rafmacalaba/datause-encoder-data huggingface dataset:
# Table D.5: **Regression Results** – Household Consumption Expenditure

This table reports treatment effects of debt relief on household consumption. The dependent variables are (a) consumption of staples over the last 30-day period and (b) consumption of durable goods over the last 12 month period after debt relief. Regressions in panel A are estimated using the full sample of surveyed households. Panels B.1 and B.2 report restricted sample robustness checks. Panel B.1 reports estimates from the sample of housheolds with audited and matching land records. Panel B.2 reports estimates from a reduced bandwidth sample, which excludes observations outside the bottom 25% of observations below and the top 25% of observations above the program cutoff. Respondent controls are as previously defined. Loan controls include years since disbursal of the loan and its interaction with treatment status and a dummy variable equal to one for loans from cooperative banks. Standard errors, in brackets, are clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

||Model 1||Model 2||Model 3||Model 4||
|---|---|---|---|---|---|---|---|---|
|_Panel A: Full Sample_|Basics<br>[30-day]<br>(1)|Durable<br>goods<br>(2)|Basics<br>[30-day]<br>(3)|Durable<br>goods<br>(4)|Basics<br>[30-day]<br>(5)|Durable<br>goods<br>(6)|Basics<br>[30-day]<br>(7)|Durable<br>goods<br>(8)|
|100% Relief|-0.08+|-0.54**|-0.02|-0.21|-0.04|-0.24|-0.02|-0.21|
||[0.05]|[0.25]|[0.04]|[0.22]|[0.03]|[0.26]|[0.05]|[0.22]|
|Observations|2,832|2,832|2,832|2,832|2,687|2,687|2,832|2,832|
|R-squared|0.155|0.403|0.15|0.382|0.373|0.404|0.15|0.382|
|_Panel B: Restricted Sample_|||||||||
|Audited accounts|||||||||
|100% Relief|-0.14**|-0.80**|-0.08|-0.44|-0.10**|-0.47|-0.08|-0.43|
||[0.06]|[0.31]|[0.07]|[0.37]|[0.05]|[0.37]|[0.07]|[0.37]|
|Observations|1,805|1,805|1,805|1,805|1,713|1,713|1,805|1,805|
|R-squared|0.156|0.397|0.151|0.373|0.377|0.394|0.152|0.373|
|Reduced bandwidth sample|||||||||
|100% Relief|-0.08|-0.47+|0.00|-0.12|-0.02|-0.2|0.00|-0.12|
||[0.05]|[0.28]|[0.05]|[0.21]|[0.04]|[0.25]|[0.05]|[0.21]|
|Observations|2084|2084|2084|2084|1964|1964|2084|2084|
|R-squared|0.156|0.389|0.16|0.374|0.379|0.397|0.159|0.374|
|Interviewer effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*district effects|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Respondent controls|No|No|No|No|Yes|Yes|No|No|
|Local linear control fctn|No|No|Yes|Yes|Yes|Yes|No|No|
|Quadratic control fctn|No|No|No|No|No|No|Yes|Yes|

60
# **7. CONCLUSIONS**

Accumulation and increased ownership of durable goods are important indicators of the improvement in the overall welfare as individuals and households tend to engage in purchase of more durable goods and other long-term assets than basic consumption commodities as they and their national economy become more prosperous. However, very little attention in the literature has been paid to the understanding of individual and household welfare dynamics captured by the accumulation of durable goods in the transition countries of Eastern Europe and the former Soviet Union.

This paper used repeated cross-sections of Bulgaria’s Household Survey data (1995, 1997, 2001 and 2003) and a comparable list of durable goods to investigate the dynamics and the distribution of durable goods over time, including during the economic crisis of 1996-1997 and the subsequent period of relatively robust economic growth leading up to 2003. It examined the dynamics of the ownership of durable goods by wealth classes, geographic locations, and various ethnic groups, including the Roma.

The analysis suggests that all wealth (as measured by consumption) classes in Bulgaria gained in respect to the quantity of major assets (durable goods) between 1995 and 2003, although the rates of improvement varied.

While the quantity of assets has been growing during that period across all wealth classes, we find evidence of the rising divergence in assets distribution among wealth classes between 1995 and 2001, followed by convergence between 2001 and 2003.

Despite some signs of recent convergence among classes in the distribution of assets, significant gaps in the level (quantity) of durables between the poor, the middle class and the rich still remain. This is especially true when it comes to such expensive goods as personal computers, which seem to become increasingly affordable to the upper middle class, but are still owned by only a very few households at the bottom of the distribution.

- 17 -
program mean school size of 252 students in phase-3 and phase-4 schools based on data from program applications and a per-student subsidy of Rs. 300, the bonus amount represents 6% of the expected mean annual subsidy payment to program schools. Again, the bonuses are transferred electronically to the bank accounts of the school awardees. To date, three rounds of school bonuses have been awarded.

Once schools qualify and join the FAS program, the partnership contract stipulates several conditions for maintaining benefit eligibility. The conditions that are stringently applied by PEF are (1) schooling is offered to students without charging them any tuition or fees (and displaying this status prominently on a PEF-issued signboard outside the school gate) and (2) participation of the program school in the QAT and that at least 66.67% of the tested students score 40% or higher on the QAT. A one-time violation of these conditions typically results in a warning and the capping of enrollment figures for the subsidy payment until the next QAT round. A second violation results in the permanent disqualification of the school with immediate effect. With the QAT-related condition, the school is permanently disqualified if it fails to achieve the minimum pass rate in the QAT in two consecutive attempts. Since the start of the program, there have been 51 schools that have been disqualified from the program due to double failure on the QAT.

There are also other conditions for maintaining benefit eligibility. These include (1) registering the school with the District Registration Authority within one year of joining the program; (2) conducting only one class in a classroom in any period; (3) maintaining or upgrading the quality of the school’s physical infrastructure (e.g., adequate classroom space, properly-constructed rooms and buildings, sufficient ventilation, and sufficient artificial and natural light); (4) adequate furniture and teaching tools (e.g., benches, desks, and blackboards); (5) monthly reporting on enrollment figures; (6) maximum student-teacher and studentclassroom ratios of 35:1; (7) a minimum enrollment of 100 students; and (8) no after-hours classes or tutoring services at the school. These additional conditions are applied more leniently; typically, when PEF detects a violation among this subset of conditions, schools are provided with a warning and a grace period within which to comply. To date, no program schools have been disqualified for repeated violations of these conditions.

10
**Table 3. Value of net exports, by groundnut product, 1996-2000**

||**Val**|**ue of Net Exports**|**in Million US $**||
|---|---|---|---|---|
||Edible Groundnut|Groundnut Oil|Prep.Groundnut|Total|
|China|193.79|2.82|111.06|307.68|
|India|86.85|-0.13|7.27|93.99|
|USA|126.43|-12.77|28.26|141.92|
|Argentina|160.98|51.52|25.82|238.32|
|Nigeria|-3.29|4.64|0.00|1.35|
|Senegal|3.34|48.99|0.60|52.92|
|South Africa|16.01|4.68|0.27|20.95|
|Malawi|0.77|0.00|0.00|0.77|
|Gambia|4.49|1.09|0.05|5.63|
|European Union (15)|-378.47|-115.12|-4.54|-498.13|
|Canada|-76.67|-1.19|-3.31|-81.18|
|Japan|-44.00|-1.85|-71.46|-117.31|
|Korea, Republic of|-4.55|0.01|-14.31|-18.86|

Source: FAOSTA, 2002 Note: Prepared groundnut = roasted, salted, flavored groundnuts. Peanut butter not included.

Figure 3 and 4 show the structure and trend of EU’s edible groundnut (Figure 3) and groundnut oil (Figure 4) imports. Competition among exporters in this market has increased. The US has lost share in the EU edible groundnuts market between 1996 and 2001 to Argentina, which has become the top exporter of that particular product in that market (Figure 3).

**Figure 3. Competition in the EU edible groundnut market**

<!-- Start of picture text --> EU Impo rts o f Edible Gro undnut by<br>Origin in 1996<br>ROW<br>14%<br>SSA USA<br>2% 28%<br>India<br>5%<br>China<br>26%<br>Argentina<br>25%<br><!-- End of picture text --> <!-- Start of picture text --> EU Imports of Edible Groundnut by Origin in<br>2001<br>ROW USA<br>15% 18%<br>SSA<br>5%<br>India<br>3%<br>Argentina<br>China 32%<br>27%<br><!-- End of picture text --> Source: Oil World Imports of groundnut oil are much more concentrated than that of edible groundnut, but here also, the EU represents a sizable block, with more than 60 percent of world imports for the 1996-2000 period. Demand from the EU has declined overtime as cheaper vegetable oils were increasingly substituted for groundnut oil. As Figure 6 shows, even as EU’s demand for groundnut oils has increased substantially, groundnut oil lagged behind that of palm and rapeseed.

9
Policy Research Working Paper 6161

# **Abstract**

After 18 years of negotiations, Russia has joined the World Trade Organization. This paper assesses how the tariff structure of the Russian Federation will change as a result of the phased implementation of its World Trade Organization commitments between 2012 and 2020 and how it has changed as a result of its agreement to participate in a Customs Union with Kazakhstan and Belarus. The analysis uses trade data at the ten digit level, which allows the first accurate assessment of the impact of these policy changes. It finds that World Trade Organization commitments will progressively and significantly lower the applied tariffs of the Russian Federation. After all commitments are implemented, tariffs will fall from 11.5 percent to 7.9 percent on an un-weighted average basis, or from 13.0 percent to 5.8 percent on a weighted average basis. The average “bound” tariff rate of Russia under its World Trade Organization commitments will be 8.6 percent, that is, 0.7 percentage points higher than the applied tariffs. Russia’s commitments represent significant tariff liberalization, but compared with other countries that have acceded to the World Trade Organization, the commitments of the Russian Federation are not unusual, especially when compared with the Transition countries.

This paper is a product of the Trade and Integration Team, Development Research Group; and the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. 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 author may be contacted at dtarr@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
|Chen et al|Prefecture|China|Urban growth|Inter-city infrastructure (road, rail) accounts for 6% of city growth in the|
|---|---|---|---|---|
|(2016)*||||long-term, favoring core cities at the expense of more peripheral ones.|

Source: Compiled by the authors. Note: Findings column is based on the main finding of the paper. An asterisk identifies peer-reviewed papers. Plus identifies papers following the instrumental variable approach.

# **5.5 Ports**

Port efficiency is an important determinant of shipping costs in which distance, volumes, and product characteristics all matter. For most Latin American countries, transport costs are a greater barrier to U.S. markets than import tariffs. The limited empirical studies in our dataset, focusing mainly on Latin America, find clear evidence that port efficiency materially reduces maritime transport costs, thereby reducing economic distance, boosting competitiveness and expanding trade.

Clark et al (2004) investigate the determinants of shipping costs to the United States from different ports around the world. Improving port efficiency from the 25<sup>th</sup> to the 75<sup>th</sup> percentile reduces shipping costs by 12 percent. Bad ports are equivalent to being 60 percent farther away from markets for the average country. Inefficient ports also increase handling costs. At the same time, reductions in country inefficiencies, associated with transport costs, from the 25<sup>th</sup> to 75<sup>th</sup> percentiles imply an increase in bilateral trade of around 25 percent. In turn, factors explaining variations in port efficiency include excessive regulation, the prevalence of organized crime, and the general condition of the country’s infrastructure. The authors also emphasize that some level of regulation increases port efficiency, but excessive regulation can be damaging.

The positive impact of port efficiency on trade is confirmed by several other studies. By using port efficiency measures from a survey of Latin American port terminals in 1999 and conducting a principal component analysis, Sanchez et al (2003) show that trade cost decreases by 0.06 percent because of a 1 percent increase in port efficiency. Similarly, Wilmsmeier et al (2006) show that a 1 percent increase in port efficiency leads to a 0.38 percent reduction in trade costs in 16 Latin American countries in 2002. Herrera Dappe et al (2017) find that the more efficient the port, the lower the maritime transport costs in the Indian and Western Pacific Oceans. An efficient port sector would reduce countries’ average maritime transport costs by up to 14 percent and increase exports by up to 2.2 percent.

**Table 15: Survey of selected papers on the development impact of ports**

|**Author**|**Level of**<br>**Analysis**|**Country/**<br>**Region**|**Indicator of**<br>**Development**|**Main Findings**|
|---|---|---|---|---|
|Clark et al|County|Global|International|Improving port efficiency from the 25th to the 75th percentile reduces shipping|
|(2004) *+|||Trade|costs by 12%. Bad ports are equivalent to being 60% further away from<br>markets.|

42
# **Figure 1. Possible global and national effects of high-skilled international migration**

|**SENDING ECONOMIES: POSSIBLE NEGATIVES**|**RECEIVING ECONOMIES: POSSIBLE NEGATIVES**|
|---|---|
|· “Brain drain”: lost productive capacity due to at<br>least temporary absence of higher skilled workers<br>and students<br>· Less support for public funds for higher<br>education|· Decreased incentive of natives to seek higher<br>skills<br>· May crowd out native students from best<br>schools<br>· Language and cultural barriers between native<br>and immigrant high-skilled workers<br>· Technology transfers to possibly hostile<br>economies|
|**SENDING ECONOMIES: POSSIBLE POSITIVES**|**RECEIVING ECONOMIES: POSSIBLE POSITIVES**|
|· Increased incentive for natives to seek higher<br>skills<br>· Possibility of exporting skills reduces<br>risk/raises expected return of personal<br>education investments<br>· May increase domestic economic return to<br>skills<br>· Knowledge flows and collaboration<br>· Increased ties to foreign research institutions<br>· Export opportunities for technology<br>· Return of natives with foreign education and<br>human capital<br>· Remittances and other support from diaspora<br>networks|· Increased R&D and economic activity due to<br>availability of additional high-skilled workers<br>· Knowledge flows and collaboration<br>· Increased ties to foreign research institutions<br>· Export opportunities for technology<br>· Increased enrolment in graduate<br>programs/keeping smaller programs alive|

# **POSSIBLE GLOBAL EFFECTS**

· Better international flow of knowledge · Better job matches · Greater employment options for workers/researchers · Greater ability of employers to find rare/unique skill sets · Formation of international research/technology clusters (Silicon Valley, CERN)

**·** International competition for scarce human capital may have net positive effect on incentives for individual human capital investments

Source: Regets, 2001 Nevertheless, governments may have policy goals beyond attracting FDI that justify, in their view, certain restrictions on skilled migration. As such, a broader policy debate needs to be considered when evaluating restrictions on skilled migration. We acknowledge the fact that this paper measures two different issues: temporary entry of skilled expatriates who intend to seek permanent immigration and temporary entry of skilled expatriates who do not intend to seek permanent immigration. While the former is a social and highly sensitive political issue, and is predominantly a South-North phenomenon, temporary entry of skilled personnel is clearly an FDI issue which does not necessarily entail a SouthNorth flow of skilled expatriates.

5
<!-- Start of picture text --> Utility Characteristics<br>a(0.357)<br>FA a<br>(0.152) | (0.221) | (0.150)<br>eae aie(0.090) | (0.248) lst | (0.149) | (0.084)<br>remoerms(0.392) | (0.245)<br>Tage esal |<br>sai<br>(0.527) | (0.422) | (0.298)<br>oe Tae(0.436) | (0.299)gen [al<br>a<br>Ar |<br>| (0.961) (0.195)<br>paar Tae (0.15 30 ) | (0.481) | a<br>(0.092)<br>ramen Tat(0.161) | (0.258)TL| (0.250) | (0.137)<br>aE(0.197)<br>A<br>cearremrers<br>eet tT<br>(0.114)<br>career [ett(0.122 |<br>Peers<br>a et |<br>rere Te(0.165) |[aco (0.339) lee]<br>Teo| (1.066) | (0.760)<br>emt (2. 635018 ) | (0.449) |[ate (0.272) |<br>aa<br>4 A<br>(0.034)<br>poem et(1.615) tT (0.312)<br>paneer(0.066)<br>[eT TT<br>La(0.061)<br>Notes: Based on 8,112 on 8,112 8,112 observations for share share moments; 1,269 observations for demographic demographic moments and 153 observations for neighborhood neighborhood<br>moments. Except where noted, parameters are GMM GMM estimates including campus, grade and and year fixed effects. Asymptotic standard errors are<br>given in parentheses. “Baseline utility” corresponds to parameters from 6, except for the coefficients on distance, which correspond to p.<br>Coefficients marked with (*) are significant at the 5% 5% significance level and (**) denotes significance at the 10% level. Estimates and standard<br>errors in Italics were obtained through minimum-distance estimation of campus campus fixed effects on time-invariant school characteristics (number<br>of observations observations in this regression = 281 281 campuses).<br><!-- End of picture text -->

Notes: Based on 8,112 on 8,112 8,112 observations for share share moments; 1,269 observations for demographic demographic moments and 153 observations for neighborhood neighborhood moments. Except where noted, parameters are GMM GMM estimates including campus, grade and and year fixed effects. Asymptotic standard errors are given in parentheses. “Baseline utility” corresponds to parameters from 6, except for the coefficients on distance, which correspond to p. Coefficients marked with (*) are significant at the 5% 5% significance level and (**) denotes significance at the 10% level. Estimates and standard errors in Italics were obtained through minimum-distance estimation of campus campus fixed effects on time-invariant school characteristics (number of observations observations in this regression = 281 281 campuses).
WPS4010

# **Rural Vulnerability in Serbia***

## **Lire Ersado**

Human Development Network Europe and Central Asia Region The World Bank

## **Abstract**

In the presence of risk and uncertainty, measures such as poverty rates are inadequate to analyze the well-being of poor households. The poor are not only concerned about the current low levels of their income or consumption, but also the likelihood of experiencing stressful declines in these levels in the future. Risks to livelihood are particularly important in rural areas where there is generally high dependence on agriculture and the environment. In this study, we analyze the nature, extent and causes of rural vulnerability in Serbia using panel national household data from the 2002 and 2003 Serbia Living Standard Surveys. Rural vulnerability is measured as a function of non-stochastic determinants of poverty as well as exposure to risk. While low levels of consumption (i.e., poverty) explain about 70 percent of vulnerability, we identify risk and uncertainty as crucial dimensions of rural life in accounting for the remaining 30 percent of household vulnerability. Households and regions with a greater share of their livelihood depending on agricultural activities are more at risk of vulnerability than those with a significantly higher share of their income coming from non-agricultural sources. Dependence on agricultural income is directly associated with higher aggregate risk, underscoring the agricultural sector’s lopsided exposure to covariate shocks in general and the negative impact of the 2003 drought in particular. Rural vulnerability to poverty and risk is also strongly associated with asset ownership and access to markets to mobilize them in time of need.

World Bank Policy Research Working Paper 4010, September 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Address all correspondence to Lire Ersado, 1818 H St NW, MSN: H7-157, Washington, DC 20433; Email: lersado@worldbank.org; Phone: (202) 473-2377. This paper is part of the Serbia Programmatic Poverty Assessment work (P088245).  I would like to thank Asad Alam and Ruslan Yemtsov for review and useful comments, and for providing overall guidance for Serbia poverty work.