rafmacalaba/datause-encoder-data huggingface dataset:
# Executive summary

This study explores the determinants of interstate migration in Mexico between 2015 and 2020 and the earnings gains from the migration decision. Internal migrants are identified using 2020 Census data, which provide information on individuals who resided in Mexico in 2015 and moved to another state. First, the study provides descriptive statistics on internal migration patterns and the socio-demographic profile of movers and stayers. Then, the study analyzes both spatial characteristics (push and pull factors in the origin and destination states) and individual factors that influence the decision to migrate and where to migrate. Push and pull factors are assessed using a gravity-type model. Individual factors are analyzed using a multinomial regression model that accounts for migration reasons. Subsequently, the study measures the impact of internal migration on labor income. Earnings gains are estimated using a double selection model that accounts simultaneously for the decisions to migrate and to work.

At the aggregate level, the results from the gravity model suggest that migration costs (proxied by the distance between the origin and destination) are more relevant for women than men. Employment is a key pull factor, while informality is a push factor. Besides, the perceived levels of corruption also appear to be a significant push factor. In addition, the availability of social networks in the destination is an important pull factor attracting migrants. At the individual level, the results from the multinomial regression indicate that younger individuals, especially women, are more likely to migrate for economic reasons. Women and men with higher levels of education are more likely to migrate than their lesseducated counterparts, regardless of the reasons for migration. Being single increases women's odds of migrating for economic reasons but reduces men's odds. Having young children reduces women's odds of migrating for economic reasons. Furthermore, the results also confirm that access to migration networks is fundamental for the individual migration choice.

Overall, the results also show that men who migrated benefit more than women from the migration decision. However, when migration happens for economic reasons, women benefit as much as men. Indeed, women and men who moved internally for economic reasons earn around 25 percent more than their counterparts who stayed in the state of origin.

Finally, we discuss some policy recommendations that could help leverage internal migration potential for improving women's labor market outcomes.

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Policy Research Working Paper 10903

# **Abstract**

The Syrian Civil War in 2011 led to a substantial influx of refugees into Jordan, with more than 660,000 Syrians arriving by 2015. More than half of these refugees were of working age. This study shows that Syrian refugees have less education than their Jordanian counterparts, and policies attempted to help them to assimilate into manufacturing. The study tests two hypotheses related to refugee assimilation. The first hypothesis examines the 2016 Jordan Compact with the European Union, which aimed to integrate Syrian refugees and improve Jordan’s export profile with simplified rules of origin for certain industries. If the Jordan Compact was effective, a relative increase in exports to the European Union, compared to other regions, would be expected. The second hypothesis suggests that the successful integration of Syrian workers into the manufacturing sector contributed to a boost in manufacturing exports to all destinations relative to other exports. The study conducts a gravity difference-in-differences analysis to evaluate these two hypotheses. The findings show little, if any, evidence supporting the first hypothesis but strong support for the second. These findings suggest that although the simplified rules of origin had limited impact on exports to the European Union, the Jordanian government effectively integrated Syrian workers into the manufacturing sector. Labor force surveys indicate that a skill mismatch impeded the integration of Syrian workers into the industries targeted by the Jordan Compact, but refugees were successfully assimilated into the manufacturing industry.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gacevedo@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
# increase in rural unemployment.

# Notes to Section 1:

1. The OECD, Economic Surveys, Turkey, 1986-87, June 1987, Table

3, seems to 'believe chat this is an estimate of actual seasonal unemployment which is certainly not the case.

2. SPO estimates of both labor force and its distribution by sectors differ substantially from census counts of economically active population and its distribution by activities.

3. The 1969 survey gives a detailed specification of the group "unemployed not seeking a job". Out of 56,580 persons, seasonal workers accounted for no less than 41,520, persons convinced that there are no jobs available in his area, 4,020, not needing to work, 2,820, found a job and will start to work, 1,320, temporary illness, 1,140, other, finally, 5,760.

4. An alternative method would be to follow Richard Stone's suggestion to correlate existing unemployment observations with observations for variables such as GDP and in this way infer values of unemployment for years without observations. I have not had the opportunity to apply this method, suggested to me by Dr. Mehran, ILO.

Tables to Section 1:

- 21 -
and the bilateral resistance term, _δ_<sup>_ij,s_</sup> , is equal to Finally, using destination and origin fixed effects, we construct the option value term, Ω<sup>_i,s_</sup> _t_<sup>,as</sup> The remaining parameters _η_<sup>_i,s_</sup> and _ν_ are calibrated in the second stage. After calibrating, _η_<sup>_i,s_</sup> , and the production functions, the model can be fully parameterized.

# _Stage 2: Calibrating the Production Functions_

After estimating the labor supply parameters in Stage 1, the production functions have to be parameterized to derive the labor demand (wage) equations. Without loss of generality, we normalize the productivity parameter to unity for formal workers, _a_<sup>_j,s_</sup> = 1 for formal sector _j_ . Also by assumption, the informal productive multiplier is equal to _a_<sup>_k,s_</sup> = _wt_<sup>_k,s/w_</sup> _t_<sup>_j,s_,</sup> which can be pinned down from observed average wages. The Cobb-Douglas labor share can be pinned down from the ratio of an industry’s wage bill over the industry’s value added (which come from national input-output tables). The wage equation multiplier _A_<sup> ̃</sup><sup>_i_</sup> is calibrated such that the aggregate labor allocation reflects the observed average wage in the data from each country. Finally, the fixed utility parameters _η_<sup>_i_</sup> can be calibrated by matching the simulated labor allocations with the labor allocations observed in the data. The simulation methodology is similar to Artuc, Chaudhuri and McLaren (2010) and based on a shooting algorithm. In the simulations, we consider a counterfactual tariff reduction in the manufacturing sector that implies a 30% price reduction in manufactured goods prices.

11
# **References**

- Aghion, P., Caroli, E., & Garcia-Penalosa, C. (1999). Inequality and economic growth: the perspective of the new growth theories. _Journal of Economic Literature_ , 37(4), 1615-1660.

- Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. _Review of Economic Studies_ , 58(2), 277-297.

- Barro, R. J. (2012). _Convergence and modernization revisited_ (No. w18295). National Bureau of Economic Research.

- Barro, R. J., Sala-i-Martin, X., Blanchard, O. J., & Hall, R. E. (1991). Convergence across states and regions. _Brookings Papers on Economic Activity_ , 107-182.

- <mark>Baselgia, E., & Foellmi, R. (2022). Inequality and growth: a review on a great open debate in economics.</mark> _<mark>WIDER Working Paper Series</mark>_ <mark>, (wp-2022-5).</mark>

- Benjamin, D., Brandt, L., & McCaig, B. (2017). Growth with equity: income inequality in Vietnam, 2002–14. _Journal of Economic Inequality_ , 15(1), 25-46.

- <mark>Berg, A., Ostry, J. D., Tsangarides, C. G., & Yakhshilikov, Y. (2018). Redistribution, inequality, and growth: new evidence.</mark> _<mark>Journal of Economic Growth</mark>_ <mark>,</mark> _<mark>23</mark>_ <mark>, 259-305.</mark>

- <mark>Bourguignon, F. (1979). Decomposable income inequality measures.</mark> _<mark>Econometrica</mark>_ <mark>, 47(4): 901920.</mark>

- <mark>Bourguignon, F. (2003). The growth elasticity of poverty reduction: explaining heterogeneity across countries and time periods. I</mark> n T. S. Eicher & S. J. Turnovsky (Eds.), _Inequality and Growth: Theory and Policy Implications_ . MIT Press.

- Bui, T. P., & Imai, K. S. (2019). Determinants of rural-urban inequality in Vietnam: Detailed decomposition analyses based on unconditional quantile regressions. _Journal of Development Studies_ , 55(12), 2610-2625.

- <mark>Cerra, M. V., Lama, M. R., & Loayza, N. (2022). “Links between growth, inequality, and poverty: a survey”. In Cerra et al. (2022). (Eds.).</mark> _How to Achieve Inclusive Growth_ . United Kingdom: Oxford University Press.

- <mark>Chen, S., & Ravallion, M. (2010). The developing world is poorer than we thought, but no less successful in the fight against poverty.</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>125</mark>_ <mark>(4), 1577-1625.</mark>

- Dang, H. A. (2012). “Vietnam: A widening poverty gap for ethnic minorities. Indigenous Peoples, Poverty and Development”. In Gillette Hall and Harry Patrinos. (Eds.) _Indigenous Peoples, Poverty and Development_ . Cambridge University Press.

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The paper can also be read as a cautionary note against knowledge management that is based on counting analytical tasks. For quite some time, it has been a common practice at the World Bank to try to align the number of active ASA budget codes with some “capacity indicator” established in a relatively crude way, based on what was delivered in previous years. In an institution like the World Bank there are simply too many analytical products underway at any point in time for this to be an effective approach, especially when a large share of such products is not even “visible” through information systems.

Instead, the methodology and findings of the paper can be seen as a call for a different knowledge management approach, one that relies on stronger information systems, a better alignment of incentives, and regular monitoring and evaluation based on statistical analysis.

Starting with information systems, a single identifier for analytical tasks would allow connecting data on all the resources they use, all the outputs they deliver, all the downloads they receive, and all the feedback from internal and external clients. Budget identifiers (P-codes) are a practical way to connect information that is currently scattered across various disconnected systems. At present, no TF can be created in the World Bank without a link to a P-code. Arguably, it should be the same for every document entered into the IB and OKR digital platforms.

The process could be hardwired, facilitating an automatic linking of the three databases used in this paper. A similar approach could be used to connect budget codes with the user feedback collected from country engagement surveys. Proceeding this way, a time will come when it will be possible to conduct data-driven studies like the one in this paper without having to go through a laborious reconciliation of entries.

On incentives, thousands of analytical tasks running in parallel call for decentralized management. But knowledge products differ in the objectives they serve, their expected outcomes, and the risks they pose to the World Bank. Ideally, the decision maker in each case should be the unit that benefits the most from a high-quality analytical product and suffers the most from a flop. This is the approach currently used by the World Bank for research products, which are mainly managed by DEC with an eye on its academic standing and without much interference from other units.

To some extent, the current Accountability and Decision-Making (ADM) framework does the same for other analytical tasks, spelling out clear protocols on how the requesting unit and the responsible unit should interact over critical steps in the process. But the ADM’s focus is more on processes rather on incentives. A second practical recommendation of this paper would thus be to use its proposed tiering of analytical tasks to better align responsibilities in the case of high-return-high-risk tasks, and to streamline processes in the other cases (table 5).

29
areas of better economic opportunities. In specific circumstances, territorial interventions aimed at turning around the economy of a region may be the best approach to improve the livelihoods of the poor.

REFERENCES Bourguignon, Francois, Francisco Ferreira, and Phillippe Leite. 2002. “Beyond Oaxaca-Blinder: Accounting for Differences in Household Income Distributions across Countries.” World Bank Policy Research Working Paper 2828.

Jalan, Jyotsna and Martin Ravallion (2002) “Geographic Poverty Traps? A Micro Model of consumption Growth in Rural China,” _Journal of Applied Econometrics_ , **17** : pp. 329-346.

Kanbur, Ravi. 2006. “The policy significance of inequality decompositions” _Journal of Economic Inequality,_ 4(3): pp 367-374.

Nguyen, T.; J. Albrecht; S. Vroman; and M. Westbroo (2007): “A quantile regression decomposition of urban–rural inequality in Vietnam”. In: Journal of Development Economics 83 (2007) 466–490.

Ravallion, M. and Q. Woodon (1999): “Poor areas or only poor people? Journal of Regional Science”, Vol 39, No. 4, 1999, pp.

World Bank (2005) “Ecuador Poverty Assessment”. Mimeo World Bank (2008) “Regional Study on Welfare Disparities Across and Within Regions in LAC”. Office of the Chief Economist.

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Energy inflation reached 23.3 percent (y-o-y) in the urban areas and 22.7 percent in rural areas in the first 11 months of fiscal year 2022 compared to 3.1 percent in urban areas and 5.6 percent in rural areas during the same period in fiscal year 2021. The increase in global commodity prices, accompanied by the rupee depreciation, resulted in an increase of domestic prices of petroleum products in Pakistan. In addition, the increase in electricity tariffs by the government also resulted in elevated energy prices. Figure 3 summarizes the recent evolution of energy prices in Pakistan.

**Figure 3: Energy Inflation in Rural and Urban Areas in Pakistan**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>-10<br>Energy Urban (YoY) Energy Rural (YoY)<br>Energy Inflation<br><!-- End of picture text --> Source: Inflation Monitor, State Bank of Pakistan (SBP) Figures 1 to 3 provide the macro situation of inflation in the country. But do this inflation and its components affect all households homogenously regardless of which income groups they belong to? This requires estimating the household level and, subsequently, decile-specific inflation rates in Pakistan. How this is done is discussed in the next section.

# **1.2. LITERATURE**

The literature on the relationship between inflation and household welfare is fast growing. Several studies have found the cumulative impact of inflation dispersion across the welfare distribution and other regional and demographic dimensions to have resulted in large disparities over a longer time horizon (Baez et al, 2021). The inflation heterogeneity and its subsequent impact on poverty and inequality in developed and developing countries are well documented (see, for instance, Kaplan and Schulhofer-Wohl, 2017; Oosthuizen, 2013; Arndt et al., 2015). Inflation is typically higher for lower-income households (Kaplan and Schulhofer-Wohl, 2017; Tavares, 2021), and the burden on this segment of the income distribution is mainly driven by substantial increases in the prices of food and non-alcoholic beverages (Baez et al., 2021). The potential reasons for these adverse welfare effects of rising inflation for poor households include 4
power. In the long run, firms’ behavior reshapes the institutions so that, as emphasized in equations (8) and (9), the effect of alternation on governance differs across countries, and the crosscountry regressions that we analyze here would be too blunt an instrument to test the predicted effect of political alternation.

# **III. DEFINING AND MEASURING ALTERNATION IN POWER**

Testing the theory calls for relating measures of expectations about the probability of political alternation, to indicators of the quality of governance. We do not observe expectations, but certain events that occur during political liberalization may have a large effect in shaping expectations and thus proxy for expectations. We assume that the greater the number of alternations in power since the onset of political liberalization, the greater the expectation of political alternation in the _current_ period. We have constructed measures of political alternation for all 27 post–communist countries for which data are available. The measures represent hypothetical answers to the question, What kinds of political turnover are likely to render “corruption contracts” unenforceable?

To define political alternation in a meaningful way, we need to decide three things: Where is the seat of power in the national government? What constitutes a change in the occupant of the seat of power? When can this change be said to constitute a change in ideology, _e.g_ ., a left-wing coalition replacing a right-wing coalition? We consider each question in turn.

In identifying the seat of power, we distinguish _authoritarian, presidential democratic_ and _parliamentary democratic_ systems. At a given point in time, _authoritarian_ countries are countries whose democracy scores are less than or equal to -4 according to the _Polity2_ variable from the PolityIV data base. The _Polity2_ score ranges from -10 (full autocracy) to +10 (full democracy). It is derived by subtracting a scale of autocracy (0 to 10) from a scale of democracy (0 to 10). (Democracy and autocracy are distinct indicators; they do not have common components; see Marshall and Jaggers, 2000, p. 14.) 16
cross-sector interconnections (Timmer et al., 2015; OECD-WTO, 2014; Lenzen et al., 2013), and represent a key statistical tool for macroeconomic analysis that takes the international fragmentation of production into account. New analytical methodologies have been proposed to exploit these data, like those developed by Hummels et al. (2001), Johnson and Noguera (2012) and Koopman et al. (2014). These seminal contributions have significantly improved the investigation of productiondemand linkages and knowledge of the value-added composition of gross trade flows when productions are unbundled across different countries and industries.<sup>1</sup> Nevertheless, relevant questions remain unanswered or, in some cases, are only partially addressed with the above methodologies. In general, bilateral exporter-importer relations and the sectoral dimensions of trade flows are overlooked in these works. This impairs a deeper analysis of the direct and indirect trade linkages between countries and sectors.

We fill this gap with a more detailed examination of the various dimensions of trade flows, which is also useful for arriving at a more precise representation of certain aggregate-level phenomena (e.g. exporting/importing country, region, world). There are several reasons why the bilateral and sectoral dimensions matter. First, firms trade with bilateral partners, even when participating in more complex multi-country production networks, and trade policies are usually implemented at the bilateral and sectoral level. Second, when studying the implications of GVCs, it is relevant to consider the position of a country (or sector) within the production chain and identify its direct upstream and downstream trade partners. This may be relevant in order to geographically map the production networks and analyze the international propagation of macroeconomic shocks, such as exchange rates’ variations and inflation spillovers (Patel et al. 2016, Auer et al. 2017, De Soyres et al. 2018). Deepening the knowledge of these mechanisms is also useful for interpreting the short-term dynamics of trade flows. Finally, macroeconomic shocks and trade policies may produce heterogeneous effects on the different trade components, depending on the extent to which they are involved in GVCs (Ruta, 2017; Hofmann et al., 2018; Cook and Patel, 2018).

Through the decomposition of bilateral trade flows, we also provide a novel measure of the share of trade components related to GVCs, which consistently refines the vertical specialization index proposed by Hummels et al. (2001). This can be important, among other things, to assess how the evolution of GVCs influences

> 1Another strand of the literature has focused on the length of production chains and on how countries and sectors are specialized in the different stages of the production process (Fally, 2012; Antras et al., 2012; Antras and Chor 2013; Wang et al. 2016).

3
**Table 3: Inequality measures**

||Gini|90/10|75/25|Mean log<br>dev|Theil-T|
|---|---|---|---|---|---|
|_Per capita household consumption_||||||
|Total consumption|47.11|7.70|3.02|0.38|0.43|
||(1.06)|(0.36)|(0.13)|(0.02)|(0.03)|
|Non-food consumption|62.72|19.48|5.23|0.77|0.82|
||(1.21)|(1.10)|(0.28)|(0.03)|(0.05)|
|Food consumption|39.17|5.93|2.44|0.26|0.27|
||(0.71)|(0.29)|(0.07)|(0.01)|(0.01)|
|_Per capita cell consumption_||||||
|Total consumption|49.67***|8.72***|3.12|0.42***|0.50***|
||(1.02)|(0.42)|(0.09)|(0.02)|(0.03)|
|Non-food consumption|67.16***|26.39***|5.91***|0.92***|1.00***|
||(1.09)|(1.54)|(0.27)|(0.03)|(0.05)|
|Food consumption|40.39***|6.48***|2.54**|0.27***|0.29***|
||(0.69)|(0.26)|(0.08)|(0.01)|(0.01)|

Source: “Pauvreté et Structure Familiale” (PSF) survey, N=1762, authors’ calculations.

Note: The first panel presents inequality measures computed using per capita household consumption as a measure of individual consumption. The second panel presents inequality measures computed using per capita cell consumption. Bootstrap standard errors (250 replications) between parentheses. *,**,***: difference with per capita household consumption inequality measure is significant at the 10%, 5% or 1% level (see Note 16 and Appendix S2 for details).

39