rafmacalaba/datause-encoder-data huggingface dataset:
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" percent as appropriate, with special emphasis on infrastructure coverage among households in the poorest deciles and quantiles of 5 percent.

# **3. Who has infrastructure services?**

# 3.1. Global infrastructure coverage

Over 65 percent of the households in the pooled cross-national sample had electricity in their homes at the time of the LSMS survey.<sup>12</sup> By contrast, only 38 percent of households had in-house water taps, 36 percent had sewer connections, and 24 percent had telephones.'<sup>3</sup> The distribution of these utility connections among households is highly correlated with our income proxy (i.e. monthly aggregate household consumption): a higher percentage of wealthy than poor households have electricity, in-house taps, sewer connections, and telephones in their homes.

Figure 1 shows how coverage of these services varies by income level in the cross-country pooled sample. Each dot on the graph represents one quantile of 5 percent of households. The dots plot the quantile's median "income" against the coverage of electricity, in-house water taps, sewer connections or telephones within that group.

Aggregate consumption among households in the poorest 5-percent quantile of the pooled sample was less than US$ 1.00 per household per day (US$27 per month, on average). These households came from all countries in the sample, but the majority live in Vietnam, Nepal, and Kyrgyz Republic -- the poorest of the fifteen countries.<sup>14</sup> Electricity was the only service with

> 12 These households may obtain electricity from a utility connection or from an electrical generator. Unfortunately, it is only possible to differentiate between these sources in four of the 15 countries (Panama, Ecuador, Nicaragua, and Nepal). In these four countries, electrical generators for household use are rare. In Nicaragua and Nepal, less than I percent of households with electricity reported obtaining the electricity from a generator. In Panama and Nicaragua, 3.5 percent and 1.5 percent of households with electricity rely on generators. In Panama, poor households are somewhat more likely than rich households to rely on generators for electricity: nearly 12 percent of those in the poorest quintile who have electricity versus 2.3 percent of the richest quintile use generators. In Nicaragua, very few of the richest or poorest obtain electricity from generators; the Nicaraguan generator users are disproportionately concentrated in the middle-income quintiles.

> _13_ Information on telephone use is only available in 10 ofthe 15 countries. Sewer information is available in 12 countries, and electricity data in 14. These coverage figures reflect the percent of sample householdsfor _whom data on the service is available_ who have the service in their homes.

14 Most of the richest households come from South Africa, Panama, Russia, Ecuador, and Jamaica - the wealthiest countries in the sample.

6
data for the sample of 694 that received the home visit. Of those, 596 had valid balance data and this is the sample we use in the structural estimation.

Appendix Table A4 reports balancing tests for the choice environment treatments implemented in 2016 and the same variables in panels A and B of Table 1. Judging by the p-values of the F-tests that all the variables are jointly zero reported in column 6, there are no statistically significant differences, indicating that the randomization of the show-up bonus and timing of the bank visit were balanced.

# **4 Empirical strategy and results**

Consider an account holder deciding what to do with the account. We assume that the utility that account holder _i_ obtains from alternative _j_ (i.e. _Uij_ ), which is fully known to the decision-maker, can be decomposed into a part known to the researcher _Vij_ and an unknown part _eij_ such that _Uij_ = _Vij_ + _eij_ . We assume further that the unobserved part of the utility (to the researcher) for all alternatives is jointly distributed as a generalized extreme value.

In particular, we assume that the set of alternatives can be partitioned into two subsets or nests that depend on whether the account holder decides to visit the bank. If the bank is visited, the account holder can keep the old account, close the account or open a Pafupi account. For any two of these three choices, we assume that the ratio of probabilities is independent of existence of other alternatives. That is, there is no correlation between alternatives and the independence from irrelevant alternatives (IIA) holds. Alternatively, the only option available to account holders that do not visit the bank is to keep the old account. In contrast to the assumption of zero correlation between alternatives when the bank is visited, we allow for some correlation between any of the alternatives when visiting the bank and the choice of keeping the old account when the bank is not visited. We therefore estimate a nested logit model with the tree diagram shown in Appendix Figure A1.

The known part _Vij_ for the different alternatives can be written as the value of the account chosen (or the balance amount if the account is closed) and the net benefit from visiting the bank if a bank visit occurs: 9
7 The second principal component predicts a blip in inflation in 1975, lower than average inflation in 1976 and 1977, and higher than average inflation from 1982 to 1987. In a regression of each country's CPI on the first two components, the second is insignificant in the case of three countries, France, Burkina Faso and Gabon. In three other cases, Cote d'Ivoire, Niger and Togo the weighting is negative, while in the remainder, the weighting 17 is positive . Were it not for the positive loading in the case of Senegal, this second component could be seen as a BEAC/UMOA distinguishing factor. However, its small contribution to overall variance suggests that it may not be an independent common factor. Accordingly we proceed to look at the individual country residuals after subtracting only the first principal component.

# ExDlaininz residual inflation differences,

Subtracting the (re-scaled) first principal component from the individual country CPIs leaves us with a residual inflation factor which displays quite a variety of patterns between the different countries.

Burkina Faso and Congo have a positive residual in the 1970s, negative in the 1980s; the experience of Gabon is almost the reverse.

Ciameroon and the Central African Republic have positive residuals chiefly after abLut 1983, whereas Cote d'Ivoire and Niger have positive residuals chiefly before 1983.

In addition to unobservable country-specific disturbances which may have contributed, these residuals might be explained on the basis of the different individual country experiences with regard to expenditure or monetary variables. To explore this possibility, data on monetary aggregates, oxports and government and private consumption were assembled for the five UNOA countries for which CPI data was available. These variables were expressed for each year 1970-86 as a deviation from the mean share in the union. (Thus in the case of exports for Niger in 1975, for example, the variable used was the difference between Niger's percentage share in total UMOA exports in 1975 and Niger's mean share 1970-86.) Regression analysis of the pooled country data was then used to identify possible correlations between these measures of monetary and spending conditions on the one hand and residual inflation differences on the other hand.

17 This can also be seen in Figure 2 where, for example, the actual CPI (marked by squares) lies above the first principal component during 1976-83, a time when the second principal component takes on negative values.
- (ii) How world market shares evolve for these products (markets). This shows on the position of the bubble relative to the red line, which indicates that the growth of world demand equals the growth of exports in the country in question, suggesting no change in the country’s world market share for that product (market).

In Azerbaijan and Kazakhstan, the increase in importance in the export basket of the top 5 products, documented in Figure 15a, is clearly explained by increases in exports of crude petroleum oils, since once we exclude these products, we see that these countries have been losing market shares (as for most of the products, their export growth is outperformed by world demand). In addition, for many important products, export growth is negative over the period (for Azerbaijan this is the case for two important products like hazelnuts and cotton; for Kazakhstan it is the case for titanium, aluminum oxide and ferrous waste). Russian performance in non-oil and gas exports seems to be more balanced, gaining world market shares in wood products, diamonds, and semi-finished steel products, while losing world shares in, for example, ferrous waste and non-alloyed aluminum. For Tajikistan, on the other hand, export growth has been positive over the period for the majority of its most important products, while for the most important one (non-alloyed aluminum), market shares remained unchanged. Uzbekistan, on the other hand, has lost market share in cotton, which was its most important export product in 2005.

In Armenia, Kyrgyz Republic, Moldova and Ukraine, exporters seem to have lost market share in the majority of their most important products, while in Belarus, export performance in the most important products is relatively more balanced.

Figure 15a: Growth Orientation of Export Products Resource Rich <!-- Start of picture text --> AZE: Growth Orientation of Products<br>Top 10 Products<br>Sections,U,i/nas,nfw than hot rlld,draw<br>Aluminium not alloyedOther fruit<br>Tomatoes<br>Polyethylene<br>Propan-1-olApples<br>Hazelnuts w.o. shells<br>Cotton<br>Sections Li/nas.<br>-60 -40 -20 0 20<br>WLD: CAGR of Exports 05-10(%)<br>20<br>0<br>-20<br>-40<br>AZE: CAGR of Exports 05-10(%)<br>-60<br><!-- End of picture text --> Figure 15b: Growth Orientation of Export Products Resource Poor <!-- Start of picture text --> ARM: Growth Orientation of Products<br>Top 10 Products<br>Copper ores<br>Diamonds Copper unrefined<br>Spirits<br>Molybdenum<br>Ferro-molybdenum<br>Portland cementDiamonds<br>Art. Of jewelry<br>Brassiers<br>-100 -50 0 50<br>WLD: CAGR of Exports 05-10(%)<br>50<br>0<br>-50<br>ARM: CAGR of Exports 05-10(%)<br>-100<br><!-- End of picture text --> 16
- **Ruiz, Isabel, and Carlos Vargas-Silva.** 2013. “The Economics of Forced Migration.” _The Journal of Development Studies_ , 49(6): 772–784.

- **Sarvim ̈aki, Matti, Roope Uusitalo, and Markus J ̈antti.** 2019. “Habit formation and the misallocation of labor: evidence from forced migrations.” _Available at SSRN 3361356_ .

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2 infant deaths each year could have been averted and tens of millions of families could have been healthier and happier.

# The Problem of Excass Female Mortalty

**The** experience of different countries as reflected in the statistical record provides a natural stardng point for any discussion of development policy. I want to start with one of the most basic of all national statistics- the demographic composition of the population. In examining these statistics, many observers have been struck by the differences between countries in what one might have expected to be a biologically determined constant-- the share of the population that is female. My Harvard colleague Amartya Sen has reccntly highlighted how large these differences are by calculating that worldwide more than 100 million women I are missing and labelling the fate of these women, "one of the more momentous problems facing the contemporary world." **2** 'his problem is symptomatic of an even larger problem of hidden underinvestment in the human development of the women who survive and are counted.

In the industrialized world females comprise over 51 percent of the population. In 3ubSaharan Africa the percent of the population that is female is a little bit lower, ranging from 50.9 to 49.2 percent (Table 1). Likewise, the percentage of females in Latin America ranges from 50.7 to 49.5 percent. But, as seen in figure 1, Asia in general and Pakistan in particular stand out in any examination of sex ratios. The female share of the population is 48.5 percent in China, 48.1 percent in India, and 47.6 percent in Pakistan-- the lowest measured share in the developing world.

Why are there such large differences in sex ratios across countries? As a matter of logic there are four possibilities-- differences between men and women in migration patterns, differences in the share of female babies born, differences in male and female population shares counted by census data, or differences in female survival rates. There is no evidence that differing patterns of migration between women and men can explain such large differences in national sex ratios, nor are there noticeable differences across countries in the share of female births. 3 Claims that women are undercounted in certain societies are largely unsubstantiated Ansley Coale (1991), using slightly different assumptions about expected masculinity ratios, calculates that the total number of missing females is approximately 60 million. As Coale says, even this lower independent estimate "confirms the enormity of the social problem." 2 Amartya Sen. "Women's Survival as a Development Problem." Comments prepared for the 1700th Stated meeting of the American Academy of Arts and Sciences on March 8, 1989.

> ' There is some evidence that in higher income Asian countries modern medical technologies have been used to selectively abort female fetuses. Where selective abortion is not practiced, male births generally slightly outnumber female births. The median ratio of the number of male to female births in 24 countries in Europe from 1962 to 1980 was 1.059, with 71 percent falling between 1.055 and 1.064 (Chahnazarian, 1986).
29 meanhgM fordg exChange and trde lealization siullor load to their reversl each time the budgt and the balance of paymen get out of control.<sup>27</sup> A truly taable macroonomic stability, however, requires not a simple suppresion of exediure which In SSA tends to fal on priority development ad rehabilitation/maintenancar **e** a. Rather, t entails a permant and st nctural chang in the level of government revenues as well s in the level of govennt ependiures mad in the composidon of both the sources of revenues and the reas of eedire. Thoory and prienc suggest, for instance, that fisl adjustment uhat fal disproportionately on capital ependiture may lead to a reducdon of private investment,28 or to real exch rat pprociation that countrs atoempts at export promotion.29 The ten cas sudies demonstra that very few cou.o!s have achieved any appreciable progress towards the realization of these goS^.30 Second, the cas studies show tdat a realistic level of exchange rate and a ransparent and auomatc mechanism of foreip exchange allocation remain indispensable for a sustainable and meaningful outward-oriened trade reform. In fact, in the presenc of a totally misaligned exchange rate, when foreip exchange availability Ii the most binding barrier to importing, changes in other impediments to imports convey little or no information as to the true degree of protection and anti-export bias of an eonomy. As for any other carce good, the level and allocation mechanism are of course closay related problem. That is, some rationing mechanism becomes necesary only when the price is not allowed **to** clear the market. However, for any level of exchange rate that is not market clearing, the allocation mechnism plays an Important role either in foiling or assistn import liberalization/export promotion.

27Moreover, although balanc..of-payments consideraions may logically dictate restricing aUl imports uniformly, sy through a uniform surcharge, the presure by various interest groups and social couldeatons, such as not taxing certain food or medicinal imports, inevitably generate many exemptions that ocalteho the structure of protectdon.

28See for example Faini and de Melo (1991).

29Seo Matin (1992b).

30See Secdon _5_ for ils.
# **2.3. Attrition and Drop-out**

Like many development programs, the TEVETA program suffered from several administrative setbacks which affected the implementation of the program. For example, between the time that the original 1,900 youth were selected and the time that the baseline survey was conducted and the treatment participants were invited to begin training, over a year went by. Thus at the time that the training was offered, about 9% of the people invited to training chose not to participate (we explore the possible reasons – including other potential opportunities or barriers facing these people – in greater depth below). In addition, owing to administrative errors, a large number (about 30%) of those who were supposed to be invited to participate in the training report in our follow-up that they never received the invitation. Lastly, even among those who were invited to the training and who chose to participate, not all completed the training. We treat all of these (not invited, did not participate, or did not complete) as drop-outs (as labeled in Figure 1), as they were assigned to treatment but did not participate. For analysis purposes, however, we distinguish between those who dropped out because of the administrative error (not invited) and those who chose to drop out. More than half of all people who dropped out did so due to the administrative error.

In addition to people who dropped out of the training, there was also survey attrition between the baseline and follow-up surveys. Specifically, about 1/3 of the respondents in the baseline survey could not be found for the follow-up survey (242 from the treatment group, and 125 from the control group). This poses identification issues, since attrition from the survey is correlated with participating in training, and therefore with our outcome variables. People who participated in training were very easy for us to track since we conducted our follow-up survey very soon after the completion of training. Thus it is likely that, of the attriters in the treatment group, most are drop-outs. This attrition is particularly problematic if we only successfully tracked a non-random sample of the drop-outs. In Table 3a, we examine whether the attriters are statistically different from the drop-outs who we were able to track in terms of their baseline characteristics. It is encouraging to note that the two groups are statistically similar across most dimensions, which indicates that our extra efforts in tracking drop-outs may have reduced some of the selection bias introduced by non-random attrition. This is especially true when we restrict our attention to drop-outs who chose to stop participating (and not the administrative errors). It appears that TEVETA ultimately chose to not invite a few participants who were originally 8
Charts of the simulation analysis can also be produced by checking the appropriate box, if desired. Figure 10 gives an example of the chart output for the distribution of the net present value of cash flow.

Figure 10: _INFRISK_ Chart Output

|_-sent value:Fr_<br>266.38<br>275.52<br>288.87<br>297.12<br>30.37<br>317.62<br>27.87<br>338.12<br>348.37<br>3M8.62<br>368.87|_equencypmnu_<br>24<br>1}<br> <br>9.<br> <br>2l<br> <br>42<br> <br>as<br>113M<br> <br>19<br>163<br>i<br> <br>184<br> <br>94<br> 24S.87<br>t<br> <br>420|_ative_<br>%9<br>SR80<br>.10%<<br>4%3<br>4%<br>1.30%<br>4.20%'<br>**_8.41%_**<br>17.32%2<br> <br>2<br>43.54%<br>59.86%<br> <br>76.2%<br>85.89%<br>7<br>.89%<br> 970%<br>9.0%|1<br>:<br>T<br>Histogram<br>180-<br>100%<br>~<br>160<br>0<br>140-S0%<br> <br>08.3<br>-<br>7012<br>100<br>%<br>80<br>X<br>4<br>X0<br>3<br>4020<br>0<br>i<br>0%<br>0|
|---|---|---|---|
|379.12<br>388.37<br>399.62<br>409.87<br>More|<br>1**29**<br> <br>5<br>2<br>,<br>2<br>0|**9.10%5 **<br>99.60%<br>89.80<br>100.00%<br>00.00%|~ ~~<br>_11_<br>_e_<br>_r_<br>§<br>**~OI**<br>N et pre s ent valIue of NC F|
|**Sumnm**<br>Men<br>Standard i<br>Median<br>Min<br>Mx<br>Count|**ary**<br>321.117<br> <br>24.76<br>321 .768<br>245 .877<br>409.867<br>1000_|.<br>|_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|

The _economic viability analysis_ can be undertaken from the perspectives of the parties involved in an infrastructure project: the project promoter (investor), the creditor, and the host government. This analysis will show whether the probability of a given variable meeting the specified criterion falls within the accepted confidence level. A "pass" result indicates that the estimated probability generated by the Monte Carlo simulation is below that specified by the user "confidence level." The viability analysis provides the following options:

- _From the investor's perspective:_

- (a) Probability of the internal rate of return being smaller than a specified level.

- (b) Probability of the net present value being smaller than zero should be smaller than the specified level.

- (c) Probability of dividend being negative or smaller than the specified level.

- _From the creditor's perspective:_

- (a) Probability of the interest coverage ratio being smaller than a specified level. (b) Probability of the debt service ratio being smaller than a specified level.

- _From the government's perspective:_

- (a) Probability of social benefits from the project being smaller than a specified level.

25