rafmacalaba/data-use-mentions-extended huggingface dataset:
3 country like Bangladesh where half the population is poor, poverty lines also represent valid price indices faced by the population. Hence, they can also be used to estimate real rather than nominal consumption and inequality. Consumption will be the preferred indicator of well-being because it incorporates the life cycle hypothesis, and because it is measured more precisely. Therefore, all measures of poverty, growth and inequality in this paper will be based on consumption rather than income, and they are all computed using successive rounds of the nationally representative Household Expenditure Surveys (HES) Regional poverty lines were estimated according to the cost of basic needs method. Details on the implementation of the method can be found in Wodon (1997), hence the exposition will be brief here. Three steps were followed for the estimation of the poverty lines. First, the country was divided into fourteen geographical areas. The list of areas and their sample size for the various years is given in Table Al in appendix. A food bundle representative of actual consumption patterns in the country and providing 2,122 kcal per day and per person was chosen. The bundle is given in Table A3, and the same bundle applies to all areas. In each area, the price of each item in the food bundle was estimated, using regressions to control for the impact of household characteristics on the quality of the food consumed. The resulting prices for 1995-96 by area are given in Table A3. Given the estimates of the food prices by area, the cost of the food bundle (the food poverty line denoted by ZF in Table A4) was computed in each area. The second step consisted in computing a cost of basic non-food needs. The non-food expenditures of households whose food or total consumption is equal to their area food poverty line were estimated as, respectively, lower and upper bounds for the cost of non-food needs. Third, lower and upper poverty lines were obtained by summing up the food poverty line with respectively the lower and upper allowance for non-food consumption. The
22

|Country,<br>L|ast publis|hed censu|s|||
|---|---|---|---|---|---|
|economy, or|P|opulation|Source for population|Source for fertility|Source for mortality|
|territory|Date|(1000) *|(mid-1985)|(1985-90, unless indicated)|(1985-90, unLess indicated)|
|Netherlands<br>Antilles|Feb 81|172 J|Bank est.|Based on USSOC<br>1985|Based on USBOC<br>1985|
|Netherlands, Th|e Feb 71|13060 J|Eurostat 1987|Eurostat 1987|Eurostat 1987|
|New Caledonia|Apr 83|145 F|USBOC<br>1985|Based on JNPVSR<br>1984<br>Special Supplement|Based on UNPVSR<br>1984<br>SpeciaL Supplement|
|New Zealand|Mar 86|3307 F|U.N. 1984 assessment|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|icaragua|Apr 71|1878 J|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|
|iger|Nov 77|5098 F|Bank projection from census|Bank assessment of 1959-60<br>survey and age data from<br>1977 census|U.N. 1988 revision (prelim.)|
|igeria|Nov 63|55670 F|Bank projection from<br>official<br>data|Official<br>est.,<br>based on<br>U.N. 1980 assessment|U.N. 1988 revision (prelim.)|
|Niue|Sep 76|4 F|UNPVSR<br>4/87 (officiat<
_Note:_ The values show the percentage of each category of workers with each level of education. For example, 40.2 percent of the manufacturing workers in the sample who work in administration belong to unions.

_Source:_ Round Three of the 1994 Regional Program on Enterprise Development for Ghana.

Workers in administration and support occupations have the highest share of workers in unions; workers in manager and professional occupations have the lowest share (see table 5). It appears that unionization makes a difference in wages because the union premium in the Ghanaian manufacturing sector is statistically significant and positive (see appendix table B-1). Union members earn more than nonunion members do, when controlling for firm and individual characteristics in the wage equation.

In addition, union members have statistically significantly higher productivity than nonmembers do. The test of the hypothesis that the wage and productivity gaps are of equal size is highly rejected (F=68.44, Prob > F=O.00). The estimated wage differential associated with being a union member is approximately 16 percent (see appendix table B-1). This outcome is in line with results found for industrial countries, which usually reveal that union members earn more than nonunion members. These results may provide evidence that trade unions do not reduce wage differentials and, therefore, that unions negatively affect the distribution of income.

The finding for Ghana may be surprising because other African studies of the impact of unions on wages report a negative association. Both results for CFA countries applying a cross-section dataset of African countries (Rama 1997) and results from wage analyses in Zimbabwe (Verner 1998) show that trade unions impact wages negatively. The presence of trade unions is generally associated with more firm-provided training. Trade union bargaining at the firm level may increase social welfare by counterbalancing 22
policies and the other related to internal migration. To our knowledge, it is the first paper to shed light on the unique interaction between school fees and child migration.<sup>2</sup> On the empirical front, we offer an empirical analysis of household survey data from China, which is a most interesting case to analyze the links between migration and access to education. Indeed, due to the _hukou_ (household registration) system, migrant workers in China are only granted limited access, if any, to the subsidized education and other social services available to local city residents. This contributes to many migrants leaving their children behind when migrating to cities for work. As a result, more than one-fourth of all children in China age 0 to 17—amounting to almost 70 million children—are estimated to be left behind by their migrant parents (UNICEF, 2013).<sup>3</sup> The focus on China offers relevant insights into important policy issues as recent studies overwhelmingly point to the detrimental impacts of parental migration on LBCs on educational outcomes including enrollment (Wang, 2014), grade attainment (Meyerhoefer and Chen, 2011), standardized test scores (Zhang et al., 2014; Zhou, Murphy, and Tao, 2014), and health outcomes including overweight and underweight measures (de Brauw and Mu, 2011) and anxiety levels and self-esteem (Bai et al., 2016). In addition to having worse education and health outcomes, Meng and Yamauchi (2015) find that LBCs spend less time studying after school, receive fewer tutoring lessons outside school, and are more likely to be enrolled in

> 2 The literature so far has mostly focused on the impacts of school fees on enrollment. For China, Yi et al. (2015) find that an unconditional financial aid program (fee-reduction program) had small effects on upper secondary school enrollment for Grade 9 students, but no effects for Grade 7 students; Shi (2012) finds educational fee reductions to
bank lending to local governments are displayed graphically in Figure A2.<sup>33</sup> Prior to the Law, relative lending to local governments and firms followed a relatively similar trend across banks. However, in the quarters following the implementation of the FD Law, banks ex ante more exposed to local public debt begin contracting relatively more their lending to local governments.

# **4.3. Impact of the FD Law on Firm-Level Outcomes**

Finally, we examine the impact of public debt limits on real outcomes of firms. To do this, we apply our identification strategy on firm-year level data obtained from Orbis. With this data, we compare changes in the real activity of firms across states of varying ex ante indebtedness before and after the FD Law. Since Orbis identifies the municipality where the firm is headquartered and given that bank lending tends to be local, we additionally examine heterogeneity in firms’ real activity across municipalities of varying exposure to the FD Law. We define the exposure of a municipality to the FD Law by averaging the exposure to local government debt of the banks that operate in the municipality. Our intuition is as follows. If changes in credit supply are concentrated in banks that were ex ante more exposed to local public debt, then firms headquartered in municipalities where these banks operate should be relatively more impacted by the FD Law. Our specification is outlined in equation 6.

The outcomes of interest _yf,y_ in equation 5 correspond to the total liabilities, assets, fixed assets, and sales (all in logs) of firm _f_ in year _y_ . The variable _DebtBankMunim,Mar16_ is our measure of exposure of a municipality to the FD Law and is calculated as the weighted average of _BankExposureGovs,b,Mar16_ of the banks operating in municipality _m_ in March of 2016. All other variables are as defined above. We include year fixed effects to control for yearly shocks, as well

> 33 The test of non-linear trends is based on regressions akin to equation 3, where
27

**34. Some illustrative computations provide a sense of the potentially significant magnitude by which European bank deleveraging could affect EMDE credit.** Subject to a number of assumptions and caveats associated with the limited availability of granular data, the computations attempt to get a suggestive estimate of the effects of the deleveraging, using the regional credit distribution of European banks under various scenarios. Starting from the situation where European banks collectively have €1.64 billion of EMDE credit, or about 7.3 percent of their total loan portfolio (table 1, figure 21),<sup>18</sup> we consider two deleveraging objectives: (i) a short-term objective, to comply with the EBA recapitalization requirements by June 2012; and (ii) a longer-term objective, to reduce the assets/equity ratio to a desirable level. We account for the following drivers: (a) the ability of banks to raise capital (through rights issues, retained earnings, and the like); (b) the extent to which deleveraging is necessary through asset reduction; and (c) the extent to which necessary asset reduction occurs through the loan book. Simplifying assumptions are discussed in box 3.<sup>19</sup>

# **Box 3. Simplifying Assumptions Underlying the Simulations of the Credit Impact of Deleveraging**

- All factors mentioned above are taken equally across European countries. For example, it might be assumed that all countries are able to raise their capital levels by 20 percent. However, banks in core European countries are likely better positioned to raise capital than their peers in the periphery.

- Any reductions in credit occur proportionately across regions. This assumption ignores the possibility that emerging markets might be hit disproportionately due to home biases. On the other hand, foreign bank branches and subsidiaries of European banks that are funded in local markets might be able to better absorb loan deleveraging pressures.

- Assets are 2.8 times RWAs. This figure is based on the median in
of green transition,<br>growth from NGEU reforms and policy 2021<br>targets<br>Percentage points Index, 10 = Frontier<br>1.0 8 Min-max range Median<br>0.8<br>6<br>0.6<br>0.4 4<br>0.2<br>2<br>0.0<br>Ageing Inclusiveness Institutions Digital<br>ECA CE WBK EE SCC CA<br><!-- End of picture text --> F. EBRD assessment of green transition, 2021 Sources: EBRD (2020, 2021); Haver Analytics; IMF; Oxford Economic Model; Penn World Tables; UNHCR; United Nations (2020); UN Population Prospects; World Bank; World Bank Development Indicators database; Enterprise Surveys database.

Note: CA = Central Asia; CE = Central Europe; ECA = Europe and Central Asia; EE = Eastern Europe; RUS = Russian Federation; SCC = South Caucasus; TUR = Türkiye; WBK = Western Balkans. Period averages of real GDP-weighted averages.

A.B. Potential growth estimates based on production function approach. Sample includes 53 EMDEs. of which 9 countries are from ECA.

C. Refugees indicate those registered for national protection schemes. Migrants indicate migrant stock in mid-2020. D. Percent of firms identifying practices of competitors in the informal sector as a major constraint. Data for the EU-26 country grouping and the euro area exclude Germany. Aggregates are calculated as averages.
years.<sup>7</sup> The population of preschool-aged children comes from the annual<sup>8</sup> National Socioeconomic Survey (SUSENAS) cross-sections of respective PODES years.

Following decentralization reform in 1999, regional governments have been entrusted with more policy-making decisions. Province, district, and village-level governments have legislative power<sup>9</sup> to make policies and to allocate public goods, in this case, pertaining to preschool access. We aggregate preschool access at the district-level because, apart from PODES, the other two

> 5 For more details, see: Frankenberg and Thomas (2000), Strauss et al. (2004), and Strauss et al. (2009). 6 For each individual mother, we need at least two observations. Some of our analyses, such as on main activity, are not available in the historical employment module. Hence, we have to ensure that individuals are observed in at least two rounds.

> 7 Specifically, between 1990 and 2014 Podes was administered in 1990, 1993, 1996, 2000, 2003, 2005, 2008, 2011, and 2014.

> 8 SUSENAS are sometimes fielded more than once per year. In such cases, we use the round with the biggest sample. 9 Sub-district is an administrative level between district and village. It serves to demarcate geographic boundaries, but it does not have any legislative power.

7
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# b. Water

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Q1 Q2 Q3 Q4 Q5<br>income quintile<br>piped water standposts<br>wells/boreholes surface water<br>% of population<br><!-- End of picture text --> _Source:_ Banerjee and others 2009.

Assuming that households can afford to spend 5 percent of their budget on power and 5 percent on water, subsistence consumption bundles of water and power at cost recovery prices would be affordable for the vast majority of Tanzanian households (figure 19). For power, with a cost recovery tariff of $0.16 per kilowatt hour for power and a subsistence consumption of 25 kilowatt hours per month (enough to power two 100 watt light bulbs for four hours per day), the monthly power bill would be $4. For water, with a cost recovery tariff of $0.68 and a subsistence consumption of 4 cubic meters per month (25 liters per person per day for a family of five), the monthly water bill would be $4. Based on analysis of household income distribution, monthly utility bills of $4 would be affordable to all but the poorest 20 percent of Tanzania’s population. Given that access to these services is currently confined to the more affluent 10 percent of the population, affordability is unlikely to become a real issue for some time to come until service access is much more widely available than at present.

Even if all inefficiencies could have been eliminated, an infrastructure funding gap of $0.7 billion per year (5 percent of GDP) remained as of 2006 (table 12). Looking across sectors, the overall funding gap was mainly associated with water (55 percent of total) and to a lesser extent power (30 percent of total
First, the absence of long-run positive effects associated with the subtler reintegration benefits could be due to the low predictive power given the small number of officially demobilized soldiers. Our analysis of spillover effects does not suffer from this caveat, as the combination of household panel data and the variation in the number of demobilized combatants across collines allowed us to quantify these effects both in the short and the longer run. Second, our analysis focused on an area in north-western Burundi in which ex-combatant density was above the country average, and it should not be seen as representative of the country. The theoretical framework highlighted that the impact of cash and in-kind transfers may vary according to the context and the way the program is implemented. More research is needed to assess the external validity of our findings. Finally, our interpretation of the evolution of the impact of the DDR program on CNDD+ demobilized combatants relies on the indirect observations of a short-run positive impact identified in the literature (Ndayiziga et al, 2008; Verwimp and Bundervoet, 2009; Willems et al, 2010). More research is needed to test the relevance of our interpretation and, more generally, to assess how impacts of demobilization programs evolve over time.

In spite of its limitations, we believe that our study sheds light on some important effects of the DDR program on both ex-combatants and civilians. We hope that our study, together with other analyses of the effects of the DDR program, can pave the way towards innovative designs for evaluating the impact of such important programs.

# **References**

- Ajayi, O. (2006) Experiences in the application of the core welfare indicator questionnaire (cwiq) survey technology in africa: the journey so far. _The African Statistical Journal_ 3, 189–210.

- Alderman, H., Behrman, J., Kohler, H.-P., Maluccio, J., Watkins, S. (2001) Attrition in Longitudinal Household Survey Data. _Demographic Research_ 5(4), 79–124
I assume that: To calibrate the optimal rates of return to government investment implied by proposition 2, it is necessary to take a stance on the values of _rt_ +1. I assume that _rt_ +1 is given by the Euler condition, which relates the interest rate to the rate of consumption growth.<sup>3</sup> The calibration based on the conservative parameterization described by Table 1 suggests that the optimal rate of return on government investment should be negative in most countries, and always below 2%. Given that most estimates suggest high rates of return to government investment, the conclusion is that, given current tax revenues, governments should divert resources from cash transfer programs to government investment projects.

However, this conclusion is a result of the highly conservative parameterization, and may not hold up in the presence of less conservative (and perhaps more realistic) parameters. Table 2 illustrates how the optimal rate of return to government capital varies with alternative calibration parameters.

Depending on the parameterization, the average optimal rate of return on government investment can be as high as 21%. This estimate is in the range of plausible estimates of rates of return to government investment projects, although those are notoriously imprecise. The calibrated optimal rates of return vary somewhat by country, and are reported in Appendix C.

> 3The Euler condition is _β_ (1 + _rt_ +1) = _u′_ ( _ct_ ) _/u′_ ( _ct_ +1). I assume that the rate of consumption growth is given by the rate of GDP per capita growth, which is calibrated based on data from the World Development Indicators.

15
POLICY RESEARCH WORKING PAPER 2302

# **Summary** findings

Deininger and Olinto use a large panel data set from Zambia to examine factors that could explain the relatively lackluster performance of the country's agricultural sector after liberalization. Zambia's liberalization significantly opened the economy but failed to alter the structure of production or help realize efficiency gains. They reach two main conclusions.

First, not owning productive assets (in Zambia, draft animals and implements) limits improvements in agricultural productivity and household welfare. Owning oxen increases income directly, allows farmers to till their fields efficiently when rain is delayed, increases the area cultivated, and improves access to credit and fertilizer markets.

Second, the authors reject the hypothesis that the application of fertilizer is unprofitable because of high input prices. Rather, fertilizer use appears to have declined because of constraints on supplies, which government intervention exacerbated instead of alleviating. (Extending the use of fertilizer to the many producers not currently using it would be profitable, but increasing the amount applied by the few producers who now have access to it would not be.) Policies to foster accumulation of the assets needed for agricultural production (including draft animals and implements) and to provide complementary public goods (education, credit, and good agricultural extension services) could greatly help reduce poverty and improve productivity.

- - This paper a product of Rural Development, Development Research Group is part of a larger effort in the group to analyze determinants of rural growth and market participation. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Fernandez, room MC3-508, telephone 202-473-3766, fax 202-522-1151, email address mfernandez2@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may be contacted at kdeininger@worldbank.org or polinto@Hworldbank.org. March 2000. (22 pages)

_The Policy Research Working Paper Series disseminates the findings of work
the role of suppliers in developing countries in the global textile industry and the barriers they face to integration in the global production network. That work draws mostly on case studies and is supplemented by firm surveys in several countries.

The web site www.globalvaluechains.org is a depository for work on GVCs and it contains 74 studies focusing specifically on African countries and 220 studies on the textile and apparel industries. Twelve studies are exactly at the intersection of textiles & apparel industry in Africa and there are three more studies that investigate the footwear & leather industries in Africa. The evidence in Gibbon (2003) illustrates how detailed information from specific industries in a few countries can supplement the type of cross-country evidence that we will present here.

A second reason for our interest in studying GVCs in textiles and apparel is that this industry is a common entry point into the global manufacturing industry for less developed countries (Vogel 1991). Developing countries often have a comparative advantage in laborintensive, light manufacturing of which textile is a prime example. This was true historically, but is still supported by current data. In Figure 1, we show the average GDP per capita across all exporters for different product categories. Importantly, we use each country’s export market share in global trade for the product category as weight. When most exports of a particular commodity originate from relatively poor countries, the product category will show a low average GDP per capita. This approach was used by Hausmann, Hwang and Rodrik (2007) to construct their widely-used PRODY measure of product quality.

4