rafmacalaba/datause-encoder-data huggingface dataset:
much more prominent role in rural India. In our analysis, we pay close attention to the implications of these cross-country differences.

The standard model of intergenerational educational mobility where parent’s education is the sole indicator of family background is not suitable for our analysis because it ignores occupational dualism in the rural economy.<sup>6</sup> Children born into a nonfarm household may face different educational opportunities compared to the children born into a farming household even when the parents have similar educational background. At a given level of education, the parent’s nonfarm occupation may affect investment in children’s education through two major channels. First, higher income from nonfarm occupations may relax binding credit constraints on investment in schooling.<sup>7</sup> Second, the probability of a child getting a nonfarm job may be higher when the parents themselves are employed in nonfarm occupations because of network, referral, and role model effects (Emran and Shilpi (2011)), and this would increase the optimal investment when returns to education are higher in nonfarm occupations.

There is substantial evidence that structural change in favor of non-farm occupations is an important source of increasing income inequality in villages of many developing countries.<sup>8</sup> According to the estimates of Lanjouw et al. (2013) based on the data from Palanpur in the Indian state of Uttar Pradesh, the contribution of non-farm income to income inequality was only 4 percent in 1974/75, which increased to 67 percent in 2008/09.<sup>9</sup> The evidence on China also suggests that nonfarm income contributes to income inequality in rural areas (Rozelle ( 1994), Yang and An (2002)). Such a rise in inequality associated with the expansion of the nonfarm sector is of special concern when it reflects lower intergenerational mobility.

We develop a theoretical model in the tradition of Becker and Tomes (1986) that incorporates the role played by parental farm and nonfarm occupations in shaping children’s educational opportunities, and yields the almost universally used linear-in-levels estimating equation.<sup>10</sup> As emphasized by Mogstad (2017), in the absence of an explicit theoretical

> 6Although there is a long tradition in development economics following Lewis (1954) that emphasizes dualism as a central feature of underdevelopment, to the best of our knowledge, ours is the first paper to incorporate occupational dualism in an analysis of intergenerational educational mobility.

> 7However, in some countries, low-skilled nonfarm activities might be occupations of last resort, with low income, lower than the income of the farming households (Lanjouw and Lanjouw (2001)).

> 8For an excellent survey of the literature on rural nonfarm economy in developing countries, see Lanjouw and Lanjouw (2001). On rural China see Rozelle (1994), Yang and An (2002), and on rural India see Lnjouw et al. (2013).

> 9The Gini coefficient of income in Palanpur was 0.253 in 1974/75 and 0.427 in 2008/09.

> 10The estimating equation used in the literature on intergenerational income mobility is log linear. In

2
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defined microregions, grouping the 5571 municipalities in the data. To ensure a consistent definition of microregions over time, when necessary we merge microregions whose boundaries changed over the period of analysis.<sup>11</sup> We merge RAIS with customs records on export transactions by microregion, industry, and destination each year from the administrative data collected by _Secretaria do Com ́ercio Externo_ (SECEX) of the Ministry of Development, Industry and Foreign Trade. These data are defined at the level of the municipality, detailed product category, and destination market, and are available since 1997. For consistency with the RAIS data and our definition of the labor market, we restrict the analysis to the post-2003 period and aggregate the customs records to the microregion-sector level. We further use yearly data on the industry-level imports of each of 189 Brazilian destinations reported in the customs data (sourced from all countries except Brazil) to construct the instrument and link them to the information on industry-level imports from the UN COMTRADE. We use the data of the year 2003 only for initial weights in our instrument and in the regressions, and the main analyses draw on yearly changes of variables of interest during 2004-2015 as specified in the next subsection.

# **2.2 Econometric Model**

We now describe the econometric strategy for examining the effects of export shocks on labor markets. Our baseline specification is: where _Yt_<sup>_k_</sup> denotes the log of the outcome variable of interest in the region-sector pair, i.e., labor market, _k_ in year _t_ ; _Zt_<sup>_k_isthelogofexportrevenueoriginatedin</sup><sup>_k_;</sup><sup>_κt_denotesayearfixedeffect;and</sup> _ε_<sup>_k_</sup> _t_<sup>istheerrorterm.The∆operatordenotesthelinearchangeofavariablebetweenyear</sup><sup>_t_andyear</sup> _t −_ 1 throughout the paper.

Figure 2 depicts the change in export revenue observed in different microregions over the period of our main analysis, 2004-2015. It reveals that there exists substantial heterogeneity in the direction and the magnitude of the change in exports across space, which is convenient for identification. Notice also that because some labor markets were initially more export-oriented than others, they differ in the extent to which they are exposed to a given percentage change in export revenue. This heterogeneity is illustrated in Figure A1, which depicts the distribution of export revenue per worker across microregions in 2003. While the figures show only spatial heterogeneity, initial exports per worker also vary across sectors within each microregion. The same percentage change in exports would therefore be expected to have a stronger impact on labor market outcomes in labor markets where exports per worker were higher, to begin with. To account for this, in the regression analysis each observation is

> 11See Appendix A.3 for more details.

7
_proportionality assumption_ to approximate the main use of imports. That is, every domestic sector is assumed to import inputs in the same proportion as its economy-wide use of that input. For example, if an industry such as electronics relies on semiconductors and 10% of all semiconductors are imported, it is assumed that 10% of the semiconductors used by the electronics industry is imported. With this assumption, scholars have been able to link the flow of imported inputs to the flow of exported goods within the same global production network. Hummels, Ishii and Yi (2001) and Johnson and Noguera (2009) have used this approach to quantify the import content embodied in a country’s exports. Recent studies, then again, have questioned the accuracy of the proportionality assumption. Winkler and Milberg (2009) have shown that, in Germany, the cross-sectoral variation in the use of imported inputs differs significantly from the cross-sectoral variation in the use of domestic inputs. Koopman, Wang and Wei (2008) show that China’s policy preferences for processing exports has led to a significant difference in the intensity of imported inputs in the production for processing exports than in other productions (for domestic final sales and non-processing exports).

A third approach has been to use firm-level data on multinationals to measure the dispersion of GPNs. Collinson and Rugman (2008), Rugman, Li and Oh (2009) and Rugman and Oh (2009), for example, use data on the geographic distribution of assets for large multinational firms to measure the dispersion of GPNs. Hanson, Mataloni and Slaughter (2005) use BEA data on U.S. multinationals to estimate the drivers of trade in intermediate inputs for further processing between parent firms and their foreign affiliates. These studies, however, give an incomplete and potentially biased picture of the organization of GPNs since many multinationals outsource a large portion of their manufacturing activities to external firms (Gereffi, 1999; Gereffi, Humphrey and Sturgeon, 2005; Swenson, 2006; Bonham, Gangnes and Van Assche, 2007; Desai, 2009). If these outsourced activities are more dispersed geographically than the assets owned by the multinational firms, the existing estimates on the dispersion of upstream activities will be biased.

In this paper, we will exploit a unique data set that allows us to overcome some of the shortcomings in the existing literature. Specifically, we will exploit a data set collected by the _General Administration of Customs of the People’s Republic of China_ on China’s 7
mixed results on the relationship between tenure status and land-attached investments. Using survey data from Central Ethiopia, Gavin and Ehui (1999) did not find any empirical basis to support the hypothesis that land tenure was a constraint on agricultural productivity. Fafchamps and Pender (2001) similarly suggest that variable input use was not affected by the variety of rural tenure contracts under which production takes place. Their results indicated that farmers apply more or less the same amount of inputs on land under informal and less secure contracts (rented, sharecropped and borrowed) and on lands formally allocated to them via the local authorities. Arguably, the concern in these studies is with variable inputs, and since their returns are captured in the short run, security and transferability are unlikely to be a negative constraint on production decisions.<sup>2</sup> Studies focusing on more long-term investments also do not necessarily find negative effects from tenure insecurity. Holden and Hailu Yohannes (2002) investigated the planting of perennial crops using data from 15 different sites in Southern Ethiopia. They showed that tenure insecurity has little effect on the decision of farmers to plant perennials. On the other hand, they identified resource poverty as the main factor that has led to under investment in tree crops. Based on nationally representative survey data, Deininger et al. (2003) argued that the impact of tenure insecurity varies across types of investments. In line with this, they found out that tenure insecurity has encouraged planting (any) trees while discouraging investment in terraces. There is little or no evidence that resource constraints have adversely affected both investments. Gebremedhin and Swinton (2001) suggest that farmers’ perceived land tenure security in Tigray was significantly and positively associated with long-term durable soil conservation investments such as stone terraces. Gebremedhin et al. (2003) argued from village level data that perceived tenure security increased land investments.

Many of these studies suffer from specific data or methodological limitations. For example, typically only a cross-section is available, measures of security and transferability are incomplete and endogeneity of tenure security cannot be appropriately addressed. Deininger et al. (2003) can account for these issues to some extent, but only observe propensities to invest and only over a

> 2 Indeed, there would be incentives related to tenure insecurity to overexploit the soil in the short run, leading to higher productivity in the short run.

6
Policy Research Working Paper 5051

# **Abstract**

This paper examines newly available data from the World Bank-sponsored Global Antidumping Database tracking the worldwide use of trade remedies such as antidumping, countervailing duties, global safeguards and China-specific safeguards during the current economic crisis. The data indicate a marked increase in WTO members’ combined resort to these instruments beginning in 2008 that continued into the first quarter 2009. The use of these import-restricting instruments is increasingly affecting “South-South” trade, i.e., developing country importers initiating and imposing new protectionist measures primarily affecting developing country exporters, with a special emphasis on exports from China. However, the collective value of imports in 2007 for the major (G-20) economies that has subsequently come under attack by the use of importrestricting trade remedies during the period of 2008 to early 2009 is likely less than $29 billion, or less than 0.45 per cent of these economies’ total imports, though there is substantial variation across countries. While the level of trade affected thus far may be small for most of these economies, a first assessment of some of the caselevel data identifies a number of ways in which the crisis use of these import-restricting trade remedies may have economically important welfare-distorting effects on economic activity.

This paper—a product of the International Trade Department, Poverty Reduction and Economic Management Network and the DFID supported Global Trade and Financial Architecture project—is part of an effort to monitor trade-related policy responses to the crisis. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@brandeis.edu _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
# A Dynamic Spatial Model of Rural-Urban Transformation with Public Goods

## 1 Introduction

Urbanization - defined as the percentage of a country’s population that lives in urban areas- among developing countries exhibits significant variation across different world regions and countries. The Middle East and North Africa region is the most urbanized region, with around 70% of its population living in cities. By contrast, South Asia has a surprisingly low urbanization, around 28%. Differences in income per capita cannot account for this variation. Figure 1 shows the level of urbanization and annual GDP per capita in PPP terms in selected developing countries around the world in 1960.<sup>1</sup> South Asia and East Asia are clustered together, while Latin American economies are slightly detached given their higher GDP per capita PPP and larger urbanization levels. In this figure the size of the bubbles represent countries with their population size — the larger the bubble the larger the population. Figure 2 shows that in half a century East Asia followed the Latin American path with some countries like the Republic of South Korea and Malaysia even surpassing Latin American economies both on urbanization levels and on GDP per capita PPP, but South Asia lagged well behind. The eight countries of South Asia (Afghanistan, Bangladesh, Bhutan, India, The Maldives, Nepal, Pakistan, and Sri Lanka) display very low urbanization given their income levels.<sup>2</sup>

> 1 This figure is similar to the one presented in Henderson (2009) which shows the variation in urbanization rates at different levels of income for different countries. He emphasizes that China’s urbanization rate is also too low at its stage of development.

> 2 These figures are constructed grouping countries by region according to the World Bank classification. See http://www.worldbank.org/depweb/beyond/beyondco/beg_ce.pdf

1
## **<u>Table 2:  Summary of perceptions of main social programs</u>**

|**He**|**alth**|
|---|---|
|_Positive_|_Negative_|
|The professional character of COF<br>Improvements in the growth and<br>development of children<br>Improvements in the relationships between<br>mother-child<br>Development of new social interventions in<br>the family and between neighbors<br>Awareness ofpreventative actions|Limited number of spaces|
|**Educ**<br>|**ation**<br>|
|_Positive_|_Negative_|
|The continuity of services<br>The range of activities<br>Expanding educational and occupational<br>opportunities for youth (particularly<br>through the cooperatives)<br>The presence of resource people<br>(educators) in the community<br>Indirect impacts on the family and their<br>habits|Low coverage<br>Difficulty in access (long waiting list)|
|**Training and Emp**|**loyment Programs**|
|_Positive_|_Negative_|
|Opportunities for cooperatives in the labor<br>market<br>Some level of education is required for<br>entrance to the program<br>Existence of skilled workers in the area<br>Integrated approach|Coverage restricts the youngest<br>Perception that the cooperatives are closed<br>to new members|

# **III. COST EFFECTIVENESS**

The project area of Novos Alagados II included 2020 households, or 7800 people. A total of R$22.1 million was spent from 2001-2004 on physical infrastructure, equivalent to R$11,000 (US$3928) per household. Unit cost data for a range of 11 indicators presented in Figure 1 show substantial variation.

The highest expenditure under the Project was for the construction of new houses for those resettled from the precarious _palafitas_ . The costs ranged significantly 7
<!-- Start of picture text --> 6<br>GEO<br>5<br>4<br>3 KAZ<br>2<br>ARM<br>AZE<br>1<br>SVK<br>HUN 0 BGR MDA UKR<br>LTU EST<br>-3 -2 -1 0 1 2 3 4 5<br>LVA<br>RUS<br>-1 MKD<br>POL<br>ROM<br>HRV<br>-2<br>TUR<br>-3 SRB more vulnerable<br>-4<br>VULNERABILITY<br> more adaptable<br>ADAPTABILITY<br><!-- End of picture text --> Source: World Bank Staff calculation. See Annex 5 for details on data.

Note: Countries with high vulnerability (higher values) are those with higher shares of employment and value added originating from energy-intensive industries, as well as those that expect a higher energy price spike. Countries with high adaptability (higher values) are the ones with high LM flexibility, high skills, high social protection readiness, and higher spending on ALMPs. The value 0 represents the average position in terms of vulnerability and adaptability across all countries in our sample. This analysis was performed taking into account the overall manufacturing sector. To see a more detailed breakdown of the manufacturing sector resulting in a similar analysis, see Annex1.

The analysis is designed as a simple tool to see each county’s individual stance as well as how they compare with the rest of ECA; for policy-making, however, what is important is to identify the areas within each dimension in which they may have weaknesses. FYR Macedonia and Russian Federation are highly vulnerable and not very adaptable while Slovak Republic has low vulnerability as well as high adaptability which mean it is the most favorable country in our analysis.

The policy challenges are likely to be different for all countries depending on their position in the above graph. The reform agenda for countries in the bottom right part of Figure 8 is the most urgent. Moreover, the reform agenda will differ by country since, as discussed in section 4, “unprepared countries” are unprepared in their own way. Based on the existing literature, we have identified five key policy areas where governments should focus in order to better prepare their firms and workers for an energy price shock: labor market costs and flexibility; skills, training and innovation; active labor market policies; social protection; and economic policies supporting green growth.

Poland can serve as an informative example to policy makers across the region as a country that is highly dependent on coal and other energy-intensive inputs, but that has put in place policies to help weather the negative impacts of a carbon shock or other economic shocks; at the same time, it is a country that illustrates the need for a continuous reform agenda in social sectors (Box 1). The Polish case illustrates 18
|ElectricityService Access Project|Christopher Saunders|
|---|---|
|ElectricityService Access Project|Christopher Saunders|
|ElectricityService Access Project|Christopher Saunders|
|TurkeyEnergyEfficiencyin Public Buildings|Jasneet Singh|
|TurkeyEnergyEfficiencyin Public Buildings|Jasneet Singh|
|Forest Investment Program|Salimata D. Follea|
|Forest Investment Program|Salimata D. Follea|
|Forest Investment Program|Salimata D. Follea|
|Forest Investment Program|Salimata D. Follea|
|Forest Investment Program|Salimata D. Follea|
|Forest Investment Program|Salimata D. Follea|
|StrengtheningPartnerships to Protect Endangered Wildlife in Vietn|Thu Thi Le Nguyen|
|StrengtheningPartnerships to Protect Endangered Wildlife in Vietn|Thu Thi Le Nguyen|
|StrengtheningPartnerships to Protect Endangered Wildlife in Vietn|Thu Thi Le Nguyen|
|Improvingthe Performance of Non-Criminal Justice Services|Nicholas Menzies|
|Improvingthe Performance of Non-Criminal Justice Services|Nicholas Menzies|
|IMPROVING ECONOMIC OPPORTUNITIES FOR WOMEN AND YOUTH|Laila Ashraf AbdelKader Ahmed|
|IMPROVING ECONOMIC OPPORTUNITIES FOR WOMEN AND YOUTH|Laila Ashraf AbdelKader Ahmed|
|India EnergyEfficiencyScale-upProgram|Ashok Sarkar|
|India EnergyEfficiencyScale-upProgram|Ashok Sarkar|
|India EnergyEfficiencyScale-upProgram|Ashok Sarkar|
|India EnergyEfficiencyScale-upProgram|Ashok Sarkar|
|Public Financial Management Improvement Project|Riham Hussein|
|Public Financial Management Improvement Project|Riham Hussein|
|Public Financial Management Improvement Project|Riham Hussein|
|Public Financial Management Improvement Project|Riham Hussein|
|Public Financial Management Improvement Project|Riham Hussein|
|The Gambia - Education Sector Support Program|Alison Marie Grimsland|
|The Gambia - Education Sector Support Program|Alison Marie Grimsland|
|The Gambia - Education Sector Support Program|Alison Marie Grimsland|
|The Gambia - Education Sector Support Program|Alison Marie Grimsland|
|The Gambia - Education Sector Support Program|Alison Marie Grimsland|
|The Gambia - Education Sector Support Program|Alison Marie Grimsland|
|Liberia Land Administration Project|Linus Benedikt Pott|
|Liberia Land Administration Project|Linus Benedikt Pott|
|Liberia Land Administration Project|Linus Benedikt Pott|
|Liberia Land Administration Project|Linus Benedikt Pott|
|Liberia Land Administration Project|Linus Benedikt Pott|
|Liberia Land Administration Project|Linus Benedikt Pott|
|Liberia Land Administration Project|Linus Benedikt Pott|
|Djibouti Integrated Slum UpgradingProject|Hazem Mohamed Kamaleldin Abdelf|
|Djibouti Integrated Slum UpgradingProject|Hazem Mohamed Kamaleldin Abdelf|
|Djibouti Integrated Slum UpgradingProject|Hazem Mohamed Kamaleldin Abdelf|
|ElectricityAccess and Renewable EnergyExpansion Project|Mitsunori Motohashi|
|ElectricityAccess and Renewable EnergyExpansion Project|Mitsunori Motohashi|
|ElectricityAccess and Renewable EnergyExpansion Project|Mitsunori Motohashi|
|ElectricityAccess and Renewable EnergyExpansion Project|Mitsunori Motohashi|