholesale price index.

Table 6 documents the impact of household and district characteristics on household consumption. The outcome of interest in columns (1)-(3) is logged per capita monthly household consumption; in columns (4)-(6) a dummy equaling 1 if the household is situated below the poverty line. Columns (1) and (4) include dummies indicating whether any household member is engaged in non-farm activities. Columns (2) and (5) disaggregates non-farm employment by employment type, while columns (3) and (6) does likewise for employment sectors. All specifications restrict the sample to rural households, and use household weights. Standard errors continue to be clustered by district, allowing for arbitrary correlation in consumption/poverty outcomes across households located in the same district.

Table 6 shows robust correlations between non-farm employment, education and household consumption. Column (1) shows that while having a literate member in the household increases per capita monthly consumption by almost 7 percent, the presence of a member

> 9 The consumption data obtained through the NSS survey rounds are used to construct state-specific poverty line thresholds corresponding to each round. Households with monthly per capita consumption less than this threshold are classified as below the poverty line or BPL.

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# **Estimating the Magnitude of Water Supply and Sanitation Subsidies**

**Luis Andrés, Gonzalo Espiñeira, George Joseph, Germán Sember, and Michael Thibert**<sup>**1**</sup>

JEL Classification: H20, L95, Q25 Key Words: Water supply and sanitation; Subsidies.

> 1 Luis Andrés: World Bank (landres@worldbank.org); Gonzalo Espiñeira: MacroConsulting (gespineira@macroconsulting.net); George Joseph: World Bank (gjoseph@worldbank.org); Germán Sember: MacroConsulting (gsember@macroconsulting.net); and Michael Thibert: World Bank (mthibert@worldbank.org).
confirms broad consistency, though far from perfect, between the collected sample price data and one of the existing comprehensive data sources for telecommunications prices (World Bank, 2006).

<!-- Start of picture text --> Figure 11. Consistency Check for Collected Price Variables<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6<br>Prepaid mobile unit rate per minute (US$)<br>Price basket for mobile per month (US$) 1/<br><!-- End of picture text --> 1/ Price basket for mobile is calculated based on the prepaid price for 25 calls per month spread over the same mobile network, other mobile networks and mobile to fixed calls and during peak, off-peak and weekend times. The basket also includes the price of 30 text messages per month.

Sources: World Bank (2006); and author’s calculations from various telephone operator websites.

Telecommunications services are assumed to be differentiated and characterized by availability of financial schemes, such as business and friend discount and tie-in arrangements, and value-added functions such as voice mail and international roaming services. These dummy variables indicate whether such characteristics are available if a consumer chooses subscription plan _j_ . It may or may not be relevant to whether subscribers are actually using those characteristics. Unfortunately, there is no productcharacteristic-based quantity data available.

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Given the importance of high teacher quality, a promising candidate for improvement is the specific form of accountability that aims incentives directly at teachers. While convincing evidence on the effects of performance-related teacher pay is scarce, the more rigorous studies in terms of empirical identification tend to find a positive relationship between financial teacher incentives and student outcomes (cf. the survey in Atkinson et al. (2004) along with the discussions in Vegas (2005) and Vegas and Umansky (2005)). Thus, monetary incentives for teachers based on their students’ performance have been shown to improve student learning in Israel immensely (Lavy (2002, 2004)). Similarly, Atkinson et al. (2004) find that the introduction of performance-related pay had a substantial positive impact on student achievement in England.

# **_7.4 Summary of How to Improve the Quality of Education_**

From the perspective of policies to obtain better outcomes, a simple picture emerges. First, the evidence from extensive research shows clearly that “pure resource policies” – i.e., policies that simply provide more resources within the current incentive structure of schools within most countries – are unlikely to produce substantial systematic gains in student outcomes. There are of course qualifications and caveats. Some schools will use added resources effectively. And, some systems that fail to provide minimal resources such as basic textbooks could, if resources were applied to the key shortage areas, get improvement. Yet, both developed and developing countries demonstrate an improbable but tangible disconnect between simple resource solutions and student achievement.

Second, the evidence about what specifically counts in terms of schools is rather limited. But the central candidate is high teacher quality. Unfortunately, the characteristics of good teachers are not well understood, making regulatory solutions very difficult and supporting the development of rewards for high quality teaching. On this score, most countries – both developed and developing – have incentive systems built into teacher salaries and teacher contractual arrangements that resist significant change and that are not aligned well with teacher quality.

Third, the key to improvement appears to lie in better incentives – incentives that will lead to management keyed to student performance and that will promote strong schools with high quality teachers. Here, three interrelated policies come to the forefront: promoting more competition, so that parental demand will create strong incentives to individual schools; autonomy in local decision making, so that individual schools and their leaders will take actions to promote student achievement; and, an accountability system that identifies good school performance and leads to rewards based on this.

We have sketched three separate components of improved incentives, but it is also important to point out that they form a package. Local autonomy without strong accountability may be worse than 74
<!-- Start of picture text --> 7 Total outlays 706 Housing and community anvenities<br>TO General public services 7061 Housing development<br>7Oll Executive and egisktive organs, flnanelal and fiecal 7062 Community development<br>affalrs, external affairs 7063 Warer supply<br>7OI2 Forelgn economic ald 7064 Street lighting<br>TOS General services 7065 R&D Housing and community amenities<br>7Ol4 Basle research 7066 Housing and cornrmuntey amenities reac.<br>7OIS R&D! General public services TOT? Health<br>7016 General public services nec? 70? | Medical products, appllances, and equipment<br>FOI? Public debt transactions FOF2 Outpatient services<br>TOs Transfers of a general character between different TOFS Hospital services<br>levels of government 7Or4 Public health services<br>Tol Defense Fors R&D Health<br>702) Military defense 7076 Health nec.<br>7022 iil defense 708 = Recreation, culture, and religion<br>7023 Forelgn rollitary abd 708! Recreational and sporting services<br>7024 R&D Defense 7082 Cultural services<br>7025 Defense nec. 7083 Broadcasting and publishing services<br>703 Public order and safety 7084 Religious and other community services<br>7031 Pollee services 7085 R&D Recreation, culture, and religion<br>7032 Are protection services 7086 Recreation, culture, and religion nec<br>7033 Law courte 70?) = Education<br>704 Prisons 709] Pre-primary and primary education<br>7035 R&D Public order and safecy 7092 Secondary education<br>7036 Public order and safety re.c. 7093 Postsecondary nontertary education<br>704 Economic affairs 7094 Tertiary education<br>704] General economic, commercial,and labor affairs 7095 Education not definable by level<br>742 Agriculture,forestry, flehing, and hunting 7036 Subsidiary services to education<br>7043 Fuel and energy 7097 R&D Education<br>744 Mining, manufacturing, and canetruction 7098 Education nec.<br>7045 Transport 710) = -Social protection<br>7046 Communication 7Idl Sicknege and disability<br>7047 cher Industrks 7102 Old age<br>7048 R&D Economie affairs 7103 Survivors<br>7049 Economie affairs rie. 7104 Farnlly and children<br>TOS Environmental protection Flos Unemployment<br>705] Waste management 7106 Housing<br>7052 Waste water management 7107 Socal exclusion nec.<br>7053 Pollution abarennient 7108 R&D Socal protection<br>7054 Protection of blodiversity and landscape 7109 Sockal protecthon mec.<br>TOSS R&D Environmental protectlon<br>7056 Environmental protection nec.<br><!-- End of picture text -->
in the economy has been decreasing relative to non-state sectors, even though as a group PSUs have been growing at comparable rates to the non-state sector. The reason is there is an ongoing privatization process in the state sector, including selected PSUs being publicly listed. Second, our empirical results on _listed firms_ of the non-state sector are based on a sample of more than 850 firms over the period 1995 - 2004. We find that the equity ownership is highly concentrated within the founder’s family and/or the controlling shareholder. These findings are similar to those for other Asian countries (e.g., Claessens, Djankov and Lang 2000; Claessens, Djankov, Fan, and Lang 2002). When compared to listed firms studied in LLSV (2000b, 2002), both the dividend ratio and valuation of Indian firms are much lower compared to similar firms operating in countries with strong investor protection, but similar to those listed firms in countries with weak protection.

More interesting results are found for the _non-state, non-listed_ firms, and in particular, smalland medium-sized firms. These firms have grown faster than the rest of the economy during the past 15 years, while the financing of this sector is clearly different from the state and listed firms. Due to the lack of publicly available firm-level data, our evidence, by necessity, is mainly based on a survey of 213 entrepreneurs and executives of firms located in and around the _southern_ Indian city of Hyderabad (76 firms) and the Delhi-Gurgaon area of _northern_ India (136 firms). Our survey firms operate in manufacturing industries with firms ranging in age from less than a year to 85 years (the median is 21 years). These firms employ two to 350 workers (the median is ten).

In about 85% of the firms surveyed, the largest owner is the founder’s family, while over half of the firms have _unlimited_ liability. When asked how the owners (with unlimited liability) would protect their personal assets in case of business failure, 151 out of 157 respondents would negotiate with lenders for an extension; only 22 respondents said they would also file personal bankruptcy. The three most important financing channels for these firms during their start-up and growth periods are founders’ family and friends, trade credits and loans from financial institutions, including state4
Policy Research Working Paper 10525

# **Abstract**

This paper analyzes extreme child poverty ($2.15/day poverty line) trends, as well as child poverty based on the higher international poverty lines of $3.65 and $6.85. The paper provides a trajectory of extreme child poverty (children living in extremely poor households) from 2013 to 2019 (based on the most recent surveys included in the Global Monitoring Database), complemented by nowcasting for 2020 to 2022. Children continue to be disproportionately affected by extreme poverty. Children who are younger than 18 years comprise more than 50 percent of those living in extreme poverty, although their share of the population is 31 percent. The paper estimates that in 2019, 15.8 percent of children in the world (319 million) younger than 18 years lived on less than $2.15 (2017 purchasing power parity) per day, as opposed to 6.6 percent of adults ages 18 and older. More recent “nowcasted” estimates suggest that at least 333 million children were expected to be living in extremely poor households in 2022, implying that 14 million more children were extremely poor in 2022 than in 2019. Following an increase in extreme child poverty at the height of the pandemic in 2020, nowcasted estimates show that the rate of extreme child poverty fell again in 2021 and 2022, but only at the slow rate of progress seen prior to the COVID-19 crisis. If the COVID-19 pandemic had not occurred, an estimated 79.7 million fewer children would have been living in extreme poverty between 2013 and 2022; however, the estimates suggest that the number of children living in extreme poverty decreased by 49.2 million, due to pandemic disruptions.

This paper is a product of the Poverty and Equity Global Practice; the Social Sustainability and Inclusion Global Practice; and the Development Data Group, Development Economics. 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 dnewhouse@worldbank.org.

_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
**Figure 5: 2000-11 Average FDI Inflows to South Asia**

(as percent of GDP) <!-- Start of picture text --> percent<br>10 9.43<br>9<br>8<br>7<br>6<br>5<br>South Asia Region Average: 1.53%<br>4<br>3<br>2 1.69 1.56 1.48 1.22 1.21<br>1 1.01<br>0.15<br>0<br>Maldives Pakistan India Afghanistan Bhutan Sri Lanka Bangladesh Nepal<br>Note: South Asia Region Average is weighted by size of South Asian economies<br>Source: UNCTAD<br><!-- End of picture text -->

**FDI inflows, like portfolio capital inflows, move with the global business cycle; during the recent global crisis, FDI dropped in nearly all South Asian countries—some more than others.** In relative terms, Pakistan was the most affected by the global crisis. It experienced an 83 percent drop in inward FDI flows as a share of GDP during in 2007-11 (Figure 6). Looking at absolute levels, both India and Pakistan had significant declines in FDI inflows after the crisis in 2008 (Table 1). India experienced a 44 percent slide in FDI between 2008 and 2010. Pakistan was affected even more. Annual FDI to the Pakistani economy fell by 63 percent during the same period and continued to fall in 2011. Foreign investors, particularly from Europe and the United States, suffered higher economic uncertainty, which may have led to the decrease in FDI. The fall was compounded by Pakistan’s weakening macroeconomic environment, combined with security issues and political uncertainty.  Unlike Pakistan, India’s absolute inward FDI flows rebounded in 2011. The country’s FDI saw additional but more modest growth in 2012.<sup>11</sup>

**Most countries have seen a recovery in FDI flows after the global crisis.** In 2009, Maldives’ FDI was

17 percent less than its 2008 level—but it has since more than recovered and is now higher than before

> 11 As noted by India’s Planning Commission: “To achieve rapid growth, the economy will have to overcome constraints posed by limited energy supplies, increase in water scarcity, shortages in infrastructure, problems of land acquisition for industrial development and infrastructure, and the complex problem of managing the urban transition associated with rapid growth. Greater efforts also need to be made in agriculture, health and education to ensure inclusion of the most excluded and sometimes invisible parts of our population.”

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