rafmacalaba/datause-encoder-data huggingface dataset:
In our identi...cation strategy below, we will instrument the endogenous share of village out-migrants, Mjt, with village level instruments, identifying the size of the village migrant labor force, interacted with period t � 2 lagged land per capita, lpcit�2, in order to allow for di¤erences in the e¤ective value of the village migrant network for households with di¤erent amounts of land.

# 3.5 Identi...ying the Migrant Network

To identify the village migrant network, we make use of two policy changes that, working together, a¤ect the strength of migrant networks outside home counties but are plausibly unrelated to consumption growth. First, a new national ID card (shenfen zheng) was introduced in 1984. While urban residents received IDs in 1984, residents of most rural counties did not receive them immediately. In 1988, a reform of the residential registration system made it easier for migrants to gain legal temporary residence in cities, but a national ID card was necessary to obtain a temporary residence permit (Mallee, 1995). While some rural counties made national IDs available to rural residents as early as 1984, others distributed them in 1988, and still others did not issue IDs until several years later. The RCRE follow-up survey asked local o¢ cials when IDs had actually been issued to rural residents of the county. In our sample, 41 of the 90 counties issued cards in 1988, but cards were issued as early as 1984 in three counties and as late as 1997 in one county. It is important to note that IDs were not necessary for migration, and large numbers of migrants live in cities without legal temporary residence cards. However, migrants with temporary residence cards have a more secure position in the destination community, hold better jobs, and would thus plausibly make up part of a longer-term migrant network in migrant destinations. Thus, ID distribution had two e¤ects after the 1988 residential registration (hukou) reform. First, the costs of migrating to a city should fall after IDs became available. Second, if the quality of the migrant network improves with the years since IDs are available, then the costs of ...nding migrant employment should continue to fall over time.

As a result, the size of the migrant network should be a function of both whether or not cards have been issued and the time since cards have been issued in the village. Given that the size of the potential network has an upper bound, we expect the years-since-IDs-issued to 21
<!-- Start of picture text --> Graph 2<br>Evolution of Mexico's Exports<br>16,000 l<br>14,000 - _ . ___<br>12,000<br>cf 10,000 .<br>0 8,000- - _<br>6,000 - =__-_- _<br>2,000 - _ _ _ _<br>0<br>Source: INEGI ------ Total Oil Non-Oil<br><!-- End of picture text --> <!-- Start of picture text --> Graph 3<br>Mexico's Exports<br>100.00%<br>80.00%<br>60.00%<br>40.00%<br>20.00%<br>0.00%<br>1991 1992 1993 1994 1995 1996 1997 1998 1999<br>Source: INEGI *Oil 0 Non-Oil<br><!-- End of picture text --> 54
Table 4: Risky sexual behavior outcomes measured at health camps

|**Panel A: Demand for condoms**|||||||
|---|---|---|---|---|---|---|
||_Full Sa_|_mple_|_Fem_|_ales_|_Ma_|_les_|
|_Dep. Var. =1 if chose condoms over N50_|(1)|(2)|(3)|(4)|(5)|(6)|
|Treated|0.013|-0.024|0.012|-0.006|0.014|-0.045|
||(0.021)|(0.043)|(0.026)|(0.054)|(0.028)|(0.066)|
|Treated * # packs ofered||0.018||0.009||0.028|
|||(0.019)||(0.028)||(0.030)|
|# packs ofered|0.059***|0.046***|0.049***|0.043*|0.068***|0.049*|
||(0.009)|(0.016)|(0.012)|(0.023)|(0.014)|(0.025)|
|Constant|-0.061|-0.033|-0.304**|-0.292*|-0.085|-0.039|
||(0.116)|(0.118)|(0.148)|(0.160)|(0.187)|(0.194)|
|Observations|3,827|3,827|1,844|1,844|1,983|1,983|
|R-squared|0.137|0.137|0.054|0.054|0.063|0.063|
|Controls<sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean Dep. Var. (Control)|0.383|0.383|0.229|0.229|0.520|0.520|

# **Panel B: STD biomarkers**

||_Full Sample_|_Females_|_Males_|
|---|---|---|---|
|_Dep. Var. =1 if tested positive for Chlamydia_|(1)|(2)|(3)|
|Treated|-0.014|-0.017*|-0.013|
||(0.012)|(0.010)|(0.015)|
|Observations|3,820|1,839|1,981|
|R-squared|0.010|0.024|0.014|
|Controls<sup>(</sup><sup>_a_)</sup>|Yes|Yes|Yes|
|Mean Dep. Var. (Control)|0.029|0.031|0.013|

Notes. Standard errors in parentheses adjusted for clustering at the screening centre level (80 clusters). ***, ** and * denote significance at the 1, 5 and 10 percent level, respectively.

(a) Controls in each regression include: female, age, currently attending school, years of education, english spoken, single, does not live with the family, household size, wealth index, home owner, father obtained higher than secondary education, mother obtained higher than secondary education, muslim, speaks Yoruba as a native language.

39
connectivity of ports and sub-regional hubs and transshipment centers. In fact, it is difficult to find a definite relation between freight tariffs and distances with the available data (Figure 38b).

The second consideration is the relation between import costs and tariffs. This relation seems more promising and shows a positively sample correlation of over 0.5 (Figure 38a).

**Figure 38: Correlation between Freight Tariff and Various Factors**

<!-- Start of picture text --> a. Import Costs and Freight Tariffs to Destination b. Distance and Freight Tariff to Destination<br>3000 3500<br>Paramaribo<br>Castries<br>3000<br>2500 Basseterre Georgetown<br>St. Georges 2500 Bridgetown Kingstown<br>Port of St. Georges<br>2000<br>Spain Castries Roseau<br>St. John's<br>St. John's<br>2000<br>Basseterre<br>Bridgetown Kingstown<br>1500 Port au<br>Port of Prince Roseau 1500<br>Spain Paramaribo<br>1000 Port au<br>1000<br>Prince<br>Georgetown<br>500 500<br>1,000 2,000 3,000 4,000 1,000 2,000 3,000 4,000<br>Freight Tariff from Miami to Destination Freight Tariff from Miami to Destination<br>(US$/TEU) (US$/TEU)<br>Import Costs (US$ per container) Distance (,00 Kms)<br><!-- End of picture text --> _Source: Based on data from World Bank (2012b) and World Bank (2013a)_ .

Finally, there is the competition element. The correlation between the lack of competition, expressed in the HH, and high freight is clearly positive, and roughly equal to 0.6 (Figure 39).

89
Policy Research Working Paper 5652

# **Abstract**

Food price inflation in Brazil in the twelve months to June 2008 was 18 percent, while overall inflation was seven percent. Using spatially disaggregated monthly data on consumer prices and two different household surveys, we estimate the welfare consequences of these food price increases, and their distribution across households. Because Brazil is a large food producer, with a predominantly wage-earning agricultural labor force, our estimates include general equilibrium effects on market and transfer incomes, as well as the standard estimates of changes in consumer surplus. While the expenditure (or consumer surplus) effects were large, negative and markedly regressive everywhere, the market income effect was positive and progressive, particularly in rural areas. Because of this effect on the rural poor, and of the partial protection afforded by increases in two large social assistance benefits, the overall impact of higher food prices in Brazil was U-shaped, with middle-income groups suffering larger proportional losses than the very poor. Nevertheless, since Brazil is 80 percent urban, higher food prices still led to a greater incidence and depth of poverty at the national level.

This paper is a product of the Office of the Chief Economist and the Social Protection Unit, Latin America and the Caribbean Region. 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 fferreira@worldbank.org, afruttero@worldbank.org and pleite@ 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
country-specific city and settlement definitions, which means there is no consistency across countries. Second, although occasionally definitions are somewhat density based, most defintions are based on qualitative and subjective criteria including governance elements. Many countries define cities based on status in the political-spatial hierarchy, local political boundaries defined historically, or through an application or evaluation process to redefine rural areas as cities, which tends to under-represent newer fast-growing agglomerations due to delays both in application and evaluation as well as the granting of status.

We employ a consistent density based definition across our African countries, using LandScan (2012) population per grid square. We set population per grid square, or density thresholds to define cities and settlements. To do so, we apply a smoothing algorithm so that each own grid square is assigned the average density of neighbors within 7km. Smoothing is essential to avoid large doughnut holes in cities, due to terrain factors, airfields, parks, big open public spaces and the like. We define a core city as a set of contiguous grid squares all of which have a density greater than or equal to 1,500 per sq. km. and the population of these contiguous squares must sum to 50,000 or more. The area included in these contiguous squares over 1,500 per sq. km. defines the area and population of what we call the city core. We then add in a fringe to each city core, which includes all contiguous grid squares with population density over 500 per sq. km. The core combined with a fringe is called a city.

For smaller urbanized places that are stand-alone, we require a collection of contiguous grid squares all with (a smoothed) population density over 500 per sq. km., which collectively sum to 5,000 or more. We call these low density [LD] settlements. Full details of these urban definitions are given in Appendix B.

The process and impact of threshold decisions are illustrated in Figure 3 for Nairobi. Core city areas are in dark blue, and overall cities are also outlined in dark blue. There are two cities in the figure, Machakos to the bottom right and Nairobi. Nairobi consists of the main core and three small core areas, essentially satellite towns now falling under the umbrella of Nairobi. The fringe of Nairobi consists of pink and light blue areas, within the dark blue outline. Our choice of 500 per sq. km. is based on the idea that a lower threshold such as 300 per sq. km. (yellow areas) is too loose and extends too far into more rural 9
(2). Finally, the point estimates and their corresponding standard errors are obtained by computing respectively the average and the standard deviation over _R_ imputed poverty rates. Note that the standard errors of the poverty estimates are driven both by model error and idiosyncratic error. The contribution of the idiosyncratic error (the area- and the household error) will tend to zero as the size of the target population of a small area (in the population census) tends to infinity. Similarly, the contribution of the model error tends to zero when the sample size of the survey (which is used to estimate the model parameters) tends to infinity.

# **2.2 Remote-sensing-based small area estimation of poverty**

Relying on remote sensing data to estimate small area poverty rates alters the dimensions of the model with which we work. The unit of observation will be a geographic unit, think of a small administrative unit such as a village. The dependent variable in this case will be village level poverty rather than household level consumption expenditure. Furthermore, restricting the set of the covariates that can be derived from publicly available remote sensing data could conceivably increase the magnitude as well as the spatial correlation structure of the error term. Adopting a spatial error model will help fit this spatial structure (e.g. Anselin, 2001).

We use a spatial error model (henceforward SEM) that assumes iid normal shocks to local poverty spilling over to other locations depending on distance. It is assumed that village level poverty _q_ can be described (in matrix notation) by: where _X_ is the matrix with geo-covariates (rows indicating villages and columns indicating covariates), _u_ satisfies _u_ = _ρWu_ + _ε_ , where _ε_ is a vector of independent local errors satisfying _ε ∼ N_ (0 _, σ_<sup>2</sup> _I_ ) and _E_ [ _ε|X_ ] = 0, and where _W_ is a spatial weight matrix with elements _wij_ measuring the spatial correspondence between villages _i_ and _j_ (relative to other villages). We define _wij_ = _t_<sup>_−_</sup> _ij_<sup>_τ_</sup> (for _i_ = _j_ ) as a function that is inversely related to travel time _tij_ between _i_ and _j_ with _τ >_ 0. We take _W_ to be symmetric, with _wii_ = 0, and normalize it by its largest eigenvalue, i.e. _W_ = _W_<sup>_∗_</sup> _/λmax_ , where _W_<sup>_∗_</sup> is the un-normalized spatial weight matrix and _λmax_ is the largest eigenvalue of _W_<sup>_∗_</sup> (see e.g. Bell and Bockstael (2000) and Kelejian and Prucha (2010)). Locations that are in close proximity to each other are exposed to the same latent geographical features which induces spatial correlation. These correlations are modeled as the product of _W_ and _ρ_ , where _ρ_ is a scalar, expected to be positive. The spatial covariance structure of

8
revealed high shares of toxic chemicals which have known to cause cancer, genetic damage, fetal damage, and severe allergic responses in exposed populations (for example, carbamates and organophosphates in insecticides, and dithiocarbamates and inorganics in fungicides ) (Zahm, Ward and Blair, 1997).

Many pesticides used in Bangladesh are also banned or restricted under international agreements (Meisner, 2004; NOVIB, 1993; SOS-arsenic.net, 2004; SUNS, 1998). Pesticide suppliers in Bangladesh even continue to sell the 12 particularly controversial pesticides known by activists campaigning worldwide as the “dirty-dozen” (SUNS, 1998); SOS-arsenic.net, 2004). In addition, several studies have shown that inadequate product labeling and users’ lack of information have lead to widespread overuse and misuse of dangerous pesticides. Anecdotal evidence suggests that the consequence of these practices are numerous, with pesticide poisonings abound (Ramaswamy, 1992). Although monitored health data on the effects of pesticides is not available in Bangladesh<sup>4</sup> projections suggest an annual incidence of organophosphate poisoning alone is as high as 900/100,000 population (World Bank estimate, 2004).<sup>5</sup>

# **3. The survey of traders**

_Background of the supply chain_ Bangladesh does not produce any active ingredients, it only imports and formulates. The marketing channel of pesticides in Bangladesh consists of pesticide companies, distributors, wholesalers, wholesaler-cum-retailers, retailers and farmers (Sabur and Moila, 2000). At the top, there exist approximately 66 officially registered companies, with 6 of these being multinational in nature. Distributors buy almost all of their products from pesticide companies. However, the pesticide companies also directly sell to wholesalers. Distributors, in turn, sell their products to the wholesalers-cum-retailers, retailers and large farmers. Wholesalers sell to retailers as well as farmers. Retailers, in turn, sell their product to farmers, but farmers frequently buy directly from the

> 4 The 1999 DGHS data, produced by the Health Information Unit of DGHS, indicates that over 30,000 cases of poisoning were reported in 1997, however, they do not classify information by the source of poisoning.

> 5 The comparable figure for the US is 71/100,000.

6
# **3.6  Other Regulations and the Business Environment**

We have focused on the regulation of entry. There are, however, other regulations and aspects of the business environment that might affect entry. Let us consider some in this sub section.

# _3.6.1. Labor regulation_

First consider labor market regulation, specifically laws that prevent a firm from firing employees. This could cut both ways. One could argue that strict labor regulations protect employees and give them the confidence to join small, untested firms (much in the way that good corporate governance offers investors confidence), thus reducing start up costs. There may be other forces at work in the same direction. Regulations may hamper the growth of large incumbent firms, whose adherence to regulations is more easily monitored, thus creating the space for new firms to enter. However, one could argue for the opposite effect of labor regulations on entry: the cost of compliance with regulations may have fixed components, which make them particularly costly for small businesses to meet, and could inhibit entry. Small firms may not be able to afford to keep their employees through downturns, and thus might under hire in the face of strict labor regulations.

We use a measure of firing costs obtained from Nicoletta et al. (2000) that captures the costs of individual dismissals of employees with regular contracts. The measure is calculated using factor analysis of original data collected by the OECD (1999) and ranges from 0 to 6, with a higher score indicating a higher cost. The data refer to 1998. We refer to this index as the cost of dismissals, or LabCost. Unfortunately we do not have this measure for all firms in our sample, so we also use a more general index from Botero et al. (2003), which indicates the strictness of labor regulations in the country in 1997. This index, the Employment laws index, was constructed by examining the detailed provisions in the labor laws regarding alternative employment contracts, conditions of employment, and job security. The index takes values between 0 and 3, 26
**<u>Figure 2. The role of taxes and transfers in Latin America and Europe</u>**

<!-- Start of picture text --> Difference between Gini coefficients of gross income and market income<br>Panel A. Latin America Panel B. Europe<br>0.00 0.00<br>-0.04 -0.04<br>-0.08 -0.08<br>-0.12 -0.12<br>-0.16 -0.16<br>Difference between Gini coefficients of disposable income and gross income<br>Panel C. Latin America Panel D. Europe<br>0.00 0.00<br>-0.04 -0.04<br>-0.08 -0.08<br>-0.12 -0.12<br>-0.16 -0.16<br>Argentina Brazil Chile Colombia Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembour Netherlands Portugal Spain Sweden UK EURO 15<br>Argentina Brazil Chile Colombia Mexico Peru LAC 6 Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembour Netherlands Portugal Spain Sweden UK EURO 15<br><!-- End of picture text -->

**Table 1 The role of indirect taxes in the UK** **<u>(Gini coefficients)</u>**

|||Income|Definition||
|---|---|---|---|---|
||Market|Gross|Disposable|Post-tax|
|This Study|0.52|0.39|0.34||
|Jones (2006)|0.52|0.37|0.34|0.38|

Source: Tables 1 and 2 and Jones (2006) Importantly, our definition of disposable income only deducts direct taxes, which in general tend to be more progressive than the overall tax package comprising both direct and indirect taxes. In this regard, Jones (2006) performs calculations similar to ours for the UK using 2003-04 data and extending the analysis to consider also the role of indirect taxation. As shown in Table 1, the conclusion is that indirect taxes increase the Gini coefficient of post-tax income (household income after transfers have been received and both direct and indirect taxes paid) by about 4 percentage points above that of disposable income, thus more than offsetting the progressive impact of direct taxes. That 7
Wessells 2006; Dwyer and Cagoco‐Guiam 2011; ICRC 2001; McKay, Robinson, Gonsalves, and Worthen 2006), which also focus on conflict processes, community structures and institutional changes at the local level.

Micro‐level research has made significant contributions to measuring conflict’s effect on livelihood choices and poverty dynamics. One strand of the micro‐level conflict literature suggests a positive correlation between violence exposure and various measures of deprivation at the household level. Mercier, Ngenzebuke, and Verwimp (2016) compare three waves of household panel data in Burundi over 1998‐2012 and deduce that violence exposure seems to trap already poor and economically vulnerable households into chronic poverty. Non‐poor households exposed to violence do no exhibit the same adverse impact on welfare. Douarin, Litchfield, and Sabates‐Wheeler (2011) similarly find that exposure to violence has different impacts on household welfare depending on the labor and livelihood choices adopted. Households with more diverse livelihood opportunities demonstrate greater economic resilience and ease in increasing consumption levels. War‐affected households in Rwanda that suffer loss of real estate or land due to conflict tend to be at greater risk of falling into chronic poverty after conflict, particularly for households accustomed to cultivation and land usage prior to the conflict (Justino and Verwimp 2013). Other studies offer evidence suggesting that the decline of infrastructure, economic opportunities, and social services due to conflict increases the likelihood of chronic poverty regardless of pre‐existing assets, skills, or social capital (Bozzoli and Brück 2009; Bozzoli, Brück, and Muhumuza 2015; Bratti, Mendola, and Miranda 2016).

Analytical work linking conflict to human capital accumulation indicators finds that conflict exposure causes household trade‐offs that negatively impact child schooling retention and investment in health care (Justino, Leone and Salardi 2014; Brown, Ryan, and Velásquez. 2015; Minoiu, Camelia and Shemyakina 2012; Debalen and Paul 2012). These studies echo the view that conflict induces risk aversion and short‐term time preferences, which, combined with real conflict‐imposed economic constraints, detracts from human capital accumulation post‐conflict. Micro‐level studies also explore the relationship between exposure to conflict and other behaviors, such as the impact of civilian casualties on wartime informing (Shaver and Shapiro 2016) and degrees of depression (Bratti, Mendola, and Miranda 2016).

Yet data on whether women’s greater market participation and altered engagement in the domestic sphere results in welfare gains and long‐term social empowerment are more ambiguous (Justino, et. al 2012; Bozzoli 2012). One strand of the literature suggests that the economic and social gains women 6