rafmacalaba/datause-encoder-data huggingface dataset:
# Table A.9: Child Birth and Number of Children

||Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|---|
|**Panel A: Gave birth**||||
|Fraction of female leaders|-0.021|-0.017|-0.036|
||(0.016)|(0.018)|(0.028)|
|Observations|235,036|166,801|68,235|
|Outcome mean|0.092|0.099|0.076|
|**Panel B: Number of children**||||
|Fraction of female leaders|-0.054|-0.033|-0.113|
||(0.038)|(0.039)|(0.075)|
|Observations|235,036|166,801|68,235|
|Outcome mean|2.394|2.505|2.124|
|State fxed efects|x|x|x|
|District characteristics|x|x|x|
|Individual characteristics|x|x|x|

_Notes:_ Data are from the retrospective woman-year panel data based on the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are an indicator for giving birth in a given year in Panel A and the number of children born up to a given year in Panel B. Column (1) presents estimates for the full sample of women, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and year fixed effects, and control for the share of seats in the district that had close elections between women and men and the number of children at the time of the previous election. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

36
<u>Table 11. Program Heterogeneity in Enrollment by Competition and Age of school</u>

||Number of se|condary schools within 10<br>km||Age of school|
|---|---|---|---|---|
||Treatment|Treatment*Interaction|Treatment|Treatment*Interaction|
|Panel A. Check 1|||||
|Log (Total Enrollment)|0.404*|-0.073|0.682**|-0.057|
||(0.24)|(0.06)|(0.35)|(0.04)|
|Log (Students, grade 1)|0.761***|-0.137**|0.770**|-0.045|
||(0.24)|(0.06)|(0.37)|(0.04)|
|Log (Students, grade 2)|0.219|-0.096|0.568|-0.069*|
||(0.25)|(0.07)|(0.37)|(0.04)|
|Log (Students, grade 3 to 6)|0.398|-0.05|1.138**|-0.107**|
||(0.33)|(0.09)|(0.46)|(0.05)|
|Number of observations||82||80|
|Panel B. Check 2|||||
|Log (Total Enrollment)|0.493**|-0.074|0.575*|-0.035|
||(0.24)|(0.06)|(0.34)|(0.04)|
|Log (Students, grade 1)|0.771***|-0.129**|0.673*|-0.031|
||(0.24)|(0.06)|(0.36)|(0.04)|
|Log (Students, grade 2)|0.264|-0.064|0.562|-0.053|
||(0.24)|(0.06)|(0.36)|(0.04)|
|Log (Students, grade 3 to 6)|0.41|-0.035|0.810*|-0.063|
||(0.31)|(0.08)|(0.43)|(0.05)|
|Number of observations||80||78|
|Panel C. Check 3|||||
|Log (Total Enrollment)|0.474**|-0.085|0.646*|-0.045|
||(0.23)|(0.06)|(0.35)|(0.04)|
|Log (Students, grade 1)|0.571**|-0.103|0.765**|-0.053|
||(0.25)|(0.06)|(0.38)|(0.04)|
|Log (Students, grade 2)|0.816***|-0.130*|0.847**|-0.042|
||(0.26)|(0.07)|(0.41)|(0.04)|
|Log (Students, grade 3 to 6)|0.181|-0.049|0.455|-0.046|
||(0.25)|(0.06)|(0.37)|(0.04)|
|Inverse PLE Score (from low to high)|2.084**|-0.297|1.486|-0.03|
||(0.84)|(0.22)|(1.32)|(0.13)|
|Number of observations||79||77|

Note: OLS Estimation; coefficient of impact. Model includes treatment status, the variable of heterogeneity (competition and age) and the interaction between treatment and variable.

28
_Effect of proxy respondent characteristics and of differences in characteristics between proxy and the subject (on whom the proxy reports)_ Focusing on the proxy assignment, we investigate whether the responses are affected by the characteristics of the respondent or by the difference in characteristics between the respondent (the proxy) and the person about whom the response is reported (the subject). We focus on differences in age, gender, and schooling. In Table 11a, we examine labor statistics across the quartiles of proxy-subject age differences (proxy age minus subject age). Quartile one (Q1) reflects the smallest age difference in proxy age minus subject age, where the proxy is actually younger than the subject. In quartile four (Q4), there is the largest age difference with the proxy being much older than subject. We compare the proxy-subject age difference for each quartile to the mean age difference, disaggregating the sample by subjects that are less than or equal to the median age and those who are older than the median age. This disaggregation of the sample by the median subject’s age controls for potential life cycle effects of the subject’s employment, which might otherwise be partially captured in age differences between the proxy and subject. For younger subjects (less than or equal to the median age), reports of LFP, weekly hours worked, and earnings all decrease significantly as the age of the proxy increases relative to that of the subject. For instance, there is a 16 percentage point decline in LFP between the first quartile (proxy much younger than the subject) and fourth quartile (proxy much older than subject). The difference in reported weekly hours decreases by 19 hours between quartile one and four. For subjects older than the median age, we find less variation across the proxy-subject age gap, and less of a clear pattern. The age difference, either 28
**<u>Table 7: Impact of female reservation on perceived ability to hold official to account</u>**

||**Getting local problems**<br>**fixed easy**|**Holding local officials to**<br>**account easy**|**Ensuring transparent**<br>**selection of beneficiaries easy**|
|---|---|---|---|
|**_Model 1_**||||
|Reserved now|0.0314***|0.0184|0.0156|
||(2.681)|(1.581)|(1.350)|
|Reserved now|-0.0132|-0.0134|-0.0122|
|* female|(-1.059)|(-1.081)|(-0.983)|
|Reserved lag1|0.0493***|0.0451***|0.0606***|
||(4.128)|(3.728)|(5.059)|
|Reserved lag1|0.00862|0.0106|-0.00399|
|* female|(0.650)|(0.820)|(-0.307)|
|Observations|25,055|25,055|25,055|
|R-squared|0.108|0.114|0.112|
|Tests|3.15**|0.24|0.11|
||28.46***|26.51***|27.66***|
|**_Model 2_**||||
|Reserved now|0.00945|0.00211|-0.00385|
||(0.692)|(0.156)|(-0.286)|
|Reserved now|0.0153|0.0105|0.0132|
|* female|(1.063)|(0.741)|(0.927)|
|Reserved lag1|0.0399***|0.0320**|0.0453***|
||(2.929)|(2.316)|(3.322)|
|Reserved lag1|0.0382**|0.0367**|0.0236|
|* female|(2.543)|(2.501)|(1.610)|
|Reserved lag2|-0.00162|-0.00450|-0.00393|
||(-0.110)|(-0.303)|(-0.270)|
|Reserved lag2|0.00780|-0.00334|-0.00322|
|* female|(0.526)|(-0.225)|(-0.224)|
|Observations|19,245|19,245|19,245|
|R-squared|<br>0.105|<br>0.107|<br>0.109|
|Tests|4.27**|1.16|0.61|
||39.18***|31.02***|31.28***|
||0.32|0.37|0.31|

_Note:_ The dependent variable is a zero-one indicator of whether an individual thinks it is „easy‟ or „very easy‟ to achieve the desired outcome.

Regressions are clustered at household level and information on education, age, gender, marriage status, household size as well as dummies for cast, religion, land ownership, and overall wealth are included in all regressions but not reported.

26
in the transition economies. The BEEPS consist of a series of 3 snapshots of virtually all transition economies in 1999, 2002 and 2005 and covers random and representative samples from these countries. In addition, its last wave contains firm level data from a number of developed market economies, which makes it possible to directly benchmark TEs against these economies.

Our analysis of firm growth, sectoral changes and changes in size distribution of firms provides a clear picture of the convergence process. Overall, the BEEPS data show a faster growth of firms in the TEs compared with the developed market economies. The pattern of growth at the country, sectoral and firm level shows more rapid growth in the private and especially new private sectors, movement in the size distribution of firms towards the pattern of large numbers of small firms as seen in developed market economies, more evidence of convergence in the EU8 members than in the poorer TEs, as well as evidence of Kuznet-Chenery type structural change across sectors.

We see clear signs of convergence of the TEs to the developed market economies of the EU across the two dimensions of the business environment that we investigate in detail, competition and finance. In terms of competition and market structure, the EU8 members are furthest along in this process, with the SEE and CIS countries following. We offer an interpretation in terms of an initial move by firms into niches to exploit local market power and avoid domestic competition. Later in transition, there is more entry and domestic competition becomes stronger. In finance, there are again clear signs of convergence, albeit incomplete. We find some evidence that the gaps may be related to within-country duality in TEs (developed urban vs. undeveloped rural) that we do not observe to the same degree in the developed EU countries. Our decomposition analysis suggests that structure of financing received by firms in TEs is related to the observed characteristics, and the main difference remaining between TEs and developed market economies is a lower autonomous reliance on bank financing in the former (unrelated to observable firm characteristics) and correspondingly higher reliance on retained earnings. The increasing reliance on retained earnings in TEs that we observe in the BEEPS surveys over time is not a sign of reversal; rather, it represents a “maturation” of the 43
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39
# **Introduction**

Guinea, a low-income country located in West Africa, has a population of approximately 13 million.<sup>[1]</sup> Although it has seen improvements over the past 20 years, health statistics show suboptimal outcomes. [2-4] For example, the under-five mortality rate was 99 deaths per 1,000 live births in 2019, which is higher than the sub-Saharan Africa average of 76 deaths per 1,000 live births for the same year.<sup>[3]</sup> In addition, the maternal mortality ratio was 576 deaths per 100,000 live births in 2017, which is also higher than the sub-Saharan Africa average of 534 deaths per 100,000 live births for that same year.<sup>[4]</sup> Universal health coverage (UHC) is not yet a reality in Guinea. In 2011, a health fee waiver program for children under five and pregnant women, which includes prenatal care and delivery services, such as cesarean sections, and free care for severe malaria cases was introduced.<sup>[5]</sup> In addition, in 2012, a mandatory health insurance scheme was introduced for civil servants.<sup>[5]</sup> However, these initiatives have been hampered by insufficient budget allocations and allocative and technical inefficiencies, among others. Indeed, from 2000 to 2019, not more than 4% of the national budget was allocated to the health sector.<sup>[6]</sup> Over the same period, it was estimated that Guinea could have increased its health budget by an average of 14%, given its macroeconomic characteristics, compared to other low- and middle-income countries.<sup>[7]</sup> In addition, health outcomes could have been achieved with only 69% of the financial resources used.<sup>[7]</sup> Due to the lack of UHC, a large share of total health expenditures (THE) is borne by households (60% in 2019).<sup>[6]</sup> The substantial proportion of out-of-pocket (OOP) health <mark>payments</mark> is a huge burden on these households, especially considering the extreme poverty rate that was estimated to be 24% in 2020.<sup>[8]</sup> Nevertheless, data on the extent of catastrophic and impoverishing health expenditures (CHE and IHE, respectively) in Guinea are scarce and outdated.<sup>[9, 10]</sup> <mark>OOP</mark> health <mark>payments a</mark> re considered catastrophic when their share in an individual's total income or consumption expenditure is equal to or exceeds a <mark>defined threshold.</mark><sup>[9]</sup> <mark>OOP h</mark> ealth <mark>payments a</mark> re considered impoverishing <mark>when they cause an individual's total income or consumption expenditure to</mark> fall below or further away from a defined poverty line.<sup>[9]</sup> Furthermore, little is known about the drivers of health spending, including those that have the greatest impact on CHE and IHE.

3
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Another reason why procurement issues are attracting increasing attention is that they may condition access to foreign markets. International trade negotiations—whether bilateral, regional, or multilateral—are a mechanism through which governments address the terms upon which foreign firms can compete in domestic public procurement markets. WTO disciplines on procurement are currently voluntary, in that they apply only to signatories of a so-called plurilateral agreement, the Agreement on Government Procurement (GPA). Most developing countries have not joined the GPA, although they have been encouraged to do so by OECD countries. Procurement has also been the subject of negotiated disciplines in numerous bilateral and regional trade agreements, examples being the various treaties associated with the European Community and its successor the European Union, the North American Free Trade Agreement (NAFTA), and recent agreements between the US and EU on the one hand and partner countries (industrialized and developing) on the other.

Given the growing prominence of public procurement law and practices in developing countries the question immediately raises as to what evidence and national experience is available to inform reform strategies and international rule making. As a first step in considering the evidentiary base on public procurement practices in developing countries it is useful to assess the available data on the size of public procurement markets, which is the subject of the next section.

# **III. Establishing the factual record: public procurement in developing countries**

Data on _total_ government expenditure levels, in particular on central government outlays, include certain payments (such as compensation for government employees) that are not usually the subject of state contracts with private sector firms or other suppliers. There have been a few attempts to strip out such payments from total government spending levels, so as to provide a more accurate guide of the amount of public outlays that might be influenced by state procurement laws and practices. The latest comprehensive study is OECD (2002a) which employs the System of National Accounts (SNA) to compute the magnitude of procurement of goods and services by state bodies in 106 developing 5