rafmacalaba/datause-encoder-data huggingface dataset:
In addition to the quality of education, the efficiency of public expenditures on education will play a large part in determnining additional resource requirements. Work at the country level in several African nations underscores the importance of the policy environment in determining the costs and resource requirements of meeting the goals. Table 13 shows that the public financing gap depends upon assumptions about the education policies, the level of teacher salaries, and the priority given by the country for publicly financed primary education. If we consider the total for the four countries, we see that the financing gap amounts to $487 million if no policy changes are undertaken in the countries. Assuming that policy changes are introduced just to improve education quality, the financing gap increases to $683 million, because these quality-enhancing interventions are costly. But if policies are introduced to adjust teacher salaries to levels proportional to average incomes, the financing gap falls to $394 million, a drop of almost $290 million. Finally, if further policy changes are made such that public budgetary support for primary education is adjusted to adequate levels, the financing gap falls again, to $258 million. Calculating the financing gap on an annual basis indicates that, for the period 2000-2015, the average annual financing gap to reach universal primary education of a reasonable quality in 2015 in the four countries is around $191 million -- $48 million per country on average.

The total number of children who will be enrolled in primary school in the four countries is about 3.1 million (see Table 14). Thus, an annual additional expenditure of $191 million will enable 3.1 million children to enroll in primary school, meaning the per-student cost is about $62, which is close to the sustaining cost in Africa and South Asia as well. In other words, if we extrapolate from these country studies to the population at large in Africa and South Asia, and apply appropriate multipliers for the other regions (to reflect their much higher per-student expense), the cost of reaching universal primary enrollment taking into account some of the additional policy and institutional changes required could amount to as much as $10 billion but could be less if countries bring additional resources to bear.

24
increase would ease the household’s budget constraint, increase the set of feasible consumption allocations and could affect the demand for all goods consumed in the household. Though the income elasticity could of course differ between goods, one would by definition expect a positive income elasticity for normal goods. The much larger and significant effects on female compared to the small, noisy, and insignificant effects on male good expenditures provide a further suggestion that a re-allocation of the household’s budget towards female expenditures is taking place, rather than a Marshallian income effect.

As a third piece of evidence, subgroup analyzes in Table 4 support the limited role of an income effect. Columns (1) to (3) show that the impacts on female expenditures and remittances remain large even for women who had not yet been officially promoted with an income increase at the time of the survey, but who had been in a supervisory position as part of the promotion program and who may have still been waiting for a promotion. For this group, an income effect is excluded by definition and the large effects shown in Columns (1) to (3) in Table 4 further support that a reallocation is taking place. As an aside, I also do not find any effects on the expenditures of unmarried women who live in one-person households, for example in factory dormitories (See columns (4) to (5) of Table 4). For this subsample, intra-household bargaining is excluded by definition, but any possible Marshallian income effects will still be at play. Since the number of observations is small, I am mainly interested in the direction and size of effects. Table 4 shows a very small positive effect on women’s expenditures of 0.35 percentage points (3% of the comparison mean), and a negative effect on remittances of -3.74 percentage points (about 19% of the comparison mean).<sup>69</sup> Taken together, these three pieces of evidence strongly support the argument that a re-allocation of expenditures towards female goods and remittances is taking place.

There is some evidence that the promotion effect operates by improving the selected women’s attitudes and beliefs about themselves. Table 5 shows the results for several different measures of attitudes and beliefs, as well as an overall index in column (1).<sup>70</sup> I find positive point estimates for women’s confidence, their involvement in negotiation, gender attitudes and their aspirations, though none of these are individually significant. The summary index of all these outcomes is positive and significant at the 10% significance level. While this is not conclusive evidence, these results are consistent with the leadership position improving the selected women’s self-beliefs and attitudes.

> 69Note that 47% of the selected women living in one-person households have been officially promoted with an income increase at the time of the household surveys, so that this lack of effect cannot be attributed to a lack of income increase.

> 70All variables are defined in Table C.4 in the Online Appendix.

26
sampling, ordering PSUs by the average household size and prevalent economic status of households, as described in a document from the previous survey (INS, 2010). Moreover, it has been verified _ex-post_ that further implicit stratification was also introduced by ordering PSUs, at the minimum, by delegation, thus forming a sort of geographic serpentine. These three factors contributed to better implement the design and the resulting errors became smaller than those expected when planning the design. Taking into account such implicit stratification, Betti and Pavelesku (2019) have estimated the real design effect of the EBCNV 2015 and the relative standard errors for estimates of mean per capita consumption expenditures at the governorate level. These results are reported in Table 4.

Since the aim of the EBCNV 2015 was to reach significative estimates at the governorate level, sub-sample sizes were planned to be as proportional as possible to sub-population sizes. This fact has resulted in homogenous post-stratification or calibration weights and small Kish effects, mostly well below 1.10. Moreover, the maximum errors estimated _ex-post_ have been very small; even for the Governorates of Manouba and Mahdia have not been higher than 7%, well below the planned maximum percentage error of 10%.

**Table 4. Per capita consumption expenditure, standard error and its** **<u>components</u>**

|**Gov**|**Domain**|**Expenditure**<br>**per capita**|**Standard**<br>**error**|**Cv %**|**Kish**|**Deft**|
|---|---|---|---|---|---|---|
|1|Tunis|5,810.12|296.78|5.11%|1.03|1.73|
|2|Ariana|5,461.10|274.30|5.02%|1.08|1.82|
|3|Ben Arous|4,878.05|177.31|3.63%|1.03|1.73|
|4|Manouba|4,377.36|296.80|6.78%|1.07|1.79|
|5|Nabeul|3,919.35|113.59|2.90%|1.00|1.68|
|6|Zaghouan|3,052.25|115.37|3.78%|1.00|1.68|
|7|Bizerte|2,867.93|88.08|3.07%|1.00|1.68|
|8|Béja|2,471.60|101.92|4.12%|1.02|1.71|
|9|Jandouba|2,943.07|96.03|3.26%|1.03|1.74|
|10|Kef|2,362.78|69.36|2.94%|1.05|1.77|
|11|Siliana|2,934.32|135.72|4.63%|1.06|1.78|
|12|Sousse|3,777.08|119.08|3.15%|1.02|1.72|
|13|Monastir|5,115.06|208.10|4.07%|1.02|1.72|
|14|Mahdia|3,195.92|210.05|6.57%|1.28|2.16|

16
with similar findings that households with more educated members are more likely to choose cleaner fuels. Based on 2008 Nigerian Demographic and Health Survey data, Oyekale (2012) finds that access to electricity and modern cooking energy sources significantly increased among urban dwellers and educated household heads but declined with resident in rural northern Nigeria. Another factor is fuel pricing. Jain (2010) finds that Indian households continue to depend on traditional and inefficient fuels mainly due to high price of clean and modern fuels. Schlag and Zuzarte (2008) find similar results that high fuel prices made household more likely to use traditional fuels in SSA. In fact, income and price factors complement to each other (Farsi et al. 2007). In addition, based on the data from household surveys in ten developing countries of SSA, South Asia (SA) and Latin America and the Caribbean (LAC) regions, Kojima et al. (2011) finds that increase in level of education and price of alternative cooking fuels, in general, increases use of LPG. However, a study by Zhang and Kotani (2012) in rural Beijing finds that fuel prices did not exhibit substitution effects between cooking fuels (coal and LPG), but an increase in these prices had strong demand effect, i.e., reduces the use of these cooking fuels.

Moreover, most socio-economic factors influence household decision-making together. For instance, Narasimha Rao and Reddy (2007) finds that household expenditure, household size and education all act together in determining fuel choices in rural and urban areas in India. Andadari et al. (2014) finds that the same in Indonesia. Using regression analysis, Peng et al. (2010) finds incomes, fuel prices, demographic characteristics and topography had a significant effects on household's fuel choices in rural China. The study also finds that biomass is often substituted by coal in rural households which contributed to negative impacts on health. However, Jingchao and Koji (2012) finds that changes in prices of coal and LPG, mainly used for cooking, had no substitution effects with other energy sources in rural Beijing areas mainly due to high variations in income level, consumption customs and the availability of energy resources.

Factors such as household size could also influence cooking fuel decision. For example, Nnaji et al. (2012) find that fuelwood is by far the fuel of choice for a majority of households with relatively larger size in rural Nigeria. Liu et al. (2003) and Carr et al. (2005) also find that household size linked to increase in fuelwood consumption because of increased energy demand 13
social protection programs can only be deployed to assist a certain proportion (say, 20 percent) of the vulnerable population, this proportion can be a good starting point to derive the vulnerability index. Second, and perhaps more importantly, this approach allows the vulnerability line (and index) to be estimated using cross sectional household surveys, or the synthetic panels that are constructed from these cross sections.<sup>7</sup> Given a vulnerability line vj, we can extend Equality (1a) to analyze the dynamics for these three categories: poor, vulnerable, and middle class. For example, the percentage of poor households in the first period that escape poverty but still remain vulnerable in the second period (joint probability) can be calculated using the following quantity _P_ ( _yi_ 1  _z_ 1 _and z_ 2  _yi_ 2  _v_ 2 ) .

Table 1 shows a range of values of the vulnerability line that correspond to different vulnerability indexes for all countries. The vulnerability index falls within the range [10, 33], which is comparable to those for India or countries in the Middle East and North Africa region, but higher than that for the United States and Vietnam (Dang and Ianchovichina, 2016; Dang and Lanjouw, in press, forthcoming).<sup>8</sup> The vulnerability line ranges from $2.1 to $9.3 dollars per day, in 2011 PPP prices. We will employ a vulnerability index of 15 percent and the associated vulnerability line of $4.3 for our welfare analysis in the next section.

# _Shared Prosperity_

To provide a summary measure of the different growth rates for the three welfare groups, we employ a simple typology of growth scenarios (Dang and Lanjouw, 2016). This typology has a

> 7 In addition, other differences are that the target population consists of the currently non-poor households rather than all households; and this approach employs simpler non-parametric estimation methods to estimate vulnerability as a function of consumption alone. See Dang and Lanjouw (in press) for a more detailed comparison of this approach with existing studies. See also Hoddinott and Quisumbing (2010) for a recent review of other approaches to measuring vulnerability.

> 8 All numbers are in 2011 PPP dollars per capita per day.

9
# **Imports of Intermediate Inputs and Country Size**

Mohammad Amin* and Asif Islam** Keywords: Country size, Imports, Inputs JEL: F10, F14, F19 _____________________________

*Enterprise Analysis Unit, World Bank, Washington DC, 20433. Email: mamin@worldbank.org. ** Corresponding author, Enterprise Analysis Unit, World Bank, Washington DC, 20433. Email: asif.m.islam@gmail.com
preferred host of armigera and in many areas cotton is attacked only after alternative (preferred) hosts have senesced (i.e. have developed past the full-blooming stage).

Nobility: The ability to undertake extensive local and interregional movements is the second important factor resulting in armigera becoming a major agricultural pest. H. armigera is a facultative migrant i.e. it migrates in response to poor local conditions for reproduction (shortage of adult nectar sources or oviposition sites) and the occurrence of weather systems conducive to such movement. There is extensive evidence of substantial wind assisted movement by H. armigera. Pedgley (Pedgley, 1985) has shown that armigera migrates upto 1000 kms to reach Britain and other parts of Europe, from sources in southern Europe and North Africa. Evidence from cotton in the Sudan Gezira also suggests considerable mobility of armigera within the irrigated tract.

There is considerable circumstantial evidence from India to indicate widespread migration of the pest. Using light trap catches at ICRISAT for the period 1977-1983, Pedgley et al. (Pedgley et al., 1987) find that catches rise to a maximum in August-September as well as in November-December whence they exhibit a larger maximum. After controlling for the local cropping practices, it is concluded that the August-September peak is most likely due to migration of moths from the coastal and hilly parts of western Maharashtra assisted by the persistent western winds. In fact it is believed that the moths could have reached ICRISAT in three to four 36
# **1. Introduction:**

The importance of international migration for development has received increasing attention from both the research and policy communities. The United Nations Global Commission on International Migration recently issued a comprehensive report calling for greater consultation and cooperation between states and greater attention to the rights and integration needs of migrants once they arrive (GCIM, 2005). The World Bank has devoted its 2006 Global Economic Prospects report to remittances and migration, and emphasizes the importance of competition and regulation for lowering the cost of sending money transfers. Ambitious calls for greater freedom of movement under GATS Mode 4 negotiations, temporary worker programs, and other forms of bilateral or global cooperation have become part of the global policy debate.

However, far less attention has been given to barriers that countries place in the way of their own citizens who wish to emigrate. The right to emigrate is enshrined in Article 13 of the Universal Declaration of Human Rights<sup>2</sup> , but there remains a sizeable number of countries who place legal restrictions on exit, especially in the case of women. Even when no legal restrictions are in place, countries can put a sizeable burden on potential migrants through the imposition of large passport fees, and cumbersome procedures for obtaining a passport. However, little is known about the size of such fees across countries or their importance for migration.

This paper presents new data collected on passport costs in 127 countries around the world and documents the large amount of heterogeneity in costs across countries. The

> 2 See http://www.unhchr.ch/udhr/lang/eng.htm

- 2 -
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 1: Regional log Formal Earnings Premia - 2000, 2010

|Change in log formal earnings premium:||1991-2000|||1991-2010||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Panel A: RAIS<br>|||||||
|Regional tariff change|0.472*|0.536**|0.358|1.891***|1.898***|1.665***|
||(0.256)|(0.237)|(0.217)<br>|(0.478)|(0.327)|(0.298)<br>|
|Formal earnings pre-trend (86-90)|||-0.344**|||-0.467***|
||||(0.150)|||(0.144)|
|State fixed effects (26)||✓|✓||✓|✓|
|R-squared|0.019|0.183|0.236|0.142|0.409|0.454|
|Panel B: Census Earnings<br>|||||||
|Regional tariff change|0.379|0.399**|0.353*|1.342*|1.178***|1.126***|
||(0.459)|(0.198)|(0.195)<br>|(0.735)|(0.292)|(0.273)<br>|
|Formal earnings pre-trend (86-90)|||-0.098*|||-0.113|
||||(0.054)|||(0.075)|
|State fixed effects (26)||✓|✓||✓|✓|
|R-squared|0.013|0.583|0.587|0.071|0.707|0.710|
|Panel C: Census Hourly Wages<br>|||||||
|Regional tariff change|0.584|0.631***|0.572***|1.229*|0.957***|0.894***|
||(0.477)|(0.199)|(0.191)<br>|(0.670)|(0.257)|(0.241)<br>|
|Formal earnings pre-trend (86-90)|||-0.126**|||-0.139**|
||||(0.056)|||(0.066)|
|State fixed effects (26)||✓|✓||✓|✓|
|R-squared|0.029|0.595|0.601|0.057|0.704|0.708|

Positive coefficient estimates for the regional tariff change imply larger declines in formal earnings/wages in regions facing larger tariff declines. 475 microregion observations. Standard errors (in parentheses) adjusted for 112 mesoregion clusters. Weighted by the inverse of the squared standard error of the estimated change in log formal earnings or wage premium. Pre-trends computed for 1986-1990 period. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

63
safety net that protects previous gains in reducing poverty. The aim of targeting the program on the population that is currently experiencing poverty is to reduce deprivation now. However, risk-induced vulnerability involves fluctuations in welfare, that is, the likelihood of households currently above the poverty line to fall into poverty after exposure to unforeseen shocks. In the realm of social assistance, this means the method described here can help enhance the targeting of the RNU to contribute more effectively to the ability of households to cope with risks, a crucial step toward increasing the resilience of the population to risks and shocks (Skoufias, Vinha, and Beyene 2024).

Accounting not only for poor households, but also for households at risk of becoming poor reflects a recognition that expanding the safety net to cover a portion of nonpoor households is essential for sustaining the gains of past spells of poverty reduction during economic downturns and shocks. In practice, using vulnerability rates along with poverty rates, instead of poverty rates alone, increases program eligibility among households in urban and peri-urban communes. For instance, urban Dakar’s eligibility rises by 84 percent with respect to the eligibility rate if only the poverty rate is considered. Nonetheless, rural areas still exhibit consistently higher program eligibility rates because of their comparatively high poverty rates. Taking risk-induced vulnerability into account broadens the scope of social protection to encompass households that, in the face of a shock, could slip into poverty, a scenario that the current targeting mechanism may not address sufficiently.

The paper is structured as follows. Section 2 explains the use of poverty maps in support of the targeting of social programs, and discusses of the concept of the vulnerability to poverty. Section 3 describes the data and the methods used in estimating poverty and vulnerability and clarifies the assumptions involved in adding vulnerability to the concepts behind poverty mapping. Section 4 provides estimates of poverty and vulnerability using data on Senegal, the distribution of communes by poverty- and risk-induced vulnerability rates, and several robustness checks. Section 5 provides background on the RNU, the recent expansion of the registry, and the operational implications of using vulnerability as part of the eligibility quota system. The last section concludes.

# 2. Expanding poverty mapping to measure vulnerability

## Targeting in social programs

The use of targeting in antipoverty programs in the context of limited resources has been widely studied, and there is a general consensus on the positive relationship between effective targeting and a program’s impact (Alatas et al. 2012; Brown, Ravallion, and van de Walle 2016; Coady, Grosh, and Hoddinott 2004).

3
covering up to an additional 15 percent and 14 percent of non-oil imports in the US and EU, respectively. This projected new coverage would have added roughly five to seven times as much import protection as the entire US and EU stock of imports cumulatively covered by TTBs immediately prior to the crisis (Bown, 2011a).

Finally, in order to understand why the predicted surge of import protection did not materialize, our third step is to re-estimate the empirical model on data that includes 2008:Q42010:Q4. This enables us to compare how the responsiveness of import protection policies to macroeconomic shocks changed during the crisis, relative to the earlier period. First, we find robust evidence that policy-imposing economies refrained from imposing new import restrictions against those trading partners with weak GDP growth. This contrasts strongly with TTB determination before 2008, and it is an important force for dampening the overall incidence of import protection during the Great Recession given that so many trading partners were undergoing periods of macroeconomic contraction. Second, our estimates indicate that bilateral real appreciations are typically associated with more TTBs. Thus, for the United States and European Union, sharp real currency depreciations, especially in late 2009 through early 2010, likely contributed to a dampening of the trade policy response throughout 2010.

This paper contributes to the empirical literature on the determinants of import tariffs and other trade restrictions. Trefler (1993), Golberg and Maggi (1999), and Gawande and Bandyopadhyay (2000) pioneered empirical investigations into political-economic theories of endogenous trade policy formation (Hillman, 1982; Grossman and Helpman, 1994). While the evidence indicated that lobbying for protection impacts trade policy; the quantitative effect of such political-economic determinants was perhaps smaller than had been expected. More recent empirical research explores classical economic theories regarding the role of the terms of trade in tariff determination (Bagwell and Staiger 1990, 1999). For example, Broda, Limão and Weinstein (2008) directly test the optimal tariff theory in a cross-section of hundreds of imported products for a set of countries that faced no internationallybinding external constraints in setting trade policy. Their evidence supports the theory that governments set higher tariffs when export supply is more inelastic. In a different setting, Bagwell and Staiger (2011) find broad empirical support for terms-of-trade models of international agreements through a cross-sectional examination of negotiated tariff reductions for countries acceding to the WTO. Finally, Bown and Crowley (forthcoming) analyze a panel of time-varying US trade policy 3
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