rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1 – Stock and Flow of U.S. Antidumping Measures, 1990-2009**

<!-- Start of picture text --> 600<br>Number of<br>unique<br>products<br>(HS08) 500<br>400<br>300<br>Stock: products under AD import barrier<br>Flow: products subject to newly initiated AD<br>investigation (not all resulting in new barriers)<br>200<br>100<br>0<br>1990 92 94 96 98 2000 '02 '04 '06 '08<br><!-- End of picture text --> Source: Compiled by the authors from Bown (2010a). The stock is computed on a yearly basis as the number of 8-digit Harmonized System (HS) products subject to U.S. preliminary and/or final AD measures. The flow is computed on a yearly basis as the number of 8-digit HS products subject to U.S. AD investigations, some of which may not result in a duty. Since the data relies on the HS system, the stock does not reflect any imposed or removed AD measures that were imposed before 1988 under the TSUSA product classification system.

# **Scope – Countries Affected:** Despite a dispute settlement history which has

mainly entailed industrialized countries challenging the U.S.’s use of zeroing in AD cases, there is every reason to believe that zeroing is just as important for developing country exporters. First, developing countries are increasingly affected by U.S. AD. In Figure 2 we report the stock of U.S. AD measures in effect for each year from 1990 through 2009. In this chart we include information both for the products and also the exporting country.

We divide the exporting countries into three groups: developed countries, China, and other – 8 –
# **1. Introduction**

The World Bank 2009 World Development Report _Reshaping Economic Geography_ put trade at the heart of the holy trinity of factors promoting growth. “Cities, migration, and trade have been the main catalysts of progress in the developed world over the past two centuries [and] these stories are now being repeated in the developing world’s most dynamic economies” (World Bank, 2009: 20). Although promoting trade is acknowledged to lead to greater territorial disparities (World Bank, 2009: 6 and 12), this may not matter in the medium- and long-term as “evidence from today’s industrial countries suggests that development has largely eliminated ruralurban disparities” (World Bank, 2009:62). Hence, from this perspective, the best way to deal with territorial inequality is not through ‘spatially balanced growth’, which has been a “mantra of policy makers in many developing countries” (ibid: 73), but through the promotion of growth resulting from increases in trade and economic integration.

This approach to promoting economic development rests, however, on three assumptions for which existing scholarly literature provides no firm answer. Namely that a) increases in trade lead to rising territorial inequalities; b) these inequalities subsequently recede as a country develops; and c) the emergence of spatial disparities does not represent a threat to future development, implying that developing countries should be more concerned about the promotion of growth rather than worry about inequalities (ibid: 12). However and despite the surge of attention on the relationship between globalization, the rise of trade, and inequality whether these assumptions hold remains very much unanswered.

Most of the work conducted so far on the link between trade and inequality has been concerned with the impact of increasing global market integration on inter-personal income inequality, both in the developed and the developing worlds (e.g. Wood, 1994; Ravallion, 2001; Alderson and Nielsen, 2002; Williamson, 2005). The spatial dimension of inequality has attracted far less attention. This means that, as Kanbur and Venables (2005) underline, both the theoretical and empirical relationship between greater openness and spatial inequality remains ambiguous (see also 2
Policy Research Working Paper Serles

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS1411|Income Inequality, Welfare, and<br>Poverty: An Illustration Using<br>Ukrainian Data|NanakKakwani|January 1995|G. Evans<br>85783|
|WPS1412|ForeignTechnology Importsand<br>EconomicGrowth in Developing<br>Countries|MaomingZhang<br>Heng-fuZou|January 1995|C. Jones<br>37754|
|WPS1413|Endogenous Distortions In Product<br>and Labor Markets|MartinRama<br>Guido Tabellini|January 1995|S. Fallon<br>38009|
|WPS1414|TheWorldBankand LegalTechnical<br>Assistance: Initial Lessons|TheWorldBank<br>Legal Department|January 1995|K. Mathernova<br>82782|
|WPS1415|China’sGDP in U.S. DollarsBased<br>on Purchasing PowerParity|Ren Ruoen<br>Chen Kai|January 1995|E. O’Rielly-Campbell<br>33707|
|WPS1416|Informal Regulation of Industrial<br>Pollution in Developing Countries:<br>Evidence from Indonesia|Sheoli f-argal<br>DavidWheeler|February 1995|E. Schaper<br>33457|
|WPS1417|Uncertaintyand GlobalWarming:An<br>Option-Pricing Approach to Policy|AndreaBaranzini<br>MarcChesney<br>Jacques Morisset|February 1995|C. Dell<br>85148|
|WPS1418|TheimpactofLaborMarket<br>Regulations|Lyn Squire<br>Sethaput Suthiwart-<br>Narueput|February 1995|G. Bayard<br>37460|
|WPS1419|Industry Structure and Regulation|Martin C. Stewart-Smith|February 1995|N.James<br>82758|
|WPS1420|Lagisiative FrameworksUsed to<br>FosterPetroleum Development|WilliamT.Onorato|February1995|W. Onorato<br>81611|
|WPS1421|Distribution ofIncomeandthe Income <br>TaxBurden in Bulgaria|Zeljko Bogetic<br>Fareed M.A. Hassan|February1995|F. Smith<br>36072|
|WPS1422|Efficiency in Bulgaria’s Schools:<br>ANonparametric Study|Zeljko Bogetic<br>Sajal Chattophadyay|February1995|F. Smith<br>36072|
|WPS$1423|TheRoleofCommercial Banksin<br>Enterprise Restructuring in Central<br>and Eastem Europe|Millard Long<br>Izabela Rutkowska|February 1995|R.Garner<br>37670|
|WPS1424|Terms-of-TradeShocksandOptimal<br>Investment: Another Look at the<br>Laursen-MetzlerEffet|Luis Serven|February 1995|E. Khine<br>37471|
<!-- Start of picture text --> fee, NIGERA TM < OS % + . r<br>\ a: 3 ] CENTRALAFRICANREPUBLIC |“. © souTHsUDAN ern 4 J<br>nent, CAMEROON |. : ~ re ik ssi y<br>ant ‘ a a ie ba ot7 y<br>EQUA i naff% { ls y \ / SOMMM a a<br>&—_ ann ( UGANDA -# y<br>a as € , KENYA |<br>= x ~ GABON '|conGco . \wf<br>» ie af ANG 3<br>yo gy<br>intend>RUS ;<br>><br>d<br>\J &<br>ae | B :<br>Le‘a : eS con OROS (Fr ¢<br>ALI F MAYOWIE<br>J *<br>PY s<br>f = = ne Ly<br>\ ites | y<br>ee BOT on & '<br>LS]<br>3<br>| i y<br>: il U<br>= | ECONOMIC<br>GDP per 100km2 (in million)<br>< 10<br>® w-1w0<br>@ 100-1000<br>@ 1000-5000<br>@ > 5000<br><!-- End of picture text -->
- 20 -

for most groups, except possibly for those with the lowest and highest levels of education. For females, there is some evidence that work hours are increased by training, but these do not translate into higher monthly earnings. Unlike their male counterparts, the earnings and hours effects of training do not vary by level of schooling. The results also suggest that, for males, program participation increases the pro'jability of finding employment in a large firm. Since large firms tend to pay higher wages, provide more training opportunities and thus have steeper earnings profiles, finding a job in a large firm is likely to imply increased earnings opportunities over time for trainees relative to controls.

# VI. CONCLUSIONS AND IMPLICATIONS

Our evaluation of Mexico's PROBECAT program sought to measure the impact of training on a larger set of labor market outcomes than is traditionally investigated in manpower training evaluation studies. In these analyses, training outcomes were estimated by comparing PROBECAT trainees to a control group of unemployed individuals, and an effort was made to account both for non-random selection of individuals into the program and for the choice-based design of our datasets.

# Summary of Findings

We arrived at the following general conclusions. First, trainees move out of unemployment more quickly than do members of the control group. However, training does not appear to reduce the duration of unemployment for trainees without prior work experience. Second, training improves the likelihood of employment for program participants over a longer period of time. Compared to the control group, male trainees are more likely to be employed three and six months after training; female trainees with prior work experience also benefit, but unlike male trainees, these positive employment effects appear to persist over a full year. Third, program participation for males, increases the likelihood of employment in large firms, which typically provide more training opportunities and higher wages. Fourth, program participation raises the post-training earnings of men but not women. For male trainees, these earnings effects vary systematically by level of schooling attainment, being greatest for those with 7 to 9 years of schooling. Finally, for both men and women, training induces an increase in the number of hours worked per week. For males, this hours of work effect translates into higher monthly salaries; for females, it does not.
# **<u>Table 5. Predicted Poverty Rates Based on Imputation, from TA 1 to TA 2 using 2020/21 Poverty Line, Tanzania (percentage)</u>**

|**Method**|||**T**|**A 2**|||
|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3.1**|**Model 3.2**|**Model 8**|**Model 9**|
|1) Normal linear reression model|**21.0**<sup>**a**</sup>|**20.6**<sup>**a**</sup>|29.7|**20.0**<sup>**a**</sup>|**20.0**<sup>**a**</sup>|**20.4**<sup>**a**</sup>|
|g|(2.6)|(2.7)|(2.7)|(2.1)|(2.7)|(2.8)|
|2) Emirical distribution of the error terms|**21.1**<sup>**a**</sup>|**20.4**<sup>**a**</sup>|30.1|**20.0**<sup>**a**</sup>|**19.8**<sup>**a**</sup>|**20.6**<sup>**a**</sup>|
|p|(2.6)|(2.7)|(2.7)|(2.1)|(2.7)|(2.8)|
|_Control variables_|||||||
|Food expenditures|||Y|Y|||
|Utilities: water, kerosene, lighting|||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.34|0.51|0.89|0.89|0.55|0.41|
|N1(base survey)|711|711|711|711|711|711|
|N2(target survey)|701|701|701|701|701|701|
|**True poverty rate in TA 1**<br>|||2<br>(2|1.0<br>.2)|||

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Model 3.1 employs non-standardized variables. Model 3.2 employs variables in TA 2 standardized by those in TA 1. Food expenditures, household size and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 2 use the estimated parameters based on the TA 1. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.7.

36
# Poverty-Specific Purchasing Power Parities in Africa<sup>‡</sup>

YURI DIKHANOV<sup>*</sup> , NADA HAMADEH, WILLIAM VIGIL-OLIVER, TEFERA B. DEGEFU AND INYOUNG SONG _THE WORLD BANK_

**JEL codes** : C43, F31, I32 **Keywords** : PPPs, Poverty Measurement, Price Indexes

> ‡ All authors are with the World Bank’s Development Economics Data Group (DECDG). Funding from DFID under the World Bank’s Strategic Research Program “Research on poverty-specific PPPs” (P151176) is gratefully acknowledged.

> * Corresponding author: Yuri Dikhanov (ydikhanov@worldbank.org). The World Bank, Washington, D.C.
For medicine and health care, we find a decrease in the respective expenditure shares as a reaction to the program. We also observe an increase in the perceived constraint on access to pharmaceuticals for households subject to the program. We do not find a significant effect on the perceived constraint on health services.

The interpretation of the empirical results on health expenditures and constraints in the context of BFP should be undertaken cautiously. A comparison of the treatment effect with the Engel Curves appears unreliable given that the shape of some of the ECs did not prove robust. The baseline ECs would suggest a decrease in the expenditure share of health care, pharmaceuticals and the perceived constraint on pharmaceuticals, while the ECs resulting from the IV procedure suggests an increase in the expenditure shares and the constraint on pharmaceuticals.

What does this decrease in the expenditure shares mean in light of the expansion of the overall household income? For health care and pharmaceuticals, we see a significant decrease in expenditure share. However, the decrease in share does not imply a decrease in absolute terms. In fact, the decrease in the expenditure share on medication by -2.8 percentage points and health care by -2.6 percentage points translates into a net increase in spending on medication, considering that the transfer constitutes a mean increase in income of 18 percent for households around the threshold. Hence, it is more accurate to state that the health expenditure increases disproportionally little compared to the overall expansion of the household budget.

Abstracting from the above, the decrease in the expenditure share on medical expenditures taken together with an increase on the perceived medication constraint seems to be still puzzling. In Brazil health services are available without charge. This is, in general, not the case for medication.<sup>84</sup> The program stimulates demand for health services which in turn could stimulate the demand for medication, which some of the households cannot afford; this channel may constitute a candidate explanation for the observed significant increase in the reported constraint on medication, but not for the decrease in the share of medical expenditure.

In sum, we observe some interesting impacts. In line with previous evidence from the literature on CCTs we find a strong increase in food expenditures; given that food is a necessity around the threshold, this result strongly indicates that transferring cash to women, conditionalities or features other than the pure cash transfer impact household expenditure decisions. We find a similar but weaker effect for spending on school materials and education which increases by proportionally more as would be expected from a pure cash transfer.

The results for health expenditures are harder to interpret: We observe that households

> 84In some instances, medication is provided by the government free of charge.

46
# **2.1 Infrastructure and development in Africa**

A strand of recent papers has focused on the development impact of infrastructure in Africa. Ndulu (2006) offers an overview of the big issues, and Ayogu (2007) surveys the empirical literature. Most of the latter deals with the growth and productivity effects of infrastructure development. For example, Estache, Speciale and Veredas (2005) present pooled OLS growth regressions based on an augmented Solow model including a variety of infrastructure indicators, one at a time. Their main conclusion is that roads, power and telecommunications infrastructure – but not water and sanitation -- contribute significantly to long-run growth in Africa. Other studies follow a production function approach. Ayogu (1999) applies it to regional panel data from Nigeria, finding a strong association between infrastructure and output. Kamara (2006) uses data from African countries to calculate various dynamic panel estimates of the effects of infrastructure in an aggregate production function augmented with indicators of the quality of macroeconomic policy. Boopen (2006) likewise presents panel estimates of the output contribution of transport infrastructure using similar data.

South Africa (along with Nigeria) has attracted special attention in this literature, partly reflecting the significantly better quality of its data relative to that of other countries in the region. Perkins, Fedderke and Luiz (2005) use a detailed database on infrastructure investment and capital stocks, spanning as long as a hundred years, to test for the existence of a long-run relation between different infrastructure measures and GDP. Their results suggest a bi-directional relation in most cases. Kularatne (2005) explores the effects of infrastructure investment (as well as social spending on health and education) on GDP. He also finds bi-directional effects, although the impact of infrastructure investment appears to occur indirectly through private investment. Dinkelman (2008) finds a significant impact of household electrification on employment in South Africa’s rural labor markets.

# **2.2 Caveats**

Much of the literature reviewed above is subject to some major caveats. There are three main concerns: identification, measurement and heterogeneity. We discuss them 7
FORWARD YjstU = ∑∀k ρj,k ∗HORIZONTAL YkstU **,** where ρj,k is the share of industry _j_ ’s inputs <u>purchased from industry</u> _k_ , and ∑∀k ρj,k = 1 ∀ j. When Y is output or employment this index is the weighted total of unorganized suppliers’ output or employment in state _s_ . When Y is the total factor productivity of unorganized plants in that cell then this index measures the average productivity of unorganized sector suppliers to industry _j_ weighted by input shares.

Further we construct a measure of backward linkages that captures the presence (or productivity) of unorganized plants in state _s_ and in industries that buy from industry _j_ : BACKWARD YjstU = ∑∀k φj,k ∗HORIZONTAL YkstU , where φj,k is the share of industry _j_ ’s inputs sold to industry _k_ , and ∑∀k ρj,k = 1 ∀ j.

# **b. Measure of Spillovers from the Organized sector to the Unorganized Sector**

Using notation and intuition from part (a) we measure the extent of spillovers from organized plants in a state-industry cell.

HORIZONTAL Y<sup>O</sup> jst = Yjst O , Y∈ {Employment, Output, TFP} BACKWARD YjstO = ∑∀k φj,k ∗HORIZONTAL YkstO , where φj,k is the share of industry _j_ ’s inputs <u>sold to industry</u> _k_ , and ∑∀k ρj,k = 1 ∀ j. This index of backward spillovers captures the presence (or productivity) of organized plants in state _s_ and in industries that buy from industry _j_ Finally we measure forward spillovers as the weighted total (average) of organized suppliers’ to inputs to industry _j_ output or employment (TFP) in state _s_ .

FORWARD YjstO = ∑∀k ρj,k ∗HORIZONTAL YkstO **,** where ρj,k is the share of industry _j_ ’s inputs purchased from industry _k_ , and ∑∀k ρj,k = 1 ∀ j.

# **Section IV. Data**

# **a. Data**

We construct a time-series cross-section of the Indian unorganized manufacturing sector using establishment-level surveys carried out by the Government of India. The four snapshots of the 10
**Figure 3. Selected Plot Area Coefficients w/ 95% Confidence Intervals - All Plots**

**Notes:** SR, CC, RS, and FP stand for self-reporting, sub-plot crop cutting, remote sensing, and full-plot crop cutting, respectively. MAPS II SR estimates are based on the plot sample net of those subject to full-plot crop cutting. Household and Parcel Panel are defined as in Table A4.1.

**Figure 4. Selected Plot Area Coefficients w/ 95% Confidence Intervals – Pure Stand Plots**

**Notes:** SR, CC, RS, and FP stand for self-reporting, sub-plot crop cutting, remote sensing, and full-plot crop cutting, respectively. MAPS II SR estimates are based on the plot sample net of those subject to full-plot crop cutting. Household and Parcel Panel are defined as in Table A4.2.

39
it is difficult to locate registered properties for tax administration because there is no proper address system and the tax database is not geocoded. The addresses provided at the time of registration are often vague (e.g., “18th Street” or “Catholic Hospital community”). As a result, officials from the LRA typically rely on information from community members to identify buildings from the tax database, especially in cases where there is no readily available photograph of the property.<sup>9</sup> Because of the lack of addresses, tax bills are not routinely delivered to property owners; instead, they are required to visit the LRA to request their bills.

Besides the challenges in identifying properties, the LRA faces legal, political, and logistical constraints to implementing the penalties for noncompliance. The most common consequence for noncompliance is that penalties (up to 25 percent of the tax liability per year) and interest (an average of 15 percent per year) are added to the tax liability. Although the revenue code allows for the closure and seizure of delinquent properties, these actions require a court ruling, which implies significant time and administrative costs. As a result, in practice, enforcement consists of repeated written notices and in-person visits to property owners and, at most, the posting of a delinquent notice on the property for 72 hours. Furthermore, given the limited staff and resources of the LRA, these enforcement actions are focused on select properties that have the highest revenue potential, leaving the majority of the tax base essentially free from enforcement.

An additional key feature of real estate tax in this context is the tax rate. It is 1.5 percent of the property value per annum for commercial properties and 0.25 percent for residential properties.<sup>10</sup> Given the relative illiquidity of the real estate market and the lack of transaction data, it is challenging to determine property values. To facilitate extensive margin compliance, property owners are allowed to self-declare their property value, subject to the caveat that the government reserves the right to purchase the property from them at said value. Commercial properties with high revenue potential are valued by a valuation firm

> 9 Although property owners are required to submit a photograph at the time of property registration, these records are typically kept in hard copy and not organized in a way that allows for easy matching with electronic payment records for enforcement purposes.

> 10 Commercial properties are those that are used for income-generating activities, such as a rented apartment, a store, or an office building, whereas residential properties are used strictly for housing and do not generate any income.

8