rafmacalaba/datause-encoder-data huggingface dataset:
been digitized, so we do not have access to that information. For that reason, the level and composition of income in tax records for that period is very different from the most recent 2011 - 2019 period.

Nonetheless, we do observe PIT filings and undistributed corporate profits for those years, and therefore can use the ratio of income in PIT filings + corporate income to total income in recent years to adjust total income the 2003-2010 period. We do not change directly the microdata for the 2003-2010 period. Instead, we adjust the g-percentiles used when merging tax records to survey data at the top. We proceed as follows:

1. We first create the g-percentiles used for the merging process with survey data, and compute for all years and g-percentiles the ratio between the sum of income in PIT filings plus undistributed corporate profits to total income. For 2011-2019, total income will include all forms of withholding that we do not observe in 2003-2010.

2. We then take the average value of these ratios across all periods, for each g- percentile. In Figure G1, we show the average adjustment factor, which we use to scale total income over 2003-2010, together with 95% CI of the mean to illustrate dispersion of the adjustment factor across years. What we observe is that for individuals just on the top 1%, the adjustment factor is on the order of 2 - 2.5 - since most of their income, as we observe in other years, is composed of wage income, we miss a lot of income in 2003-2010 when withholding of wages is not observed. As we move towards higher incomes, the adjustment factor slowly converges towards unit, consistent with the fact that at the very top most of the income is from undistributed corporate profits, which we observe across the entire period.

3. We then use these adjustment factors to scale up total income in each g-percentile in the period 2003-2010. Note that we do not scale them to the average _level_ of income observed in 2003-2010 - we simply scale it considering the ratio of PIT + corporate profits to total income, so the underlying assumption to recover the correct income level is that this ratio is stable over time.

4. Finally, when adjusting our microdata to national account aggregates, we need the composition of total income, since we perform the adjustment componentby-component. We compute the share of each component, by g-percentile, in the period 2013-2019, then apply those shares to g-percentiles in 2003-2010, recovering estimated income components that are then scaled-up to national accounts.

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<!-- Start of picture text --> 90+ HR vate<br>80-8425-88 ME Fra<br>75-79<br>70-74<br>65-69<br>60-64<br>55-59<br>Qa<br>> 50-54<br>2<br>5 45-49<br>oO2 40-44<br>< 35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>59<br>0-4<br>0<br>4 5 4 -3 -2 Population-1 . 0 shares1 (%)2 3 4 5 6<br><!-- End of picture text -->
The paper is structured as follows. The next section provides some background information on the Brazilian case and motivates our approach. Section 3 describes the macro and micro models. The results of our simulations are reported and commented in section 4. The last section summarizes and concludes.

# **2 Background and Motivation**

The main objective of this paper is to assess whether trade reform favors the Brazilian poor. It is therefore important to know who the poor are, where they live, and especially how they earn their living. In addition, it should prove helpful to identify economic trends that have been particularly important for the poor.

Brazil’s per capita income has virtually stagnated for the past 25 years and the very unequal distribution of income has remained more or less unchanged. Accordingly, poverty in Brazil has been roughly constant over the past 25 years (Bourguignon, Ferreira, and Lustig 2005; Verner 2004). In light of the substantial structural changes that have occurred in this period, especially increasing urbanization, a massive decline in agricultural employment, an important educational expansion and demographic changes, this appears “paradoxical”, as Bourguignon et al. (2005) put it. Yet, microsimulation exercises by Ferreira and Paes de Barros (2005) show that each of the features of structural change affects poverty and inequality, but they tend to cancel out each other.<sup>2</sup> Notwithstanding its considerable variation across multiple dimensions, poverty in Brazil is particularly high in rural areas, small and medium towns and the metropolitan peripheries of the North and the Northeast (Ferreira, Lanjouw, and Neri 2001). In 1996, the North and the Northeast accounted for 55 percent of the poor and for 34 percent of the Brazilian population. At the national level, about 20 percent of the population lived in rural areas contributing 35 percent to total poverty.<sup>3</sup> The high poverty rates in rural areas, particularly in the Northeast, are related to this region’s predominance of employment in agriculture. The Northeast has the highest share of agriculture in employment with 34 percent in 2001 compared to only 11.5 percent in the Southeast.<sup>4</sup> According to Ferreira,

> 2 Note that their analysis compares the income distribution of 1976 with the 1996 distribution. For detailed results see Ferreira and Paes de Barros (2005).

> 3 Poverty is measured by the headcount ratio. The poverty figures in this paragraph are taken from Ferreira, Lanjouw, and Neri (2001).

> 4 The figures on agricultural employment are own calculations based on the PNAD 1997 and the PNAD 2001. The PNAD (Pesquisa Nacional por Amostra de Domicílios) is a regularly conducted representative household survey. The sample had a size of about 380 000 individuals in 2001.

5
procedure of getting credit" (Bezanson _et al_ ., 2000). Moreover, private enterprises are at a disadvantage as compared to SOEs, due to their lack of access to both preferential and non-preferential government loans. They must rely on their own capital resources for R&D.

**_Figure 1 R&D Source Distribution in Enterprises in 2002_**

<!-- Start of picture text --> R&D Source Distribution in Enterprises in 2002<br>100%<br>90%<br>other resources<br>80%<br>70% own capital<br>60%<br>50% loans from preferential<br>government treatment<br>40% loans not from preferential<br>30% government treatment<br>20% government<br>10%<br>0%<br>total state- non-state foreign<br>enterprises enterprises enterprises<br><!-- End of picture text --> Source: General Statistics Office 2004 Although a range of studies<sup>4</sup> have pinpointed a need by firms for technological services for innovation, this is not met at this juncture by R&D organizations and universities. The situation is much worse outside of Hanoi and Ho Chi Minh City, where there is complete lack of supply for some services (Miehlbrandt, 2002).

Among research institutes and universities, the share of cooperation with enterprises is low. As depicted in Table 2, a survey of linkages between R&D institutes, universities and industries reveals that universities are doing even less cooperation with firms than research institutes are.

9
# **Foreign Job Opportunities and Internal Migration**

# **in Vietnam**

# **Emiko Fukase**<sup>**1**</sup>

# **The World Bank**

JEL Classifications: F16, F23, O15 Keywords: foreign ownership, internal migration, job, Vietnam Sector Board: Poverty Reduction and Economic Management

> 1 Development Economics Research Group, World Bank. I would like to thank Wim P. M. Vijverberg for guidance and very useful comments at each stage of this research; the late Robert E. Lipsey for discussions and insightful comments for an earlier draft of this paper; Zadia M. Feliciano, Jonathan R. Nelson and Jing Sun for comments and suggestions; Chad P. Bown and Will J. Martin for guidance; Tran Thi Van for help in obtaining the data; the General Statistics Office of Vietnam for providing the data; and anonymous referees for very useful and detailed comments. Any remaining errors are mine.
wage elasticity of labor demand. Our calculations assume that the distribution of earnings (wage rate times employment) does not change above the Basic Income Guarantee. This will hold if the wage elasticity of labor demand is -1. This seems to be a reasonable assumption for the average wage elasticity based on past research on labor demand in the US.<sup>7</sup> However, there is undoubtedly variation around this mean elasticity, so we can expect (positive and negative) effects on earnings for many “non-Basic-Income-Guarantee” workers not captured by our analysis. Third, it is of interest to see how the size of this market compares with the current count of undocumented migrants. However, this count is uncertain, with current estimates in the (wide) range of 11 million to 22 million. The lower bound is derived from the Census Bureau’s survey-based estimates of the total migrant population (foreign-born people who have not become naturalized citizens) after deducting the count of legal migrants based on administrative data.<sup>8</sup> There are naturally concerns that illegal migrants will be reticent to reveal this fact, and may instead report that they are citizens; some illegal migrants may also be outside the sample frame of the household surveys. The upper bound of 22 million is based on Fazel-Zarandi et al. (2018), who used a time-series accounting of flows with demographic modeling. There is a continuing debate on the size of the undocumented migrant population in the US.<sup>9</sup>

**Calibrating migration and other costs** Demand curve (3) depends on parameter Γ _id_ , which represents the out-of-pocket cost of moving to the US (travel expenses to the US and back and visa fees). A reasonable assumption for the out-of-pocket cost of moving (and returning) is US$4,000, covering the costs of obtaining a visa as well as travel and relocation costs.<sup>10</sup> In addition, Clemens et al. (2019) provide a useful clue to the plausible inner range of values for migration costs by comparing the wages in the mainland US of migrants

> 7See, for example, Beaudry et al. (2018), who find a wage elasticity of labor demand of -1 based on the variations across cities and industries over 40 years.

> 8The methods and results using this method are described in Passel and Cohn (2018).

> 9Capps et al. (2018) question the Fazel-Zarandi et al. (2018) estimates. Rodilitz and Kaplan (2021) find similar results to Fazel-Zarandi et al. (2018) using data from Mexican surveys of prior illegal migrants to the US.

> 10We took an approximate amount of US$1,700 for processing of an H1B visa.

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4 machines and increase their demand for skilled labor (Acemoglu, 2003; Behrman, Birdsall, and Szekely, 2000; Harrison and Hanson, 1999). Furthermore, many developing countries are rich in natural resources. Trade would stimulate this sector rather than labor-intensive ones.

From a dynamic perspective, economic growth is key to sustained poverty alleviation and trade liberalization is argued to lead to the needed increases in productivity to sustain growth. Freer trade provides greater incentives for investment, the benefits of scale and competition, limitation on rent-seeking activities favored by trade restrictions and openness to new ideas and innovations (Berg and Krueger, 2003; Grossman and Helpman, 1991; Lucas, 1988).

Empirically, cross-county growth regressions have produced mixed evidence. Using either trade shares or indices of trade liberalization, Sachs and Warner (1995), Edwards (1997), Frankel and Romer (1999) Dollar and Kraay (2001) and Lee et al. (2004) find a positive association between liberalization and growth. However, noting that trade liberalization often occurs at the same time than other policy reforms, Rodriguez and Rodrik (2001) have criticized the literature which associates trade openness measured by trade shares with more rapid growth. Considering trade policy measures instead, Harrison (1996), Edwards (1997), and Irwin and Terviö (2002) find a significant negative effect of trade on economic growth, while Vamvakidis (2002) still shows that trade enhances growth.

Examining the effect of trade on poverty more directly, Krueger (1983) shows in her case studies that developing countries’ manufactured exports were, indeed, labor-intensive, but that the employment effects of freer trade policies were generally rather limited. A number of cross-country studies on poverty, while not dealing with trade explicitly, incorporate trade openness as a control variable and showed similar results: at best the benefits of greater trade openness seem to have bypassed the poor.

Looking at developing countries only, Beck et al. (2007) and Kpodar and Singh (2011) find no effect on the poor. Similarly, Dollar and Kraay (2001) find a lack of any evidence of an impact of openness on the income of the poorest quintile in a sample of advanced and developing economies. By contrast, looking at a sample of developing countries, Guillaumont-Jeanneney and Kpodar (2011) find a negative relationship between trade openness and the income of the poorest quintile. Similarly, Singh and Huang (2011) focusing on a sample of sub-Saharan African countries suggest that greater trade openness increases headcount poverty, widens the poverty gap, and reduces the income of the poorest quintile.
There is evidence that most of the recent growth in Chinese exports is due to foreign firms. Several studies also argue that foreign firms, which are typically engaged in processing trade activities, drive the skill content upgrading of China’s manufacturing exports (Amiti and Freund 2010; Xu and Lu 2009). Moreover, estimations of growth equations indicate that the income gains from export performance and export upgrading are confined to the improvements made by domestic firms. Jarreau and Poncet (2012) find that the positive association between GDP per capita growth and export sophistication at the province level is limited to ordinary export activities undertaken by domestic firms. These results, together with the results emphasized by Amiti and Freund (2010), suggest that the export activities of foreign firms in China do not matter for the economic growth of Chinese provinces once domestic exports have been controlled for. However, although there are no direct gains from the export upgrading of foreign firms in terms of GDP per capita, there may be room for foreign firms to indirectly affect domestic firms through export spillovers. There are two channels through which export spillovers can act. Foreign firms can provide specific information on export markets that can help domestic firms to reduce their fixed export costs (e.g., information about the tastes of foreign consumers or distribution networks abroad). Foreign export spillovers can also be linked to the mutualization of fixed or variable export costs (participation in international fares, marketing, transport costs). Thus, it is worth investigating whether foreign firms in China act as export catalysts that foster the creation of new export transactions by domestic firms.

Using panel data from Chinese customs that record provincial export flows for the period 1997-2007 by product, destination country, type of firm, and type of trade, this paper shows that the capacity of Chinese domestic firms to begin exporting new products to new markets responds positively to the export activity of neighboring foreign firms. These export spillovers are found to be very specific both in terms of activity and in terms of the geography of exports. Furthermore, the effect of these export spillovers exhibits a spatial decay consistent with the 3
monitoring mechanisms. Pressure from these stakeholders has created political costs that have forced the government, however reluctantly, to incorporate their requests.

The proposed political economy framework can be replicated beyond Chile and to other human rights policies beyond NHRAPs. Some governments fully engage in policies pursuing the realization of human rights, fighting impunity for human rights violations, and ensuring the inclusion of rights-based principles in their domestic policies. Others ignore them completely, violate human rights, and provide impunity to perpetrators. Which approach a given government takes likely reflects consensus, cost/benefit considerations, and capacity.

In particular, some of the cost/benefit considerations when it comes to policy decisions on human rights include the desire to mobilize supporters, divide the opposition, encourage/discourage the judiciary or legislature from taking independent decisions, preventing social unrest, and complying with international pressure. Context matters too: political regime, proximity to elections, domestic and international financial or political crises, and the presence of separatist or nationalist movements, may all tilt the cost-benefit balance. Which set of benefits dominate likely varies across context, time and specific issues. A political economy framework provides a simple, hypothesis-based way to approach this open question. An area of further analysis refers to process, namely, how countries can advance the delivery of human rights action plans in a more piecemeal fashion when these conditions do not hold (as it is being observed in other developmental agendas such as climate change).

# **References**

Acemoglu, D., and Robinson, J. A. (2006). Economic backwardness in political perspective. American Political Science Review, 100(1), 115–131.

- Armijo, L. E., Biersteker, T. J., and Lowenthal, A. F. (1994). The problems of simultaneous transitions. Journal of Democracy, 5(4), 161–175.

- <mark>Baeza, A (2019). "Ministerio de Justicia ingresó Plan Nacional de DDHH a Contraloría para toma de razón " https://www.latercera.com/nacional/noticia/ministerio-justicia-ingresoplan-nacional-ddhh-contraloria-toma-razon/810952/</mark>

- <mark>Biblioteca Nacional del Congreso de Chile. 2018. H</mark> istoria de la Ley N° 20.885.

- https://www.bcn.cl/historiadelaley/fileadmin/file_ley/4687/HLD_4687_37a6259cc0c1dae 299a7866489dff0bd.pdf

- Björkdahl, A., and Mannergren Selimovic, J. (2015). Translating UNSCR 1325 from the global to the national: protection, representation and participation in the National Action Plans of Bosnia-Herzegovina and Rwanda. Conflict, Security and Development, 15(4), 311335.

- <mark>Blofield, M., Ewig, C., and Piscopo, J. M. (2017). The reactive left: Gender equality and the Latin American pink tide.</mark> _<mark>Social Politics: International Studies in Gender, State and Society</mark>_ <mark>,</mark> _<mark>24</mark>_ <mark>(4), 345-369.</mark>

- Borzutzky, S. (2017) Human Rights Policies in Chile: The Unfinished Struggle for Truth and Justice. Palgrave McMillan, Cham, Switzerland.

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# **Figure 12. Infrastructure indicator**

_The quality of infrastructure in the most of the region is substantially below OECD average. Port container traffic is limited, highlighting the region’s reliance on road, air, and rail transport. The quality of air and road transport infrastructure remains well below the OECD average in most of the region. The region is energy intensive and heavily reliant on non-renewable energy_ .

# **A. Overall infrastructure quality**

# **B. Port container traffic**

<!-- Start of picture text --> Score Range Median Container traffic per GDP<br>6 20<br>5 15<br>4<br>10<br>3<br>5<br>2<br>1 0<br>0<br>C. Quality of air transport D. Quality of roads<br>Score Range Median OECD median Score Range Median OECD median<br>6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>E. Energy use intensity F. Share of renewable energy<br>Energy use per GDP Percent Range<br>200 Median<br>150 70 EMDE median (energy exporter)<br>60 EMDE median (energy importer)<br>100<br>50<br>50<br>40<br>0 30<br>20<br>10<br>0<br>ECA ECA<br>energy exporter energy importer<br>Asia Pacific Africa<br>Eastern Europe Central Europe Central South Caucasus Western Balkans East Asia & Middle East & N. Africa Latin America & Caribbean South Asia Sub-Saharan Europe & Central Asia<br>Asia Asia<br>Eastern Europe Central Europe Central South Caucasus Western Balkans Eastern Europe Central Europe Central South Caucasus Western Balkans<br>OECD<br>Africa Pacific<br>Sub-Saharan East Asia & Europe & Central Asia Middle East & N. Africa South Asia Latin America & Caribbean<br><!-- End of picture text --> Sources: EBRD (2015a), Haver Analytics, World Bank, World Economic Forum. A. The score is overall quality of infrastructure. The score from 1 to 7 (best). Investment is the share of fixed capital formation as a percent of GDP. OECD average is the average investment share of OECD countries from 1990 to latest.

B. Sum of container port traffic (TEU: 20 foot equivalent) per current U.S. dollar GDP in millions in 2014. C.D. The score is from 1 to 7 (best). The OECD and EMDE average are the simple average of all the countries in the respective subgroupings.

E. Regional aggregates. Data are for 2014 or latest available. GDP data are in constant 2011 “international dollars.” F. Share of renewable energy consumption as percent of total final energy consumption in 2012. EMDE averages are the simple average of all the countries in the respective subgroupings.

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Table 15: Trade, Legal Origins <u>and Corruption, Rich Countries vs. Poor</u> Dep. Var.: log of average trade flow

|||All|Rich|Partner|Poor|Partner|
|---|---|---|---|---|---|---|
||(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|(5)<br>OLS|(6)<br>IV|
|Different Legal Origin|-0.08|-0.09|-0.25|-0.19|-0.1|-0.11|
||[1.70]|[1.78]|[4.42]|[3.33]|[2.01]|[2.08]|
|Control of Corruption|0.27|0.28|0.17|0.07|0.29|0.3|
||[25.11]|[12.65]|[11.89]|[2.19]|[23.52]|[11.17]|
|log distance|-1.08|-1.08|-0.83|-0.85|-1.14|-1.14|
||[37.03]|[36.24]|[24.99]|[24.68]|[33.24]|[33.25]|
|log product of GDP|1.13|1.12|1.12|1.13|1.14|1.14|
||[108.66]|[96.37]|[91.33]|[87.20]|[99.52]|[91.34]|
|log product of land area|-0.21|-0.21|-0.19|-0.19|-0.22|-0.21|
||[23.48]|[22.34]|[17.79]|[17.38]|[21.76]|[20.44]|
|no. of landlocked|-0.35|-0.35|-0.27|-0.25|-0.32|-0.33|
||[7.84]|[7.84]|[5.76]|[5.37]|[6.84]|[6.85]|
|common border|-0.03|-0.03|-0.13|-0.11|-0.13|-0.14|
||[0.55]|[0.64]|[2.55]|[2.04]|[2.59]|[2.64]|
|common border|0.91|0.91|0.12|0.17|1.09|1.1|
||[6.33]|[6.33]|[0.76]|[1.02]|[6.93]|[6.93]|
|common language|0.62|0.62|0.49|0.48|0.63|0.63|
||[11.05]|[11.06]|[7.93]|[7.73]|[10.10]|[10.11]|
|currency union|2.15|2.15|1.2|1.03|2.09|2.1|
||[8.59]|[8.60]|[11.54]|[9.00]|[8.33]|[8.33]|
|Observations|77594|77594|39961|39961|69014|69014|
|R^2|0.67|0.67|0.77|0.77|0.64|0.64|
|First stage R^2||0.40||0.32||0.35|
|% increase in trade due to<br>1s.d. institution|70%|74%|34%|13%|71%|74%|

Notes: t-values in Brackets. Standard errors are calculated taking into account country-pair clusters. A constant and year dummies are not reported. IV estimators use the sum of absolute latitude as an instrument for Control of Corruption. The Rich Partner subsample includes all country pairs where at least one is "rich". The Poor Partner subsample includes all country pairs where at least one is "poor". "Rich" countries are defined as those with PPP GDP per capita larger than 10,000 dollars in 2000 and the rest are "poor". The percent increase in trade due to 1s.d. institution is calculated as exp(coef*sd)-1 and takes into account different standard deviations in each subsample. Summary statistics for the institutional quality variable in each subsample are reported in Table 16.

50