rafmacalaba/datause-encoder-data huggingface dataset:
percent are four times the best practice 10 percent benchmark, resulting in $116 million in potential savings. Another $15 million per year could be saved by raising bill collection efficiency from 94 to 100 percent. Distributional losses are the driver of operational inefficiencies in the water sector. Uganda could avoid this cost by reducing nonrevenue water losses from 2009’s 36 percent level, to the 20 percent benchmark of a well-functioning utility. Bill collection, on the other hand was close to 99 percent in 2009.

**Figure 32. Uganda’s power and water utilities burden of inefficiency, 2001-2009**

<!-- Start of picture text --> a. Uncollected bills and unaccounted losses in the power sector, b. Uncollected bills and unaccounted losses in the water sector, as a<br>as a percentage of GDP percentage of GDP<br>1.000 0.20<br>0.800<br>0.15<br>0.600<br>0.10<br>0.400<br>0.05<br>0.200<br>0.000 0.00<br>Uganda LIC-Non-Fragile Uganda LIC-Non-Fragile<br>Collection inefficiencies Unaccounted losses Collection inefficiencies Unaccounted losses<br>Percenatage of GDP Percenatage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

# **Annual funding gap**

_Uganda’s infrastructure funding gap amounts to $425 million per year, or about 5 percent of GDP, once efficiencies are captured._ In contrast to many of its peers, Uganda does not seem to face a significant funding gap for power or transport. In the power sector, this is mainly as a result of the substantial efficiency gap, as well as the potential for reallocating expenditure within the sector from operating to capital expenditure. The largest funding gap is to be found in the water and sanitation sector(table 19). Overall, Uganda’s infrastructure funding gap looks manageable relative to its GDP and compared to funding gaps found in other LICs in Africa.

**Table 19. Funding gaps by sector**

<u>US$ millions</u>

||ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Spending needs|(161)|(231)|(447)|(221)|(348)|(1,409)|
|Existing spending*|130|0|281|218|147|776|
|Reallocation potential within sectors|0|0|166|3|0|169|
|Efficiency gains|0|0|243|24|39|307|
|Funding gap|**(31)**|**(231)**|**-**|**-**|**(163)**|**(425)**|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

49
for years. As no official datasets of school locations could be obtained for this analysis, just as with hotels, volunteer-contributed geolocations of schools were sourced from the OSM project (OpenStreetMap, 2019). Consequently, all the caveats mentioned in section 3.5 for the hotel dataset also apply here. The OSM school dataset contains 1,546 schools, 864 of which are located in coastal provinces. According to 2017 data from GSO, these 864 schools represent about 12 percent of all schools in coastal provinces (General Statistics Office of Vietnam, n.d.).

Importantly, the schools in the dataset do not seem to represent a random subsample of all schools, as some provinces contain only very few school, and three provinces have no schools at all in the dataset. Table 3.2 gives an overview over the provincial distribution of the geolocated dataset from OSM and compares it to the number of schools in each district according to the official statistics.

**Table 3.2: School numbers comparison across datasets**

|**Coastalprovince**|**Number of schools(GSO)**|**Number of school locations(OSM)**|**Coverage rate(%)**|
|---|---|---|---|
|Ba Ria-VungTau|167|27|16|
|Bac Lieu|88|0|0|
|Ben Tre|178|13|7|
|Binh Dinh|338|31|9|
|Binh Thuan|193|33|17|
|Ca Mau|132|4|3|
|Da NangCity|199|38|19|
|Ha Tinh|273|7|3|
|Hai PhongCity|311|58|19|
|Ho Chi Minh City|1208|505|42|
|Khanh Hoa|199|32|16|
|Kien Giang|155|10|6|
|Nam Dinh|266|10|4|
|Nghe An|541|6|1|
|Ninh Binh|152|9|6|
|Ninh Thuan|92|20|22|
|Phu Yen|138|7|5|
|QuangBinh|182|3|2|
|QuangNam|260|10|4|
|QuangNgai|214|5|2|
|QuangNinh|216|2|1|
|QuangTri|167|0|0|
|Soc Trang|135|3|2|
|Thai Binh|229|7|3|
|Thanh Hoa|669|1|0|
|Thua Thien-Hue|207|17|8|
|Tien Giang|228|6|3|
|Tra Vinh|122|0|0|

Sources: General Statistics Office of Vietnam, 2019, OpenStreetMap, 2019.

14
(Table 6).5 However, the number of<sup>firms in 2000 was below that in1995.Allin all,</sup> the rate of new enterprise growth<sup>wasinsufficientto offset theemployment reductions</sup> taking place in existing firms.

Table 6 **Firm** growth by firm<sup>**size,**1993-2000</sup> Index of growth, 1993=100

||**1994**|1995<br>|1996<br>|1997<br>|1998<br>|1999<br>|2000<br>|Avg.<br>**annuat**<br>growth<br>|
|---|---|---|---|---|---|---|---|---|
|Numberoffirms('000)<br>|139.7|168.3|174.5|182.0|174.3|168.5|165.1|7.4|
|Employment(1993=100)|||||||||
|Allfirms|97.5<br>|92.4<br>|92.3<br>|93.0<br>|92.2<br>|89.7<br>|92.9<br>|-1.0<br>|
|251+|93.3<br>|85.3<br>|81.5<br>|79.7<br>|76.4<br>|72.9<br>|72.0<br>|-4.6<br>|
|51- 250|89.5<br>|80.7<br>|81.7<br>|83.0<br>|82.7<br>|76.2<br>|75.9<br>|-3.9<br>|
|-50|119.5|127.9<br>|136.6<br>|143.5<br>|148.9<br>|155.0<br>|174.4<br>|8.3<br>|
|Stateowned|61.0<br>|48.1<br>|45.7<br>|46.0<br>|46.6<br>|37.9<br>|39.6<br>|-12.4<br>|
|Mixed|114.2<br>|110.1<br>|104.1<br>|96.2<br>|84.7<br>|84.8<br>|78.3<br>|-3.4<br>|
|Private|145.3|155.2|166.7|178.2|187.5|192.8|210.4|11.2|

Note: Data refer to registered<sup>legalpersons.</sup> Source: FINA, Author's calculations.

_5_ Data refer to enterprises which are<sup>registered as legalpersons.Since a substantial proportion</sup> of all enterprises are natural persons,<sup>the data presented may not be representativeof all enterprises.</sup> Moreover, there is likely to be a discrepancy<sup>betweenthe number of registered enterprisesand the</sup> number of actually active enterprises,<sup>the latter often being substantiallysmaller(EUROSTAT, 2000).</sup> _-14-_
Table 1: Call engagement estimates after wave 1 and 2.

||Bayesian Bi|nomial GLM|
|---|---|---|
||Wave 1|Full sample|
||(1)|(2)|
|T1A|_−_2_._84<sup>_∗_</sup>|_−_2_._63<sup>_∗_</sup>|
||[_−_3_._09;_−_2_._60]|[_−_2_._81;_−_2_._46]|
|T1B|_−_2_._64<sup>_∗_</sup>|_−_2_._49<sup>_∗_</sup>|
||[_−_2_._87;_−_2_._42]|[_−_2_._63;_−_2_._36]|
|T1C|_−_2_._75<sup>_∗_</sup>|_−_2_._78<sup>_∗_</sup>|
||[_−_3_._00;_−_2_._52]|[_−_2_._93;_−_2_._63]|
|T2A|_−_2_._94<sup>_∗_</sup>|_−_2_._89<sup>_∗_</sup>|
||[_−_3_._19;_−_2_._70]|[_−_3_._11;_−_2_._68]|
|T2B|_−_2_._83<sup>_∗_</sup>|_−_2_._67<sup>_∗_</sup>|
||[_−_3_._08;_−_2_._60]|[_−_2_._85;_−_2_._50]|
|T2C|_−_3_._46<sup>_∗_</sup>|_−_3_._32<sup>_∗_</sup>|
||[_−_3_._74;_−_3_._20]|[_−_3_._57;_−_3_._07]|
|Num. students|1283|2462|
|Period|1|1 and 2|

Notes: _∗_ Value of zero lies outside of the 95% credible interval. We simulate 4 independent Markov chains of 4,000 posterior draws each and discard the first 2,000 as warm up. The remaining 8,000 draws are used to generate the posterior distributions of the coefficients. The Split- _R_<sup>ˆ</sup> of every posterior distribution is below 1.01 and there are no divergent transitions.

27
**Table 2: Impact of financial support on higher education enrollment among high school graduates, household survey**

|DAID|DENROL<br>DENROL<br>DENROL<br>0.248<sup>***</sup><br> <br>0.216<sup>***</sup><br> <br>0.241<sup>***</sup><br>|
|---|---|
|INCOME|(0.064)<br>(0.069)<br>(0.070)<br>0.005<sup>***</sup><br>0.005<sup>***</sup><br>0.007<sup>***</sup>|
|(×1000 pesos)|(0.002)<br>(0.002)<br>(0.002)|
|DFEMALE|0.017<br>-0.027<br>0.027|
||(0.028)<br>(0.029)<br>(0.031)<br><br><br>|
|AGE|0.068<sup>***</sup><br>0.094<sup>***</sup><br>0.119<sup>***</sup>|
||(0.007)<br>(0.008)<br>(0.009)<br><br><br>|
|DHIGHSCHOOL_FATHER|-0.131<sup>***</sup><br>-0.133<sup>***</sup><br>-0.107<sup>**</sup>|
||(0.047)<br>(0.049)<br>(0.051)<br><br><br>|
|DUNIVERSITY_FATHER|0.163<sup>***</sup><br>0.130<sup>***</sup><br>0.109<sup>**</sup>|
||(0.043)<br>(0.046)<br>(0.048)<br><br><br>|
|DHIGHSCHOOL_MOTHER|-0.174<sup>***</sup><br>-0.208<sup>***</sup><br>-0.185<sup>***</sup>|
||(0.046)<br>(0.046)<br>(0.049)<br><br><br>|
|DUNIVERSITY_MOTHER|0.229<sup>***</sup><br>0.236<sup>***</sup><br>0.241<sup>***</sup>|
||(0.051)<br>(0.051)<br>(0.051)<br><br>|
|WORKHOURS|-0.007<sup>***</sup><br>-0.004<sup>***</sup>|
||(0.001)<br>(0.001)<br>|
|DPRIVATE_school|0.404<sup>***</sup><br>(0.029)|
|N<br>|1,442<br>1,442<br>1,442|
|R<sup>2</sup>|0.094<br>0.148<br>0.232|
|Note: The regression method is<br>the10%-level,** indicates sign|PROBIT. Standard errors are between brackets. * indicates significance at<br>ificance at the 5%-level, and*** indicates significance at the1%-level.|

For the treatment effect of student loans, we find that the probability of university enrollment for the eligible population (aged 18-24, with completed upper secondary school) markedly increases when the students receive financial support from the government. Students with financial support have a 24%-point larger probability of university enrollment than students without financial support, and the effect is highly significant.<sup>13</sup> This impact is not sensitive to the set of controls included in the regression model. The effect is more or less the same when hours of work and type of school are included.

We should mention that these results need to be interpreted with caution. First, we cannot control for some other important variables in the estimation. For instance, we do not have data on academic aptitudes. When financial aid is merit-based, the coefficient on _DAID_ may be biased upward. Hence, students enroll not because they receive financial aid, but because they are academically proficient, which makes them eligible for student financial aid. Second, there are some interpretation difficulties. With regard to the student support variable, the amount of the financial support, its conditions (e.g. merit-based, or need-based), and timing of the student aid (some of the financial support may have been granted for primary and secondary education) are not

> 13 The increased probability of enrollment of 24 % from the regression based on household data is similar in magnitude to the response in the SOFES questionnaire, where 29% of students would not have enrolled in university in the absence of student loans.

16
workers who are working at home or in a mobile place of employment, i.e. those with less stable work. Wage employees do report working fewer days and hours when interviewed three times a week instead of once a week.

These results point to the possibility that the reference period of a week may systematically underestimate the incidence of certain types of self-employment, but more research is needed to substantiate this conclusion. Future studies seeking to assess the role of the reference period, in contexts with significant self-employment, would be well advised to interview respondents on a daily basis and/or use time diaries. The use of administrative and/or observational data is of course ideal, but highly unlikely to be available in such settings.

Although identifying the mechanisms that cause differences in reporting are beyond the scope of this paper, our findings demonstrate that labor market statistics are very sensitive to the method by which they are collected, with those in the least stable types of self-employment being most impacted by changes in survey mode and the reference period. It is important to pay attention to these differences when examining the determinants of labor supply, especially in contexts where self-employment is widespread.

18
of these surveys is particularly useful because the surveys either preceded or followed the bulk of the fighting.<sup>4</sup> For each DHS, a household survey collected the usual individual demographic and education data as well as household-level socioeconomic information. More detailed information was then collected from all women and a subset of men of reproductive age (if ever married, in the case of the 2001 survey). The data used in this analysis come from the household survey as well as migration information from the detailed interviews with women in the 2006 DHS.<sup>5</sup> Summary statistics by district conflict intensity and specification subsample can be found in table S1.

The data used to measure conflict intensity are taken from electronic files provided by INSEC, an independent, well-regarded human rights NGO based in Kathmandu with reporters in each of the 75 Nepalese districts who monitor human rights violations. The INSEC data files contain the number of conflict-related deaths per month per district of Nepal between February 1996 and December 2006 as well as the total number of school destructions and abductions by Maoists at the district level, which are used to construct most measures of exposure to conflict used in this paper. Data from INSEC have been extensively used in the media, international agencies, and government reports and in a number of academic studies, including those by Bohara et al. (2006) and Do and Iyer (2010). However, conflict deaths and school destructions are easier to monitor than abductions, and there are some surprising figures in the abduction data provided by INSEC, such as only 284 abductions by Maoists in Rolpa during the entire conflict. A degree of measurement error is likely to affect any conflict event data. If uncorrelated with the actual number of conflict events, this classical measurement error would lead to attenuation bias. However, the measurement error would have to be both inversely related to the true number of conflict events and very severe for it to lead to a reversal of the sign of the estimated effect of conflict. Such a result appears implausible given the degree of consensus on INSEC conflict data. Furthermore, findings using the number of casualties are consistent with the simple difference-in-difference calculations in table 1, in which 9
# **FACTORS AFFECTING LEVELS OF INTERNATIONAL COOPERATION IN CARBON ABATEMENT PROJECTS**

Ariel Dinar<sup>1</sup> , Shaikh Mahfuzur Rahman<sup>2</sup> , Donald Larson<sup>3</sup> , and Philippe Ambrosi<sup>4</sup>

> 1 University of California, Riverside; at the time of this research World Bank, Development Economics Research Group.<sup>2</sup> Texas Tech University, Lubbock, and consultant to the World Bank.<sup>3</sup> World Bank, Development Economics Research Group.<sup>4</sup> World Bank, Carbon Finance Unit. This paper is one of the products of the study “Local Actions for Global Impact Inference of the Role of the Flexible Mechanisms in Reducing GHGs” conducted in the World Bank DECRG-RU that was funded by the Knowledge for Change Trust Fund. We thank Rama Chandra Reddy for useful comments.
|**Table 3:**|(1)<br>**Effect of sele**|(2)<br>**cted variables**|(3)<br>**on returns to**|(4)<br>**education**|
|---|---|---|---|---|
|Transition|-1.596**|-0.108|-0.846|-0.306|
||(0.625)|(0.622)|(0.625)|(0.658)|
|Schooling|0.470***|0.169|0.060|-0.023|
||(0.169)|(0.141)|(0.159)|(0.170)|
|Polity||0.238***||0.134***|
|||(0.039)||(0.052)|
|Trade|||0.021***|0.020***|
||||(0.007)|(0.007)|
|Constant|3.965**|4.409***|5.951***|5.443***|
||(1.657)|(1.236)|(1.333)|(1.314)|
|Prob > chi2|0.002|0.000|0.004|0.000|
|Observations|219|216|199|196|
|Groups<br>Robust standard<br>*** p<0.01, ** p|40<br>errors in parenth<br><0.05, * p<0.1|39<br>esis|40|39|

To assess how transitioning from a centrally planned economy to a market economy affects the returns to education in post-socialist countries, we run a set of panel regressions using the difference-in-difference (DiD) estimation (see Table 4). The first model of this set shows that postsocialist economies encountered a significantly lower rate of return to schooling in comparison to non-transition countries before the fall of the Soviet Union by the end of 1991 and that the fact of transitioning significantly increased their returns to education in comparison to non-transition countries. The second regression includes the polity variable to take into account changes in governance and shows that the strengthening of democracy has a positive and significant effect on the returns to education, although the positive effect of transitioning on returns to education in post-socialist countries diminishes when including this variable. The third model uses trade instead of polity and reveals that commercial openness has a positive and significant effect on returns to schooling, while being a post-socialist country remains related to a significantly lower rate of return before 1992, although this trend is reversed after transitioning from a command to a market economy. The fourth model includes polity and trade simultaneously in the DiD estimation; in this case, commercial openness has a positive and significant effect on the returns to education of all sample countries, while transitioning preserves a positive and significant effect on the returns to education of post-socialist countries, although the returns gap between transition and nontransition countries before 1992 seems smaller when taking into account polity and trade.

We can conclude from this set of panel regressions that the event of transitioning from a centrally planned economy to a market economy (captured by the variable _DiD_ ) increased the returns to schooling in transition countries positively and significantly compared to non-transition countries. All else equal, the transition helped workers in post-socialist countries to increase their returns to schooling by 1.6 to 3.2 points, which translates into an increase ranging from 36.0 to 71.9 percent when considering the average returns to education of all post-socialist countries included in the 11
financial system can influence both aggregate production and the allocation of credit, each of which may alter the demand for low- and high-skilled workers with concomitant ramifications on the distribution of income (Townsend and Ueda, 2006). For example, improvements in finance that boost the demand for low-skilled workers will tend to tighten the distribution of income, expanding and equalizing economic opportunities. Thus, theory illuminates an assortment of direct and indirect mechanisms through which changes in the operation of the financial systems can intensify or reduce the inequality of economic opportunity.

The emerging bulk of empirical research points tentatively toward the conclusion that improvements in financial contracts, markets, and intermediaries expand economic opportunities, reduce persistent inequality, and tighten the distribution of income. For example, access to credit markets increases parental investment in the education of their children and reduces the substitution of children out of schooling and into labor markets when adverse shocks reduce family income. Moreover, a growing body of evidence suggests that better functioning financial systems stimulate new firm formation and help small, promising firms expand as a wider array of firms gain access to the financial system. Besides the direct benefits of enhanced access to financial services, research also indicates that finance reduces inequality through indirect, labor market mechanisms. Specifically, cross-country studies, individual-level analyses, and firm-level investigations show that financial development accelerates economic growth, intensifies competition, and boosts the demand for labor, disproportionately benefitting those at the lower end of the income distribution.

In our critique, we argue that economists under-appreciate the potentially enormous impact of financial sector policies on inequality. For example, while a growing body of theoretical and empirical research suggests that finance exerts a first-order impact on inequality, the three volumes of the _Handbook of Income Distribution_ do not mention possible connections between inequality and formal financial sector policies, such as bank regulations and securities law. While finance plays a crucial role in the preponderance of theories of persistent inequality, researchers generally take financial market imperfections as given and unalterable (e.g., Becker and Tomes, 1979, 1986; Galor and Zeira, 1993; Mookherjee and Ray, 2003). In some theories, credit constraints are simply taken as exogenous. In others, static information and transaction costs endogenously produce adverse selection and moral hazard that impede financial 3
low management public school (via teacher selection). In the numerical example shown in Figure 7, the vertical height of the blue point in the top panel is greater than the vertical height of the orange point in the bottom panel.

Column (2) of Table 4 shows that, consistent with the theory, the partial effect of operations score on the teacher motivation index is positive and significant at 1 percent; a one standard deviation increase in operation score is associated with a 0 _._ 238 standard deviation _increase_ in the teacher motivation index, significant at the 1 percent level.

**Teacher effort.** The expected effort level of a teacher hired to an intermediate management public school is higher than the expected effort level of a teacher hired to a low management public school (via teacher selection)

Column (3) of Table 4 shows that one standard deviation higher operations score is correlated with 0 _._ 076 standard deviations higher teacher effort, significant at the 5 percent level.

**Household effort** The expected level of household effort in an intermediate management public school is higher than the expected level of household effort in a low management public school (via augmentation of student intrinsic motivation): _a_<sup>_I_</sup> _> a_<sup>_L_</sup> .

We explore this prediction by using the school climate section of the school principal questionnaire to construct an index of household effort, combining student behavior questions (relating to how often students are truant, late, disrespectful and/or disruptive) and parental involvement questions (relating to the extent to which parents: are interested in, and discuss, their child’s progress and behaviour; volunteer for school activities; and participate in school governance or other forms of accountability). Column (4) of Table 4 shows that, consistent with the theory, one standard deviation higher operations management score is correlated with 0 _._ 160 higher household effort, significant at the 1 percent level. For completeness, Figure 9 plots the coefficients for country-level regressions using the same specifications in Columns (1) to (4) in Table 4. Again, the results 24
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