rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2: Evolution of the number of drug trafficking organizations, 2006-2011**

<!-- Start of picture text --> Year 2006 2007-2009 2010 2011<br>Cártel de Sinaloa Cártel de Sinaloa Cártel de Sinaloa<br>Pacífico Sur<br>Pacífico Sur La Mano con Ojos<br>La Nueva Administración<br>Cártel de Sinaloa<br>Beltrán Leyva<br>Cártel del Charro Cártel del Charrro<br>Cártel Independiente de<br>Cártel Independiente de Acapulco<br>Acapulco<br>Cártel de Juárez Cártel de Juárez Cártel de Juárez Cártel de Juárez<br>Cártel de Tijuana<br>Drug-Trafficking Cártel de Tijuana Cártel de Tijuana Cártel de Tijuana<br>Organization Francción del El Teo<br>Cártel del Golfo Cártel del Golfo<br>Cártel del Golfo Golfo-Los Zetas<br>Los Zetas Los Zetas<br>Los Caballeros Templarios<br>La Familia Michoacana La Familia Michoacana La Familia Michoacana Los Incorregibles<br>La Empresa<br>La Resistencia La Resistencia<br>Milenio Milenio Cártel de Jalisco Nueva Cártel de Jalisco Nueva<br>Generación Generación<br>La Nueva Federación<br>Total 6 8 11 16<br><!-- End of picture text --> Source: using data from Guerrero (2011)

# **_2.2 THE RISE AND FALL OF THE NORTHERN STATES_**

The period 1995-2007 in Mexico saw a rapid expansion of international trade fueled mainly by the country’s comparative advantage in the production of manufactured goods. The North American Free Trade Agreement (NAFTA) increased demand for Mexican exports to the United States, particularly manufactured products. Exploiting their proximity to the United States and favorable tax conditions, _maquiladoras_<sup>_6_</sup> flourished, creating many jobs in the northern Mexican states. The maquila boom brought about improvements in employment opportunities and increased real wages of relatively unskilled workers, particularly in municipalities bordering the United States (Hanson 2004 and Robertson
efficacy of this large-scale intervention at an early stage is critical from a policy perspective. Our study provides short-run results based on data from three states where the program has had considerable reach and intensive support through the NRLP. The gradual roll-out of the program over the last five years gives us an opportunity to rigorously analyze the impact and we find four noteworthy results.

First, the intervention has been able to achieve its objective of retaining women in the labor force by increasing opportunities for self-employment, including transitioning agriculture to higher value activities and investments. Our estimates suggest that overall 5.5 percent more women are working (part-time and full-time) due to the intervention overall, with a 7.7 percent increase evident for women of productive ages (15-64 years old), and these effects remain strong in all our robustness checks.

Second, we find large and significant increases in the amount and share of formal savings, probably because of SHGs as a commitment device and as more women have opened bank accounts. Consistent with almost all other studies on the self-help groups, we find that the overall amount and productivity of borrowing at the household level have increased. Similar to Hoffmann et al. (2017), we find a large and significant reduction in the average interest paid by households in the treatment areas. However, the impacts on these aspects are not very different for any sub-population.

Third, contrary to previous studies on similar programs, we find weaker results in the asset status of households. We do find minor increases in livestock assets and consumptive assets, but no increase in other productive assets and nature of landholdings. As the primary mechanism to build assets was through loans, it is possible that the average loan size at INR 12,830 is too small to purchase big and productive assets.

Lastly, we find significant results in median household income as well as large and significant increases for the 30<sup>th</sup> , 40<sup>th</sup> , and 75<sup
**Weak regulatory framework for financial agencies and business disputes resolution underscored the strong demand for financial inclusion reform in OIC countries.** The statistics show that OIC countries fell behind other developing countries in terms of regulatory supervision coverage. For example, only 59% of microfinance institutions are under regulation compared with the average level of 72% in low income countries (Table ). Current laws are also insufficient to provide enough protection for disputes resolution. Take the instance of client confidentiality protection, only 68% of OIC countries provide laws to restrict such unfair treatment, while 78% of developing countries do (See Figure 11).

**Figure 11. Disputes resolution: provisions in existing laws and regulations to restrict unfair treatment**

<!-- Start of picture text --> 78% 76%<br>70% 68% 71%<br>51% 50%52%<br>42%<br>38%<br>35%<br>32% 31%<br>27% 26% 24% 24%<br>21% 21%<br>16%<br>Deceptive Unfair or high‐ Abusive Breach of client No fair treat‐<br>advertising pressure selling collection confidentiality ment provi‐sions<br>practices practices<br>OIC countries Developing countries OIC‐GCC low‐income countries<br><!-- End of picture text --> Source: CGAP (2010)

**However, the statistics also shows that OIC countries have been aware of the issue and taking actions more actively than other developing countries.** According to the CGAP survey, OIC governments have been more actively promoting reforms in all aspects of financial inclusion, especially in fields of financial consumer protection and microfinance expansion. The proportions of countries taking reforms on financial capability (38%) and SME financial access (49%) are also higher than those of developing countries (37% and 43%), much
|Yes|
|Observations|10,800<br>10,800|10,800|10,800|
|log likelihood|-5533<br>-5412|-4457|-4436|
|Sigma u|0.352<br>0.348|0.267|0.261|
|Sigma e||0.264|0.264|

_Notes_ : The dependent variable is the share of imported capital goods over total sales of the firm _i_ in year _t_ . All specifications include capital goods and output tariffs and the Herfindahl index. Columns (2) and (4) also include firm-level controls. All control variables are defined in table 3. Heteroskedasticity-robust standards errors are reported in parentheses. Errors are corrected for clustering at the 3-digit industry level.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5 and 10 percent levels respectively.

> 35In the random effects Tobit, firm unobserved heterogeneity is assumed to be part of the composite error. Random-effects Tobits are unbiased if firm characteristics are exogenous (uncorrelated with the regressors). Honore (1992) has developed a semiparametric method dealing with this issue which captures unobserved time-invariant individual heterogeneity. He proposes a trimmed least squares estimator of censored regression models. Nevertheless, this semiparametric estimator for fixed-effect Tobits is not suitable here due to the relatively small sample size.

45
- National Research Council, 1986, _Population Growth and Economic Development: Policy Questions_ , Washington, DC: National Academy Press

- Paes de Barros, R. & Sergio Firpo & Roberta Guedes & Phillippe Leite, 2015. "Demographic Changes and Poverty in Brazil," Discussion Papers 0096, Instituto de Pesquisa Econômica Aplicada - IPEA.-

- <mark>Rajan, R. G., and A. Subramanian. 2008. “Aid and Growth: What Does the Cross-Country Evidence Really Show?”</mark> _<mark>Review of Economics and Statistics 90</mark>_ <mark>(4): 643–65.</mark>

- Emmanuel Saez & Gabriel Zucman, 2016. "Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data," The Quarterly Journal of Economics, doi:10.1093/qje/qjw004.

- Szreter, S. 1993. “The Idea of Demographic Transition and the Study of Fertility Change: A Critical Intellectual History”. _Population and Development Review_ 19(4), pp. 659-701.

- Treisman, D. 2007. _The architecture of government: Rethinking political decentralization_ . Cambridge University Press.

- UN (United Nations). 2013a. _National Transfer Accounts Manual: Measuring and Analysing the Generational Economy_ . New York, United Nations.

- UN (United Nations).  2015. _World Population Prospects: The 2015 Revision,_ New York: United Nations, Department of Economic and Social Affairs, Population Division.

- World Bank. 2013. Poverty Assessment: Assessing a Decade of Progress in Reducing Poverty 2000–2010. Washington, DC: World Bank.

- World Bank. 2015a. _<mark>Global Monitoring Report 2015/16: Development Goals in an Era of Demographic Change.</mark>_ <mark>Washington DC: World Bank.</mark>

- ______. <mark>2015b.</mark> _Aging in East Asia and Pacific:
of distinct trade policy settings, that market power motives can affect trade policy determination.<sup>6</sup> In particular, we utilize newly available data on inverse foreign export supply elasticities provided by Nicita, Olarreaga, and Silva (2015) to examine the theory that changes in Argentina’s import market power in particular – such as those that might arise due to the customs union with a larger trading partner (Brazil) – may have acted as the stumbling block channel discouraging additional MFN liberalization. We are able to provide only very weak evidence of this potential effect arising from our data. Nevertheless, we conclude in Section 6 with a potential institutional explanation behind this (non-) result by questioning the extent to which Argentina and Brazil really have formed a common external trade policy (that is jointly responsive to economic shocks), given that each country can (and does) implement its temporary trade barrier policies toward third countries independently from the other, thus resulting in substantial deviations from a common MFN tariff This paper contributes to a number of other literatures in empirical trade policy. First, it is one of only a handful of papers that has attempted to consider the interplay between TTBs (in particular, antidumping) and preferential trade agreement implementation. Notable papers from this literature include Blonigen (2005), which studied the impact of NAFTA on US antidumping use, and Prusa and Teh (2010) which provided a cross-country study of the effects of PTAs on the incidence of new antidumping import restrictions, finding they are increasing on PTA outsiders (relative to insiders) after implementation of the agreements.

This paper also contributes to a related literature on the intertemporal substitution of different trade policy instruments that may be arising due to a variety of different types of shocks. For example, Limão and Tovar (2011) study Turkey’s trade policy substitution away from tariffs and toward a broad class of non-tariff barriers in response to its accession to the WTO in 1995 and its formation of a customs union with the
to ensure that risks are detected before they materialize (World Bank 2007). Recent examples of hidden debt and discrepancies among debt statistics point to continued low debt recording capacity, weak legal frameworks, and governance challenges. Debt Management Performance Assessments (DeMPA) suggest that, of the 17 LICs with available data, minimum requirements in debt recording are met by only eight, and monitoring guarantees are met by only four. Due to shortcomings in accuracy, timeliness, coverage and completeness of debt records, only four of these 17 countries met the minimum requirements for debt reporting and evaluation (Figure 5). Only a third of the 59 countries eligible for International Development Association borrowing report private sector external debt statistics (World Bank and IMF 2018d).

12
Table B3: Difference-in-Difference Regressions [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 2<br>Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity<br>Mild Moderate Severe Index<br>46<br><!-- End of picture text --> _Note:_ The figures plot results using our second fractional index that spans pre- and
.0030<br>mple<br>n per<br>nder, age,<br>rial,<br>ter, and<br>and World|0.5724<br>|

# Section 6. Conclusion and policy discussion

This paper demonstrates the dramatic effects that seasonality has on Sahelian households’ welfare, especially in rural areas. Household consumption is approximately 10.5 percent lower in the lean season than the non-lean season, as captured by the two waves of the EHCVM sample. Since households across the entire consumption distribution are affected by these welfare losses, this leaves poverty – measured at each country’s national line – 13.7 percentage points higher in Burkina Faso, 6.6 percentage points higher in Niger, and 8.1 percentage points higher in Senegal in the lean season. Moreover, the types of consumption losses that households incur appear to be consistent with particularly extreme forms of deprivation, as the quantities of cereals, or key staple goods, that are consumed are reduced. In-keeping with these results, subjective poverty appears to be 4.4 percentage points higher in the lean season too.

The large seasonal swings in welfare observed in the EHCVM data are unsurprising given the limited strategies that Sahelian households have to manage and cope with seasonality. Households’ income sources are concentrated in agriculture and raising livestock and are not especially well diversified. There is a shift towards work in non-farm household enterprises when the lean season arrives, but this option may not be available to all households, especially in rural areas. Additionally, financial penetration is low, so smoothing consumption through saving or borrowing may be difficult. Finally, social protection is rare compared with the extent of poverty and vulnerability, with only 10.5 percent of Sahelians living in a household that received food assistance during the previous 12 months, and 3.6 percent living in a household receiving cash transfers.

The results in this paper have three clear policy implications; first, expanding the coverage of _regular_ safety
. This observation suggests that industries’ranking in the degree of intra-...rm ...nancial linkages is mainly dependent on characteristics innate to each industry and thus unlikely to change structurally in response to short-term idiosyncratic shocks. Second, we compute the variable for each major MNC parent country and ...nd high rank correlations across countries. For our empirical analysis, we do not use country-speci...c measures to mitigate potential endogeneity even though in doing so our estimates could be potentially biased downward.

According to our measure, industries that exhibit the strongest ...nancial linkages between parents and subsidiaries include cut stone and stone products, newspapers: publishing or publishing and printing, fats and oils, ordance and accessories except vehicles and guided missiles, grain mill products, broadwoven fabric mills, manmade ...ber and silk, paperboard mills, and petroleum re...ning.<sup>12</sup> We also consider an alternative approach to examine the ...nancial linkage hypothesis. We follow Rajan and Zingales (1998) and Braun and Larrain (2005) in constructing a measure

> 12 As a robustness check, we also computed the ...nancial linkage variable using the average share of a¢ liate assets ...nanced by parent ...rms in each industry based on the U.S. BEA Direct Investment Abroad Benchmark Data. The results were largely similar. This measure has been considered in Antras, Foley and Desai (2009). Their analysis shows that host-country characteristics can exert a signi...cant e¤ect on the level of ...nancial linkages within MNCs. But to the extent that the e¤ect of country characteristics applies symmetrically across industries as shown in their paper, the ranking of industries is likely to be relatively similar across countries.

11
|**D**<br>**(lowest)**|<br>No single institution in charge of monitoring<br>and different undocumented methodologies<br>are used for available data; or<br><br>No information on large parts of the country;<br>or|52|39|
|---|---|---|---|
||<br>Only outdated data are available|||

_Note_ : Assessment of methodological robustness was based on a review of end-2014 data and associated methodologies.

At an aggregate level, the headline statistic of 65 million forcibly displaced persons is an estimate, and the data are the least robust for IDPs (who represent the bulk of the forcibly displaced and the largest part of the recent growth in numbers). Based on existing data, it is not possible to assess how close it may be from the actual situation on the ground, nor whether it provides an over- or an under-evaluation. In the absence of accurate data, existing figures provide the only source to determine orders of magnitudes, relative numbers, and trends. They may provide important insights into the forced displacement crisis, but they ought to be taken with a great degree of caution. Data users should be aware of their limitations and of the corresponding error margins.

# **E. Key data required for development policy and programming**

Conflict-induced forced displacement is increasingly being recognized as a development issue by both humanitarian and development actors. Forced displacement poses significant challenges to achieving the SDGs and the Bank’s own goals of eradicating extreme poverty and boosting shared prosperity. Displaced people are often (though not always) more likely to be poor than non-displaced people and they also have unique vulnerabilities that are impediments to achieving higher incomes. Additionally, the burden of hosting displaced populations is overwhelmingly carried by developing countries, which can undermine their efforts to reduce poverty and promote development. In particular
across sectors is neither a necessary nor a sufficient condition of inefficiency. When production follows a Cobb-Douglas technology in presence of perfect competition, marginal productivity is simply the product of average productivity and the labor share of income. The deviation in labor income shares across sectors drives a wedge between marginal and average productivity gaps. If the sector with higher average productivity also has a higher relative labor share of income, then marginal gaps are larger than observed average gaps. To estimate labor shares at the sectoral level, I use the data from Global Trade Analysis Project (GTAP). I find significant deviation between marginal and average gaps and as such considering the gaps at margin is not only a theoretical concern. On average, distortions estimated using average gaps in productivity are around 35% and 22% lower than when estimated using marginal gaps for Industry and Services respectively. Vollrath (2009) also considers differences in marginal productivity to measure impact of misallocation. Though, in his calculations he assumes the labor shares to be invariant across countries and hence uses the US labor shares to estimate marginal productivity in each country. However, labor shares obtained from GTAP show that there is a considerable variation in sector labor shares across countries. Thus, using a common set of labor shares may not be an appropriate strategy.

In order to quantify the productivity impact of the sectoral gaps, I develop a simple model of resource allocation in which the gaps arise due to distortions in the form of asymmetrical taxes on labor inputs across three broad sectors: Agriculture, Industry and Services. These taxes are a stylized way of capturing the net effect of a menu of factors that create barriers to movement of resources across sectors. A model of resource allocation is essential in contrast to a shiftshare analysis of aggregate productivity because productivity in both sectors change as a result of resources flowing from the low productivity sector to the high productivity sector. An important point is that the growth impact of changes in distortions depends crucially on the amount of resources that get reallocated. If the sector-specific
# **Reasons for Low Migration Rates**

Three stylized facts presented below suggest three interrelated reasons for Egypt’s reduced internal migration rates. The first is low educational attainment, as migration rates tend to increase with level of education. Second, labor is absorbed by low-productivity agricultural activities, which relates to the third reason for low migration, that of households producing significant portions on their total food consumption, in what has been labled the “food problem” (Gollin, Parente and Rogerson, 2008).

# **Migration and Educational Attainment**

Migration rates increase with educational attainment (Table 14). The correlation may reflect both the larger numbers of workers in lower educational levels, and the greater tendency of highly-educated individuals to migrate. Migration rates among those with the least educational attainment (from illiterate to technical secondary) are low, with a maximum of 8.3 percent for graduates of technical secondary schools. As educational attainment increases, migration rates spike to reach more than 20 percent among migrants possessing post-graduate degrees.

Table 14 – Migration Rate by Educational Attainment

|Education Level|Non-Migrants|Migrants|Total|
|---|---|---|---|
|Less than 6 years|99.2|0.8|100|
|Illiterate|91.9|8.1|100|
|Read & Write|94.4|5.6|100|
|Less than intermediate|94.6|5.4|100|
|General Secondary|93.1|6.9|100|
|Tech. Secondary|91.7|8.3|100|
|Above Intermediate|88.8|11.2|100|
|University|87.7|12.3|100|
|Above University|78.9|21.1|100|
|Total|93.8|6.2|100|

Source: Authors’ calculations using Labor Force Survey Most
of the regressions for subjective welfare, as used in calibrating social-subjective poverty lines, or measuring welfare more generally. If the likely errors in subjective welfare data are white noise—classical measurement errors—we would not be concerned; the R<sup>2</sup> will be lower but the regression coefficients will still be unbiased under standard assumptions. However, there are good reasons to expect the errors in subjective data to be correlated with other explanatory variables of interest, as argued by Bertrand and

> 24 The authors note the endogeneity concern and provide instrumental variables (IV) estimates although it is not obvious that there is any theoretically valid IV for this problem.

> 25 Examples include Clark and Oswald (1994), Theodossiou (1998), Winkelmann and Winkelmann (1998) and Ravallion and Lokshin (2001).

15
opposed to no earnings and lower IPV risk (-4.7 percentage points) and membership of certain ethnic groups is associated with lower IPV risk. <mark>Our findings are consistent</mark> with other research using Nigeria’s DHS data; being Hausa and Fulani as opposed to being Yoruba is associated with lowering risk of physical or sexual IPV by -5.3 and -5.2 percentage points respectively.<sup>25</sup>

> 25 <mark>Nwabunike and Tenkorang’s (2015) analysis of the 2008 NDHS finds that Hausa women are less likely to experience physical and sexual violence, compared with Yoruba women.</mark>

17