rafmacalaba/datause-encoder-data huggingface dataset:
||Country Environment Flag [dummy]|||||0.0335|
|---|---|---|---|---|---|---|
|oject||||||(0.0673)|
|f Pr|Country Record Flag [dummy]|||||-0.155***|
|alf o||||||(0.0599)|
|rst H|Project Management Flag [dummy]|||||-0.0647|
|he Fi||||||(0.0598)|
|ng t|Safeguards Flag [dummy]|||||-0.157|
|Duri<br>tion||||||(0.124)|
|able<br>enta|Counterpart Funding Flag [dummy]|||||0.0131|
|serv<br>plem||||||(0.0655)|
|s Ob<br>Im|Number of Risk Flags|||||-0.0128|
|cator||||||(0.0175)|
|Indi|Minimal ISR “DO” Rating|||||0.0364**|
|Risk||||||(0.0164)|
|and|Minimal ISR “IP” Rating|||||0.0266|
|cess||||||(0.0192)|
|Pro|Projects at Risk|||||-0.0621**|
|||||||(0.0293)|
||Region Controls|Yes|Yes|Yes|Yes|Yes|
||Observations|674|674|619|1,012|579|
||McFadden’s R<sup>2</sup>|0.093|0.099|0.106|0.055|0.190|
||McKelvey and Zinova’s R<sup>2</sup>|0.200|0.213|0.223|0.113|0.379|
||Percent Correctly Predicted|72.55%|73.15%|71.57%|69.27%|77.03%|
||Proportional Reduction in Error|8.42%|10.40%|5.38%|0.64%|20.83%|
||_t_statistics in parentheses<br>*_p_< 0.10,**_p_< 0.05,***_p_< 0.01||||||

_Note:_ GDP = gross domestic product; PPP = purchasing power parity; ODA = official development assistance; DAC = Development Assistance Committee; IBRD = International Bank for Reconstruction and Development; IDA = International Development Association; DOD = debt outstanding and disbursed; PCN = project concept note; DO = development objectives; IP = implementation progress; ISR = implementation status and results report.

49
-1 -

# 1. **URBAN** SECTOR

## A. **COUNTRY BACKGROUND**

1.1 Tanzania's GDP per capita is amongst the lowest in the world' with about 50% of the population (mid-1993 estimate of 28 million) living in poverty. Although life expectancy is only 51 years and infant mortality is 92 per 1,000 live births, population growth is high at 3 percent. Agriculture constitutes the largest activity in the country, contributing to 61 percent of GDP, followed by services (26 percent) and industry (12 percent). GDP growth during 1980-1992 averaged 4 percent. The exports to GDP ratio is 1 to 6 with coffee, cotton, tobacco and tea being the main export commodities. Both imports and exports declined by 1.2 percent per year on average during the last decade due to Government controls and a sharp deterioration of the terms of trade. Machinery and transport equipment constitute the largest share of merchandise imports (43 percent), followed by other manufactured goods (33 percent), fuels (13 percent) and food (6 percent). Tanzania's external debt amounts to 250 percent of its GDP, resulting in a correspondingly high debt service burden.

1.2 Tanzania became independent in 1961. With the Arusha Declaration in 1967, the public sector took on the leading role in the economy. Social progress was achieved in the 1970s, but the structural imbalance of the economy continued. By the mid-1980s the economy was characterized by prolonged stagnation and a worsening macroeconomic performance. Since 1986 Tanzania has undertaken a prograrn to reduce economic controls and encourage the private sector, but the reforms have not sufficiently restructured traditional exports, parastatals, and the financial sector. GDP and export growth averaged 4 percent per year during the period of 1986-1993.

1.3 The Government's medium-term economic development objective is to raise the annual rate of economic growth to 6 percent and to reduce poverty through the provision of social and physical infrastructure. IDA's role has been to support the Government in achieving its goal of poverty reduction through the provision of financial resources in the form of credits and through policy advice and technical assistance.

### B. **URBAN SECTOR OVERVIEW**

1.4 Tanzania's approximately 6 million urban dwellers represent 21 percent of the total population. The urban population was growing at a rate of 6.6 percent annually from 1980 to 1992, more than twice the national rate of population growth during the

> The estimate given in the 1995 World Bank Development Report of $90 per capita is based on official statistics and is likely to be an underestimate. A recent survey of household expenditure in Tanzania estimates per capita yearly expenditure at US$ 280. The same report gives a population estimate of 28 million.
Policy ReseaRch WoRking PaPeR 4366

# **Abstract**

In view of its increasing importance, and the dearth of information on return migration and its impacts on source households, this study uses data from the 2005 Albania Living Standards Measurement Study survey and assesses the impact of past migration experience of Albanian households on non-farm business ownership through instrumental variables regression techniques. Moreover, considering the differences in earning potentials and opportunities for skill acquisition in different destination countries, the impact of household past migration experience is differentiated by main migrant destinations, namely Greece and Italy. The study also tests for the hypothesis of the existence of migration cycles, by differentiating the time spent abroad based on the year of return. The empirical results indicate that household past migration experience exerts a positive impact on the probability of owning a non-farm business. While one additional year in Greece increases the probability of household business ownership by roughly 7 percent, a similar experience in Italy or further destinations raises the probability by over 30 percent. Although past migration experience for the period 1990-2000 is positively associated with the likelihood of owning a household enterprise, a similar impact does not materialize for the period 2001-2004. The latter finding seems suggestive of the fact that more recent migrants are yet to attain a target level of required savings and skills in order to successfully establish a new business upon return.

This paper—a product of the Development Research Group—is part of a broader collaboration with the Food and Agriculture Organization of the United Nations to study the impact of migration on investments and rural development in source countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gcarletto@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
|Power Outages<br>|||||-0.157**|-0.145**|-0.146**|
|---|---|---|---|---|---|---|---|
|(days)||||||||
||||||(0.068)|(0.068)|(0.067)|
|Courts Obstacle (cell<br>|||||-0.098|-0.536|-0.404|
|average)||||||||
||||||(0.735)|(0.738)|(0.734)|
|Crime Losses Y:1<br>|||||-0.625|-0.664|-0.671|
|N:0||||||||
||||||(0.670)|(0.670)|(0.670)|
|Firm Inspected Y:1<br>|||||-0.813|-0.869|-0.899|
|N:0||||||||
||||||(0.607)|(0.606)|(0.604)|
|How Much Of An<br>|||||-0.199|-0.243|-0.231|
|Obstacle: Tax Rates||||||||
||||||(0.231)|(0.229)|(0.228)|
|How Much Of An<br>|||||0.012|0.032|0.034|
|Obstacle: Labor||||||||
|Regulations?||||||||
||||||(0.301)|(0.301)|(0.300)|
|Population (logs)||||||-38.37***|-37.069***|
|||||||(10.833)|(10.729)|
|Urbanization (%)||||||1.630***|1.633***|
|||||||(0.406)|(0.411)|
|WBL Index||||||15.209*|14.790*|
|||||||(8.451)|(8.393)|
|Women can work the||||||2.882|2.999|
|same night hours as<br>||||||||
|men Y:1 N:0||||||||
|||||||(2.879)|(2.861)|
|Women can work in||||||-1.485|-0.858|
|the same industries<br>||||||||
|as men Y:1 N:0||||||||
|||||||(2.114)|(2.094)|
|Ratio of Female to||||||2.474***|2.720***|
|Male Population||||||||
|||||||(0.556)|(0.566)|
|Fertility Rate||||||-14.64***|-13.769***|
|||||||(3.089)|(2.959)|
|Starting a Business||||||0.076*|0.075*|
|(DTF)||||||||
|||||||(0.039)|(0.039)|
|Inflation (%)||||||0.041|0.030|
|||||||(0.063)|(0.062)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry<br>fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

57
**Annex Table 2: Elasticities for preferred model and alternative model that includes improved seeds.**

||Preferred M|odel|||Improved s|eeds included|||
|---|---|---|---|---|---|---|---|---|
||Ordinary lea<br>|st squares<br>|Instrument<br>|al variables<br>|Ordinary le<br>|ast squares<br>|Instrument<br>|al variables<br>|
|Inputs|Elasticity|z-score|Elasticity|z-score|Elasticity|z-score|Elasticity|z-score|
|Fertilizer use (kg/ha)*|0.013|5.40|0.097|2.50|0.013|5.23|0.178|1.94|
|Manure use (kg/ha)|0.001|0.61|0.004|1.12|0.001|0.62|0.006|1.04|
|Family labor (days/ha)|0.086|6.99|0.107|5.37|0.086|6.96|0.141|3.38|
|Hired labor (days/ha)|0.060|7.43|0.111|2.12|0.060|7.32|0.165|1.84|
|Area planted to improved seeds (share)|||||0.005|0.70|-0.212|-1.28|
|Maize area|-0.092|-3.10|-0.062|-1.44|-0.092|-3.11|-0.048|-0.70|
|Risk management|||||||||
|Share of maize area in pure stands|-0.020|-0.66|-0.083|-1.73|-0.022|-0.73|-0.034|-0.41|
|Maize area/ plots managed|-0.057|-2.06|-0.084|-2.11|-0.058|-2.11|-0.029|-0.39|
|Number of crops managed|0.264|4.98|0.167|2.12|0.261|4.89|0.263|1.85|
|Farmer characteristics|||||||||
|Female head of household|-0.019|-1.97|0.000|-0.02|-0.018|-1.88|-0.015|-0.55|
|Age of household head|-0.034|-0.71|0.043|0.58|-0.031|-0.66|-0.008|-0.07|
|Weather effects|||||||||
|Difference from average rainfall (mm)|-0.056|-0.123|-0.175|-0.50|-0.060|-0.29|-0.047|-0.09|
|Late start for season 1 rains (weeks)|-0.040|-0.068|-0.080|-3.49|-0.040|-3.34|-0.066|-1.81|
|Late start for season 2 rains (weeks)|-0.035|-0.052|-0.063|-1.38|-0.034|-1.10|-0.139|-1.48|

25
99 LATIN AMERICA AND THE CARIBBEAN (LAC), BORROERS ONLY Population projection (thousands) Sumary for 25 Year Periods

|...<br>**..**<br>_....... .........<br>AGE**GROJP**|..<br>1985|....<br>........<br>2000|...................<br>2025|...............<br>2050|............<br>2075|2100|2125|2150|
|---|---|---|---|---|---|---|---|---|
|...<br>..<br>.....<br>.........|..............|....|...........|......................|..|.<br>.....|..<br>........||
|TOTALN4F|387617|517288|706239|820568|860331|879499|891415|896548|
|MALES|||||||||
|0-4|27467|29252|28750|28729|28774|28840|28833|28815|
|5-9|24864|28800|28320|28536|28655|28735|28770|28787|
|10-14|22822|27579|27913|28484|28625|28700|28740|28766|
|15-19|20754|26664|28047|28361|28587|28643|28693|28731|
|20-24|18761|24236|28327|28358|28477|28539|28621|28674|
|25-29|15877|22031|28376|28211|28289|28411|28528|28609|
|30-34|13284|19817|27933|27750|28073|28290|28445|28542|
|35-39|10718|17793|26552|27218|27913|28178|28349|28459|
|40-44|8665|14959|25418|27160|27641|28026|28200|28330|
|45-49|7384|12356|22783|27132|27393|27m76|27931|28100|
|50-54|6247|9730|20220|26623|26783|27158|27460|27671|
|55-59|5256|7546|17415|25215|25480|26201|26649|26904|
|60-64|4141|6008|14530|22430|23565|24754|25316|25589|
|65-69|3023|4562|10847|19271|21306|22452|23185|23460|
|70-74|2188|3237|7403|14479|18083|19173|19904|20184|
|75+|2173|3195|7055|16904|26100|29484|31796|32695|
|TOTAL|193623|257765|349887|404861|423743|433313|439419|442315|
|FENALES|||||||||
|0-4|26628|28367|27823|27641|27629|27646|27604|27564|
|5-9|24292|28064|27505|27518|27562|27578|27571|27560|
|10-14|22352|26943|27170|27512|27569|27570|27563|27558|
|15-19|20352|26078|2734|27545|27582|27552|27549|27552|
|20-24|18578|23882|27782|27657|27551|27510|27528|27541|
|25-29|15860|21838|27970|27627|27451|27453|27487|27527|
|30-34|13342|19733|27677|27280|27323|27404|27459|27509|
|35-39|1om|17926|26458|26873|27258|27374|27429|27484|
|40-44|8798|15240|25452|26966|27201|27332|27375|27439|
|45-49|7543|12724|23102|27161|27163|27207|27265|27352|
|50-54|6497|10131|20829|27009|26877|26935|27075|27185|
|55-59|5566|8073|18385|26177|26106|26498|26m|26916|
|60-64|4511|6642|16059|24165|24998|25888|26289|26444|
|65-69|3423|5323|12739|21864|23813|24814|25352|25515|
|70-T4|2600|4016|9386|1m1|21759|22873|23484|23679|
|75+|2879|4544|10633|24982|38748|44551|48191|49408|
|TOTAL|193993|259523|356352|415706|436588|446186|451996|454234|
|BIRTH RATE||26.4|18.8|14.9|13.6|13.2|12.9|12.7|
|DEATH RATE||6.7|6.4|9.0|11.7|12.3|12.4|12.5|
|NET MIGRAT|ION<br>RATE|-. 5|-. 1|.0|.0|.0|.0|.0|
|GROWTH<br>RAT|E|1.92|1.25|.60|.19|.09|.05|.02|
|TOTAL FERTI|LITY|3.219|2.277|2.093|2.091|2.089|2.081|2.067|
|**e(O)**- BUT|H SEXES|67.85|71.91|74.11|75.69|77.62|78.90|79.27|
|<br>**INR**- BOTH|SEXES|40.7|24.7|17.3|15.1|13.6|11.2|7.3|
# **6 Main Results**

## **6.1 Levels and composition of income at the top**

We start this section characterizing the levels and composition of total income, adjusted to national accounts aggregates, at the top of the distribution.

In Figure 2, we present the average income for individuals within the top 10% highest earners in 2019, where we separate them in 37 bins – 9 bins for those between the top 10% but below the top 1% ( _≈_ 50,000 individuals per bin); 9 bins for those in the top 1% but below the top 0.1% ( _≈_ 5,000/bin); 9 bins for those in the top 0.1% but below the top 0.01% ( _≈_ 500/bin); and 10 bins for those at the top 0.01% ( _≈_ 50/bin). We present income levels both in local currency (Lempiras) as well as in USD PPP.

In 2019, the average income of individuals just in the top 10% of the distribution was L213,000 or USD 20,000 per year. This level is rather close to the exemption threshold for Personal Income Tax (PIT) filing in 2019, suggesting that 90% of the adults in Honduras receive incomes below the exemption level.<sup>19</sup> Average incomes increase fast once we move towards those at the very top – those just above the top 1% receive L590,000 or USD 56,000 per year, those at the top 0.1% receive almost five times as much at L3 million or USD 285,000; and the 500 highest earners at the top 0.01% receive more than L30 million or USD 3.1 million in 2019.<sup>20</sup> Consistent with findings from other countries in Latin America, the highest earners in Honduras receive incomes that are not too different from top earners in high-income countries: in Table A.3 we document the average income for the top 10%, top 1% and top 0.01% in several countries across the globe. While the average income of the top 10% in Honduras is substantially below most countries in Latin America, the average income of those at the top 1% and 0.01% are much more in line with other countries in LAC.

The composition of income is also substantially different within the top 10% highest earners in the country. In Figure 3a, we decompose total income into income from wages, mixed income, distributed capital income and undistributed corporate profits.<sup>21</sup> We first note that within the top 10% highest earners but below the top 1%, income composition is mostly stable at 80% from wages and 20% from mixed income. Within the top 1%, income from capital steadily increases as the wage share decreases – and within the top 0.1% the entire increase in capital income is driven by undistributed profits, which steadily rises to represent close to 80% of total income within the top 0.01%. It is noteworthy that the most important source of income at the very top of the distribution is usually completely absent from personal income tax decla-

> 19Note this is total income, not taxable income, including imputed rent and likely also informal incomes not observed by the tax authority.

> 20We document these income levels for selected quantiles in Table A.2

> 21We present the average shares for the period 2011-2019, to reduce noise from individual years.

13
exceed that of Ghana, yet the additional cost of alleviating urban poverty induced by food price increases is much higher in Nigeria (2.8 percent of GDP) than in Ghana (0.2. percent of GDP).

Figure 2 shows how countries facing similar increases in poverty rates might also face very different costs (i.e. changes in poverty deficit) implied by the shock. For instance, India and Haiti’s urban poverty rates increase by a similar magnitude (3.3 and 3.5 percentage points respectively) in the central scenario. But the change in poverty deficit would exceed 1 percent of GDP in Haiti, while it would be 0.1 percent in India. This is explained by the different initial urban poverty rates in these two countries, with that of Haiti being significantly higher.

**Figure 2. Estimated changes in urban poverty deficits and poverty rates (US$1 a day)**

<!-- Start of picture text --> 3.0<br>NGA<br>2.5<br>2.0<br>1.5 NIC<br>HTI<br>1.0<br>MDG UGA<br>0.5 KHM<br>BEN<br>TZA<br>BOLGINSLVECU MRTCMR BDIMLICIV GHAPAKIND BFA BGD ETH<br>0.0<br>0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0<br>Change in poverty rate (central estimate, percentage points)<br>Change in poverty deficit (central estimates, percentage points)<br><!-- End of picture text -->
- [36] edlund.l and e.korn (2002) “A Theory of Prostitution,” _Journal of Political Economy_ 110: 181-214.

- [37] evans.d.k, m.p.goldstein and a.popova (2015) “Health-care Worker Mortality and the Legacy of the Ebola Epidemic,” _Lancet Global Health_ 3: e439-40.

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- [40] fluckiger.m, m.ludwig and a.sina onder (2018) “Ebola and State Legitimacy,” _Economic Journal_ , forthcoming.

- [41] fogli.a and l.veldkamp (2016) Germs, Social Networks and Growth, mimeo NYU.

- [42] gertler.p, m.shah and s.m.bertozzi (2005) “Risky Business: The Market for Unprotected Commercial Sex,” _Journal of Political Economy_ 113: 518-50

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43
42 ‘achievement’ index? One possibility is to conduct an exit survey of users of health facilities, the aim being to gather information not only on the health services received and the need for them, but also on the living standards of the person’s household; the latter data can be used to construct a wealth index using the same scoring system as in a national survey (like the DHS), so that users can be placed in the national wealth distribution. As Wagstaff and Waters (2005) report, several studies in the ‘Reaching the Poor’ volume (Gwatkin et al. 2005) used such an approach. The increased use of electronic personal identification systems to target public resources on the poor and vulnerable, such as the smart card used in India’s aforementioned RSBY program, provide another potential source of information on the living standards of a user’s household; sometimes this may only indicate whether the household is above or below a poverty line, but over time more granular data may be recorded in the system, such as the household’s score on whatever means test (or proxy means test) was used to determine the position vis-àvis the poverty line or eligibility for a subsidy.

An additional area for future work is using the index to shed light on policy. A couple of approaches suggest themselves. One is to use the index to quantify the impacts of reforms on UHC. Reforms and programs often focus on specific challenges, sometimes those deemed most important by the international development community, and will, because of their limited focus, likely affect only some of the indicators in the UHC index. If one has good evidence on the impacts of a reform or program on a subset of indicators, one could use these estimates to see 42
**A1.6. The Matrix-Representation of the Flow-of-Funds**

In the preceding sections of the appendix we presented the budget constraints of all but the private sector individually. In the theoretical section of the paper we discussed the matrix representation of the budget constraints. This identifies the bilateral relations between the various sectors in concise format. Table **Al.10** presents this matrix with actual data for 1988. The nom,an!cl3ature of the variables is identical to that of the Tables A1.4 through Al.9 without the prefix H. Balance of payments data are multiplied by the average exchange rate to obtain T1 values. In the banking system and the Central Bank accounts we disaggregate the net foreign asset position (excluding cross-currency, timing and revaluation effects) in order to show all bilateral flows.

30
consists of 915 households interviewed up to four times, from fall 1991 to January 1994, at 6 to 7 month intervals (World Bank, 2004). The sample was stratified based on illness and mortality prior to the first round (see World Bank, 2004 for details) and this stratification is controlled for in all regressions. The follow-up, the KHDS 2004, was conducted in the first half of 2004 (Beegle _et al._ , 2006).

# **2.2. Older adults**

We analyze outcomes for the 2004 sample of older individuals who were in the original KHDS baseline interview in 1991.<sup>5</sup> In keeping with the literature on older persons in developing countries, we define older adults as above the age of 50 in 2004; thus, these individuals were as young as 37 years in 1991. 1,060 individuals in the data fall under the category of older adults, as defined here. Of these, 613 are re-interviewed in 2004.

We make note of two things with respect to the sample. First, the youngest of our sample of older individuals (those who were just above 50 years old in 2004) were below 50 years for the majority of the time span we study. Approximately 25% of the sample was below 50 years old at baseline. That is, our sample of “older adults” is actually not so old at baseline. However, give the long span of the data set, we face a tradeoff between two definitions for the sample of older adults: (1) not-so-elderly at baseline and aging into older adult status by 2004, or (2) elderly at baseline and very old (or already deceased) by 2004. We choose to focus on the former definition.

Second, the main cause of attrition in the sample of elderly individuals over the 13-year span of the panel is death. Of the 1,060 individuals at baseline who would have been over 50 in 2004, 403 (38%) died in the thirteen year span of the panel and 44 (4%) were not relocated. We return to this issue below when we explore the correlates of attrition. Of the 613 respondents who were re-interviewed, 87% live in the same community in which they were interviewed 13 years earlier. This age group is much less geographically mobile compared to younger age groups in the KHDS, of which 59% of those re-interviewed live in the same community. Few of the older adults live alone,

> 5 Since we are focusing on long-run impacts, we do not use data from the 2<sup>nd</sup> -4<sup>th</sup> rounds (collected at roughly 6 month intervals from mid 1992 to late 1993) of the KHDS. Earlier studies, including Ainsworth and Dayton (2003) and Beegle (2005) do explore labor and health impacts using the 4 rounds from 1991 to late 1993.

7