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

During the last two decades, there has been a significant increase in anti-dumping (AD) activity in the world economy. Prusa (2005) documents that the number of anti-dumping cases filed with the WTO tripled between the early 1980s and the late 1990s. Also, while two decades ago the overwhelming majority of AD cases was filed by the United States, the European Union, Canada and Australia, today India, Argentina, Mexico, Brazil, South Africa and New Zealand are additional heavy users. Figures from Zanardi (2004) show that 46 countries adopted AD laws between 1990 and 2001, which may yet lead to a further increase in anti-dumping activity in the near future.

The empirical literature on anti-dumping has traditionally focused on aggregate issues like changes in international equilibrium prices (Debaere, 2005; Prusa, 1997), pass-through to domestic prices (Blonigen and Haynes, 2002; Blonigen and Park, 2004), changes in trade volumes, trade deflection and trade depression (Bown and Crowley, 2007a; Staiger and Wolak, 1994; Prusa, 1997), impacts on aggregate welfare costs (Gallaway, Blonigen and Flynn, 1999), and retaliation and further trade liberalization (Blonigen and Bown, 2003).<sup>1</sup> In this paper, we are instead interested in exploring household adjustments to trade policy, in particular to anti-dumping measures. In light of the increasingly heavy use of AD, our estimates of these microeconomic impacts should become valuable additions to the set of current evaluations of AD policies.

We study the anti-dumping duties imposed by the United States on imports of catfish fillets from Vietnam in 2003. After the U.S. lifted the embargo on Vietnam in 1994, Vietnamese catfish burst into the U.S. market, which by 2002 became the main export destination and accounted for 50 percent of total production. However, while catfish farming is an important source of income for households in the Mekong delta in Southern Vietnam, it is also an important industry in the Southern United States (mainly in Mississippi, Arkansas, Alabama, and Louisiana). Faced with this increasing competition from cheaper Vietnamese catfish, the Association of Catfish Farmers of America (CFA) initiated a successful campaign

> 1This list refers only to empirical papers on anti-dumping. For an overview of this literature, see the survey in Blonigen and Prusa (2003).

2
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<!-- Start of picture text --> 0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Buyer County<br><!-- End of picture text --> Buyer County Buyer County
# **3 Example Application: Sectoral Mobility in the US**

In this section we present an application of the estimation method. First, we estimate a disaggregated variant of Artuc, Chaudhuri and McLaren (2010) using the exact same data, which is the Current Population Survey from years 1975 to 2001 (henceforth CPS). Recently, Kaplan, Lederman and Robertson (2013), Artuc and McLaren (2012) and Artuc, Bet, Brambilla and Porto (2013) used the estimation method we introduce herein. _Model_ To elaborate on the generic model we presented in the previous section, consider that sectors are industries in which workers choose to work in each time period. For each choice, workers receive a payoff _wt_<sup>_i_andanidiosyncraticutility</sup><sup>_ηi_commontoallworkersinsector</sup><sup>_i_.</sup> Assume that _η_<sup>1</sup> = 0 for normalization. For simplicity, we consider one type of worker, hence drop the state superscript _s_ . We allow the deterministic moving cost to change over time such that _Ct_<sup>_ij_=</sup><sup>_ct_if</sup><sup>_i_=</sup><sup>_j_and</sup><sup>_C_</sup> _t_<sup>_ij_= 0if</sup><sup>_i_=</sup><sup>_j_.</sup> We use two regressions to estimate structural parameters of the model. First, the Poisson regression equation is where the regression coefficient Ψ _t_ = _−ct/ν_ and the indicator function 1 _i_ = _j_ is equal to one when _i_ = _j_ and zero otherwise, and _e_<sup>_ij_</sup> _t_<sup>istheresidual.Inmanycases,thediscountrate</sup> can not be identified, therefore we assume that it is equal to _β_ = 0 _._ 97, and known by the econometrician.

Second, the regression equation based on the Bellman equation is where _ξt_<sup>_i_istheresidual,</sup><sup>_φi_</sup> _t_<sup>= Λ</sup><sup>_i_</sup> _t_<sup>+</sup><sup>_β_Γ</sup><sup>_i_</sup> _t_ +1<sup>_−_log(</sup><sup>_Li_</sup> _t_ +1<sup>)isthedependentvariable,</sup><sup>_η_�</sup><sup>_i_isasector</sup> dummy, and _ζt_ is a time dummy. We set _η_ �<sup>1</sup> = 0 for the first sector.

In the “Alternative specification” we use different set of sector dummies, we allow the 13
comparing a cross-section of beneficiaries to a matched set of control households with children in the age 2-5 range. The controls were selected by matching beneficiary children to other children from the same neighborhood, of a similar age and gender and whose mothers were working. Impact estimates were obtained by propensity score matching.

Because all mothers had to be working to be a part of the evaluation, it is impossible to estimate the effect of the program on mothers‟ rate of working. However, to get an idea of the potential effect, Ruel and Quisumbing (2006) compare the labor force participation rate for a random sample of mothers who have children aged newborn to 6 years with that of women who do not. The comparison suggests that the program could potentially increase labor force participation by 25 percent.

The program‟s impact on mothers‟ earnings and job choice is assessed by comparing beneficiary mothers to matched control mothers. This comparison indicates that beneficiary mothers had 30% higher earnings than mothers using alternative childcare arrangements and were more likely to be employed in the formal sector. The program had the largest benefits for younger and older women with low levels of education. A limitation of the analysis is that all mothers in the control group were restricted to be working. It is likely that some of the mothers in the treated group would not have been working had the program not been available to them. For this reason, the evaluation probably understates the effect of the program on mothers‟ incomes. The results from interviews with focus groups show that the program was very well received and much appreciated by the beneficiaries.

_A comparison of Guatemala and Ghana:_ Quisumbing, Hallman and Ruel (2003) analyze the determinants of mothers‟ joint labor supply and daycare utilization choices in Guatemala City and in Accra, Ghana. The analysis for Guatemala is based on a random sample of mothers with preschool age children from one zone of Guatemala City. The analysis for Accra is based on a household survey.

The urban settings of Guatemala and Ghana are quite different with regard to the type of work that women typically do. In Guatemala City, the landscape is becoming increasingly urbanized and women often engage in formal work, such as factory work. About one-fifth of households are headed by single women, about half of which are poor or indigent. In Accra, 41
based on its economic risk to natural hazards (CEPAL et al. 2011).<sup>1</sup> Similarly, the Global Climate Risk Index puts Guatemala in 12th place worldwide based on the number of extreme weather events recorded between 1991 and 2010 –and in 2nd place for events recorded only in 2010 (Harmeling 2011).

Tropical Storm Agatha exemplifies the high vulnerability of Guatemala to natural risks. Triggered by a tropical wave that moved westward from the coast of Africa on May 8, 2010, Agatha originated as a tropical depression on May 29, 2010 in the eastern Pacific. A few hours later the tropical depression developed into a cyclone, making landfall in Champerico, southwest of Guatemala, near the border with Mexico, at 16:40. The surface circulation of Agatha weakened as it continued northeastward into the Sierra Madre Mountains and it began to dissipate on May 30 over northwestern Guatemala.

Reaching top winds of nearly 80 kilometers/hour, Agatha produced torrential rains, widespread floods and landslides across several countries in Central America. Guatemala, however, was the hardest hit. Some parts of the country received more than 910 millimeters of rainfall, the highest levels recorded in over 60 years, making Agatha the strongest tropical cyclone to ever strike Guatemala in terms of amount of rain dropped since records have been kept. The human losses, the destruction of homes, crops and critical infrastructure –including schools and health centers– and their subsequent disruption of economic and institutional systems forced government officials to declare a state of emergency for the entire country. Assessments conducted jointly by national and international institutions estimated that nearly 400,000 people (around 3% of the total population) needed humanitarian assistance and the total damages attributed to the storm amounted to 2.2% of the GDP. Donations centers spread across the country started deploying relief aid started on May 31, but anecdotal evidence suggests that this assistance was far from sufficient to mitigate the immediate consequences of the disaster (CEPAL et al. 2011).

# **3. Data**

Two main sources of data underlie the empirical analysis.. The first source is the Living Standards Measurement Survey ( _Encovi_ for its acronym in Spanish) developed by the Guatemalan Statistics Bureau (INE). _Encovi_ is a comprehensive, multi-purpos,e cross-sectional

> 1 It is estimated that 83% of Guatemala’s GDP is generated in areas expecially prone natural disasters.

6
# **Figure 11: Direct and indirect job content per $1 million of gross exports across macro sectors, South Africa**

<!-- Start of picture text --> Other commercial services<br>Construction<br>Trade, sales and accomodation<br>Agriculture<br>Public services<br>Electricity, gas and water<br>Manufacturing<br>Financial services<br>Transport and communication<br>Mining and Energy<br>Real estate, renting<br>0 20 40 60 80<br>Jobs per $1 mil. exports<br>Direct JOBX Indirect JOBX<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

One drawback of the classification is that manufacturing is a very broad and heterogeneous category. In order to explore this heterogeneity further, we use sectoral employment data from South African labor force surveys instead of ILO, shown in Figure 12. Machinery, other metal products and ferrous metal products becomes the largest sector in terms of the number of jobs contained in its exports (which was all contained within the broad manufacturing sector in Figure 10).<sup>15</sup> It also witnessed the largest increase in jobs contained in exports across all sectors between 2001 and 2011, from 837,000 to 992,000 jobs. This growth was entirely explained by jobs in inputproviding sectors; in 2011, indirect jobs represented three quarters of the total jobs contained in machinery, metal products and ferrous metal exports. Minerals also experienced an important growth in the number of jobs contained in exports, jumping from 273,000 jobs in 2001 to 400,000 jobs in 2011. The indirect content grew as well, though at a much slower rate than that experienced by machinery and metals. But for each of these sectors, the growth in jobs was substantially lower than the growth in gross exports, resulting in a sharp decline in the number of jobs per $1 million of gross exports.

On the other hand, export jobs in agriculture fell dramatically in the 2000s, from over 300,000 to less than 200,000 jobs between 2004 and 2011, as did jobs contained in trade and transport services exports. These trends – a rising number of jobs for capital-intensive exports and a declining number

> 15 The sector ‘ferrous metals’ includes manufacture of basic iron and steel and casting of iron and steel, which by its ISIC concordance is considered manufacturing (ISIC 27) rather than minerals (ISIC 12-14).

19
Policy Research Working Paper 8028

# **Abstract**

This paper evaluates effectiveness of microfinance using Propensity Score Matching (PSM) method applied to data collected in a recent randomized control trial. This method allows one to answer an additional set of questions not answered by the original study and provide more nuanced evidence by comparing Microfinance Institution (MFI) borrowers to those without any loans and those with prior loans from other sources. It is argued that this unique setting with two comparison groups allows us to shed light on the unobservable entrepreneurial spirit bias and provides upper and lower bounds on the true microfinance impact. The results suggest that microfinance can make a modest difference for some households in several expenditure categories.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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 inna.cintina@lewin.com.

_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
**<u>Table 6: Determinants and impact of knowledge on reporting corruption</u>**

||**Knowledge**<br>||**Hous**<br>|**ehold subject to**<br>|**corruption wit**<br>|**h respect to**<br>||
|---|---|---|---|---|---|---|---|
||**on reporting**|**Overall**|**Health**|**Education**|**Police**|**Administrative**|**prod. services**|
|Predicted||-0.265**|-0.311**|0.075|-0.925***|-0.353***|-0.787***|
|knowldege||(2.40)|(2.53)|(0.84)|(3.79)|(2.58)|(3.64)|
|Consumption exp.|0.022***|0.064***|0.018***|0.006|0.056***|0.027***|0.050***|
|<br>(log)|(6.72)|(13.57)|(3.45)|(1.53)|(5.15)|(4.54)|(5.39)|
|Female head|-0.061***|-0.113***|-0.077***|0.002|-0.161***|-0.037**|-0.075**|
||(5.73)|(8.31)|(5.20)|(0.17)|(4.64)|(1.98)|(2.37)|
|Permanent house|-0.009|-0.014|0.005|-0.013*|0.005|-0.022*|0.014|
||(1.09)|(1.45)|(0.49)|(1.70)|(0.23)|(1.88)|(0.75)|
|Owns a bicycle|0.008|0.028***|0.006|0.004|-0.004|0.022**|0.057***|
||(1.11)|(3.20)|(0.63)|(0.59)|(0.20)|(2.04)|(3.28)|
|Business|-0.006|-0.024**|-0.023*|-0.025***|-0.055**|-0.006|0.001|
||(0.60)|(2.02)|(1.74)|(2.65)|(2.24)|(0.39)|(0.05)|
|Professional|0.055***|-0.043**|-0.010|-0.026**|-0.058|0.009|0.007|
||(4.33)|(2.50)|(0.54)|(2.03)|(1.54)|(0.41)|(0.21)|
|Rural|<br>-0.022***|<br>0.022**|<br>0.007|<br>-0.001|<br>0.049**|<br>0.007|<br>0.049**|
||(2.68)|(2.30)|(0.66)|(0.13)|(2.31)|(0.61)|(2.50)|
|East|<br>0.036***|<br>0.114***|<br>0.095***|<br>0.009|<br>0.120***|<br>0.043***|<br>0.129***|
||(3.52)|(9.33)|(7.19)|(0.90)|(4.33)|(2.78)|(5.40)|
|North|0.048***|0.031**|0.093***|-0.021|-0.004|0.047**|-0.022|
||(3.69)|(2.07)|(5.31)|(1.62)|(0.09)|(2.13)|(0.71)|
|West|<br>0.096***|<br>-0.007|<br>-0.042**|<br>-0.028**|<br>0.198***|<br>0.072***|<br>0.032|
||(9.21)|(0.44)|(2.46)|(2.33)|(5.52)|(3.51)|(0.92)|
|Lower primary|0.028<br>(1.48)|||||||
|Upper primary|<br>0.054***<br>(3.24)|||||||
|Secondary|<br>0.103***<br>(6.14)|||||||
|Tertiary|<br>0.177***<br>(7.90)|||||||
|Observations|11,888|11,888|7,855|5,204|2,664|4,717|2695|
|Loglikelihood rat.|-5,772.49|-6,780.85|-3,932.70|-1,306.37|-1,688.90|-1,954.05|-1484.87|

Absolute value of z-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 22
study further analyzed the contribution of pension funds to capital market development in Chile, finding that the growth of pension fund assets has impacted the size and the structure of the domestic financial market. However, the investment strategy of administrators had little effect on the market efficiency (Yermo 2005).

# **IV.3.3. Macroeconomic Environment**

At the time of the pension reforms, some LAC countries presented unstable macroeconomic environments (Figure 21 and Figure 22) or were emerging from an economic crisis that resulted from unsustainable fiscal and monetary policies coupled with weak financial systems (Singh and Cerisola 2006).<sup>57</sup> It was only in the mid-1990s that most countries managed to control inflation and to decrease average public debt levels.<sup>58</sup> The current account imbalances have improved with the fiscal discipline undertaken by governments in the region. Real GDP per capita growth in LAC has been very volatile and more than 70 percent of this volatility was due to country-specific shocks (IMF 2005).

<!-- Start of picture text --> Figure 21: Selected LAC countries: Annual inflation, boxplot (percent)<br>(a) 1980-1999 (b) 2000-2005<br>Argentina Bolivia Chile Colombia C. Rica El Salv. Mexico Peru Uruguay Argentina Bolivia Chile Colombia C. Rica El Salv. Mexico Peru Uruguay<br>400 30<br>300<br>20<br>200<br>10<br>Annual inflation (%) Annual inflation (%)<br>100<br>0<br>0<br><!-- End of picture text --> Notes: Values above 500 percent were not included in (a), which occurred in Argentina, Bolivia, and Peru. Boxplot is a graph of the five-number, in which the central box spans the 25<sup>th</sup> and the 75<sup>th</sup> percentiles, the line in the box marks the median, lines extend from the box out to the minimum and the maximum observations and “dots” represent outliers. Source: WDI.

The uncertain macroeconomic landscape did not favor the asset allocation of pension funds.<sup>59</sup> In these circumstances, it was common to restrict investments of pension funds in variable-income and foreign-issued instruments, thus favoring their asset allocation in government debt – some governments also applied such restrictions to help manage the fiscal costs of the transition from a PAYG to a funded system instead of relying on increased public savings.

> 57 See Zettelmeyer (2006) and Jaeger (2007).

> 58 Since the early 2000s, public deficits and indebtedness have been trending downwards. Active debt management and reforms are helping LAC countries to develop capital markets (Singh and Cerisola, 2006). See also Carstens and Jácome (2005).

35
comparative advantage in more than one-third of the products which it exports. For Malaysia, the share is only 16.4 percent in 2006 and for Penang, 12 percent in 2007 (see Table 7). Penang’s restricted capability stems from the smaller number of exported goods and lower share of these goods having revealed comparative advantage. Given the size of Penang’s economy, the first aspect is difficult to change. Larger economies (or economic area however defined) tend to have more diversified exports. Nevertheless, improving the competitiveness of the current mix of exports may be feasible, especially through innovation and improvements in productivity.<sup>4</sup>

**Table 7: Number of Products with Revealed Comparative Advantage for Penang, Malaysia, and China**

||1995|2000|2006/7|
|---|---|---|---|
|China|274|279|278|
|Malaysia|103|101|122|
|Penang|65|71|72|
|Share of Products with||||
|RCA (%)||||
|China|35.8|36.6|36.4|
|Malaysia|13.9|13.6|16.4|
|Penang|11.2|11.7|12.0|

Source: Authors’ calculation Comparing Penang’s performance in 1995 to that in 2007, the distribution of export categories with sophisticated products moved close to the origin (see Figure 4). This would imply that Penang has been able to build up industrial capabilities for these sophisticated products. But between 2000 and 2007, there was little further increase in capabilities (see Figure 5).

> 4 Improvement in RCA may also require additional resources where a smaller country or place would not be able to secure.  There are several ways to improve RCA.  Either improve the efficiency of production so that output can be increased or to focus resources on fewer products.

12
TABLE 10: Impact of leaders’ exposure on ECD outcomes, by closest leader’s commitment

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|ITT Leader|-0,12<sup>_∗∗_</sup>|-0,01|-0,17<sup>_∗∗∗_</sup>|
||(0,05)|(0,07)|(0,06)|
|Committed leader|-0,01<br>(0,05)|0,05<br>(0,13)|-0,04<br>(0,05)|
|ITT Leader X Committed leader|0,08|-0,03|0,12|
||(0,09)|(0,17)|(0,09)|
|Observations|2051|624|1427|
|ITT Leader + ITT Leader X Committed leader|0,56|0,74|0,59|

_Note:_ Committed leader is a leader predicted to be in the top quintile of leaders with highest participation in the quizzes. All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver subcomponents for children aged 12 and 35 month. In column 3, the dependent variables is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, memory and self-control) of children aged 36 to 83 months. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 44