rafmacalaba/datause-encoder-data huggingface dataset:
In both states, however, the majority of respondents agree that upon facing a disciplinary action, physicians seek informal resolution by approaching their immediate superiors for minor infractions or higher level officials for major infractions. Contrarily, it is also in the interest of health officials to informally resolve such matters to avoid litigation, unwanted press inquiries and perhaps more importantly, interference from physicians’ political allies.

**Table 5:  Disciplinary Processes and  in States A and B Perception Survey Results 2011,** **<u>(in percentages)</u>**<sup>**a**</sup>

|**Summary Statements**|**% Agr**<br>**State A**<br>**N=273**|**eeb **<br>**State B**<br>**N=266**|
|---|---|---|
|Disciplinary actions are not implemented effectively in the department|63.6|63.4|
|There is no proper grievance redress mechanism in the department|63.5|80.8|
|In case of small issues, MOs approach their immediate superiors|82.8|92.7|
|In case of grave issues, MOs approach higher level authorities|93.3|95.3|
|Some MOs who had committed “grave deviations” and face disciplinary action had<br>‘managed’ to come out of them without apunishment|61.2|58.6|

aPercentage based on valid responses (exclude missing data) bCombines responses for “agree” and “completely agree” About 60 percent of PS respondents in both states perceive that MOs are able to avoid sanctions when facing charges resulting from “grave deviations”. The UCT survey results (not displayed in Table 5) confirm that MOs can circumvent disciplinary actions through unofficial dealings and side payments. A much higher proportion of State A respondents (91 percent) considered this to be the case than their State B counterparts (58 percent agreement). These findings suggest that the formal processes established in State A are readily bypassed by physicians facing disciplinary actions. For example, during in-depth discussions physicians confirmed that political connections are used in overcoming charges. One MO in State A commented: _“In one instance, the District Health Officer had to accommodate the request by a Minister’s representative in not initiating disciplinary action. The MO had been found not attending PHC work for more than 4 months without formally applying for leave.”_ Another example of parallel systems involves avoiding undesired “repostings” following disciplinary actions. PS respondents consider “managing” reentry after unauthorized leaves (e.g., absenteeism) 16
# **_Table 3 gendered laws and informality: regional-level results_**

||**(1)**<br>**Sub-Sahar**|**(2)**<br>**an Africa**|**(3)**<br>**East Asia &**|**(4)**<br>**Pacific**|**(5)**<br>**Europe &**|**(6)**<br>**Central Asia**|**(7)**<br>**Latin America &**<br>**Caribbean‡**|**(8)**<br>**Middle East & N**|**(9)**<br>**orth Africa**|**(10)**<br>**South**|**(11)**<br>**Asia**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||||||**Fl**||||**N fl**|**Fl**|**N fl**|
|**Y = Pr. firm began**<br>**informally**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|**emae**<br>**participatio**<br>**n**|**No female**<br>**participation**|**Female**<br>**participation**|**Female**<br>**participation**|**o emae**<br>**participati**<br>**on**|**emae**<br>**participatio**<br>**n**|**o emae**<br>**participati**<br>**on**|
|WBL index|-0.002***|0.002*|0.000|-0.000|0.000|0.000|0.000|-0.003**|-0.002**|-0.006***|0.002|
||(0.001)|(0.001)|(0.002)|(0.001)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.002)|(0.003)|
|Firm size at start|-0.000|-0.000|-0.000|-0.000*|0.000|0.000**|-0.002|-0.000|-0.000*|0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|Exporter at start|-0.142***|-0.122***|-0.069|-0.026|-0.006|-0.042***|-0.144**|-0.094|-0.014|-0.080**|-0.140**|
||(0.049)|(0.041)|(0.048)|(0.041)|(0.016)|(0.014)|(0.061)|(0.059)|(0.046)|(0.033)|(0.067)|
|Real GDP per capita|0.038***|-0.013|0.007|-0.147***|0.007|-0.003|-0.027|-0.020|-0.021|0.130**|0.117*|
||(0.012)|(0.013)|(0.035)|(0.037)|(0.009)|(0.005)|(0.023)|(0.034)|(0.039)|(0.054)|(0.067)|
|Rule of Law|-0.044**|-0.048**|-0.035|0.062*|-0.011|-0.008|0.022|-0.060*|-0.109***|0.018|-0.070|
||(0.018)|(0.021)|(0.043)|(0.036)|(0.010)|(0.008)|(0.022)|(0.036)|(0.027)|(0.045)|(0.056)|
|Religion (ref cat: Buddh|ism)†|||||||||||
|Christianity|0.095***|0.009|-0.098***|-0.054*|0.040*|-0.014||||||
||(0.035)|(0.029)|(0.035)|(0.029)|(0.022)|(0.015)||||||
|Folk religion|||-0.170***|-0.058||||||||
||||(0.057)|(0.042)||||||||
|Hinduism|0.061|-0.064||||||||0.058|0.103**|
||(0.093)|(0.083)||||||||(0.039)|(0.051)|
|Islam|||0.065|0.265***|0.051*|-0.002||-0.285***|-0.035|-0.018|0.138***|
||||(0.050)|(0.045)|(0.027)|(0.017)||(0.087)|(0.115)|(0.037)|(0.052)|
|Judaism||||||||||||
|Unaffiliated|||-0.179***|-0.018||||||||
||||(0.044)|(0.036)||||||||
|_N|2,543|7,049|2,686|2,767|3,559|8,460|1,941|339|3,630|673|5,189|

Note: .01 - ***; .05 - **; .1 - *. Marginal effects from the estimation of a probit model are reported. Standard errors are clustered at the level of the strata; rescaled weights are applied in the regressions. The models also include controls for the region in which the firm is located, the sector in which it operates and the year in which it began operations.

† In the regressions for the Middle East and North Africa region, Judaism is the reference category. ‡ Probit model will not converge for the subset of firms in this region without female participation in ownership.

16
Policy ReseaRch WoRking PaPeR 4599

# **Abstract**

This paper examines the distribution of climate change impacts across the 16 agro-ecological zones in Africa using data from the Food and Agriculture Organization combined with economic survey data from a Global Environment Facility/World Bank project. Net revenue per hectare of cropland is regressed on a set of climate, soil, and socio-economic variables using different econometric specifications ”with” and ”without” country fixed effects. Country fixed effects slightly reduce predicted future climate related damage to agriculture. With a mild climate scenario, African farmers gain income from climate change; with a more severe scenario, they lose income. Some locations are more affected than others. The analysis of agro-ecological zones implies that the effects of climate change will vary across Africa. For example, currently productive areas such as dry/moist savannah are more vulnerable to climate change while currently less productive agricultural zones such as humid forest or sub-humid zones become more productive in the future. The agro-ecological zone classification can help explain the variation of impacts across the landscape.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to mainstream research on climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Niggol.seo@yale.edu, Robert.mendelsohn@ yale.edu, Adinar@worldbank.org, Rashid.hassan@up.ac.za, and Pradeep.kurukulasuriya@undp.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
23 :O B=13. The Recurent Cost<sup>Problom.</sup> :. ::.. op:to abn d aitenac of :nrsicw Is tX aassup tha loa ovrmtsw, even whn .:ual tecs ::t involedi th* seecio oreeuino roet,cnadwillo fe : **'" .:, .:l S** E = **S** = Ali tdepir,owee. .. ere are E :: f::E::: In :: L:~j > i **'** . , ' **,** bQ~S~ **found_ Ad tode rbli jua.oa4= o innig I- h** **_rcnetonns_** ,W 0 sr4o)ee , nfatro withothe responibility eae to raising the funds and fepicitg rtaeuir chastitens<sup>sithe mothroa</sup> fil *(ikcemetal)ir cosAtiofted adoe)iin Tehwi **e** lentn secion **sespa forute** inroe itervice.dtilsms sugges ~tios **ese** tohowtea **istia tutionl-dfrned prlojet wth oe** public secor **r p wa o hae** ntutue **god** ahelp achieve. Whis e oaime.tal~nu~f~ti ecrpin asal rjcsd n EEwea,fsaul

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12 Small firms may also want to stay below the government’s radar in Africa. Figure 9 shows the change in management time spent dealing with government regulation when a firm goes from being small (below 50 workers) to being large (above 100 workers). We see that the burden of dealing with government increases substantially with size; this may act as a brake on firm growth. To understand this and other factors, we conduct robustness checks on the equations estimated in Tables 2-4. Several variables are added separately to Equations [1] and [15] to see if they can shed light on some of the unexplained variance (Table 5). These include the exports-to-GDP ratio as a measure of openness, two indices of transport inefficiency—ease of shipping and competition in shipping (drawn from the World Bank’s Logistics Performance Index), the coefficient of variation of terms of trade (to measure volatility), government expenditures as a ratio of GDP, time to resolve insolvencies, and foreign ownership.

Table 5 shows the results of robustness checks for Equation [1] and Equation [15] with the inclusion of each of these variables. All of the variables included for the robustness checks are significant in the baseline regression and almost all are significant in the final regression as well. All have the expected sign as well—openness (as measured by the exports-to-GDP ratio) and fewer transport inefficiencies are correlated with more firm growth, as is higher government spending. Greater volatility in the terms of trade and the time to resolve an insolvency is negatively correlated with firm size. **_But with the_**

# **_exception of the coefficient of variation for terms of trade, the addition of these_**

**_variables does little to change the sign and size of the Africa dummy._** Our model is very robust to variations in specification and indicates that the Africa effect prevails, even after controlling for these additional measures of the business climate and government policy.

# **_Other Explanations_**

The overall price level in Africa could also be a factor in determining the size of firms. Analysis in Gelb et al. (2013) of 188 countries from the Penn World Tables shows that
**To measure the impact on a country’s growth, this paper draws next on the findings of the PRISM (Public Investment, Savings and Macroeconomic Vulnerability tool) to track a country’s trends in public sector revenues, expenditures and capital formation, and how these decisions are being financed** .<sup>31</sup> Two examples illustrate how PRISM can shed some light on the trends in these variables and their implications.  The first example comes from Ghana (Figure 2), where increased public sector revenues (including grants) have been accompanied by higher public sector consumption, with energy sector subsidies and public sector wages accounting for the most of these expenditure increases.  Increased public sector wages have had both fiscal and labor market implications, with important consequences for the benefits of new debt-financed public investments for the private sector.  By comparing the trends one can identify two problems: (i) public sector wages have increased as a share of GDP, crowding out other expenditure and leading to a public sector wage premium, and (ii) this premium has placed a barrier for the expansion of employment in the private formal sector, since higher public sector wages bid up private formal sector wages.  Higher private sector wages have squeezed private sector profits, leading to lower levels of private investment outside the resource-rich sectors (oil, gas and mining).  Lower investments have implied in turn lower increases in labor productivity, lower growth and lower job creation.

**Figure 2** : Ghana-Debt Composition and Public Sector Expenditure Trends (2004-2011)

<!-- Start of picture text --> Ghana<br>80 40<br>70<br>60 30<br>50<br>40 20<br>30<br>20 10<br>10<br>0 0<br>2004 2005 2006 2007 2008 2009 2010 2011<br>Concessional debt (% of GDP) Nonconcessional debt (% of GDP)<br>Domestic debt (% of GDP) Public capital formation (% of GDP)<br>Public consumption (% of GDP) Public sector interest exp (% of GDP)<br>Public sector revenue & grants (% of GDP)<br>Source : World Bank.<br><!-- End of picture text --> 31 The questions the data splits in PRISM seek to answer are: (i) whether that country is over-absorbing or underabsorbing relative to its GDP (Gross Domestic Product) and GDI (Gross Domestic Income), by how much and for how long? (ii) whether the country’s terms of trade are positive or negative, by how much and for how long? (iii) what are the country’s main external financing sources (e.g., remittances, FDI, external and domestic loans, and concessional and non-concessional debt); and (iv) to what degree is the country open to trade (goods and services) -- a factor that affects the volatility of GDP, GDI and GDE (Gross Domestic Expenditures) via the terms of trade channel).

18
UsZ q V **. j**

# Contents

|1, Introd|uction|1|
|---|---|---|
|2. Analyt|ical Framework|5|
|2.1 Wel|fare Concepts|6|
|2.2 Age|and Reproductive History|10|
|2.3 A G|raphical Exposition|12|
|3. An Ana|lytical Survey of the Literature|14|
|3.1 Fer|tility and Maternal Education|15|
|3.1.1|Theories|15|
|3.1.2|Evidence|19|
|3.2 Edu|cation and Maternal and Child Well-being|26|
|3.2.1|Decisions|27|
|3.2.2|Constraints|28|
|3.2.3|Trade offs|34|
|3.3 Fer|tility, Nutrition and Survival|35|
|3.3.1|The Effects of Nutrition on Fecundity||
||and Fertility|37|
|3.3.2|The Effects of Age and Parity on Welfare|38|
|3.3.3|The Effects of Birth Spacing on Welfare|40|
|4. Policy|and Research|45|
|4.1 Pol|icy Options|46|
|4.2 The|Ranking of Policy|48|
|4.3 Res|earch Needs|53|
|Appendix||64|
|Bibliograp|hy|66|
observation for these households during 1998Q4, 1999Ql and 1999Q2, respectively. The data we analyze contains 1766 households in three different dimensions: consumption, education and health.

# 4.1 Consumption changes during the crisis

> In terms of consumption, there was an overall deterioration in living standard for the period after the Russian Crisis. We can see this from Figure 4. This figure presents kernel density estimations of (log) consumption per capita for the pre- and post-crisis period. A (log) poverty line is also shown.<sup>6</sup> The estimated distributions of (log) consumption show a shift to the left for the post-crisis period, increasing overall poverty.

> Indeed, poverty incidence increased by about 10% (from 52% to 62%). A decomposition of poverty indexes before and after the crisis by selected household characteristics is provided in the appendix.

<!-- Start of picture text --> Desinty at t=O * Desinty at t=1<br>.8501<br>00043 ___________________ _<br>2.02217 '474I 7<br>Log Consumpton<br><!-- End of picture text --> Figure 4 Beyond this aggregate distributional shift, the crisis presumably hit households at different income levels differently. If so, their relative ranking might have changed accordingly. The transition matrix in table 1 below is a first look at this effect. If the deterioration in living standard suggested in figure 1 were felt evenly across the whole population, then households in any given quintile group would remain in their same quintile group in the second period.

Table 4.1 reveals that, indeed, the majority of the households in a given consumption quintile had moved to a different quintile after the crisis. The more "volatile" quintiles are the intermediate quintiles, for which only 28% of the households remained in their

> 5 Some possible outliers (households with the change **in** log consumption at 2 standard deviations away from the mean) were excluded.

> **6** The line is the log of the poverty line as calculated by Signoret and Murrugarra (2001). Consumption is measured as per-capita consumption m real Moldovan Lei. Price indexes used to deflate nominal consumption are estimated in the same source

8
In principle, BBP recipients should get free health care at hospitals, while the rest of the users pay fees (no official co-payments apply to the first type of BBP services). Some exceptions apply to emergency treatment, sexually-transmitted diseases (STDs), tuberculosis (TB), and malaria. In practice, due to funding shortages, BBP beneficiaries continue to pay out-of-pocket for medicines and for informal payments (known as under-the-table payments) to doctors and practitioners for treatment. Informal payments are common because government reimbursements for BBP services, which are transferred from the State Treasury directly to health care facilities, are generally low and take time to disburse. Estimates (HERA, 2004) claim that 30 to 50 percent of the costs of BBP treatments are actually reimbursed.

In this article we use data from the 2004 Armenia Integrated Living Standard Survey (ILSC) to analyze the extent to which access to poverty family benefit system (and thereby to free-of-charge BBP) promotes health care utilization. Previous research using household survey estimates (Murrugarra et. al, 2004) found that patients eligible for free services under BBP displayed higher utilization rates that non-eligible ones. The authors simulated program eligibility based on a means-testing algorithm. However, not all eligible users actually get BBP. Data used by the authors did not permit them to identify whether individuals were actually collecting benefits. In practice, only those who register and pass the eligibility criteria receive benefits.

Besides providing data necessary to infer about eligibility to the program, the 2004 ILSC data provide information on whether families are registered in the poverty family benefits system and, if registered, on whether they actually receive benefits or not. As such, ILCS 2004 data allows making a more accurate comparison of the impact of the program based on better-defined treatment and control groups (e.g. eligible and registered users getting benefits vs. eligible and registered users not getting benefits). Data from the 2004 ILSC provides an ideal scenario to perform a quasi-random evaluation of the program. First, observable characteristics between users in the treatment and control groups are likely to similar because the program administration determines eligibility based on a points-based-score according to the socio-economic 4
POLICY RESEARCH WORKING PAPER 3014

# Abstract

The objective of this paper is to assess both the aggregate growth effects and the distributional consequences of financial liberalization as observed in Thailand from 1976 to 1996. A general equilibrium occupational choice model with two sectors, one without intermediation, and the other with borrowing and lending, is taken to Thai data. Key parameters of the production technology and the distribution of entrepreneurial talent are estimated by maximizing the likelihood of transition into business given initial wealth as observed in two distinct datasets. Other parameters of the model are calibrated to try to match the two decades of growth as well as observed changes in inequality, labor share, savings, and the number of entrepreneurs. Without an expansion in the size of the intermediated sector, Thailand would have evolved very differently, namely, with a drastically lower growth rate, high residual subsistence sector, nonincreasing wages, but lower inequality. The financial liberalization brings welfare gains and losses to different subsets of the population. Primary winners are talented would-be entrepreneurs who lack credit and cannot otherwise go into business (or invest little capital). Mean gains for these winners range from 17 to 34 percent of observed overall average household income. But liberalization also induces greater demand by entrepreneurs for workers resulting in increases **in** the wage and lower profits of relatively rich entrepreneurs of the same order of magnitude as the observed overall average income of firm owners. Foreign capital has no significant impact on growth or the distribution of observed income.

> This paper-a product of Finance, Development Research Group-is part of a larger effort **in** the group to understand

> financial liberalization and its impacton growth. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Kari Labrie, room MC3-300, telephone 202-473-1001, fax 202-522-1155,

> email address klabrie@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. Xavier Ginm may be contacted at xgine@worldbank.org. April 2003. (48 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developmenzt issues. An objective of the series is to get the findings out quickly, eveni if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. Ihey do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Research Advisory Staff