rafmacalaba/datause-encoder-data huggingface dataset:
**Table 10a** . Prevalence of poverty at different thresholds with confidence intervals

|**Treatment**|Natio<br>( <br>|nal poverty line<br>TSh 1,876)<br>|( <br>|US$ 1.90<br>TSh 1,644)<br>|( <br>|US$ 2.15<br>TSh 1,860)<br>|( <br>|US$ 3.2<br>TSh 2.8<br>|5<br>12)<br>|
|---|---|---|---|---|---|---|---|---|---|
||**%**|**[95% C.I.]**|**%**|**[95% C.I.]**|**%**|**[95% C.I.]**|**%**|**[95%**|**C.I.]**|
|**T0**|**20.1**|[15.1 – 25.2]|**13.1**|[9.4 – 16.7]|**19.7**|[14.8 – 24.6]|**49.6**|[44.3|– 54.9]|
|**T1**|**23.9**|[19.2 – 28.6]|**17.6**|[13.7 – 21.6]|**23.0**|[18.4 – 27.6]|**48.3**|[42.9|– 53.7]|
|**T2**|**20.7**|[16.3 – 25.1]|**14.9**|[10.6 – 19.3]|**20.4**|[15.9 – 24.8]|**44.8**|[39.3|– 50.2]|

* _all US$ converted to local values using 2017 purchasing power parity (PPP), expressed in 2022 prices_

**Table 10b.** Prevalence of poverty at different thresholds with statistical significance tests

|**Poverty**|**T0**|**T1**|**Sig**<sup>**a**</sup>|**T0**|**T2**|**Sig**<sup>**a**</sup>|**T1**|**T2**|**Sig**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|National poverty line (TSh 1,876)|20.1|23.9|n/s|20.1|20.7|n/s|23.9|20.7|n/s|
|US$ 1.90 (TSh 1,644)|13.1|17.6|*|13.1|14.9|n/s|17.6|14.9|n/s|
|US$ 2.15 (TSh 1,860)|19.7|23.0|n/s|19.7|20.4|n/s|23.0|20.4|n/s|
|US$ 3.25 (TSh 2,812)|49.6|48.3|n/s|49.6|44.8|n/s|48.3|44.8|n/s|

_a Differences found to be statistically significant are indicated by level: * p<0.10, ** p<0.05, *** p<0.01; n/s = not significant_ 40
|Table 5.5: Age of Femal|es at First Birt|h byEducation,2000|
|---|---|---|
|Agegroup<br>None|Primary|Secondary or higher|
|20–24<br>19.9|NA|NA|
|25–29<br>19.7|20.4|NA|
|30–34<br>20.0|21.4|27.2|
|35–39<br>20.4|21.6|25.6|
|40–44<br>20.7|21.7<br>_Source:_DHS|24.9<br>2000.|

Table 5.6: Age at Sexual Initiation by Education, 2000

|Agegroup|None|Primary|Secondary or higher|
|---|---|---|---|
|20–24|16.7|17.5|19.4|
|25–29|17.0|17.2|20.0|
|30–34|17.6|18.2|20.1|
|35–39|16.7|18.2|21.0|
|40–44|17.6|18.6|19.1|

_Source:_ DHS 2000.

It is important to realize that not all teen pregnancies should be seen as unwanted in order to reduce the occurrence. In Haiti many teen pregnancies are planned because motherhood and the possibility of marriage, which motherhood makes more likely, is perceived as a way of gaining recognition and bringing meaning to life. Other reasons for teen pregnancies include a general view of children as an asset for labor and the fact that abortion is illegal in all circumstances and punishable by three to nine years of imprisonment (World Bank 2002). Because of the strict abortion law some youth may be forced into illegal hazardous abortions that place their lives at risk. In this context, religion is an important opinion maker in Haiti. Catholicism and Voodoo are the main religions besides Protestantism; the former two promote fertility by being against contraception. In fact, when asked: “what is the ideal number of children” 17 percent of married youth answer that this is left to “God’s will. _”_ With this in mind, it is surprising that 32 percent of married youth aged 20–24 claim they currently use contraception (Table 5.7). It is half as likely that married teenagers use contraception; thus young married females are at high risk of pregnancy. Forty-three percent of single but sexually active teenagers use contraception, which is also far from a desirable level. Metropolitan youth are much more frequent users of contraception than youth in rural areas. In sharp contrast to the reported relatively frequent use of contraception is the fact that 91 percent of youth who have ever been engaged to be married have children and when asked about the ideal number of children 27 percent have no upper limit.

Table 5.7: Current Use of Contraception, 2000 (percent)

|Age group|Married|Single but<br>sexually active|
|---|---|---|
|15–19|16.4|42.9|
|20–24|32.1|36.6|
|25–29|31.3|40.5|
|30–34|32.4|NA|
|35–39|31.8|NA|

_Source:_ DHS 2000.

27
20 educational performance. Thus using the 30 directly-measured countries, they estimated a statistical relationship between their educational quality indices and schooling inputs such as primary school enrollment, average years of schooling, total expenditure on education (as a share of GDP), annual population growth and regional dummies. These schooling inputs were able to account for more that 65 percent of the cross-country observed variation in educational quality<sup>33</sup> . The resulting estimates were used to generate predicted values for an additional 52 countries.

Bosworth and Collins (2003) also constructed an alternative series on educational quality. They produced this series by re-estimating the Hanushek-Kimko relationship between the quality of education and the set of schooling inputs with updated data obtained from the World Development Indicators. In addition, they modified the estimating equation to include a measure of the quality of government institutions as one of the independent variables. The quality of institutions measure substantially improves the statistical fit and the reduced the role of several other variables.

As with our data series on the Mincerian rates of return to education, we seek to increase the number of usable observations on educational quality by filling in missing values in the Hanushek-Kimko series. We employ the identical procedure of filling in missing values as before by using the simple average of a specific country’s regional and income group average of available educational quality observations. We term this series as our _extended series_ on educational quality.

# **-** **<u>Using Country Specific Rate of Return to Education to Account for Difference in Educational Quality</u>**

Some argue that qualitative differences in schooling can be accounted for by the use of country-specific rates of return to education that were mentioned above. For country specific rates of return to accurately reflect quality differences in the stock of human capital, three assumptions are required to hold. First, global labor markets need to be perfectly competitive. Second, labor is required to be perfectly mobile internationally, and lastly, employers need to be perfectly informed about the human capital quality of workers.

It should be obvious that the probability that all 3 assumptions actually hold in the real world is relatively low. We know that labor markets are not very competitive in many countries, especially given the widespread existence of collective bargaining

> 33 It was be seen later that we intend to include both educational attainment as well as the Hanushek-Kimko measure of educational quality as separate regressors in our own cross section regressions on economic growth.  We acknowledge that these two variables may be highly collinear as both measures are based, in part or in whole, on the average years of schooling.
utility by choosing a vehicle among available options, where utility is linear in product attributes and a random error term. Because the policies we consider either directly or indirectly affect vehicle prices, the baseline specification of the model allows for a random coefficient on vehicle price to generate more plausible consumer responses to the policies. On the supply side, vehicle manufacturers choose vehicle prices to maximize profits given government policies and consumer demand.

Demand and supply parameters are estimated from observed vehicle market shares, prices, and other attributes using a novel and rich data set of new registrations and vehicle attributes, fuel prices, and relevant government policies between 2015 and 2020. This level of detail is comparable to recent studies of automobile markets in high-income economies and typically is not available for developing countries. Marginal costs of each vehicle are estimated from the first-order condition for vehicle prices to firms’ profit maximization problems.

Based on estimated parameters, policy simulations evaluate the effects of Colombia’s tax policies on hybrid and electric vehicle sales, GHG emissions, and consumer and manufacturer welfare. Simulations focus on outcomes in 2019 (before the COVID-19 pandemic) and 2020 (when hybrid and electric vehicle market shares increased dramatically). Compared with the 2019 and 2020 equilibriums that include both favorable purchase and import taxes, we simulate counterfactual equilibriums that eliminate one or both policies. That is, the counterfactuals hold fixed all market conditions in 2019 or 2020 and return policies to their 2017 levels. In 2019, the two policies jointly caused the market share of electric vehicles to increase from 0.1 to 0.6 percent, with the tariff reduction having a larger incremental effect on sales than the sales tax exemption. In 2020, the policies raised the market share of electric vehicles from 0.5 to 2.6 percent.

Although the policies are effective at increasing sales, their costs are high because much of the subsidy is claimed by hybrid vehicles, which are less environmentally beneficial than electric vehicles, and because of pre-existing distortions due to market power. The average fiscal cost was $350-$510 per ton of carbon dioxide, and the average welfare cost was $40-$48 per ton of carbon dioxide. This is comparable to recent estimates of the global social value of emissions reductions (van den Bremer and van der Ploeg 2021 and Interagency Working Group 2021; our welfare results do not include the benefits of local air quality improvements).

4
We label a firm as politically connected if one of its managers, owners, or board members is a government official that can directly influence economic regulations and policy making to her own private benefit. We thus focus on clear-cut conflict of interest cases in that the government official associated with the company has the incentive and power to tilt economic regulations in her favor.<sup>1</sup> In fact, previous research has demonstrated that politically connected firms managed, owned, or having a government official as a board member receive substantial policy privileges in Egypt (Schiffbauer et al., 2015), leading the WBES team to include the following question to directly identifying connected firms in the most recent survey for the country:

- _Has this establishment ever had a (current or former) government official among its managers, owners, or board of directors?_

The question was confirmed as positive for 105 firms, corresponding to 5.8 percent of all surveyed firms.<sup>2</sup> Notably, only 3 out of the 1,813 firms refused to answer or indicated not to know the answer.<sup>3</sup> Politically connected firms operate in various sectors but are especially concentrated in manufacturing of chemicals, fabricated metal products, mineral products, and construction (see Table B.1 in the Appendix). At least 1 of these 105 politically connected firms operate in 51 of the 118 4-digit (ISIC Rev. 3.1) sectors included in the WBES for Egypt, corresponding to 43 percent of all 4-digit sectors.

The nature of the question and the consistency check of the survey answer by the World Bank trained interviewer after each face-to-face interview, make it very unlikely that these 105 political connected firms include false positives (a firm identified as connected that, in fact, has never been associated with a government official). As in all previous work on cronyism, however, it is possible that our measure includes false negatives to the extent that some surveyed firms did not reveal their ownership by government officials and could not be falsified. Several facts assure, however, that these cases cannot be prevalent.

First, consistent with previous work on Egypt and other countries, we find similar characteristics of connected firms in our sample; most importantly, they are older and significantly

> 1 Many countries have conflict of interest laws that could force individuals to sell their businesses when they take political office. A conflict of interest law was introduced in Egypt in 2012 but it has not been enforced yet. 2 After the face-to-face survey interview, the interviewers check the consistency of the answers based on available information, including the identity of the interviewee (i.e., the CEO or owner of the company), which facilitates the identification of establishments that are managed, owned, or directed by a government official.

> 3 Appendix Table 1 accordingly corresponds to 1,810 observations.

9
POLIY RESEARCH WORKIN(; PAPFK 1653

# Summary findings

Hernandez and Katada analyze the effects of bilateral debt forgiveness (part of official development assistanice) on 32 low-income countries in Africa (1984-9)3). Asking whether it makes a difference for recipient countries To receive pure grants rather than official development assistance (ODA) debt relief, they focus on how one form of aid or the other affects the countries' nimport capacity. They conclude that: Grants allowed recipient countries to siginificanitly expand their import capacity for 1984-93 as graints and import capacity have been increasing since 1984. But the increasing share of concessional lending and dcebt relief in recent years has not allowed these countries to reduce their total indebtedness and solve their debt overhan;g problem. Their arrears increased significantly.

* The biggest recipients of debt relief also received the lion's share of the increase in pure grants. Debt forgiveness and pure grants were allocatcd in a way not entirely consistent with standard econormic hicrarchies

(such as poverty levels, indebtedness, and access to alternative sources of finance).

Bilateral OD)A debt forgiveness appears to be neutral in the sense of not having any significant impact on recipienr countries' capacity to import. Bilateral ODA debt forgiveness has neither increased or curtailed the imp(ort capacity of the major recipient countries. During 1 989-93, multilateral lending replaced the decrease in bilateral lending that, in turn, was caused by an increase in grants. (Bilateral ODA debt relief implies smaller cash flows because it is "pseudo" or "accounting" money and because with it goes reduced new lending from bilateral sotirces.) Private creditors have typically withdrawn money from the countries in the sample as grants increased. And debt relief has hald a crowding-out effect on new lending. Bilateral donors are switching their development finance to Africa from concessional and nonconcessional lending to) a combination of pure grants and ODA debt relief.

- - This paper a product of the International Finance Divisioin, International Economics Department is part of a larger effort in the department to monitor developmncnts in highly indebted low income countries. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, D)li 20433. Please contact Rose Vo, room N3-068, telephone 202-473-1047, fax 202-522- 3277, Interinet address lIvo I _h_ worldbatik.org. September 1996. (28 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objec tive of the series is to get the findings_ **_out_** _quiLkly, even_ **_if_** _the presentations are less than fully polished. The papers carry the names of the autlors and skould be used and_ **_t_** _iteJ according/v. '17Te findings. interpretations, and conclusions are the authors' ou!n and shouild not be attributed to the Wlorld Batik, its lxccutiwe Board of Directors, or apny of its member countries._ Produced by the l'olicy Rcs, crclh 1)isscimnation Ceinter
regression analysis to illustrate how a regression-based analysis of the question can lead to misleading results.

Our results show that at the 5% and 10% significance level, Common Law, Latitude and Ethnic Fractionalization are all significant predictors of property rights protection where as Catholic Religion is not. However at the 1% significance level, only ethnic fractionalization is a causal determinant of property rights protection. Further, Ethnic Fractionalization is the only variable which is robust to different sample compositions where as the effect of Latitude and Common Law is strongly dependent on the sample of countries under study. The data offers only limited support for the proposition that Common Law origin or Latitude is a determinant of property rights protection and the support is not robust to different definitions of the variable or sample composition Our paper is related to Beck, Demirguc-Kunt and Levine (2003) and Ayyagari, Demirguc-Kunt and Maksimovic (2005a). Using firm-level survey data, Ayyagari, Demirguc-Kunt and Maksimovic (2005a) evaluate determinants of firm level perceptions of property rights protection and find the ethnic fractionalization and endowments view to explain a greater proportion of variation in the data compared to other historical factors However, they consider one variable at a time and do not rule out any variable. Beck, Demirguc-Kunt and Levine (2003) find more evidence supporting the law and finance view. However, in this paper we are able to illustrate how the regression-based methodologies, as normally applied, can be misleading in identifying causal factors and that legal origin is not a robust determinant of property rights protection.

4
2016b), we apply a lasso regression for feature selection.<sup>22</sup> The lasso regression allows us to follow a data-driven approach when selecting the most relevant variables from the potential set of overlapping variables. We apply a 10-fold cross-validation (CV) procedure. The lasso estimation results in 7 out of 22 possible matching variables, which are households with pensioners, households with elderly, households with unemployed, a female-headed household, urban, its income category, and the number of household members with secondary education. The resulting out-of-sample R-squared is 0.151, and the CV mean prediction error is 0.014.

**In addition to the variables identified via feature selection, we include the variables that describe the underlying sample design in the set of matching variables.** This is the variable that identifies the primary sampling unit (census enumeration units in the case of the HBS). We denote this variable as psu1 in the rest of the paper. In an alternative model specification, we include the sample weights by running weighted regressions. Additionally, we explore empirical specifications, in which we abstract from weighting regressions by the given survey weights but include survey weights as an additional matching variable.

# **III. Results**

This section presents the results of the different imputation models applied in the paper and analyzes the quality of the resulting imputation.<sup>23</sup> For all regressions, we apply 20 imputations and set the seed to 12345 to ensure the reproducibility of our results.

22 The least absolute shrinkage and selection operator (lasso) forms part of penalized least square regressions. It was originally developed by Robert (1996) and performs both automatic variable selection and continuous shrinkage. For details on the lasso regression see Meinshausen and Bühlmann (2006).

23 Before applying the different imputation models we prepare our data for the usage of the mi commands in Stata. We use the data in marginal long style (mlong) as it is more memory-efficient. We then register the energy spending share and the energy poverty indicator as imputation variables. We also make sure that there are no missing observations in any of the matching variables.

24
Differences between SI- and MI-based results presented so far are compatible with compensations in the re-ranking of individuals across the welfare distribution. The rearrangement of households along the distribution of expenditure can have important policy consequences. For this reason, in the final part of this section we focus on the non-anonymous distributional implications of switching from SI to MI, borrowing a few tools from the mobility literature.<sup>15</sup>

**Table 5. Probabilities of transition in and out of poverty when switching from SI to MI ($6.85 PL)**

|**Area**|**% of poor individuals**<br>**using SI**<br>**who become non-poor**<br>**using MI**|**% of non-poor**<br>**individuals using SI**<br>**who become poor**<br>**using MI**|**number of individuals**<br>**changing poverty**<br>**status**|
|---|---|---|---|
|National|3.7|1.2|1,620,300|
|Tehran metro Urban|9.3|1.7|686,288|
|Caspian Rural|0.2|0.5|12,923|
|Caspian Urban|5.4|0.1|62,248|
|Northwest Rural|6.2|3.8|133,883|
|Northwest Urban|0.0|0.9|49,406|
|Northeast Rural|5.3|3.6|102,378|
|Northeast Urban|0.7|0.2|22,455|
|Central Rural|2.0|1.1|49,644|
|Central Urban|2.2|0.1|62,538|
|Southeast Rural|4.8|8.8|197,767|
|Southeast Urban|0.5|1.1|40,399|
|Persian Gulf Rural|3.4|3.6|90,712|
|Persian Gulf Urban|0.3|0.2|14,949|
|Zagros Rural|4.2|2.9|95,337|
|Zagros Urban|0.6|0.2|18,733|

_Source_ : Authors calculation based on HEIS 2019 First, we focus on the binary outcome of whether a household is labeled as poor or not, under the two alternative deflation scenarios. Taking the SI approach as a starting point, Table 5 shows the proportion of individuals that change their poverty status when the MI approach is adopted. Nationally, 3.7% of individuals who were poor under SI are no longer poor when using MI, while

> 15 We follow, by and large, the analysis presented by Ceriani, Olivieri and Ranzani (2022) in their evaluation of the distributional impact of imputed rent estimation.

34