rafmacalaba/datause-encoder-data huggingface dataset:
tho hourly **wale,** they do not attempt to correct for analyzing only wage *x*ors. **becouse the** aomples **are** small, aome of the<sup>estimates</sup> are imprecise. Ovorall, **howtver, there is** no clear ***vildence** that **returns** to schooling differ systeomtlcally by sex. They do differ **considerably across** countioes, bowever, p:esumebly because of differences in macro economic conditions and the lovel of past imnestmnts in schoollng (ichults, 19680). An anAlysis of the roturns to edusation in Andre Pradesh. India illustrates how sensitive the return calculatlons are to the treatment of the labor force participation **rate** of women (Tilak, 1987). Both the private and social retur,.s to schooling at virtually every level **are** greater for women than mn when the returns **are** adjusted only for uawsployment. But when nonparticipatiaa in the labor force is also factored into the calculation<sup>as Becker</sup> propasd, the private rate of return for women is less than it is for **men** (see vast to the last two rows for Indian study in Table 1). This study also documnmts the lower public cost of female than male education, and the lower *pportunity cost of tUim for **female** than r,ale students. However, the adjustsent for nonparticipation of vomen is not Introduced to deflate the opportunity cost of female student tim but only to **deflate** the stream of benefits. **A** study of **Sri Lanka** also confirmed that womn's retutn to schooling that ar not **deflated** by nonparticipstion rates **exceed those** of smn. **The** privfte rteo of return to completing the gennral certificate of **xams at the** #ad of tb aecoodtry school **are three** tie. as high for womn as for men in urb. areas (36 vermU 13 peteent), and tiies as high in **rural areas** (14 *tt* 7 #*teftt). At **the** ufivateaity levi, hwever, **the rates** of retur in _WF_ to** _d"*a*_ tae &wtsetp bet"CtW **sen** and wmen (ahn and Aldermn. 1968: **I*i** _i7v._
**Table 10: Joint probabilities of being undernourished and wealth poor**

||Poorest|20% of Househ|olds|Poorest|40% of Househ|olds|
|---|---|---|---|---|---|---|
||Underweight<br>women|Stunted<br>children|Wasted<br>children|Underweight<br>women|Stunted<br>children|Wasted<br>children|
|Benin|0.016|0.095|0.032|0.028|0.181|0.067|
|Burkina Faso|0.047|0.072|0.031|0.085|0.136|0.060|
|Burundi|0.044|0.129|0.014|0.074|0.233|0.026|
|Cameroon|0.027|0.091|0.018|0.043|0.166|0.031|
|Congo, Rep.|0.032|0.058|0.012|0.066|0.100|0.023|
|Côte d’Ivoire|0.018|0.069|0.017|0.032|0.123|0.032|
|Congo, Dem. Rep.|0.036|0.090|0.015|0.075|0.177|0.032|
|Ethiopia|0.063|0.085|0.022|0.123|0.173|0.046|
|Gabon|0.018|0.057|0.007|0.031|0.083|0.012|
|Gambia|0.035|0.052|0.020|0.079|0.097|0.042|
|Ghana|0.022|0.042|0.014|0.038|0.080|0.024|
|Guinea|0.036|0.054|0.028|0.061|0.132|0.049|
|Kenya|0.040|0.077|0.023|0.073|0.146|0.035|
|Lesotho|0.018|0.072|0.011|0.034|0.135|0.014|
|Liberia|0.021|0.059|0.015|0.035|0.114|0.027|
|Malawi|0.020|0.098|0.010|0.039|0.191|0.019|
|Mali|0.025|0.085|0.030|0.049|0.164|0.058|
|Mozambique|0.024|0.090|0.015|0.047|0.177|0.027|
|Namibia|0.045|0.044|0.022|0.075|0.093|0.038|
|Niger|0.040|0.076|0.036|0.077|0.158|0.064|
|Nigeria|0.033|0.100|0.037|0.059|0.184|0.072|
|Rwanda|0.018|0.092|0.006|0.034|0.181|0.013|
|Senegal|0.053|0.040|0.015|0.102|0.077|0.031|
|Sierra Leone|0.022|0.077|0.012|0.041|0.150|0.030|
|Swaziland|0.009|0.058|0.002|0.016|0.114|0.009|
|Tanzania|0.036|0.086|0.011|0.061|0.163|0.018|
|Togo|0.024|0.054|0.014|0.042|0.111|0.025|
|Uganda|0.044|0.059|0.010|0.074|0.118|0.021|
|Zambia|0.030|0.083|0.013|0.054|0.157|0.025|
|Zimbabwe|0.022|0.056|0.007|0.040|0.110|0.012|
|Mean|0.031|0.082|0.021|0.058|0.156|0.040|
|Corre. coeff.|0.914|0.912|0.928|0.965|0.961|0.969|
|Elasticity of joint<br>|0.888|0.765|0.953|0.950|0.824|0.947|
|to marginal|(0.057)|(0.096)|(0.109)|(0.045)|(0.045)|(0.033)|

Note: Data are drawn from the DHS. Means are population weighted. The correlation coefficient is that between the joint probability and the relevant undernutrition rate from Table 7. Elasticities estimated by double-log regression. Robust standard errors in parentheses.

47
Appendix Figure B4 shows that _PV_ - _DV_ differences are larger after the first couple of clinic observation days. At the same time, we find that only 56 percent of patients prescribed an antimalarial on Control days received a malaria test, and there is no significant difference in this “test given prescription” rate across treatment arms, even though testing before treatment was a major focus of the training and a requirement for using the voucher. Overall this suggests the implicit monitoring that took place during the experiment had limited effects on behavior that, if anything, bias us away from detecting effects.

A related worry is that _PV_ - _DV_ differences are driven by experimenter demand effects on the patient side (e.g. if patients interpreted receiving the voucher as a signal that “buying an antimalarial is encouraged by an outside NGO”). While this would not invalidate our tests of gatekeeping costs and reluctant compliance by doctors, it is important for interpretation. We believe experimenter demand effects were minimal in our setting for two reasons. First, study staff were carefully trained to explain that the vouchers were not valid without a provider’s signature, and that the doctor was the one who would make the malaria diagnosis. Second, data from the patient information treatment ( _PI_ ) suggests patients were not influenced by experimenter demand effects. The _PI_ video communicated that suspected malaria cases should only be treated if they received a positive test. However, Appendix Table B14 shows that _PI_ actually _decreased_ the share of total patients and antimalarial prescription-holders who took a malaria test, while leaving the share of patients who were prescribed an antimalarial unchanged. Panel B shows there is no significant interaction effect with the voucher treatment arms, which suggests that effects are not simply driven by patients refusing tests in the hope of redeeming a voucher. These results are inconsistent with experimenter demand effects changing patient preferences, but are consistent with patients having preferences of their own about whether to get tested.

# **4.3 Do Doctors Use Vouchers to Increase Revenue?**

Financial motives are one of the most cited reasons for doctor-driven demand for drugs. We argued above that doctor-driven demand for severe treatment would arise if (relative to patient preferences) providers get substantially more utility from severe over simple treatment (∆ _H >_ ∆ _L_ ). A health worker who cares mostly about revenue would be a plausible explanation for this, since severe treatment is much pricier and incurs more high-margin service 30
is negative. This gives the following result which says that, in the presence of the HIV/AIDS epidemic, at a constant level of protection π1 and a constant level of infection in the population γ1, more educated people tend to have less sexual partners: This is the opposite of the result obtained in the absence of the epidemic.

Of course those exercises in comparative statistics analyze extreme cases. It is more likely that the responses will be mixed : more educated individuals might reduce more the number of their sexual partners but also use greater protection. The results derived above in this simpli fi ed model show how, in the presence of the HIV/AIDS epidemic, more educated individuals will modify more strongly their behavior.

In the present model, the way by which higher education induces a stronger behavioral change in the presence of HIV/AIDS is twofold, illustrating two of the mechanisms that can explain that more educated people modify more strongly their behavior when confronted to the HIV/AIDS epidemic. The fi rst channel operates through the fact that higher education leads to a higher level of utility in the second period giving more incentives to avoid a decrease in the survival probability. This is illustrated by the fact that the terms<sup><u>[u(c2</u></sup> u<sup><u>)+�</u></sup> (c<sup>v</sup> 1<sup><u>(</u></sup> )<sup>n2)]</sup> and A in equations (18) and (20) are larger for more educated individuals, increasing the shadow cost of unprotected sex with several partners. This points to the fact that more educated people take greater care of their health since they have invested more in their future.

The model also includes a second way through which the complementarity between health and education is operating: more educated people have an advantage in accessing or processing the information about the existence, the transmission modes and the ways to prevent the HIV/AIDS epidemic. Because more educated individuals were the fi rst to understand the information about the epidemic, they were the fi rst to optimize their behavior according to model 2, while less educated groups were still ignoring or not understanding the fact that unprotected sex was risky (baseline model).

# 3. Model 3: discount factors

In order to account for the view initiated by Farell and Fuchs (Farell and Fuchs 1986; Fuchs 28
al. (2019) compiled data on the gross fiscal costs of shocks to SOEs that exceed 0.2 percent of GDP. They identify 29 episodes in which SOEs were the source of contingent liabilities for the government, 17 of which involved infrastructure SOEs broadly defined (15 in the four sectors we study). They find an average gross fiscal cost of 3.2 percent of GDP for SOEs (3.0 percent for the sectors we study), with a maximum gross cost of 15 percent of GDP. Most of these costs were incurred during the global financial crisis of 2008–10 (see table A.1 in the appendix).

The empirical work on fiscal risk has focused on contingent liability risk, usually under extreme events. But fiscal risk—defined as “the possibility of deviations in fiscal variables from what was expected at the time of the budget or other forecast” (Cebotari aet al. 2009, 4)—is not a once in a lifetime event for an SOE. As we demonstrate with systematic cross-country data, fiscal risk is more often manifested as a series of small to medium-size deviations from budgeted figures, shortfalls in performance in SOEs, and liquidity crises that may require small fiscal injections (0.1– 0.3 percent of GDP) on a regular basis. When we examine fiscal injections that increase capitalization and/or substitute liabilities (that is, net fiscal transfers) as a percentage of assets, we see that bailouts are frequent and occur more often than assumed by the literature. This finding suggests that better ex ante monitoring of SOE financials can prevent both major catastrophes and some of the smaller instances of cash flow risk, which can compound into major events (Musacchio et al. 2015; Ter-Minassian 2018).

Following the categorization of fiscal risk of Polackova Brixi and Mody (2002), we explore a variety of implicit contingent liabilities that stem from the operation of infrastructure SOEs and from their accumulation of liabilities. We examine the fiscal risk and its key sources in the power, roads, railways, and airlines and airports sectors. We use a sample of 135 firms in 19 countries that come from an original, hand-collected database of SOE subsidies and financials for 2009–18. This sample covers fewer countries and a shorter period than the Bova et al. sample.

Using this database (which we refer to as the World Bank’s Infrastructure SOEs Database), we examine the fiscal risk that stems from the operation of infrastructure SOEs. We provide an overview of the financial performance of these SOEs, their shortfalls in performance, and the factors that constrain them from being profitable. We then build new measures of fiscal injections to SOEs that include not only the flow of subsidies from the government but also changes in the stock of government equity (recapitalizations); changes in the loans SOEs receive from the government, from financial and nonfinancial SOEs; and changes in deferred tax liabilities (when they are used to support an SOE), all net of asset increases. We examine these fiscal injections net of asset increases in order to ensure that we capture only fiscal transfers that 3
Table 15. Variable Definition and Source

|**Variable**|**Description**|**Source**|
|---|---|---|
|Fatalities|Total number killed by a natural disaster|EM-DAT, CRED (2008)|
|Affected|Total number affected by a natural disaster|EM-DAT, CRED (2008)|
|Disaster dummies|Describe which type of natural disaster occurred.|EM-DAT, CRED (2008)|
|Emergency aid|Dummy variables describing the channel<br>(bilateral vs. multilateral) and type (cash vs.<br>in-kind) of emergency relief|FTS, OCHA (2009)|
|GDP|Real GDP per capita (US Dollars in 2000 prices)|Penn World Table Version 6.2|
|POP|Total Population expressed in thousands|World Bank,<br>World Development Indicators|
|Distance|Distance between donor's and recipient's<br>capitals|World Bank,<br>World Development Indicators|
|Trade|Exports plus Imports of goods and<br>services divided by GDP|World Bank,<br>World Development Indicators|
|Corruption<br>control|Perception of the extent to which public power is<br>exercised for private gain, including<br>both petty and grand forms of corruption, as well<br>as influence of elites.|Kaufmann et al. (2008)|
|Regulatory<br>quality|Perception of the ability of the government to<br>formulate and implement sound policies and<br>regulations that permit and promote private<br>sector development.|Kaufmann et al. (2008)|
|Governance|Replaces either Corruption control<br>or Regulatory quality in Table 16|Kaufmann et al. (2008)|
|Democracy|Proxy for the political system in a country.<br>Includes information on competitiveness and<br>openness of executive recruitment, constraints<br>on chief executive, regulation and competitiveness<br>of participation.|Marshall & Jaggers<br>(2005)|
|Affinity index|Extended Gartzke index on voting patterns<br>in the UN General Assembly. Takes a value<br>between -1 (donor and recipient never voted the<br>same and 1 donor and recipient always voted the<br>same.|Voeten and Merdzanovic (2009)|
|Former colony|Dummy variable<br>1 = recipient was once donor's colony|Correlates of War<br>2 Project (2008)|

29
<!-- Start of picture text --> Figure C.14: Labor Expenditure Embodied in Net Exports<br>(A) Goods (B) Services<br>Routine Labor Routine Labor<br>Non−Routine Labor Non−Routine Labor<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>(C) Aggregate (Mio. USD) (D) Aggregate (% of GDP)<br>Routine Labor Routine Labor<br>Non−Routine Labor Non−Routine Labor<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>4000 80000<br>2000 60000<br>0<br>40000<br>−2000 20000<br>Labor Exp. in Net Goods Exports (Mio. USD) Labor Exp. in Net Services Exports (Mio. USD)<br>0<br>−4000<br>.5<br>80000<br>.4<br>60000<br>40000 .3<br>20000 .2<br>Labor Exp. in Net Exports (Mio. USD) Labor Exp. in Net Exports (% of GDP)<br>.1<br>0<br><!-- End of picture text --> _Notes:_ Panels A and B illustrate the labor expenditures embodied in net exports of goods and services, respectively. Panels C and D illustrate the sum of A and B both in millions of USD and as a percent of GDP.

40
<!-- Start of picture text --> Log TFPR: Kenya<br>iQ<br>=<br>awbar)<br>Ey co7<br>an 7 \<br>= i ‘\<br>— “aN é ‘<br>o4,—TM =<br>-4 2 0 2 4<br>——— Census ——— ES (Census Weighted)<br>——-—-—- ES(ES Weighted)<br><!-- End of picture text --> <!-- Start of picture text --> Log TFPR: Ghana<br>iQ<br>=<br>awa ‘Ka<br>ReEy fif ~<br>an \<br>%> ‘ ;<br>— 4q 4<br>° a<br>-4 2 0 2 4<br>——— Census ——— ES (Census Weighted)<br>——-—-—- ES(ES Weighted)<br><!-- End of picture text -->
# **Table 2: Baseline: Covariates**

||Not Treated/Visited<br>(N=18631)|Treated/Visited<br>(N=29226)|P-value|
|---|---|---|---|
|ln Value of Tax Object - 2021|18.28 ± 1.51|17.91 ± 1.36|<0.001|
|Dummy - Lower Tax Rate - 2021|17632 (94.6%)|28612 (97.9%)|<0.001|
|ln Land Area - 2021|5.86 ± 1.07|5.65 ± 0.91|<0.001|
|ln Taxable Value - 2021|12.39 ± 0.87|12.18 ± 0.74|0.60|
|ln Taxable Value - Land (by m2) - 2021|6.53 ± 1.48|6.54 ± 1.21|<0.001|
|Dummy - Contruction - 2021|13128 (70.5%)|25143 (86.0%)|<0.001|
|ln Value - Land (Total) - 2021|9.18 ± 5.97|11.11 ± 4.52|<0.001|
|ln Building Area - 2021|3.22 ± 2.19|3.80 ± 1.65|<0.001|
|ln Taxable Value - Building (by m2) - 2021|8.49 ± 0.72|8.54 ± 0.65|<0.001|
|Dummy - Owner Lives in The Property - 2021|1914 (10.3%)|3478 (11.9%)|<0.001|
|Dummy - Owner has multiple homes - 2021|1757 (9.4%)|1596 (5.5%)|<0.001|
|ln # properties by owner if multiple - 2021|0.75 ± 0.21|0.73 ± 0.16|<0.001|
|ln # properties by owner - 2021|0.03 ± 0.11|0.02 ± 0.09|<0.001|
|ln Value Owner’s Portfolio** - 2022|1.87 ± 5.82|1.07 ± 4.46|<0.001|
|Dummy - Property Paid >0 - 2020|13754 (73.8%)|26215 (89.7%)|<0.001|
|Dummy - Property Payable is Null - 2021|0 (0.0%)|0 (0.0%)|<0.001|
|Dummy - Tax was paid after deadline - 2020|4965 (26.6%)|6290 (21.5%)|<0.001|
|Dummy - Property fined - 2020|4870 (26.1%)|6261 (21.4%)|<0.001|
|ln Tax Fine* - 2020|2.39 ± 4.11|1.86 ± 3.64|<0.001|
|ln Tax Paid* - 2020|8.43 ± 5.13|9.79 ± 3.46|<0.001|

41
Table 7: Heterogeneous treatment effects on social networks outcomes at endline

||Covariate at ba|seline:|
|---|---|---|
||Lack of close peers in village||
||that use FP|MIL opposed to FP|
||(1)|(2)|
|**A: Number of close peers in village**|||
|Own Voucher|-0.318|-0.105|
||[0.240]|[0.089]|
|BAF Voucher|-0.287|0.135|
||[0.215]|[0.099]|
|Own Voucher _×_ Covariate|0.244|0.308*|
||[0.271]|[0.173]|
|BAF Voucher _×_ Covariate|0.664***|0.018|
||[0.246]|[0.230]|
|Observations|326|513|
|Endline control mean<br>|0.635|0.635|
|p-values:|||
|Own = BAF|0.891|0.035|
|Own _×_ Covariate = BAF _×_ Covariate|0.125|0.282|
|Own + Own _×_ Covariate = 0|0.559|0.172|
|BAF + BAF _×_ Covariate = 0|0.002|0.462|
|**B: Has peers in village that accomp**|**anied to health facility**||
|Own Voucher|-0.228*|-0.150*|
||[0.133]|[0.086]|
|BAF Voucher|-0.002|0.078|
||[0.206]|[0.076]|
|Own Voucher _×_ Covariate|0.077|0.378**|
||[0.184]|[0.173]|
|BAF Voucher _×_ Covariate|0.175|0.458**|
||[0.228]|[0.223]|
|Observations|222|336|
|Endline control mean|0.550|0.550|
|p-values:|||
|Own = BAF|0.278|0.016|
|Own _×_ Covariate = BAF _×_ Covariate|0.684|0.754|
|Own + Own _×_ Covariate = 0|0.236|0.130|
|BAF + BAF _×_ Covariate = 0|0.079|0.011|

_Notes:_ All columns are fully-interacted regressions where all controls are interacted with the covariate used to examine heterogeneous effects; the main effect of the covariate is also included as a regressor. All columns also include balancing controls (i.e., the baseline variables used to balance randomization–whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variable for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. MIL denotes mother-in-law, FP denotes family planning, and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

39
- 98 -

Table 4.1t Share of Farm Household Income from Nonfarm Sources, 1962 and 1980

|Country|Total Income from Nonfarm Sources<br>(percent)|<br>Wage Income a|
|---|---|---|
||1962<br>1980|1962<br>1980|
|Japan|49<br>80|36<br>69|
|Taiwan|34 <sup>b</sup><br>74|20 <sup>b</sup><br>52|
|South Korea|20<br>35|9<br>14|

- a Share of farm household income earned as wages from non farm sources. This share is included in the total.

b Figures for Taiwan are for 1966.

Source: Kym Anderson, Yujiro Hayami, and others. The Political Economy of Agricultural Protection (Sydney. London, and Boston: Allen and Unwin, 1986), p. 13.
22

**Figure 4:** Density of bilateral migration stocks among country-pairs with and without visa provisions: developed _vs_ developing destination countries.

<!-- Start of picture text --> Migration to developed countries Migration to developing countries<br>0 5 10 15 0 5 10 15<br>Log of migration stock<br>Migration stock density with visa provision<br>Migration stock density without visa provision<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> Source: United Nations Population Division and World Bank Deep Trade Agreement database.

**Table 1:** The probability of having visa provision in DTAs.

|Dep var:|Vi|sa|||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Right wing vote (sh)<sup>_a_</sup><br>GDP diference (ln)|-0.051***<br>(0.004)|0.004***<br>(0.001)||-0.064***<br>(0.005)<br>0.004***<br>(0.001)|
|Schenghen|||0.045***<br>(0.006)|0.012**<br>(0.006)|
|Estimator|OLS|OLS|OLS|OLS|
|Year FE|Yes|Yes|Yes|Yes|
|Country-pair Fixed Efects|Yes|Yes|Yes|Yes|
|Sample period|1990-2020|1990-2020|1990-2020|1990-2020|
|Observations|346,150|210,944|346,150|210,944|
|R-squared|0.737|0.758|0.737|0.758|

Note: _a_ Sum of the share of right wing votes in destination and origin country. Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
beneficiaries, 58 percent relied on mosque announcements for information, 47 percent relied on TV, and only 10 percent mentioned radio or newspaper (Mott McDonald, 2014).

# **VI. Conclusion**

Although electricity subsidies were introduced as a form of social safety net in Pakistan, the analysis of household survey and billing data in this paper demonstrates that they continue to be regressively targeted, and that many poor households remain exposed to high bills especially in the summer months. Subsidies are often introduced in the name of helping the poor, but implemented in isolation from other social protection programs, mainly due to the mapping of implementation responsibilities within government. Going forward, subsidies and their reforms should be part of the broader dialogue on social protection in Pakistan, and considered as one of a set of antipoverty interventions.

The qualitative assessment findings indicate that electricity is central to the lives of low‐ income and lower middle income households and that households struggle with electricity costs and they resort to coping mechanisms such as reducing necessary expenses on food, health and child care to afford electricity bills. Low‐income households, especially _katchi abadi_ residents and BISP beneficiaries, use illegal electricity (called the _kunda_ system) as a way to cope with increasing electricity costs. Despite efforts to improve service delivery, load shedding and electricity outages still negatively impact on households’ economic and social well‐being. Women are more affected by load shedding and by the household’s efforts to manage electricity expenses because they are on average the main users of electrical appliances and spend more time in the home. The majority of consumers do not trust their bills, and believe that their bill amounts do not reflect their actual consumption. Almost none of the respondents were aware that the government provides electricity subsidies. Overall, the negative attitude of respondents toward the electricity service is a result of frustration with unreliable supply and perceptions of poor sector governance. Despite struggling to afford electricity, respondents appear willing to pay higher prices provided service quality improves and governance problems are addressed. However, most respondents are skeptical that such improvements will take place.

When governments undertake subsidy reforms, they have two main levers to help households adapt to higher prices: (i) compensating households for the price increase; and (ii) encouraging households to adjust their consumption patterns (Yemtsov et al., forthcoming). In this analysis we have focused on the former, and illustrated how Pakistan can better cushion the effect of energy price fluctuations on poor and economically vulnerable families by targeting compensation using the existing NSER database. However, a pilot would be needed to examine the cost‐effectiveness of the approach given the costs of implementation.

22