rafmacalaba/data-use-mentions-extended huggingface dataset:
captured by the maritime price data, but restrictions in both are relevant because they could push up the costs of liner services.

The _markup term, q(I, J, k),_ is assumed to depend on the following four variables: _b(I,J,k) = Ak +r CR1 +yl AI + AJ._ _(4)_ The first term, _Pk_ , reflects a product-specific effect that captures differences in transport demand elasticities across sectors. Note that the transport demand elasticities are derived from the final demand for product k in the United States. The second variable is again the variable that captures the existence of cargo reservation policies, which directly limit the extent of competition from foreign liners and thus may push up markups. The third and fourth effects, _AIJ_ and _AIJ,_ are due to the existence of collusive agreements among liner companies on routes between country I and customs district J. We distinguish between two kinds of collusive agreements: price-fixing agreements (which include most conferences), and cooperative working agreements that do not have a binding rate setting authority. A single agreement typically covers routes between the ports of a foreign country and one or more US _coastal_ districts that each consist of several _customs_ districts. Since collusion between liner companies is likely to push up markups, we expect both coefficients sL and<sup>_2_</sup> to show a positive sign. But conference and other price-fixing agreements are likely to be more powerful and to have a greater impact on transport prices than cooperative working agreements, i.e. we expect tV> _i_<sup>_2_</sup> _._ Substituting (4) and (3) into (2), and inserting an error term _enk,_ we obtain: _Pijk = aJ +,Bk+ y T,,k +_ a _d,J + qj + yI/ A,, + VI'A' + o CR] +,pI PS+ p2 PS/2
a multi-dimensional phenomenon, including not only income but also other aspects of welfare; it is also important to understand the overlap between different dimensions of poverty.** In 2022, approximately one-fifth of the European Union (EU) was at risk of poverty or social exclusion (AROPE)<sup>4</sup> . When looking at individual countries within the EU, Romania and Bulgaria experienced the highest poverty and social exclusion rates, reaching 34.4% and 32.2%, respectively, while Czechia reported the lowest at 11.8%. The elderly population (aged 65 to 74) in these countries was particularly vulnerable to poverty and social exclusion, with rates of 33.7% and 37.8 %, respectively. Children also tend to experience a higher risk, with about 41.5% and 33.9% of Romanian and Bulgarian children affected by poverty and social exclusion. There are significant urban-rural differences: in both countries, over 40 percent of the rural population is considered socioeconomically deprived, according to AROPE, compared to less than one-fourth of the urban population.<sup>5</sup> Though multidimensional poverty indices are useful, as they combine information on several deprivations of well-being measured at the household level in one scalar number, it is also important to understand the extent of overlap between them (Ferreira and Lugo, 2012; Bradshaw and Finch, 2003). Moreover, the monetary welfare dimension of the AROPE indicator has a relative nature. To monitor progress

> 1 Source: Own Estimates based on the European Union Statistics on Income and Living Conditions (EU-SILC) for all countries in EU27, except Germany, for which microdata is not available. The year refers to income reference year

> 2022 and survey year 2023. <mark>Eastern and Central EU countries are Bulgaria, Czechia, Croatia, Hungary, Poland, Romania, t</mark> he  Slovak Republic <mark>and Slovenia.</mark>

> 2 An individual is
42 consequently all reported t-statistics are calculated with the White heteroskedasticity-consistent errors of OLS coefficients.

standard

# 8. Conclusions.

The analysis of the data on the structure of regional budgets for 1992-96 suggests that, while quite substantive, the relative share of local governments in the Russian consolidated budget did not expand since 1994 and they remain in control of about a quarter of total budget expenditures. However, at the subnational level, the relative size of the local budgets increased at the expense of some compression in regional budgets. 1996 was the first year, when local governments collected more budget revenues (6.4% of GDP) and spent more than regional administrations. At the same time, the relative role of local governments has been substantially increasing in all sorts of social financing including education, health, and social protection. With a growing share of federal budget spent on debt service, the role of the federal government in financing public goods and services increasingly becomes less important.

The cross-regional variation in degree of decentralization of subnational budgets is high and so far does not show any decline. Variation in revenue sharing is much more serious than variation on the expenditure side, where common and stable patterns in expenditure assignments are quite noticeable (e.g. in education and health). All major taxes remain to be shared between three government levels, and no progress has made towards a more transparent system of tax assignments.

Main factors that could explain this variation in the degree of decentralization are the level of poverty and an administrative status of the region. Regions, which have been historically less wealthy, have a more centralized budget system. This seems to represent a global phenomena: urbanization, growth in education, and decentralization are closely inter-related, and all positively related to growth.

There is a major difference in budget arrangements between ethnically Russian regions and national republics and okrugs. In both cases, the average level of expenditure decentralization is similar but the contrasts on a revenue side are striking. In the case of oblasts and krais,
Policy Research Working Paper 7548

# **Abstract**

Using all the household survey data available in Latin America during the period 1992 to 2013, this paper estimates that in 2015, 20 million youth ages 15 to 24 years in the region were out of school and not working (making them _ninis,_ for “ _ni_ estudian _ni_ trabajan”). The share of out-ofschool, out-of-work youth in Latin America, at about 19 percent, is roughly equal to the global average of 22 percent. Although women make up over two-thirds of the ninis in the region, the number of male ninis grew by 46 percent between 1992 and 2010. As a result, the absolute number of ninis rose over the two-decade period, even as women’s education and employment rates were improving. Global comparisons show that Latin America is the region of the world with the largest concentration of ninis among households in the bottom 40 percent of the income distribution. Coupled with the long-lasting harm it causes to the youth’s future labor-market outcomes, the high incidence of ninis among the poorest households tends to lock in income disparities from one generation to the next, obstructing social mobility and poverty reduction in the region.

This paper is a product of the Education Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at rdehoyos@ 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
―Archer Daniels Midland: Price Fixer to the World,‖ Working Paper 00-11, Purdue University, College of Agriculture.

- Connor, J. (2003). ―The Changing Structure Of Global Food Markets: Dimensions, Effects, And Policy Implications,‖ Working Paper 03-02, Purdue University,

- Connor, J. and G. Helmers. 2006. ―Statistics on Modern Private International Cartels, 19902005,‖ Purdue, mimeo.

- Commission of the European Communities (2009), ―Competition in the food supply chain,‖ Brussels, SEC(2009) 1449.

- Congo (2001)  ̳Negotiating proposals by the Democratic Republic of the Congo‘ World Trade Organization 12 Mar G/AG/NG/W/135.

- Datt, M. B. Hoekman and M. Malouche (2011), ―Taking Stock of Trade Protectionism since 2008,‖ _Economic Premise No. 72._ Washington DC: World Bank.

- Delpeuch, C. and A. Leblois (2011), ―The elusive quest for supply response to cash-crop market reforms in Sub-Saharan Africa : the case of cotton,‖ World Bank Policy Research Working Paper 5861.

- De Schutter, O. (2011), ―The Common Agricultural Policy towards 2020: The role of the European Union in supporting the realization of the right to food,‖ 17 June.

- DFID, Tom Fox and Bill Vorley (2004) ―Concentration in food supply and retail chains,‖ at <u>http://dfid-agriculture-consultation.nri.org/summaries/wp13.pdf.</u>

- Dodd, L. and S. Asfaha (2008) _Rebalancing the Supply Chain: buyer power, commodities, and competition policy_ , South Centre and Traidcraft (April).

- Evenett, S. Ed. (2011). _Trade Tensions Mount: The 10th GTA Report_ , London: CEPR.

- Gamberoni, E. and Newfarmer, R. (2008), ―Export prohibitions and restrictions,
8. _Leverage_ : also related to financial health, it captures a firm’s financial vulnerability in the long run. It is calculated as the ratio between total liabilities to total assets at year-end.

9. _Current Ratio_ : this is a proxy for short-term financial distress, measuring a firm’s ability to meet its short-term payment requirements. It is calculated as current assets divided by current liabilities at year-end.

Lastly, firm-level domestic stock market trading data comes from Standard & Poor’s Emerging Markets Data Base (EMDB), which was formerly collected by the International Finance Corporation. The EMDB provides data on domestic market capitalization and domestic value traded in current U.S. dollars by firm. Although the EMBD is the most comprehensive database on firm-level trading of equities around the world, it focuses on emerging markets only and does not include 100 percent of local firms (e.g., while varying by country, the EMDB typically covers about 70 percent of market capitalization). Given the importance of trading activity for our analysis, we restrict our sample to firms from developing countries. The following countries are included in our sample: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Hong Kong SAR, China, Hungary, Indonesia, Korea, Malaysia, Mexico, Peru, Philippines, Poland, Russia, Slovakia, South Africa, Taiwan, China, Thailand, Turkey, and Venezuela.<sup>19</sup>

# **III. Firm Characteristics and Mutual Fund Portfolio Holdings**

> 19 It should be noted that Hong Kong SAR, China has been included in our database because of Chinese firms listed in its exchange. However, the results documented here are robust to its exclusion from the sample.

20
the same story; our main outcome, whether the respondent believes the current area is a good place for

> 19The datasets are the Afrobarometer, Arab Barometer, Asian Barometer, European Election Study, European Social Survey, Eurobarometer, International Social Survey, Latinobarometer, Pew Global Attitudes, Transatlantic Trends, World Bank Country Opinion Survey, and the World Values Survey.

> 20The most common question asked in the different surveys is whether the respondent would be “ok with an immigrant neighbor”, for instance.

11
# **D Additional Tables**

## Table D.1: **Household Survey** – Eligible Population and Sample Frame

The Table reports summary statistics of the sample population and sample frame. Panel A reports summary statistics on program beneficiaries by bank and district. Observations cover all beneficiary accounts from the largest six commercial banks and the state’s largest cooperative bank, accounting for 91% of eligible accounts in the districts covered by the survey. Panel B summarizes all accounts included in the sample frame and qualifying for a 100% waiver or 25% conditional debt relief. Observations in the sample frame are drawn from administrative data published by the largest six commercial banks and the largest cooperative bank in the state of Gujarat. Percentages refer to the proportion of total beneficiaries included in the sample frame.

_Panel A: Program Beneficiaries by Bank and District_

||District|||||
|---|---|---|---|---|---|
||Anand|Kheda|Gandhinagar|Mehsana|Total|
|Bank of Baroda|1,941|3,644|503|1,070|7,158|
|Bank of India|877|870|343|432|2,522|
|Central Bank of India|1,384|738|243|253|2,618|
|Dena Bank|654|366|794|803|2,617|
|State Bank of India|3,412|2,711|916|3,187|10,226|
|Union Bank of India|1,013|1,428|306|84|2,831|
|Kaira District Coop Bank||21,141|0|0|21,141|
|Total||40,179|3,105|5,829|49,113|
|Other banks||3,956|491|14,933|
by female candidates in a given district by the share of seats that women won against men in a close election. We use data from the 2015–16 National Family Health Survey (NFHS) of India, the 2012–13 District-Level Household and Facility Survey (DLHS) of India, and the Election Commission of India (ECI).

We find that exposure to female leaders increases female constituents’ reported experience of physical spousal abuse. Our estimates imply that a one standard deviation (s.d.) increase in the fraction of seats held by women in a district leads to a 0.07 s.d. increase in reported physical spousal violence experienced by rural women, with no evidence of a significant impact in urban areas. We find no evidence of significant changes in attitudes toward violence, women’s labor market outcomes, or partner characteristics due to exposure to female leaders, ruling out these changes as potential channels underlying our IPV results.

Instead, we show that a plausible channel underlying the increase in IPV against women is an increase in women’s modern contraceptive use, that is potentially driven by an increase in the local availability of family planning and reproductive health services, due to exposure to female leaders. The village-level data on public health infrastructure show that districts with a higher share of seats held by female politicians experience improvements in access to health facilities, the implementation of programs which target maternal and adolescent girls’ health, and the availability of female health workers. Moreover, rural female constituents in districts with female leaders increase their use of modern methods of contraception, and experience an increase in birth spacing, with no evidence of a significant impact in urban areas. Although higher takeup of contraceptive methods can benefit women in various ways (Joshi and Schultz 2013; Miller and Babiarz 2016), as Ashraf et al. (2014) show, it can also lead to spousal conflict among couples with discordant fertility preferences. In the Indian context, married couples exhibit significant misalignment in their self-reported ideal number
Table 5: Labor-Market Responses: Fishery Workload and Having Secondary Jobs

||Weekly workload<br>(1)|Having extra Jobs<br>(2)|
|---|---|---|
|**[Panel A] Geographic control **|**group**||
|(HaTinh & QuangBinh) _×_ post|1.227<br>(1.917)|-0.122<br>(0.093)|
|(QuangTri & Hue) _×_ post|-28.825***<br>(1.085)|0.138***<br>(0.020)|
|Observations|872|872|
|R-squared|0.240|0.066|
|**[Panel B] Industry control gr**|**oup**||
|(HaTinh & QuangBinh) _×_ post|1.720*|-0.114|
||(1.015)|(0.091)|
|(QuangTri & Hue) _×_ post|-29.562***<br>(0.674)|0.141***<br>(0.017)|
|Observations|2,090|2,090|
|R-squared|0.119|0.015|
|Month FE|Yes|Yes|
|Individual FE|Yes|Yes|
|Standard errors in parentheses,|*** _p <_0_._01, ** _p_|_<_0_._05, * _p <_0_._1|

Note: the dependent variables are (1) fishery work hours per week (column 1), and (2) the probability that the affected fisher worked extra jobs (column 2). The heterogeneous labor-market responses are shown for the upstream (Ha Tinh & Quang Binh) and downstream
# **1. Introduction**

In this paper, we document the extent to which emerging market economies were resilient to the 2008–2009 global financial crisis. We interpret resilience from both an ex ante and ex post perspective. From an ex ante point of view, resilience means that economies have a lower likelihood of being affected by an external shock. From an ex post perspective, it means a better recovery when there are contagion or spillover effects from external crises. We relate the behavior of emerging economies during the global crisis to the crisis transmission mechanisms (trade and finance) and a structural break in the way they conducted their policies. We describe some of the factors that might have facilitated (or hindered) the global transmission of the crisis and analyze the policies emerging economies adopted to become more resilient to crises.

We question the usual claim that, on impact, emerging market economies suffered less than advanced economies in terms of economic activity.<sup>1</sup> Even though emerging economies had a better performance when focusing on plain growth rates, the conclusions change when considering changes in growth rates relative to the pre-crisis growth rates. Emerging economies endured declines in real gross domestic product (GDP) growth comparable to those in advanced economies. Nonetheless, emerging economies were able to avoid the amplification of external shocks that were typical of past episodes that tended to end up in banking, currency, and/or debt crises. During the recovery phase, emerging economies were more resilient. At first sight, GDP data suggest that emerging economies recovered similarly to developed economies, with most economies rebounding during the crisis aftermath according to how much they collapsed. Yet,

> 1 Our data are comprised of 183 economies, classified as ―advanced‖ (or ―developed‖) if they fall under the World Bank’s July 2010 classification of ―high-income‖ economies. All others are ―developing economies.‖ Within the latter, economies are classified as ―emerging‖ if they have access to loans
details). In these examples, the provider works 2.3 hours out of the 6-hour day when there are an average of 15 patients, and patients wait under a minute each; there is nearly never a queue. But when the provider faces 40 patients, they are serving patients the entire day, and patient wait times and queue lengths increase dramatically.

**Figure 3** generalizes this approach to show the wide variation in utilization and queue lengths that can occur given any level of “average” utilization. With 20 or fewer patients per day, providers are frequently underutilized over the course of a six-hour workday. With 30 or more patients per day, average wait times become long on average, and excess capacity quickly declines. At 40 patients per day, provider working time ranged from 60% to 100%, and patient wait times ranged from five minutes to over one hour. Any “clumping” of arrival times onto specific days of the week or times of the day substantially lengthened the queues, driving wait times to “infinity” in some cases. This means that not all patients who showed up would be seen by closing time, since a facility working at or above capacity would have a constantly-growing queue. Thus, the caseloads we estimated in many countries would be consistent with long waiting times for the typical patient under conditions of even moderate clumping.

Under these conditions of variation, caseloads were observed to be substantial at the busiest facilities in each country, but only the very busiest providers appeared to have caseloads that would commit them to consistently serving patients throughout a full workday. Combining these observations and simulation results with the facility survey

**9/30**
This paper employs a novel data set on agricultural distortions that was recently developed by the World Bank (see Anderson and Valenzuela 2008). This data set offers consistent and comparable protection indices for a large number of countries over a 50year period. Employing this data set allows us to take advantage of not only cross-country variation but also within-country variation in the data and thus to overcome the strong identification assumptions that characterize previous cross-country studies.

Because the relationship between democracy and public policy may conceal potential feedback effects, we also study the reverse causality problem and exploit the timing of democratization. To control for potential nonlinearities and to better address unobserved heterogeneities, we estimate both linear specifications and semiparametric models. Specifically, we study the effects of democratic reform using the difference-indifference (D-in-D) technique combined with propensity score matching methods, as in Persson and Tabellini (2008).

By studying agricultural and food policies, our analysis contributes to a broad body of literature on the impact of political reforms on economic policies (see Rodrik and Wacziarg 2005; Giavazzi and Tabellini 2005; Eichengreen and Leblang 2008).

The remainder of this paper is organized as follows. Section I presents stylized facts on trends in agricultural and food policies over time and across political regimes. Section II discusses conceptual issues and summarizes previous findings linking political reforms to public policies. Section III presents our empirical strategy. Section IV presents the data and key variables. In section V, the empirical results are presented and discussed, and in section VI, we test the robustness of our findings. Finally, section VII concludes.

3
that help to minimize both their vulnerability to natural hazards and the impacts of disasters. But there is evidence that such strategies-with costs to average incomes-coupled with frequent occurrences of natural disasters in certain regions, are helping to widen regional inequalities. 12 As reflected in the Vietnamese idiom "the intact leaf covers the ragged one," various informal and quasi-market community-level arrangements are used to help families; for example, the mass organizations may arrange to have someone else work the land for a family whose breadwinner is transiently ill. Informal arrangements might also be made to (in effect) hire labor in, or rent land out. Exchanging labor time among households, particularly to cope with seasonal or unexpected labor shortages, is a time honored tradition in Vietnam. 13 'The extended family remains important in Vietnamese culture, which has a strong Confucian heritage. An analysis of the VNLSS reveals that a large share of Vietnamese households either received (44 percent) or gave (46 percent) private transfers to other households. 1<sup>4</sup> In general, transfers flow from the young to the old. Such private forms of insurance tend to be more effective and reliable for some among the vulnerable in certain circumstances, but they are often not available to the poorest among the poor and they don't cope well with covariate risk and unanticipated shocks. They need to be complemented by effective public programs.

Finally, it is important to note that some informal social protection schemes also exist in various parts of the country, set up by farmers or other interest groups or whole communities." Members make regular contributions and can expect to get insurance benefits 10