rafmacalaba/datause-encoder-data huggingface dataset:
Table 12. Estimation results for “firms” with 4-6 employees

||||ln(C|A)|||||ln(rev|enue)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS||Rand|om eff|ects||OLS||Rand|om eff|ects|
||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||
|woman|-0.50|0.25|0.05|-0.50|0.33|0.14|-0.58|0.31|0.06|-0.58|0.39|0.14|
|can read|0.03|0.26|0.91|-0.01|0.34|0.97|-0.01|0.32|0.98|-0.02|0.39|0.96|
|education>2|0.25|0.28|0.37|0.22|0.36|0.55|0.13|0.34|0.70|0.12|0.42|0.78|
|age 30-39|-0.15|0.20|0.46|-0.17|0.26|0.52|-0.25|0.24|0.30|-0.28|0.30|0.36|
|age 40-49|-0.51|0.25|0.04|-0.51|0.33|0.13|-0.69|0.31|0.03|-0.70|0.39|0.07|
|age>49|-0.46|0.31|0.14|-0.47|0.41|0.25|-0.57|0.38|0.14|-0.64|0.48|0.19|
|age unknown|-0.46|0.46|0.32|-0.47|0.61|0.44|-0.70|0.59|0.24|-0.75|0.74|0.31|
|exp. 5-10|-0.67|0.35|0.06|-0.66|0.46|0.15|-0.90|0.43|0.04|-0.87|0.54|0.11|
|exp.>10|-0.34|0.35|0.32|-0.37|0.46|0.42|-0.61|0.43|0.16|-0.60|0.54|0.27|
|couture|1.49|0.38|0.00|1.51|0.50|0.00|1.01|0.47|0.03|0.95|0.58|0.10|
|electronics|0.88|0.44|0.05|0.92|0.59|0.12|0.38|0.55|0.48|0.33|0.68|0.63|
|Abidjan|-0.73|0.41|0.08|-0.75|0.55|0.17|-0.12|0.54|0.82|-0.09|0.68|0.90|
|PB|-0.01|0.17|0.96|0.05|0.18|0.79|-0.20|0.21|0.36|-0.20|0.22|0.37|
|PA|-0.01|0.17|0.98|0.04|0.18|0.84|-0.02|0.22|0.92|-0.07|0.22|0.77|
|NPB|-0.13|0.17|0.45|-0.09|0.10|0.36|-0.23|0.22|0.30|-0.22|0.15|0.14|
|constant|11.14|0.47|0.00|11.14|0.62|0.00|11.63|0.59|0.00|11.69|0.73|0.00|
|PB-NPB=0|0.51||0.48|0.61||0.44|0.02||0.89|0.01||0.92|
|PA-PB=0|0.00||0.98|0.01||0.90|0.83||0.36|1.02||0.31|
|PA-PB=-NPB|0.30||0.59|0.58||0.45|0.03||0.86|0.19||0.66|
|B&P LM-test||||37.96||0.00||||25.27||0.00|
|Hausman-test||||0.00||1.00||||0.00||1.00|
|N|200.00|||200.00|||200.00|||200.00|||

Table 13 shows the findings for firms with 7-9 employees. Again only the sectorial indicators are significant and none of the coefficients of the participation variables come out significantly neither in the models of ln(CA) nor in the models of ln(revenue). Hence, the conclusion is that for these mid-size firms PAFPA has not changed anything in terms of CA or revenue.

In table 14 the results of the larger firms are presented. In the random effects specification of ln(CA) the coefficients of the education variables and the sector variables are significantly positive and the coefficients of PB and PA are significantly negative, meaning that the CA of the nonparticipants is higher than the CA of the participants. For both the participants and the nonparticipants there is no evidence of change in the level of CA during PAFPA period, and hence no program impact is detected. In the case of ln(revenue) the outcome is slightly different. The coefficient of experience is significantly positive and so are the coefficients of the sector indicators. Furthermore, the indicators of the “before PAFPA” variables of both groups are significantly different from zero, but with opposite signs. The conclusion is that 24
variations. These determinants of productivity are associated with the efficiency with which factors, technology and material inputs are utilized. They reflect the level of competence of the entire production unit taken as a whole and depend mostly on the quality of management. The rapidly growing literature on technical efficiency is largely concerned with estimating the extent of this source of productivity variance (Coelli et al 1999). In practice however, effects deriving from scale and location externalities, tend to get bundled in with purely internal effects in the estimation of technical inefficiency.

Technical inefficiency introduces a plant-specific _diminution_ of operational productivity in relation to the domestic best practice productivity that can be realized in a particular industry. Using plant level data for four major Indian industries at the three digit level of industrial aggregation, we examine the variation of this technical inefficiency component in relation to various locational and other economic variables. In particular we are interested in testing whether mean technical efficiency is higher - and its variance lower - in relatively modern or technology intensive sectors. Higher efficiency is expected in such sectors for several reasons, which include the adoption of efficient production technologies, higher skill levels of the workforce, a greater likelihood of knowledge diffusion and uniformity of efficiency in the plants being examined.

India has one of the oldest and most widely developed manufacturing sectors in the developing world. Since it has functioned to a large extent like a closed economy for most of the post-1948 period, it is a prime candidate for the study of industrial heterogeneity as described above. Additionally, since significant liberalization has been introduced into some sectors in the early 1990s, the forces of enhanced market competition are likely to have produced further differentiation. In this study, the variation of technical inefficiency, estimated by the stochastic frontier method, is examined in relation to scale, location, extent of infrastructure investment and other determinants.

Page (1984) argues that lower efficiency variation is to be expected in relatively modern industries due to a higher degree of homogeneity in the vintage of capital stock and labor force characteristics. The demand structure of industries is also likely to influence efficiency: firms in industries mainly catering to isolated local markets are likely to exhibit higher variations in (and have lower overall) efficiency as the incentive to raise productivity is held back by limited market size. In contrast, firms in industries accessing large domestic and global markets are likely to be relatively more efficient as 2
# **Acknowledgements**

This paper was written by Carrie Turk in the World Bank office in Vietnam. Helpful detailed comments were received from: Nisha Agrawal, Bob Baulch, Jesko Hentschel (World Bank); Stefan Nachuk (Oxfam GB); Edwin Shanks (Vietnam-Sweden Mountain. Rural Development Program); Ramesh Singh (ActionAid Vietnam); and, Joachim Theis (Save the Children Fund UK).

# **Abbreviations**

|CAS|Country Assistance Strategy|
|---|---|
|CG<br>|Consultative Group<br>|
|CEMMA|Committee for Ethnic Minorities and Mountainous Areas<br>|
|DFID<br>|Department for International Development (UK)<br>|
|GSO<br>|General Statistics Office<br>|
|HEPR<br>|Hunger Eradication and Poverty Reduction<br>|
|MARD|Ministry of Agriculture and Rural Development<br>|
|MOF|Ministry of Finance|
|MOLISA<br>|Ministry of Labor, Invalids and Social Affairs<br>|
|MPI|Ministry of Planning and Investment<br>|
|MPDF|Mekong Project Development Facility|
|MRDP<br>|Mountain Rural Development Program (Vietnam-Sweden)<br>|
|NGO<br>|Non Governmental Organization<br>|
|PRSP<br>|Poverty Reduction Strategy Paper<br>|
|PPA<br>|Participatory Poverty Assessment<br>|
|PWG<br>|Poverty Working Group<br>|
|SBV<br>|State Bank of Vietnam<br>|
|SIDA<br>|Swedish International Development Agency<br>|
|UNDP|United Nations Development Program|
|WU|Women's Union|
# Restructuring Uganda's Debt

# The Commercial Debt Buy-Back Operation

# Introduction

The principal objective of this paper is to describe briefly Uganda's Commercial Debt Buy-Back operation, which was financed jointly by the IDA-Only Debt Reduction Facility (Facility), with co-financing from the Governments of Netherlands, Switzerland, and Germany and the European Economic Community (EEC). Although the Facility has been in existence since August 1989, and servicing commercial debt presents a serious burden to several low income economies throughou **t** he world, particularly in Africa, only a handful of countries have so far been able to avail themselves of the Facility's resources. This is not altogether surprising since commercial debt buy-backs can be extremely complex, requiring substantial preparatory effort to come up with a well articulated external debt strategy based on a comprehensive and detailed external debt data base. Therefore, one of the objectives in describing the operation is to discuss the requirements of the IDA Facility and to shed some light on how these were satisfied in the Ugandan context and the areas which needed particular attention.

## IDA-Only Debt Reduction Faciity

The introduction of the Brady Plan in 1989 resulted in a heightened awareness, both among creditors and among the donor community, of the need to reduce the commercial debt exposure of developing economies. While commercial debt in low income economies constitutes a relatively small proportion of their total debt, its non-concessional nature often imposes severe
# **7. Conclusions**

Both internal and international migration appear to offer large benefits for many individuals, and for the optimal allocation of labor within and across countries. Yet most people never move in their lives. There are many potential reasons for this lack of movement, including information failures, liquidity constraints, high costs and policy barriers, and risk. These reasons are important, and interventions to reduce these barriers to movement offer the potential to improve welfare. However, I argue that there are two other reasons for lack of movement that have been less a part of economic theories of migration. Fears about the uncertainty involved in migrating inhibit movement, especially when many of factors people are uncertain about may be difficult to quantify the risk of, or to insure against. Tears that accompany migration reveal the attachment people have to a particular place. Although this attachment has large value to many people, and can reduce the initial benefits of migrating, a status quo bias and an inability to picture what one is giving up by not migrating may cause an excessive attachment to the current location. Recognition of these two factors suggests new avenues for policy, such as testing ways to help potential migrants better visualize the opportunity cost of not moving, facilitating short-term visits that help reduce uncertainty, and efforts to change the default people have in mind when making location decisions.

# **References**

Allen, Treb, Caué Dobbin and Melanie Morten (2019) “Border walls”, Mimeo. Stanford.

Angelucci, Manuela (2015) “Migration and financial constraints: Evidence from Mexico”, _Review of Economics and Statistics_ 97(1): 224-228.

Atkin, David (2013) “Trade, Tastes, and Nutrition in India”, _American Economic Review_ 103 (5), 1629-1663 Bah, Tijan, Catia Batista, Flore Gubert and David McKenzie (2022) “Can Information and Alternatives to Irregular Migration Reduce “Backway” Migration from The Gambia?”, Mimeo.

Baseler, Travis (2021) “Hidden Income and the Perceived Returns to Migration”, Mimeo.

Batista, Catia and David McKenzie (2021) “Testing Classic Theories of Migration in the Lab”, _CReAM Discussion Paper_ no. 23/21.

Bazzi, Samuel (2017) “Wealth heterogeneity and the income elasticity of migration”, _American Economic Journal: Applied Economics_ 9(2): 219-55.

21
cost of PM2.5 is higher in the eastern regions than in the western regions of the country. This is due to the larger size of brick production in the eastern region than in the western region. The social cost of PM2.5 is associated with the health damage cost. It is important to note that an improvement in AAP (PM2.5) is, however, unlikely to provide immediate benefits for health outcomes (e.g., for heart disease, stroke, COPD, and lung cancer) that develop over long periods of PM2.5 exposure.

**Table 7: Social costs of PM2.5 and CO2 emissions of coal-based brick production in Nepal (NRs per brick)**

|Kiln||||PM2.5||||CO2|
|---|---|---|---|---|---|---|---|---|
|technology|Koshi|Madesh|Bagmati|Gandaki|Lumbini|Sudurpachim|Nepal|Nepal|
|CK|43|37|27|30|29|28|31|4|
|FC BTK|9|8|6|7|6|6|7|2|
|ZZK|5|5|3|4|4|3|4|2|
|VSBK|5|4|3|3|3|3|4|1|
|HK|7|6|4|5|4|4|5|2|
|TK|8|7|5|6|5|5|6|2|

_Notes_ : The estimated figures are the product of the emission factor, the average weight of the brick, and social cost. For PM2.5, e.g., it is the product of the emission factor (g/kg of brick), the average weight of the brick (kg/brick), and the social cost (US$/kg). The social cost of PM2.5 is US$ 86.85/kg and CO2 is US$ 0.04/kg taken from EPIC India, 2018).

Likewise, the social cost of CO2 varies by kiln technology, with higher costs associated with less efficient and more energy-intensive technologies (e.g., CK) and lower costs associated with more efficient and less energy-intensive technologies (e.g., VSBK). The social cost of CO2 is associated with the environmental damage cost.

# 5.3 Economics of alternative bricks

The devastating earthquake of 2015 and its aftershocks caused widespread damage to both life and property across the country, with an estimated 602,000 houses fully damaged, 285,000 houses partially damaged, and the loss of life was about 9,000 (DUDBC, 2017). As part of the government’s _Build Back Better Initiative_ , the government proposed several cost-efficient alternative materials for sustainable reconstruction of both urban and rural houses, such as interlocking brick, confined hollow concrete block, hollow concrete block (HCB), compressed 25
- _This research is not an attempt to substantiate corruption in particular Bank projects._ In order to ensure that the research is not misinterpreted in that way, and because it is based in considerable part on documentation that is for official use only, the individual projects studied will remain confidential.<sup>2</sup>

# **A database of red flags in water**

**To populate our case study database, details of water projects were selected from the relevant Bank databases** .  Countries were selected in part on the basis of comparatively full contracting documentation in Washington DC databases (therefore effectively excluding countries with a strong field-based procurement hub) and on the basis of the language abilities of the team.  Projects were selected in part on the basis of good on-line document-keeping (in particular, of bid evaluation reports).  Given that poor documentation is itself a red flag, it is worth noting that this may have introduced a negative bias into the results in terms of the number of red flags uncovered (a worrying conclusion given the results that follow).  An annex to this paper discusses the level of online archiving in the projects under review.  Again, these were some of the betterdocumented projects in the on-line system.  Of course this allows us to make no comment on the quality of physical document keeping which may be very strong.

**In addition to this procedure, we used public media reports of alleged corruption in two Bank water projects** reportedly investigated by INT to provide a small subset of 21 potentially suspect contracts.  The public reports of INT interest involve allegations, not convictions, involving one or two contracts — rather than the whole project.  This should significantly caveat any findings regarding differences or similarities between ‘potentially suspect’ contracts and ‘random’ contracts in the sample.  In total, we have analyzed 7 projects in 5 countries the water sector from three different World Bank regions, with total of 60 contracts under study. The composition of the sample is presented below in Table 1:

_<u>Table 1: Contract Sample</u>_

|**Project**|**Number of**<br>**contracts**|
|---|---|
|_Control group projects_||
|Project 1|8|
|Project 2|5|
|Project 3|4|
|Project 4|9|
|Project 5|13|
|**Subtotal for five projects**|**39**|
|_Potentially suspect projects_||
|Project 6|7|
|Project 7|14|
|**Subtotal for two projects**|**21**|
|**Total for seven projects**|**60**|

> 2 At the same time, it is worth noting that the research is not based on strictly confidential information or knowledge.  The documentation and data used is available in project files and databases that can be accessed by all World Bank staff and consultants with access to the Bank’s intranet.

5
# <u>3) Aggregate Results</u>

Figure 5 breaks down the estimated total expenditures for housing subsidies in 1995 into its main components. Different colors indicate, respectively, subsidies for housing construction (orange), housing demand (pink), and housing as capital (green). Figures have been extracted from Table 5.

<u>Figure 5 : Expenditures on Housing Subsidies by Type - Morocco – 1995</u> <!-- Start of picture text -->
- -<br>Housing Subsidies by Type   Morocco   1995<br>Interest rate  Exemption of VAT<br>subsidies on  on mortgage<br>mortgage loans interests<br>C apital gains  4% 1%<br>tax  deduction on<br>pro visions<br>12 % m ortgage interests<br>6%<br>Exemption of local<br>taxes for new units<br>and social housing<br>2% Lowered<br>registration fees<br>10%<br>Cut of 75 % o n Tax<br>low  VAT rate for<br>base for lo cal taxes<br>hou sing<br>26%<br>17%<br>Exempt ion of VAT,<br>social hous ing and<br>self-construc tion<br>Land<br>6%<br>subsidies to State- Housing ministry<br>owned companies Investment Budget<br>17% 3%<br><!-- End of picture text -->

- A few striking facts emerge:

- Total subsidies amounted to MDH 4.8 billion annually (1.5% of Morocco’s GDP). Of those, only MDH 325 million (less than 7%) were subsidies on budget, and interest rate subsidies represented only 4% of the total of subsidies.

- Budget and fiscal expenditures on mortgages (interest rate subsidies, deduction of interest from taxable income) amounted to MDH 460 million, in spite of the weak development of the mortgage market. This shows that severe problems of sustainability would show up following the development of housing financing.

39
method essentially generalizes the idea of an instrumental variables estimator to a situation in which the dependent variable contains non-ignorable errors.

We illustrate the method using high-quality socioeconomic survey data for Russia in 2002. We first show that applying standard methods from past research to these data suggests little sign of an economic gradient in the self-assessed health of Russian adults. This might not be too surprising if one was talking about Russia some 20 years earlier, at which time it might be argued that a reasonably well-funded universal health care system meant that the quality of health care varied little with income. However, the lack of a gradient in data for Russia around 2000 is surprising. Worsening public health services, increasing reliance on the market for health care, and rising poverty and inequality, would lead one to expect an emerging economic gradient in health status. Psychological adaptation to the stresses of the economic transition and health crisis could well entail that the structure of errors in _SAH_ data for Russia hides the true gradient. One can expect that it will be the poor who are least able to protect their health care during Russia’s health crisis, and they may well adapt to the situation by downplaying the health risks they are facing, at least for some time. Then the true economic gradient will be attenuated in the raw data on _SAH_ . It may well be difficult to identify the gradient in this setting by standard empirical methods, casting doubt on numerous claims found in the literature.<sup>6</sup> Applying our preferred estimation method, we show that much stronger signs of the gradient emerge from a more nuanced empirical model — emphasizing non-income factors relevant to perceived economic welfare at the micro level, and allowing for heterogeneity in _SAH_ data. This model reveals a marked tendency for the poorest to be less healthy, with dampened health gains as economic welfare rises. In short, while there is little or no income effect evident

> 6 For example, Gavrilova et al. (2002) and Brainerd and Cutler (2005) argue that the “psychosocial stresses” of the transition in Russia are a more important cause of the health crisis than poverty; however, the same (unobserved) stresses may well make it hard to identify the importance of poverty.

5
Figure 2: Intraregional Trade as a Share in Region’s Total Trade <!-- Start of picture text --> %<br>20<br>18<br>16<br>14<br>12<br>South Asia<br>10<br>East Asia<br>8<br>6<br>4<br>2<br>0<br>1980 1983 1986 1989 1992 1995 1998 2001<br><!-- End of picture text --> Source: Calculated by the authors based on United Nations COMTRADE data Note: East Asia includes: Brunei, Cambodia, China, Hong Kong, Indonesia, Lao PDR, Macao, Malaysia, Mongolia, Myanmar, Palau, Papua New Guinea, Philippines, Singapore, Solomon Islands, Thailand, and Vietnam. South Asia includes : Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka.

South Asian countries should explore more trade opportunities within the region. Figure 3 shows the observed amounts of intra- and interregional trade relative to a predicted value of trade for both South Asia and East Asia. The latter is an expected value given the average geographical distance, levels of GDP, and other relevant factors. The figure demonstrates that intraregional trade in South Asia is lower than that of East Asia—even when geographic proximity, levels of GDP, population, and trade arrangements are taken into account. Conversely, trade with the rest of the world for both regions are similar. In fact, South Asia has been engaged in trade with the major industrialized countries, most importantly, with the EU, the United States and Japan. In reality, however, the distances from these major markets impose significantly higher transport costs for exporters in South Asia. Therefore, the region is likely to gain by expanding intraregional trade through the complimentary investments in infrastructure, continued regulatory reform, and other policy initiatives discussed in this paper.

South Asia should facilitate intraregional trade in order to stabilize its economy, which is currently vulnerable to external shocks. As indicated in Figures 1 and 2; the financial crisis in the late 1990s severely affected developing countries as a whole, including those in South Asia, and most severely in East Asia. Exploring opportunities to expand intraregional trade would give South Asia greater chances to not only benefit from trade but also increase stability against external shocks. The region can and should take steps to move toward accelerated growth paths—leveraging both intra- and interregional trade.

3