rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Vietnam represents one of the great international success stories in achieving poverty reduction during recent decades. Concerns have been raised, however, that the pace of poverty reduction has slowed in recent years. In response, the Government of Vietnam has launched a variety of policies and initiatives to restore momentum in poverty reduction, and it is perceived that in order to buttress the impact of these policy measures, programs must become better targeted at the poor. One route towards better targeting is to exploit geographic variation in welfare outcomes. International evidence suggests that transfers will have a larger impact on poverty when they are targeted to finely delineated communities and localities (Baker and Grosh, 1994; Bigman and Fofack, 2000; Elbers _et al._ 2007).

Geographic targeting of the poor may be most effective in those settings where economic growth originates in areas with high factor productivity, which then results in the emergence of lagging regions. The 2009 World Development Report published by the World Bank, entitled “Spatial Disparities and Development Policy”, demonstrates that economic activity and growth is spatially concentrated in many developing countries due to agglomeration benefits deriving from networks, technological change and human capital externalities. The report argues that countries should embrace this development rather than insist on geographically balanced growth. However, the report further argues that policy makers should explore opportunities to ensure that the benefits from such spatially concentrated growth are distributed broadly across the population. One means to that end is to promote the movement of people; barriers to trade and factor mobility should be removed. Another is to implement spatially targeted re-distribution policies that permit countries to support these growth processes while pursuing an equitable distribution of wellbeing.

A major obstacle to detailed spatial targeting is scarcity of reliable information on welfare levels at the local level. Estimation of poverty in small geographical units such as districts and communes poses considerable data demands. Income or expenditure data are commonly used as indicators of economic wellbeing and such information are routinely collected in household sample surveys. While representative at a national level, their sample 2
in the dataset has been collected from original source government publications such as the _Federal Register_ ( _Official Journal of the European Communities_ ), where we are able to track the dates of investigations, measures imposed, countries affected, and 6-digit HS products that were targeted.

Our estimation examines the export growth path for products targeted by an antidumping measure for multiple years around the policy’s actual imposition. For both U.S. and EU antidumping measures examined in the estimation, we identify the focal year _t_ as the _initiation_ year of the antidumping investigation, as opposed to the year the final measure was actually imposed, though frequently they will be the same. One motivation for this choice is that there has been evidence in prior research that even antidumping investigations that do not end in imposed measures can have a destructive effect on imports, due to the uncertainty as to the final disposition of the case (Staiger and Wolak, 1994). Nevertheless, we expect that this decision could lead us to estimate a differential impact of Chinese export growth with respect to the timing of U.S. versus EU measures, and in some specifications we therefore allow for the lagged imposition of policies ( _t −_ 1 _, t −_ 2) to affect contemporaneous export growth.

# **4 Empirical Results**

## **4.1 of trade**

Do U.S. and EU antidumping duties deflect Chinese and Indian exports to third (non-U.S., non-EU) markets? Our difference-in-difference deflection estimates, presented in table 8, indicate no robust evidence of statistically significant deflection. In fact, rather than an increase in exports to third markets, U.S. antidumping duties may be associated with a “chilling” effect of a decrease in Chinese export growth to such alternative markets. With respect to EU trade policy, the only economically and statistically significant finding is a chilling effect associated with EU duties on steel products.

Our baseline specification (1) examines the response of the difference between China’s and India’s yearly growth of the volume of trade to the _contemporaneous_ initiation of an antidumping investigation that resulted in duties imposed by the U.S. and EU against China and/or India, respectively. At this short time horizon, the difference between the within-year policy changes against China and India has no effect on the difference in the growth of the volume of exports to alternative markets. Given that it could take over a year for a U.S. or EU antidumping investigation to result in the imposition of a definitive import restriction, the finding of no contemporaneous response in not entirely surprising. Our second specification (2) utilizes the same dependent variable, but includes lags of the difference in the change in the U.S. and EU duties, respectively. We include lags in case the full impact of a 14
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|Manufacturing|12.2|14.0|14.0|12.1|
|---|---|---|---|---|
|Extraction|1.1|1.0|1.1|1.7|
|Construction|2.4|3.1|3.7|4.3|
|**Tertiary Sector**|**61.9**|**61.2**|**62.5**|**65.6**|
|**_Private_**|**_42.0_**|**_42.9_**|**_44.0_**|**_46.7_**|
|Commerce|16.8|17.1|17.3|16.1|
|_Services:_|_25.2_|_25.8_|_26.7_|_30.6_|
|Transportation|4.7|4.5|4.0|4.1|
|Post & Telecoms|1.9|2.4|3.5|6.7|
|Education|5.3|4.7|3.4|3.8|
|Health/Social Serv.|1.8|1.8|1.5|1.3|
|Hospitality|1.0|1.0|1.0|0.8|
|Financial & Business Services|5.0|5.5|6.7|8.0|
|Real Estate|4.9|5.4|5.8|5.5|
|**_Government_**|**_19.9_**|**_18.3_**|**_18.5_**|**_18.9_**|
|Public Administration|8.8|8.4|6.9|6.3|
|Net Taxes|11.1|9.9|11.6|12.6|
|**Total**|**100.0**|**100.0**|**100.0**|**100.0**|

Source: Senegal country sources The growth dynamics behind the trajectory of Senegal’s economy is examined in comparison with WAEMU and Sub-Saharan Africa (SSA) representative economies. Compared to an average of 0.9 percent during 1990-1994, Senegal’s economy grew at a robust rate of 4.5 percent per year in the immediate post-reform period from 1995 to 2005, but slowed to 3.4 percent between 2006 and 2013. While growth of the tertiary sector receded slightly, rates in the primary and secondary sectors fell by nearly 50 percent between 1995-2005 and 2006-2013. The domineering role of services in Senegal stands out in comparison with WAEMU and SSA. Whereas the services sector remained the dominant growth pole in Senegal, it was the industrial sector that played the role in the representative WAEMU economy. Given Senegal’s membership in the union, it thus appears that Senegal is quite apart structurally from the remaining members of the union, which is reasonable considering Senegal’s unique historical antecedents. In contrast, the growth pole shifted directly from agriculture to services in SSA in reflection of the general shift directly from primary sector to tertiary sector occurring in the region (table 2).

**TABLE 2: ANNUAL REAL GDP GROWTH BY SECTOR, 1995-2013**

||**Sen**|**egal**|**WA**|**EMU**|**S**|**SA**|
|---|---|---|---|---|---|---|
|**Growth rate**|**1995-2005**|**2006-2013**|**1995-2005**|**2006-2013**|**1995-2005**|**2006-2013**|
|GDP|4.5|3.4|3.9|4.1|4.3|4.7|
|Agriculture|4.8|2.8|4.2|3.5|5.1|4.9|

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_9_ _WATER WHEN IT COUNTS_ information on agricultural production and irrigation practices in the study area. Each household is asked about the plots they cultivate in the scheme, as per the listing.<sup>4</sup> Table 1 shows the number of plots and households surveyed at every round.<sup>5</sup> TABLE 1—NUMBER OF OBSERVATIONS PER ROUND

|||||Round||||
|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|
|Number of listed households|72|120|121|123|124|135|138|
|Number of listed plots|90|153|156|158|158|158|157|
|Average number of plots farmed per household|1.25|1.27|1.34|1.39|1.45|1.41|1.43|
|Share of listed households surveyed|90.3%|86.7%|91.7%|84.6%|86.3%|86.7%|74.6%|

Notes: Two of the three schemes were included in round one, with the third added for round 2, causing the substantial increase in households and plots from round 1 to 2. During each round, households that weren’t surveyed in any previous round are also asked about missed rounds retroactively, so reponse rates are expected to be higher for earlier rounds, as there were multiple occasions for households to be asked about their respective periods.

We collect detailed information on the cultivation patterns on all plots farmed by that family. The frequent surveys are timed roughly with the length of cropping cycles for typical crops. We use the planting and harvest dates to estimate the growth stage in which the crops are each week, using information on average growth stage length for each crop in the region. Combining this with information on weekly water requirements in each growth stage allows us to calculate an estimate of the weekly water requirements (mm/week) for all plots. We further exploit questions in which we ask farmers to declare whether they perceive water to be sufficient for production and whether they had a dispute concerning water for each of their plots with another farmer over the period of months between the survey rounds. A total of seven rounds of survey data were collected

> 4Prior to each round of agriculture surveys, the listing is updated to track any changes of plot ownership within the scheme as well as in and out migration. In limited occasions plots are divided up or merged leading to small changes in total number of plots across survey rounds. The numbers listed refer to the number of households and plots in round 2,

> 5We addressed non-response in a given round by re-surveying these same households in subsequent survey rounds and collecting retrospective data corresponding to the previously missed interview. The main reason for non-response are people farming their rain-fed plots during the rainy season, which can be far from the irrigation schemes.
These structural changes were accompanied by an extensive transformation of the ownership of banks. The driving force of this transformation was the entrance of several new strategic investors: foreign financial institutions. At the end of 2010, the number of foreign-owned banks was 15,<sup>4</sup> of which 9 were subsidiaries of foreign banks.<sup>5</sup> For comparison, at the end of 2004 the number of foreign-owned banks was eight, only four of which were subsidiaries of foreign banks. At the end of 2010, foreign capital accounted for 72.9 percent of the overall capital of the banking system (an increase of 25.4 percentage points from the end of 2004<sup>6</sup> ). Thus, at the end of 2010, foreign-owned banks hold 92.9 percent of total assets, 95.4 percent of total loans, and 95.8 percent of total deposits of the banking system. This market consolidation and ownership transformation is interesting in and of itself, but it should be viewed in a wider context, having in mind the benefits of the organizational and managerial changes that, in the long run, should increase the level of the stability and competitiveness of the overall banking system.

In general, the shift to foreign ownership did not bring about major structural changes in the sources of financing of the banking system. Most of the banks still tend to foster their growth primarily on domestic sources of financing: on deposits from residents (Chart 1 and Chart 2). The orientation of banks toward domestic financing proved to be essential in maintaining the stability of the domestic banking system in conditions of global financial disruptions.

**Chart 1. Structure of banks' liabilities**

**Chart 2. The role of nonresidents in financing banks' activities**

<!-- Start of picture text --> 100%<br>17.0% 11.4% 10.6% 40,000 14.0%<br>90% 11.6%<br>80%70% 3.8%8.2% 8.9%3.0% 10.7%2.8% 35,00030,000 8.5% 10.2% 9.7% 10.3% 9.4% 10.6% 12.0%10.0%<br>60% 25,000<br>50% 8.0%<br>40% 69.7% 70.0% 69.9% 20,000 6.0%<br>30% 15,000<br>20% 10,000 4.0%<br>10%0% 1.3% 6.7% 6.0% 5,000 2.0%<br>0 0.0%<br>2004 2005 2006 2007 2008 2009 2010<br>2004 2005 2006 2007 2008 2009 2010<br>Deposits of banks and other financial institutions<br>Deposits of nonfinancial entities<br>Other liabilities Liabilities to nonresidents (denar million - rhs)<br>Borrowings<br>Capital and reserves Liabilities to nonresidents / Total liabilities (lhs)<br><!-- End of picture text --> Source: NBRM 4 Foreign-owned banks are considered those where foreign shareholders own more than 50 percent of the total number of issued common shares.

5 Subsidiaries of foreign banks are considered banks predominantly owned by foreign banks (more than 50 percent of the total number of issued common shares in a bank is owned by а foreign bank).

6 The biggest growth of the foreign capital share in the overall capital of the banking system was registered in 2007 (13.0 percentage points), followed by 2008 with 5.2 percentage points and 2005 with 5.0 percentage points. A decrease by 5.7 percentage points in the foreign capital share was observed in 2009.

4
<!-- Start of picture text --> OUTPUT<br>OUTPUT<br>X w N<br>oF cn .P c+ N .-<br>CDCD<br>C D<br>I-l,<br>'S,, 0<br>l<br>Cl 4 4~~~~~~ 4<br>Cp ~ - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~<br>4 +<br>4 .~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 4.~~~~~~~~~~~P<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>90<br>80 44 477 50 43<br>55<br>70<br>64 66<br>71<br>60<br>50<br>40<br>36<br>30 52 2 40 ><br>39 26<br>20 6 30 ». 7<br>29<br>10 8<br>10 10 I——_ 3 9 9 q | r<br>0 = as | 6 6 >5 7 6: as<br>1995-99 2000-08 1995-99 2000-08 1995-99 2000-08 1995-99 2000-08 1995-99 2000-08 1995-99 2000-08 1995-99 2000-08 1995-99 2000-08<br>EUIS NMS Ace Upper Middle Developing INDIA CHINA USA<br>Agriculture Industry "Service<br><!-- End of picture text -->
The withdrawal of international troops and the decline in foreign aid adversely affected consumer and investor confidence and resulted in a sharp decline in domestic demand. New firm registrations fell by almost half between 2012 and 2016, across most sectors (figure 2a). The growth rate of GDP declined from an average of nearly 11 percent per year between 2007 and 2012 to barely 2.1 percent by 2016. (figure 2b).

Figure 2. Economic activity in Afghanistan Figure 2a. Firm registration by sector <!-- Start of picture text --> 9000<br>8000 Agriculture Manufacturing<br>Construction Other industries<br>7000<br>Services<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>2012 2013 2014 2015 2016<br>Figure 2b. GDP growth rates by sector<br>25<br>20 Agriculture<br>Industries<br>15 Services<br>GDP growth<br>10<br>5<br>0<br>-5<br>-10<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Number of new firm registrations<br>Annual growth (percent)<br><!-- End of picture text --> Source: World Bank (2018).

6
avoid the downward bias associated with the second de fi nition in the presence of prohibitive tariff barriers.

As before, the slope of the import demand functions were obtained from the estimation of import demand elasticities in Kee et al. (2004). Using the notation in Kee et al. (2004) it is easy to show that dmp,n,c/dpp,n = −annGDPp − m<sup>2</sup> p,n,c<sup>/GDPp−mp,n,c.If the formula in (22) were to be applied</sup> as such, it could be severely bias. Indeed, it would include in the MA-OTRI of country c levels of protection on products that c does not export at all. Thus in order to correct for this, the formula in (22) is calculated conditional on the particular product representing more than 0.1 percent of country c exports to the world. Obviously MA-OTRI can be calculated bilaterally to obtain the level of trade restrictiveness that country p imposes on exports of country c. Again the formula after the fi rst equality in (22) can be used to answer this question. But instead of summing over n and p one would obviously only sum over p to obtain MA-OTRIc,p. By de fi nition MA-OTRIc,p = OTRIp,c. To be economically meaningful, both would need to be estimated conditional on country c’s export bundle to the world.

# 4 Data

Tariff data comes from different sources. The main sources are the WTO’s IDB and UNCTAD’s Trains. Because these sources rarely provide ad-valorem equivalents of speci fi c tariffs, we use recent computations available through the MAcMap database, developed jointly by ITC (UNCTAD-WTO, Geneva) and CEPII (Paris).<sup>13</sup> The tariff data is for the most recent year for which there is data available between 2000 and 2004. For more than half the countries the base year is 2003 or 2004 and for only three countries the data is 2000 (Peru, Kazakhstan and Egypt). MAcMap also provided us with a complete dataset of unilateral, bilateral and regional preferences which is an important component when estimating MA-OTRIs.

Calls for improving the quality of NTBs data collection are regularly done (see Deardorff and Stern, 1997). This paper is no exception. As can be seen from the fi rst column of Table 1 the best international data available to us (UNCTAD’s TRAINS) has quite an incomplete country coverage (e.g., tariff data is available for almost twice the number of countries for which NTB data

> 13 See Bou ̈et et al. (2004) for a detailed description.

14
**Figure 8: Regional variation in female employment in Bangladesh between 2005 and 2013**

<!-- Start of picture text --> (0.13,0.25]<br>(0.05,0.13]<br>(-0.01,0.05]<br>[-0.19,-0.01]<br><!-- End of picture text --> Source: Calculations based on Labor Force Surveys for Bangladesh.

# Regional Wage Dispersion

The local labor market methodology used in many recent studies depends critically on the identification of distinct local labor markets. One “heuristic” measure of the presence of local labor markets is the standard deviation of wages for different types of workers across regions overtime. This measure is “heuristic”—that is, imperfect—because there are many reasons why wages might not equalize across regions.<sup>8</sup> Table 1a shows that the standard deviation of wages in Bangladesh decreases consistently between 2005 and 2016, if assuming that highly integrated markets show lower wage differences among workers over time. Table 1a suggests that local labor markets in Bangladesh have become more integrated over time. In addition, we also compared this dispersion of wages across districts between India and Bangladesh for each year and worker type. Comparing Table 1a (Bangladesh) with Table 1b (India) shows that dispersion of wages across districts is substantially lower in Bangladesh relative to India, even when the means are very similar. This finding holds for every year and type of worker.

> 8 <mark>Wages may differ across regions due to preferences, distribution of capital, or other factors. Even if workers are perfectly mobile, wages will not equalize if there are “preference” shocks. Consider an assignment model, like Eaton-Kortum (2002, 2012), with no transportation costs, where there is a productivity or utility draw. In that model, wages will not equalize. While it might not be possible to separate mobility of labor from mobility of other factors (and the distribution of other factors) by looking at wage variances, wage variance across regions remains widely-used to measure labor market integration.</mark>

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