rafmacalaba/datause-encoder-data huggingface dataset:
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Uganda > Nonproductive investments > Macro results**

||||Reduction in<br>th bli|Nonprodu|ctive invest<br>It|ment<br>Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|875,000.00|–0.01|0.02|0.02|0.05|0.02|
|EV|Equivalent variation||–0.01|0.02|0.02|0.06|–0.61|
|s|Wage|1.00|–0.07|0.11|0.11|0.27|0.12|
|yg|Government income|163,000.00|–0.01|3.14|3.14|3.14|3.14|
|g|Totalgovernment expenditure|103,000.00|–2.50|2.49|2.49|2.49|2.49|
|ye|Firm income|113,000.00|0.09|–0.13|–0.14|–0.33|–0.15|
|It|Total private investment|105,000.00|0.06|–0.16|–0.85|–5.11|–0.55|
|GDP|GDP|834,000.00|0.00|0.00|0.00|0.00|0.00|
|e|Nominal exchange rate|1.00|–0.09|–0.12|–2.39|–15.98|–0.31|

# **Uganda > Nonproductive investments > Sectoral results**

||||Reduction in<br>|Nonp|roductive<br>|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign<br>aid|Income<br>tax|
||Crop agriculture|270,000.00|0.01|–0.09|–0.08|–0.01|–0.33|
||Export agriculture|112,000.00|0.06|–0.24|–0.27|–0.69|–0.33|
|Va|Mining and gas|577.00|–0.05|–0.18|0.06|1.19|–0.12|
|Value added or|Industries|94,100.00|0.12|–0.59|–0.64|–1.98|–0.21|
|output|Construction|66,700.00|1.47|0.95|1.05|1.93|1.18|
||Private services|260,000.00|–0.17|–0.10|–0.12|–0.10|–0.05|
||Public services|31,600.00|–2.40|2.13|2.27|5.12|2.49|
||Crop agriculture|1.00|–0.07|0.10|0.07|–0.24|–0.16|
||Export agriculture|1.00|–0.02|0.01|–0.04|–0.64|–0.21|
||Mining and gas|1.34|–0.10|0.09|1.71|–13.50|–0.25|
|pq<br>Market prices|Industries|1.10|–0.06|0.90|0.02|–6.28|–0.16|
||Construction|1.01|0.44|0.91|0.49|–2.27|0.40|
||Private services|1.02|–0.15|0.47|0.15|–2.52|0.01|
||Public services|1.00|–0.11|0.35|0.21|–2.50|0.00|
||Crop agriculture|1.00|–0.05|–0.06|–0.03|0.24|–0.52|
||Export agriculture|1.00|0.04|–0.38|–0.45|–1.16|–0.56|
|r<br>|Mining and gas|1.00|–0.29|–0.71|0.40|5.91|–0.43|
|Rental rate of<br>capital|Industries|1.00|0.19|–1.15|–1.26|–3.92|–0.33|
||Construction|1.00|2.44|1.72|1.91|3.58|2.14|
||Private services|1.00|–0.35|–0.06|–0.10|0.09|0.03|

44
Diffusion and Ideational Change Project, which include data for approximately 1,500 evermarried women and their husbands who were interviewed four times over the course of eight years. Overall, the evidence indicates that not only is the HIV prevalence rate for widowed, divorced and separated individuals very high but also, in some cases, over a third of those married have been divorced or separated and between 5 and 10 percent of those married have been widowed.

A sizable proportion of those who have been divorced or widowed remarry. Because such a large percentage of those who are widowed or divorced are HIV positive and so many remarry, this creates a scenario whereby HIV potentially spreads within marriages. Munguti et al. (1997) report that of the men ever married in their survey, 137 out of 379 men (36%) had been widowed or divorced at least once. However, at the time of the survey, only 22 were currently widowed or divorced while 111 men had remarried at the time of the survey. For women, 176 out of 524 (33%) had been widowed or divorced at least once. At the time of the survey, 69 were currently divorced, widowed or separated while 125 had remarried. In a study focused on women in rural Malawi, where between 40 and 65% of marriages ended after 25 years, remarriage was almost universal with over 40% of women remarrying within two years, 75% of women remarrying within five years, and 90% remarrying after ten years (Reniers 2003). Also demonstrating that divorce is common in rural Malawi, Bracher et al. (2003) finds that after five years, between 14 and 26 percent of first marriages ended in divorce, depending on the region. These percentages increase over time; after 15 years, between 31 and 49 percent of these couples had gotten divorced. Another study showed that 34% of males of a sample in Tanzania and 28% of females had previous marriages that ended in divorce or widowhood. It is important to note that these individuals had a higher HIV prevalence rate than those who had not remarried (Quigley et al. 1997).

4
this paper: those who terminated education at a vocational secondary and at an academic secondary school, respectively.9 / For secondary school completers that went on to post-secondary education, it was not possible to identify type of secondary school attended; they are not included in the present analysis.

Only individuals in the 25-49 age group at the time of the Census are included in this research. The upper age limit was set in order to exclude individuals who had attended secondary school before 1948, the year of statehood; the lower, to allow at least three years possible labor market experience, following 3 year compulsory military service at age 18. Since our concern is with the Israeli education system, we excluded (on the basis of age and year of migration) the large number of immigrants who had attended school abroad. Finally, the present study relates only to male full-time workers (a worker is considered "full-time" if he worked at least 35 hours in the week prior to the Census). In all, some 16000 individuals were included in the sample, nearly 11000 vocational school completers and some 5000 individuals who had completed a general secondary school; the number of observations in the regressions that follow is somewhat smaller, however, because of missing values.

9 In fact, individuals that had concluded other forms of vocational training for youth, notably the formal apprenticeship and industrial schools, were also included within the category of vocational school completers; also included were those that attended agricultural secondary schools. However these groups constitute a small, and declining, proportion of vocationally educated students.

- 10 -
Equation (I) implies an expression for the relationship between the public expenditures on health as a share of GDP and health status, that is:

|_a(HealthStatus,)_<br>_(Health Status,)_|<br>_a_|
|---|---|
|_a(HIGDP,)_<br>_(GDPIN,)_|_(H/GDP,)_|

The above relationship can be expressed in terns of the number of deaths averted and, in turn, can be inverted to derive the public sector health spending per additional death averted. An equivalent expression can be derived for the amount of GDP that is not public expenditures on health. From the difference one can calculate the amount that would need to be transferred from GDP to public sector health expenditures in order to avert an additional death. The values derived from these calculations, estimated at the median characteristics of countries with under-5 mortality rates greater than 20 are presented in Table **5.29** These results imply that the cost of averting the death of a child under-5, in terms of public sector health spending, is equal to $47,112 using the two-stage least squares point estimate. The range of the three different estimation techniques (two-stage least squares, OLS, spending from the regressions. When we include it, and impose the restriction that the coefficient on health and non-health sum to the coefficient on GDP per capita, the results are extremely similar. For simplicity however, we exclude it, and infer the value of the coefficient on the share of non-health spending ex-post.

29 The calculations are made for a country with an under-5 mortality rate of 126, a population of about 7 million, a GDP per capita of $1,816 (in 1985 international dollars), a crude birth rate of 34.15, and whose public health expenditures are 2.1 percent of GDP.

25
proxies for the parameter _φ_ in our theoretical model, and we observe that a higher _φ_ lowers a recipient country’s incentive to improve policies in equilibrium. Also, a higher _φ_ makes it less likely for a recipient country to participate in the contest. Our data support that prediction, but results are somewhat different for different specifications and measures. Across all regressions we find a positive and significant GPS coefficient for democracies. With the outlier adjusted GPS measure we also find a positive and significant but smaller coefficient for non-democracies. The difference between democracies and non-democracies is significant only in Column (3) for both our GPS measures.

Columns (5) and (6) test for an interaction with natural resource wealth. We classify a recipient as rich in natural resources if its initial value of total natural resources rents, as reported in the World Development Indicators, is above the 75th percentile. In our data, that threshold is reached when a country has natural resource rents of above 6% of GDP. The evidence related to natural resource wealth is mixed. In Table 6 we find a statistically significant positive relationship between GPS and CPIA for non-resource rich countries and a non-significant coefficient for countries that are rich in natural resources. This finding is consistent with the model’s predictions, which suggest that countries with large amounts of domestic resources have weaker incentives to participate in the contest for aid. However, the difference in the GPS coefficient between the two types of countries is statistically significant only in Column (5) that does not control for region-year fixed effects. In Table 7 the relationship between GPS and CPIA is statistically significant for both, resource and non-resource rich countries. The GPS coefficient is larger for non-resource rich countries than for resource rich countries as predicted by our theory, however, that difference is not statistically significant.

Finally, Table 8 investigates interaction effects related to financial flows from China, using data obtained from AidData (Dreher, Fuchs, Parks, Strange, and Tierney, 2017).<sup>5</sup> China’s aid and investment policies follow a principle of non-interference in the politics and policy choices of recipient countries (Dreher, Fuchs, Parks, Strange, and Tierney, 2016). Furthermore, flows from China to aid-recipient countries have become very sizeable in recent years. Comparing its aid amounts with those of other bilateral and multilateral donors, China has been among the top 10 donors in most years. A notewor-

> 5China does not provide reliable data on its financial investments in developing countries. The methodology used for the data we use here relies on identifying aid projects through a media database, followed by targeted online searches to gather more detailed information. For a full description of the methodology see Strange, Parks, Perla, and Desai (2015) and Custer, Rice, Masaki, Latourell, and Parks (2015).

21
Table 15 also shows that, in June, 44% of the households in affected areas had received assistance and that 32% had received food assistance. 37% of the respondents’ houses were partially or totally destroyed, making it impossible to live in them. 44% of the households slept in their homes, while 44% slept in a camp. 12% of the households had agricultural production as their main source of income, 37% had trade, 26% unqualified work, 17% professional work and 3% lived mostly out of transfers. Aid was received from both abroad (12%) and from within Haiti (18%).

**Table 15. Descriptive** **<u>statistics</u>**

||Mean|Std|
|---|---|---|
|Food consumption score|55.79|19.75|
|Assistance|0.44|0.50|
|Food assistance|0.32|0.47|
|Housing not damaged|0.17|0.37|
|Housing damaged but still usable|0.46|0.50|
|Housing partially destroyed|0.11|0.32|
|Housing totally destroyed|0.26|0.44|
|Sleeping in the house|0.44|0.50|
|Sleeping beside the house|0.09|0.29|
|Sleeping in the neighborhood|0.27|0.44|
|Sleeping in the commune|0.18|0.38|
|Sleeping outside the commune|0.02|0.14|
|Sleeping in a camp|0.44|0.50|
|Main income source before the earthquake|||
|Agricultural production|0.12|0.32|
|Trade|0.37|0.48|
|Unskilled work|0.26|0.44|
|Skilled work|0.17|0.38|
|Transfer|0.03|0.18|
|Other income source|0.04|0.19|
|Aid/transfers from abroad|0.12|0.32|
|Aid/transfers from Haiti|0.18|0.38|

Source: Own computations using June 2010 (EFSA II) surveys. Note: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake.

# **5.4.Pre-Earthquake Conditions**

Table 16 presents summary statistics by quintile of pre-earthquake wealth. The poorest households lived mostly in the East of the affected area (Léogane, Gressier, Jacmel, Petit Goâve, Grand Goâve and Croix-des-Bouquets). They were mostly agricultural households: 66% of them were practicing agriculture, compared to only 5% among the wealthiest, who were mostly concentrated in Port-au-Prince or Pétionville. The poorest households lived in houses with no electricity and no toilets, and do not have access privately to water. They had no car and no oven for cooking. Only few of them had a TV or a fan. Most (66%) had tools or materials for production. Compared to other groups, they took more part in associations be they religious ones (28%) or not (19%). In the population, very few people (around 1% to 2%) were part of cash and food for work 41
Table 22: IV estimates of the effect of the share of labor with Internet on labor outcomes by quartile of income-per-worker growth: Services

||1st quartile|2nd quartile|3rd quartile|4th quartile|
|---|---|---|---|---|
|**Dependent variable:** ∆**ln(Numb**|**er of white-c**|**ollar workers**|**)**||
|∆Share of labor that uses Internet|0.0171***|-0.0038|-0.0083*|0.0131***|
||(0.0040)|(0.0047)|(0.0045)|(0.0035)|
|F- frst stage|42.15|33.61|33.94|48.86|
|Sargan p-value|0.4166|0.2341|0.1015|0.5949|
|**Dependent variable:** ∆**ln(Numb**|**er of blue-co**|**llar workers)**|||
|∆Share of labor that uses Internet|0.0159***|0.0086***|0.0019|-0.0081**|
||(0.0038)|(0.0033)|(0.0028)|(0.0035)|
|F- frst stage|34.72|28.73|38.48|40.05|
|Sargan p-value|0.5867|0.2697|0.2203|0.1805|
|**Dependent variable:** ∆**ln(Wage **|**of white-coll**|**ar workers)**|||
|∆Share of labor that uses Internet|-0.0522***|-0.0270***|-0.0183***|-0.0002|
||(0.0055)|(0.0048)|(0.0062)|(0.0024)|
|F- frst stage|41.04|33.61|9.62|55.71|
|Sargan p-value|0.6008|0.8138|0.1513|0.1110|
|**Dependent variable:** ∆**ln(Wage **|**of blue-colla**|**r workers)**|||
|∆Share of labor that uses Internet|-0.0362***<br>(0.0047)|-0.0231***<br>(0.0044)|-0.0007<br>(0.0027)|0.0243***<br>(0.0026)|
|F- frst stage|34.74|23.53|27.66|89.42|
|Sargan p-value|0.3167|0.3895|0.1172|0.1109|
|**Dependent variable:**∆**white/blu**|**e-collar work**|**ers**|||
|∆Share of labor that uses Internet|0.0012|-0.0038**|-0.0007|0.0021***|
||(0.0011)|(0.0015)|(0.0011)|(0.0007)|
|F- frst stage|32.65|26.07|25.72|68.15|
|Sargan p-value|0.6403|0.3005|0.1782|0.4398|
|**Dependent variable:**∆**white/blu**|**e-collar wage**|**s**|||
|∆Share of labor that uses Internet|-0.0239***|-0.0015|-0.0119|-0.0308***|
||(0.0065)|(0.0070)|(0.0073)|(0.0044)|
|F- frst stage|30.427|24.40|26.33|85.22|
|Sargan p-value|0.2314|0.5965|0.5226|0.8058|
|Observations|1,560|1,560|1,561|1,560|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

51
# **APPENDIX TABLE 3**

**How does initial inequality relate to various subsequent growth rates?**

**<u>Varying the Beginning and End Periods, Village-Level "Cross-Section" Specification</u>**

|||**Measur**|**e of Inequality **<br>**Endpoin**|**: Mean Log De**<br>**t Period:**|**viation**||
|---|---|---|---|---|---|---|
||**(2)**<br>**1989-90**|**(3)**<br>**1991-93**|**(4)**<br>**1995-96**|**(5)**<br>**1997-98**|**(6)**<br>**1999-00**|**(7)**<br>**2001-02**|
|**Beginning Period:**|||||||
|**1987-88**|**_-0.722*_**|-0.221|**_-0.255*_**|**_-0.234*_**|**_-0.190*_**|-0.132|
||(0.257)|(0.137)|(0.084)|(0.089)|(0.078)|(0.080)|
|**1989-90**||-0.153|-0.090|-0.116|0.018|0.025|
|||(0.214)|(0.128)|(0.092)|(0.077)|(0.063)|
|**1991-93**|||0.126|0.099|0.108|0.128|
||||(0.288)|(0.201)|(0.122)|(0.097)|
|**1995-96**||||0.190|-0.002|0.105|
|||||(0.239)|(0.192)|(0.128)|
|**1997-98**|||||-0.077|0.085|
||||||(0.319)|(0.185)|
|**1999-00**||||||0.285|
|||||||(0.184)|

|||**Me**|**asure of Inequ**<br>**Endpoin**|**ality: 90-10 Ra**<br>**t Period:**|**tio**||
|---|---|---|---|---|---|---|
||**(2)**<br>**1989-90**|**(3)**<br>**1991-93**|**(4)**<br>**1995-96**|**(5)**<br>**1997-98**|**(6)**<br>**1999-00**|**(7)**<br>**2001-02**|
|**Beginning Period:**|||||||
|**1987-88**|**_-0.047*_**|**_-0.015*_**|**_-0.015*_**|**_-0.015*_**|**_-0.012*_**|**_-0.009*_**|
||(0.014)|(0.007)|(0.004)|(0.004)|(0.004)|(0.004)|
|**1989-90**||-0.004|-0.001|-0.005|0.001|0.000|
|||(0.008)|(0.006)|(0.004)|(0.004)|(0.003)|
|**1991-93**|||0.004|0.001|0.005|0.006|
||||(0.012)|(0.008)|(0.006)|(0.005)|
|**1995-96**||||0.009|0.000|0.006|
|||||(0.011)|(0.008)|(0.006)|
|**1997-98**|||||-0.009|0.001|
||||||(0.018)|(0.010)|
|**1999-00**||||||0.008|
|||||||(0.008)|

_Notes:_ 1) All specifications use the village-panel data set, cross-section specification 2) The reported numbers are the coefficients (and standard errors) of the effect of inequality on growth from a regression of growth between period "t" and "t-1" as a function of village characteristics in period "t-1"
21 Table 2: Estimates (1986:4 - 1990:4) The Determinants of Willincaness to Pav (t-statistics in parenthesis)

|Independent Variables||Equatio|ns|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|constant|0.140|0.131|0.161|0.174|0.159|
||(4.49)|(4.40)|(4.62)|(5.12)|(5.63)|
|(variation<br>in|0.131<br>10-3|0.101<br>10-|0.142<br>10-|0.08<br>10|0.860<br>104|
|reserves)/GDP|(1.47)|(1.14)|(1.53)|(0.83)|(1.04)|
|GDP|0.151<br>10-2<br>(4.31)|0.143 10-2<br>(4.19)|0.135<br>10-2<br>(3.57)||0.151<br>10-2<br>(4.68)|
|GDP growth||||0.096<br>(1.51)||
|exports/GDP|0.67110-<br>(2.22)||0.684<br>10-3<br>(2.33)|0.170<br>10-<br>(0.59)||
|U.S.<br>Industrial|-0.327<br>1042|-0.311<br>10-2|-0.339<br>10-2|-0.233<br>10-2|-0.417<br>10-2|
|index|(-4.36)|(-4.15)|(-4.31)|(-3.03)|(-5.39)|
|degree<br>of<br>openness||0.332<br>10-3<br>(1.79)|||0.411<br>10-3<br>(2.26)|
|inflation|||0.110<br>(0.24)|||
|Brady Announcement|||||0.191 lo-<br>(2.09)|
|Reserves<br>Announcement|||||-0.357<br>10.<br>(-2.82)|
|SSR|0.089|0.089|0.092|0.106|0.076|
sources are used, one cannot fully explain what the WBI “Control of Corruption” index is measuring or on what basis.

Although both TI and WBI provide thorough explanations of their aggregation methodology, replication of the indexes by independent analysts would be costly, particularly as the number of sources used has expanded over the years. Some of the sources are available only to paying subscribers or members, and some are not publicly available at all.<sup>16</sup>

# _Conceptual imprecision, uncertainty and inconsistency_

The TI and WBI indexes by construction are even more _conceptually imprecise_ than some of their broadly-defined components (e.g. ICRG, NIT and CPIA). They are also more _conceptually uncertain_ : the uncertainty in how criteria are weighted is compounded by aggregation. In contrast to any single broadly-defined indicator, the TI and WBI composite indexes suffer from having _varying_ definitions. Composite indexes have no explicit definition, but instead are defined implicitly by what goes into them. The sources used in constructing these composite indexes change over time, so the implicit definition of corruption reflected in the index values changes over time.

Moreover, the sources used in constructing the indexes vary from country to country in a given year. Estonia’s 6.4 corruption rating and Latvia’s 4.2 corruption rating in the 2005 TI index are based on two different sets of indicators, hence on differing implicit definitions of corruption.

Among the 27 ECA countries, there are 13 distinct combinations of sources used in computing the 2005 TI index, so the 27 index values reflect 13 different implicit

> 16 The CPIA indicator used in the WBI index will be available for the first time in the summer of 2006, but only for the IDA-eligible (i.e. lower income) countries.

18
community health workers in terms of the coverage and timing of household visits, and the detection and management of, and referral for, risks.<sup>21</sup>

# **3. Data, sample, and empirical strategy**

# A. _Data and sample_

The 2006 and 2013 BUHS were designed to provide data that are representative of three overall domains: (1) slum neighborhoods in city corporations, (2) nonslum neighborhoods in city corporations, and (3) municipal cities and towns. The 2006 round covered the six city corporations existing at that time, and the 2013 round covered 10 of the 11 city corporations existing in that year (Gazipur became a city corporation in the year of the survey, and thus was not included in the appropriate sample domain).<sup>22</sup> The sample frame for the survey was a complete list of mahallas (which we refer to as neighborhoods) in the city corporations and sampled municipal cities and towns. A mahalla is an Islamic parish, and an optional, non-elective administrative unit below the ward in cities and towns in Bangladesh. Focusing on city corporations, neighborhoods were randomly selected in the first stage of sampling. In each sampled neighborhood, all slum and nonslum areas were mapped through visits. An area was defined to be a slum settlement if it had at least 10 households and met four of the following five conditions: poor housing conditions, insecure housing tenure, high population density, poor sanitation and inadequate water access, and at least 75% of the households appeared to be poor.<sup>23</sup> The second stage of sampling differed slightly between the 2006 and 2013 rounds, but essentially, slum and nonslum areas of sampled neighborhoods, the primary sampling units (PSUs), were randomly drawn, stratified by slum status. See NIPORT et al. (2008) and NIPORT et al. (2015) for sample design details. We refer to the PSU as “neighborhood area”.

We use data from the neighborhood-area, household, and female questionnaires. The neighborhood area questionnaire was administered to knowledgeable leaders and key informants

> 21 In terms of other evidence, Andaleeb (2000) finds for Dhaka city that sample users rated private hospitals higher than public hospitals in terms of responsiveness, communication, and discipline.

> 22 In the 2006 BUHS, Barisal, Chittagong, Dhaka, Khulna, Rajshahi, and Sylhet were included in the city-corporation statistical domain. In the 2013 BUHS, Barisal, Chittagong, Comilla, Dhaka North, Dhaka South, Khulna, Narayanganj, Rajshahi, Rangpur, and Sylhet were included in the city-corporation statistical domain.

> 23 These conditions are consistent with UN-Habitat guidelines for defining slums (UN-Habitat 2003).

15