rafmacalaba/datause-encoder-data huggingface dataset:
and Rural Affairs and Ministry of Finance, 2021a).<sup>5</sup> Chinese fuel subsidies are determined ex post in that the amount of subsidy awarded for fishing in year _t_ depends on the policy in year _t_ + 1. For example, the fuel subsidies vessels received in 2015 depended on 2015 fishing activities and the parameters of the policy in 2016. We exclude 2015 data from our primary analysis because the policy we evaluate was publicly announced in 2016; some vessels might not have known about the new policy when choosing how much to fish in 2015 (Ministry of Agriculture, 2016). We include data from 2020 in our primary analysis because the 20162020 formula was used by most provinces to award subsidies that year.<sup>6</sup> We obtain similar results when we repeat our analysis with data between 2015 and 2020, and data between 2016 and 2019 (Section 5.3).

During our period of analysis (2016-2020), the fuel subsidy received by vessel _i_ of gear (fishing method) type _j_ in year _t_ is given by _Subsidyijt_ = _SubsidyStandardt×SubsidyFactorj×SubsidizedEnginePowerij×SubsidyDaysit_ (1) The subsidy standard is the same for all vessels but changes each year; controlling for year fixed effects does not change our results (Section 5.3). The subsidy factor is a constant that depends on the vessel’s gear; there are 10 possible gears (Appendix B). The discontinuities in subsidy amount occur because of the subsidized engine power term, which we explain when we introduce our empirical strategy in Section 4. The particular thresholds at which discontinuities occur derive from the government’s goal for firms to scrap their vessels and rebuild them in standardized sizes.<sup>7</sup> We subset the data in our primary analysis

> 5China ended explicit fuel subsidies from 2021 and replaced them with a “compliance award” based on the compliance rating scores of distant water fishing companies (Ministry of Agriculture and Rural Affairs and Ministry of Finance, 2021a). The government designed this new subsidy formula to end subsidies for unauthorized fishing, in accordance with the 2022 WTO agreement (WTO, 2022). However, compliance subsidies may also increase fishing because they are linear in “compliance hours” (e.g., hours of fishing in an authorized location).

> 6Zhejiang province was an exception (Zhejiang Provincial Department of Finance and Zhejiang Provincial Department of Agriculture and Rural Affairs, 2021). Most provinces used the 2016-2020 formula because they were not required to start applying the 2021-2025 formula until 2021 (Ministry of Agriculture and Rural Affairs and Ministry of Finance, 2021b, 2021c, 2021d).

> 7The government believes that standardization improves the safety and durability of newly-constructed vessels (Ministry of Agriculture and Rural Affairs, 2021a). We do not find evidence of differential exit with respect to fuel subsidy thresholds (Section 5.2.1).

7
# **1. Introduction**

A large body of research has attempted to explore the links between women’s autonomy and their use of reproductive health services in South Asia, but the evidence is inconclusive. Despite the consistent finding that broad socio-demographic characteristics such as education and economic status have a significant impact on reproductive health services uptake, the relationship between women’s decision-making power and their use of reproductive health services is unclear. For example, in India there appears to be no relationship between women’s freedom of movement and decision-making power and their use of reproductive health services (Bhatia and Cleland 1995; Bloom, Wypij, et al. 2001). Similarly in Nepal, Matsumura and Gubhaju ( 2001) report that decision-making power has a mixed impact on maternal health services utilization. Survey data from Pakistan also show a weak or no relationship between women’s reproductive health services uptake and measures of their autonomy (Sathar and Kazi 1997; Fikree, Khan, et al. 2001; Mumtaz and Salway 2005).

In addition to data source and methodology issues, some inconsistencies in the research findings may be due to lack of consistent measures of “autonomy” (Kabeer 1999), which in some South Asian countries is measured as women’s “mobility”. Thus, more recent literature urges the need to rethink the concept of women’s autonomy as the framework for understanding gender and reproductive health in South Asia and elsewhere (Mumtaz and Salway 2009). Although women are typically the primary points of contact for reproductive health programs, often the decisions that lead them to seek services are outside their own control and may occur in the context of families or households (Becker 1996; Beegle, Frankenberg et al. 2001).

This paper contributes to the literature by applying several constructed indices composed of eight women’s decision-making indicators to assess the relationship between women’s decision making and their reproductive health services uptake. This paper also considers the influence of male household members (household heads or husbands) on decision making and women’s uptake of reproductive health services (Mullany, Becker, et al. 2007; Mumtaz and Salway 2009).

Unlike some earlier findings on Pakistan, this study finds that women’s decision-making power has a significant positive correlation with reproductive health services uptake and that influential males’ decision-making power has the opposite effect, after controlling for socio-economic indicators and supply-side conditions. This study then suggests that empowering women and increasing their decision making in different dimensions may increase their uptake of reproductive health services. Such intervention is highly possible now in Pakistan given that the government is implementing a national cash transfer program to eligible women (the Benazir Income Support Program). In addition, interventions to increase women’s reproductive health services uptake should extend to influential males in the household.

2
responses are aggregated to give the share of firms in the country that report the issue as above their own firm-average level of constraint. The correlations with the ES indicators are relatively large and significant for all but one indicator.<sup>9</sup> This reinforces the view that the qualitative measures of constraints are capturing meaningful variations across countries and within countries.

# **5.  Are there differences across types of firms?**

In the literature, measures of constraints are often reported for a country as a whole, even if they are based on underlying disaggregated information. However, Table 4 shows there are significant sub-national differences and variations across types of firms in the constraints they report. The two dimensions highlighted here in particular are firm size and lagged firm performance.<sup>10</sup> _IC_qualitativeit= ß1 + ß2 *Size(t-1)i + ß3*Performance(t-1)I + ∑ ßk*Xkit +_  s _+_  c _+ eit_ (3) Where Xi is a set of k dummy variables identifying firms’ characteristics: size, firm age, location, ownership (domestic, foreign and government),  s and  c are sector and country dummies 9 It should be noted that there are somewhat different patterns based on the external data source used for comparisons. The first source is the Kaufmann-Kraay Governance Indicators. They are composite measures based on the aggregation of existing data sources (can be 12-20 variables used to construct each index). These draw on a number of subjective or expert opinion polls as well as some more objective measures. The second data source is Doing Business. This provides _de jure_ measures of the time and costs of fully complying with all regulatory requirements. The correlations with these variables are still generally significant, but the sizes of the coefficients are considerably smaller. This can be understood in part from the differences in what Doing Business and the surveys measure. While Doing Business measures the formal requirements, the ES report what firms actually experience. But, what is supposed to happen is not always what actually does happen. Enforcement and implementation are ultimately what matters to the firms. To the extent that there is a gap between what is on the books and what happens on the ground, the correlations between the two will be weak. Other work shows that this gap can be considerable and is generally larger in countries with weaker rule of law or greater corruption (Kaufmann, Kraay 2007; Hallward-Driemeier 2006).

10 The regressions are run as probits on the probability that a firm reports the issue as being above their average level of complaint, with marginal effects reported.

14
# **Table A4. Changes in Revenue Growth Across the Business Cycle on Compensation to Employees: Elasticities from Fixed Effects and IV Regressions for Emerging and Advanced Economies**

|VARIABLES|(1)<br>Emerging<br>economies -<br>FE|(2)<br>Advanced<br>economies -<br>FE|(3)<br>Emerging<br>economies -<br>IV|(4)<br>Advanced<br>economies -<br>IV|
|---|---|---|---|---|
|Revenue Growth * Exp|0.28<br>[0.121]**|0.32<br>[0.094]***|0.74<br>[0.167]***|0.34<br>[0.090]***|
|Revenue Growth * Rec|-0.07|0.06|0.10|-0.18|
||[0.149]|[0.110]|[0.220]|[0.152]|
|Lagged Compensation to Employees Growth|-0.04|0.26|-0.06|0.30|
||[0.093]|[0.068]***|[0.083]|[0.130]**|
|Lagged General Government Expense Growth|0.09|0.05|0.08|0.01|
||[0.039]**|[0.079]|[0.051]|[0.056]|
|Constant|0.04<br>[0.004]***|0.01<br>[0.001]***|||
|Observations|864|432|857|418|
|R-squared|0.045|0.162|||
|Number of Countries|42|18|42|18|
|Country Fixed Effect|Yes|Yes|Yes|Yes|
|Adjusted R-squared|||-0.200|0.090|
|**First Stage IV Estimates**|||**First Stage **|**First Stage **|
|Trade Weighted Growth * Exp|||1.45<br>[.2]***|1.3<br>[.08]***|
|Trade Weighted Growth * Rec|||1.68<br>[.24]***|1.39<br>[.15]***|
|**Underidentification - Weak Identification - W**|**eak Instrume**|**nts Tests**|||
|LM test statistic for underidentification|||43.97|30.51|
|p-value of underidentification LM statistic (And<br>F statistic for weak identification (Cragg-Donald|erson or Kleibe<br>or Kleibergen|rgen-Paap)<br>-Paap)|0<br>29.28|3.33e-08<br>52.01|
|Anderson-Rubin chi-sqared test of significance|of endogenous r|egressors|39.88|13.91|
|p-value of Anderson-Rubin chi-sqared test of en|dogenous regre|ssors|2.19e-09|0.000952|

Source: Authors’ calculations based on World Economic Outlook database 2019.

Robust standard errors in brackets.*** p<0.01, ** p<0.05, * p<0.1.Underidentification tests. Ho: matrix of reduced form coefficients has rank=K1-1 -underidentified. Ha: matrix has rank=K1-identified. Weak identification test. Ho: equation is weakly identified. Weak-instrument-robust inference. Tests of joint significance of endogenous regressors B1 in main equation. Ho: B1=0 and overidentifying restrictions are valid.
# **8 Statistical Annex**

Table 4 contains descriptive statistics for the eighteen country samples used in Section 6, including EU-SILC sample sizes, average incomes, relative poverty lines (60% of the median of equivalent household income distributions), FGT (0, 1, 2), the mean logarithmic deviation ( _E_ (0)) of incomes, and the between-type share of _E_ (0), as a measure of inequality of opportunity.

Figure 4 graphically depicts the 95% bootstrapped confidence intervals around _PH_ for each country in the sample.

Figure 4: 95% confidence intervals for _PH_ <!-- Start of picture text --> CZ DE AT NL FI SK BE LU HU FR CY EE PL IT LV ES LT GR<br>0.15<br>0.10<br> Op. FGT(0)<br>0.05<br><!-- End of picture text --> _Source: Authors’ calculation from EU-SILC (2005)_ 19
Table 4.1 Correlates of Inorganic Fertilizer Use (2000 ‐ 2012, balanced panel)

|_Inorganic Fertilizer_|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Log of distance to markets index, winsorized at 5%|0.00393***|0.00393***|0.00399*|0.00470***|
||(0.000741)|(0.000691)|(0.00114)|(0.000528)|
|Log of WRSI for maize, lagged one period|0.121*|0.124*|0.0938|0.135|
||(0.0516)|(0.0512)|(0.0575)|(0.0816)|
|Area of parcel planted in Ha, self‐reported|0.00325**|0.00308**|0.00231*|0.00303**|
||(0.000808)|(0.000765)|(0.000632)|(0.000881)|
|Household owns any of the land cultivated|‐0.00294|‐0.00445|‐0.00376|‐0.00428|
||(0.0120)|(0.0112)|(0.0188)|(0.0174)|
|Use of any extension services in the community|0.0158||0.00804|0.0165|
||(0.0127)||(0.0149)|(0.0157)|
|The household participated in a NAADs program||0.0377**|||
|||(0.0114)|||
|Any HH member obtained loan in past 12 months|||0.00318<br>(0.0115)||
|Average monthly real price of maize, lagged one period||||0.000112|
|||||(7.63e‐05)|
|Constant|‐0.512*|‐0.527*|‐0.387|‐0.615|
||(0.240)|(0.236)|(0.265)|(0.385)|
|Observations|3,045|3,045|1,902|1,813|
|R‐squared|0.020|0.026|0.017|0.024|

Table 4.2 Correlates of Pesticide and Improved Seed Use (2000 ‐ 2012, balanced panel)

|_Pesticide_|(1)|(2)|(4)|(5)|
|---|---|---|---|---|
|Log of distance to markets index, winsorized at 5%|0.000532|0.000429|0.000137|0.000405|
||(0.00163)|(0.00145)|(0.00188)|(0.00222)|
|Log of WRSI for maize, lagged one period|0.360**|0.370**|0.273*|0.377*|
||(0.0907)|(0.0828)|(0.0802)|(0.135)|
|Area of parcel planted in Ha, self‐reported|0.0111***|0.0108***|0.00943***|0.0117**|
||(0.00141)|(0.00132)|(0.000597)|(0.00203)|
|Household owns any of the land cultivated|‐0.00688|‐0.00933|‐0.00108|‐0.00475|
||(0.0147)|(0.0167)|(0.0187)|(0.0214)|
|Use of any extension services in the community|0.0496*||0.0624*|0.0601|
||(0.0204)||(0.0204)|(0.0320)|
|The household participated in a NAADs program||0.0895***|||
|||(0.0166)|||
|Any HH member obtained loan in past 12 months|||0.0204||
||||(0.0167)||
|Average monthly real price of maize, lagged one period||||‐3.93e‐07<br>(0.000270)|
|Constant|‐1.534**|‐1.577**|‐1.166*|‐1.615*|
||(0.403)|(0.367)|(0.360)|(0.598)|
|Observations|3,044|3,044|1,900|1,815|
|R‐squared|0.066|0.073|0.065|0.078|

29
for both Rajasthan and Rest of India.

The share of contract labor is about 6-7 percentage points lower in larger control firms, across Rajasthan and the other states. There is, however, no statistically significant difference between the treatment and control group as far as the share of contract labor in total labor is concerned. The estimated log expenditure on material inputs (relative to labor) is also not statistically different across both treatment and control, for Rajasthan as well as other states.<sup>30</sup>

# **5 Estimation results: State-level synthetic control analysis**

In this section, we present the results of the analysis using the 1980-2018 long panel, looking at aggregate output and employment in the formal manufacturing sector. We first consider the impact on total employment. Figure 3 presents the evolution of aggregate employment in Rajasthan and its synthetic control, and the MSE ratios from the placebo estimations. Table 13 in the appendix presents the balance between Rajasthan and the synthetic control for employment, and the weights on the control states in the synthetic control.

The synthetic control tracks Rajasthan closely between 1990 and 2014, adding to our confidence in its ability to replicate what would happen to Rajasthan absent the treatment (Panel A). But there is no sizable widening of the gap in total employment levels between Rajasthan and the synthetic control in the three post-treatment years. The average post-treatment gap in employment between Rajasthan and the synthetic control is 1 percent.

Panel B presents the key results of the placebo tests. Recall that inference testing in synthetic control is based on conducting placebo estimations on control units and examining the distribution of the ratio of the post and pre-treatment MSEs for the placebos. The purpose of this exercise is to assess the likelihood of obtaining a post-treatment gap (relative to the pre-treatment gap) as large as that observed in the treatment unit through sheer chance. In this case, the placebo tests indicate that Rajasthan’s MSE ratio is not unusually large compared to the ratios obtained when treatment is assigned randomly. Thus, the results suggest that the amendments did not have a significant impact on aggregate employment.

> 30As described in the previous section, this variable represents the relative adjustment cost of labor after we account for firm- specific fixed effects in the estimation

18
|Repurchase FacilitySupport Loan Project<br>Paul Levy|
|---|
|Repurchase FacilitySupport Loan Project<br>Paul Levy|
|Repurchase FacilitySupport Loan Project<br>Paul Levy|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|Public Sector Financial Management Reform Support<br>Elene Imnadze|
|EmergencyFlood RecoveryProject<br>Shekhar Shah|
|EmergencyFlood RecoveryProject<br>Shekhar Shah|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Privatization and UtilityReform Project<br>Constantine Chikosi|
|Land Reform Support Project<br>Watson C. Chidawanyika|
|Land Reform Support Project<br>Watson C. Chidawanyika|
|Land Reform Support Project<br>Watson C. Chidawanyika|
|Yangtze Flood EmergencyRehabilitation Project<br>Xiaokai Li|
|Yangtze Flood EmergencyRehabilitation Project<br>Xiaokai Li|
|Yangtze Flood EmergencyRehabilitation Project<br>Xiaokai Li|
|Yangtze Flood EmergencyRehabilitation Project<br>Xiaokai Li|
|Hurricane Georges EmergencyRecoveryProject<br>Jose Alonso-Biarge|
|Hurricane Georges EmergencyRecoveryProject<br>Jose Alonso-Biarge|
|Hurricane Georges EmergencyRecoveryProject<br>Jose Alonso-Biarge|
|Hurricane Georges EmergencyRecoveryProject<br>Jose Alonso-Biarge|
|Hurricane Georges EmergencyRecoveryProject<br>Jose Alonso-Biarge|
|Conservation & Sustainable Use of Biodiversityin High Andes RegionJuan Pablo Ruiz|
|Conservation & Sustainable Use of Biodiversityin High Andes RegionJuan Pablo Ruiz|
|Conservation & Sustainable Use of Biodiversityin High Andes RegionJuan Pablo Ruiz|
|Conservation & Sustainable Use of Biodiversityin High Andes RegionJuan Pablo Ruiz|
|Conservation & Sustainable Use of Biodiversityin High Andes RegionJuan Pablo Ruiz|
|Conservation & Sustainable Use of Biodiversityin High Andes RegionJuan Pablo Ruiz|
> <sup>19</sup>

based on Question 93, that is the fraction of five assets that households own: a radio, television, bicycle, motorcycle and car. Results are insensitive to alternative formulations, including the use of five dummy variables. Controls are also included for whether the respondent has a religious affiliation, what religion that is, and how often the respondent participates in religious services (questions 91 and 92). The survey asks for respondent’s highest level of educational attainment (question 90), gender, age and occupation; these are also taken into account.

Harding (2008) uses citizen access to services as a proxy for whether respondents are urban residents or not, a status that is not captured directly in the Afrobarometer survey. Here, the services variable is the average of dichotomous variables created from answers to question 116, indicating whether respondents have easy access to a post office, school, police station, electricity, water, sewage and/or health clinic. Consistent with the notion that clientelist appeals and networks are weaker in urban than in rural areas, access to services is strongly, negatively associated with expressions of partisan preference. The efficacy of partisan appeals to an ethnic group might also vary with the share of the group in the overall population; each group’s population share is therefore taken into account.

Though the literature on African politics does not anticipate this, parties could mobilize support through their policy stance on the role of government in the economy, as in established Western democracies. Parties that succeed in doing so would attract the support of citizens who share their views on these issues, leading these respondents to express a partisan preference. Respondent attitudes to government intervention in the economy are reflected in question 19 of the Afrobarometer survey, which asks whether the respondent believes that people should look after themselves and be responsible for their own success or whether the government should bear the main responsibility for their well-
9 ii) The disparities have fallen consistently between male/female urb4n/rural, Java/Outer Islands ana low/high expenditure groups. For all these subgroups there has been convergence so that the disparities are less pronounced than they were in the earlier period.

iii) However, many of the earlier cualitative conclusions continue to hold: Utilization is greater for males than for females, in urban than in rural areas, in the Outer Islands than in Java and for higher than for lower expenditure groups.

In summary, the 1987 data indicate level improvements generally, convergence across different subgroups, but essentially the same patterns suggested by the 1978 data. At the regional level, urban enrollments continue to be hig e-r than rural ones and the differential continues to be larger the higher the education level. Although Java and the Outer Islands have equalized at the primary school age leiel, the Outer Islands contin&e to do better at higher education levels than Java. Looking for a moment at the gender differentials, this is where convergence is perhaps most striking. In most cases the rate of improvement has been higher for girls than for boys. In primary school, female proportions are even slightly higher for some groups. At the 13 to 15 and 16 to 18 age groups in the lower panel (set out by expenditure levels), the rates of improvement for girls are quite astonishing for all incomes.

The importance of consumption as a correlate of school enrollment is still clearly evident in the 1987 data. Concentrating again on the two middle age groups in the lower panel, note the striking correlation with consumption expenditure. Again, the qualitative pattern has not changed between the survey dates, yet the disparities between exper4iture groups have certainly narrowed. At the upper secondary age (16 to 18) level we are still talking about huge disparities. Less than one quarter of low expenditure group females in this age bracket are in school compared to nearly two thirds for the unper 30 percent group in 1987. Yet, in 1978 it was less than 1 in 10 for the former group.