rafmacalaba/datause-encoder-data huggingface dataset:
7 The financial costs associated with health care are particularly problematic, both because they are unpredictable, and because they are sometimes very substantial. In the past, government provided free health care services, although both the range of services and the geographic coverage of the provider network were very limited. However, cost‐sharing has been a formal government policy since the 1990s, initially through the introduction of revolving drug funds, and later through a national policy on user fees. With the growing importance of user fees, combined with the steady expansion of the private health care sector, out‐of‐pocket payments have come to play an increasingly important role in financing health care. According to the most recent National Health Accounts estimates, out‐of‐pocket expenditures accounted for around 74% of total health expenditures. These expenditures include payment of user fees in public facilities, as well as expenditures on health care and drugs from private providers (clinics, pharmacies, drug vendors, traditional practitioners, etc.) within Laos and abroad. The heavy reliance on out‐of‐pocket payments results in considerable financial barriers to utilization of health services, contributing to low levels of utilization and significant health‐related financial risk. The Health Care Law includes provisions for health care to be provided for free to poor patients. However, in practice, these provisions are rarely implemented. In response to the growing reliance on user‐fees in the health system, the government, sometimes with support from development partners, has put in place different health protection schemes, but coverage remains low (approximately 10 percent of the population).<sup>2</sup>

# III. **THE SURVEY**

The survey was implemented in three provinces (Vientiane Capital, Phonsaly, and Attapeu) in Laos between March and June 2008. Both Attapeu, located in the south‐eastern corner of the country,

> 2 Schemes include (i) the Civil Service Health Insurance (CSHI), (ii) the Social Health Insurance Fund (SHIF), (iii) Community Based Health Insurance (CBHI), and (iv) Health Equity Funds (HEF)
**Figure 4. Time Path of Service Sector Reform, Transition Economies, 1990–2004**

<!-- Start of picture text --> S erv ice S ecto r R efo rm In d ex S erv ice S ecto r R efo rm In d ex<br>1990-2004 G ro u p (a) 1990-2004 G ro u p (b )<br>4.50 4.50<br>4.00 4.00<br>3.50 H ungary 3.50<br>3.00 3.00 Latvia<br>2.50 S lovak R epublic 2.50 Lithuania<br>2.00 C zech 2.00 E stonia<br>1.50 R epublic 1.50 S lovenia<br>1.00 P oland 1.00<br>0.50 0.50<br>0.00 0.00<br>1990 1995 2000 1990 1995 2000<br>S erv ice S ecto r R efo rm In d ex S erv ice S ecto r R efo rm In d ex<br>1990-2004 G ro u p (c) 1990-2004 G ro u p (d )<br>4.50 4.50<br>4.00 4.00<br>3.50 3.50<br>3.00 C roatia 3.00 K yrgyzstan<br>2.50 2.50<br>M acedonia G eorgia<br>2.00 2.00<br>1.50 R om ania 1.50 M oldova<br>1.00 1.00<br>B ulgaria A rm enia<br>0.50 0.50<br>0.00 0.00<br>1990 1995 2000 1990 1995 2000<br><!-- End of picture text --> _Source_ : Eschenbach and Hoekman (2006b).

# **3.  Impacts of Services Trade (Liberalization)**

In theoretical models, the impact of trade liberalization on economic growth is either absent or ambiguous. In a conventional neoclassical growth model, trade does not affect the equilibrium or steady state rate of output growth because, by assumption, growth is determined by exogenously given technological progress. In two-sector models of this kind, trade policy affects the allocation of resources between sectors and hence the steady-state level of savings and capital accumulation. This can have a one-off effect on the steady-state level of output (which can be positive or negative depending on how savings and capital accumulation are affected by trade policy), but not on the rate of growth.

In endogenous growth models, the impact of trade liberalization on output growth can be positive or negative. If the resource allocation effects of trade policy changes promote sectors or activities that generate more long-run growth, the impact is positive, 13
<!-- Start of picture text --> Panel A: Public Panel B: Private<br>Pe. wecdl Pe, oo<br>--=Bo oe can _-e--7ge-<br>0 tel o © ooa uo j#<br>2Bip . “Ss, 2 a 3<br>an o *a +ae: 2 = 3 anBip Ae AY 2 oe——<br>=a a7“ a =o a_ oat<br>o on o - o a a -<br>on an - ol a e<br>= ; a ae = ; a oo<br>= veo = a<br>" oe - of<br>ae #<br>it Pi<br>TM os<br>7990 7995 2000 2005 2010 2015 1990 7995 2000 2005 2010 2015<br>Year Year<br>—+— High & Eligible —+— Low & Eligible —+— High & Eligible —+— Low & Eligible<br>—-@—- High & Not eligible —-—G—- Low & Not eligible ——@—- High & Not eligible ——-G—- Low & Not eligible<br><!-- End of picture text -->
- Pillania, Rajesh, Madhurima Lall, and Shikha Saha, “Motives for Starting Business: Indian Women Entrepreneurs Perspectives”, International Journal of Indian Culture and Business Management 3:1 (2010), 48-67.

- Powley, Elizabeth, “Rwanda: The Impact of Women Legislators on Policy Outcomes Affecting Children and Families”, Background Paper for State of the World’s Children, UNICEF (2007).

- Rosenthal, Stuart, and William Strange, “Female Entrepreneurship, Agglomeration, and a New Spatial Mismatch”, Review of Economics and Statistics 94:3 (2012), 764-788.

- Topalova, Petia, and Esther Duflo, “Is There Discrimination Against Women in Politics? Evidence from India”, MIT Working Paper (2004).

- Verheul, Ingrid, André van Stel, and Roy Thurik, “Explaining Female and Male Entrepreneurship at the Country Level”, Entrepreneurship and Regional Development 18 (2006), 151-183.

- Washington, Ebonya, “Female Socialization: How Daughters Affect Their Legislator Fathers' Voting on Women's Issues”, American Economic Review 98:1 (2008), 311-332.

World Bank, Gender Equality and Development, World Development Report (2012).

World Bank, Reducing Gender Based Differences in Formality and Productivity (2008).

33
productivity differences that we observe across countries could be due to different endowments of such capital. Formalizing some of the stylized facts mentioned above, a standard formulation of this line of thinking would probably start from the argument that key infrastructure services, such as transport, energy or communications, matter in a strongly complementary way to other productive input, and as such may constitute major bottlenecks if not or insufficiently available.<sup>5</sup> Such a presumption underlies the inclusion of infrastructure indices in much publicized competitiveness indices such as the World Economic Forum’s Global Competitiveness Report and the IMD’s World Competitiveness Yearbook. However, to the extent that infrastructure indices are used as an input into multivariate competitiveness indices, problems of circular reasoning and potential simultaneity are obviously not absent from these indices, making their use in applied economic research problematic.

However straightforward the presumption might appear, it is subject to considerable dispute based on both theoretical and empirical arguments. Prominent scholars have contested the notion that shortage of capital could credibly account for the large productivity differences between developed and developing countries.<sup>6</sup> Prescott (1998) points to differences in the incentive structure prevailing in these countries as a leading candidate explanation. Although he does not specifically discuss infrastructure capital, considering instead general physical as well as additional forms of intangible capital such as human capital, firm-specific learning-by-doing and organization capital, his approach might prove quite useful when applied to infrastructure, as it signals incentive arguments, applied specifically to infrastructure delivery and maintenance, as a potentially important topic that has largely been ignored in the literature. Related points are made by Hall and Jones (1999) and Parente and Prescott (2000). Formal models can be found in

> 5 An O-ring type of production function (Kremer, 1993), in which capital is made part of the multiplicative formulation in which quantity of any input cannot be substituted for quality of other key inputs like infrastructure, could be used to formalize such an argument.

> 6 The fact that output-per-worker in developing countries is lower than what is implied by the amount of capital available point is discussed for example in Prescott (1998) and Banerjee and Duflo (2005). This mirrors Lucas (1990)’s initial point that the marginal product of capital implied by differences in outputper-worker between developed and developing countries is higher than the average rate we observe. Note that all these discussions are framed within a Cobb-Douglas type of specification.

5
In this paper we address these issues by adopting an empirical approach that relies on the data rather than investigator discretion to specify a model linking property rights and a set of potential explanatory variables advanced in the literature. Our approach treats the potential explanatory variables together and evenhandedly, and allows us to explore relations between them. Empirically, we use cross country data on 158 countries and evaluate four theories concerning historical determinants of property rights protection. While there are overlaps, the four theories focus on different and distinct causal mechanisms in shaping institutions, as captured by _legal origin, endowments, ethnic diversity_ and _religion_ . We begin with a set of variables suggested by the institutional theories and then allow the data to reject potential causal relations between these variables and property rights protection. At the end of the process, we are left with a set of potential causal relations between our measure of property rights protection and the set of proposed explanatory variables. The procedure also suggests possible relations among the set of proposed explanatory variables.

Specifically, we employ Directed Acyclic Graph (DAG) methodology developed in computer science that allows us to consistently evaluate the four theories concerning historical determinants of property rights protection (Spirtes, Glymour, and Scheines, 2000). This algorithm uses the correlation matrix of a set of variables to determine whether a variable meets certain criteria, derived from probability and graph theory, for it to be classified as a direct or indirect cause of another variable. Using this methodology, whose purpose is to discover causal patterns in the data, we are able to identify which historical factors are direct determinants of property rights protection and which are not. We subject the results to a battery of robustness tests and compare our methodology to 3
datasets do not include village-level identifiers and, thus, district is the smallest policy-relevant geographical boundary possible. Since decentralization, districts have also often split over time. In 1993, there were 290 districts; by 2014, there were 511 districts. To ensure equal comparisons across time, we harmonize district boundaries as they existed in 1993. We then match our constructed panel with preschool access at the consistent district boundaries circa 1993.

PODES is not available annually. We employ three strategies to account for the missing years. First, we simply restrict our constructed panel to PODES years. Second, we infer preschool data from in-between PODES years using the closest upper year available. For instance, year 1992 is sandwiched between PODES 1990 and 1993, so we infer preschool data from the 1993 round.<sup>10</sup> Third, we predict preschool density for the missing years using linear projection with the closest two data points available. For year 1992, we fit a linear projection using preschool density data in PODES 1990 and 1993. In the subsequent section on empirical strategy, we discuss the results from each strategy.

Table 1 provides summary statistics from our constructed panel. We have 227,579 femaleyear observations, with an average age of 31.5 and a 30 percent probability of having a preschoolaged child in any year. They have a 52 percent work participation rate and 51 percent live in urban areas. They are subjected to an average of 0.15-0.16 public and 4.45-4.60 private preschool density in their district of residence. There are 10,340 distinct females, who are surveyed on average in 3.54 rounds representing an average of 22 years of observations and 7.5 years coinciding with PODES years.<sup>11</sup> The average age of first marriage and first birth are 20.23 and 22.13, respectively. On average, each mother has 2.74 children and 7.75 years of education, or the equivalent of halfway through lower secondary education. There are multiple observations of preschool access per district, ideally equal to the multiple of 9 PODES rounds between 1990-2014; however, some districts in West Sumatra province are not included in the 2011 PODES and SUSENAS because probability sampling does not always cover all the districts. Ultimately, there are 2,559 districtyear observations of preschool access with an average of 0.24 public and 4.18 private preschool density.

# **4. Empirical Strategy**

> 10 In other instances, years 1988-1990 of a constructed panel are matched to 1990 PODES, years 1991-1993 to 1993 PODES, and years 2012-2015 to 2014 PODES.

> 11 IFLS surveys span over 21 years. However, the employment history which extends as far back as 1988 allows a maximum of 27 years of observations between 1988 and 2015.

8
**Figure 31: Rate of Return to Capital, Togo, 1960‐2008.**

<!-- Start of picture text --> ogo: ate o etu o Cap ta : 960 008<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Capital Stock<br>Income Accruing to Capital / Estimated<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> _Source_ : Authors’ calculations. WDI data, World Bank.

# **6.3 Geography and Regional Potential**

**Togo’s economic prospects are not constrained by its geographic characteristics.** That is, when we regard geography in a narrow sense, involving the country’s physical characteristics, access to international markets, resource endowments and its degree of exposure to natural hazard phenomena. However, our opening statement needs to be made with modesty when we extend the concept to consider the economic geography dimension. That is, the one that involves the country’s current access to dynamic, high income markets with high income, growth‐enhancing products. We develop these ideas below.

**Togo is a small, modestly population‐dense country resulting in a small domestic market, but many other characteristics compensate for this.** With an area of 56,785 square km it only ranks as the 132<sup>nd</sup> biggest country in the world. This feature, however, does not impose a physical limitation to conduct business, as the country has excellent access to neighboring Ghana, Burkina Faso and Benin; is a coastal economy with a relatively well developed deep sea port in  Lomé; is a mostly flat country with savannas in the north, not very sloped central hills, southern plateaus and low coastal plains, all of which extend through 6 distinct geographic regions; and in spite of being small, is not as densely populated as one may think. With 120 people per square km in 2007, population density is higher than the average for LICs (58.5 people/sq. km), ECOWAS (53.3) and SSA (32.3), but below the world simple country average. In this regard Togo ranks 80<sup>th</sup> among 225 economies. Togo is also relatively urbanized compared to other low‐income countries with almost 40 percent of its population living in cities (20 percent in Lomé). This is an advantage in terms of public investments that may have a very high pay‐off as they efficiently can reach a large number of the population.

**Figure 32: Arable Land, WAEMU countries, 2006.**

<!-- Start of picture text --> 50 1.2<br>45<br>40 1.0<br>35 0.8<br>30<br>25 0.6<br>20<br>15 0.4<br>10 0.2<br>5<br>0 0.0<br>Arable land (% of land area) Arable land (hectares per person)<br>% of lanf area<br>Hectares / person<br>Togo Nigeria Gambia, The Benin Ghana Burkina Faso Senegal Niger Cape Verde Cote d'Ivoire Guinea‐Bissau Sierra Leone Liberia Mali<br><!-- End of picture text --> _Source_ : WDI data, World Bank.

32
# **Does Hotter Temperature Increase Poverty and Inequality? Global Evidence from Subnational Data Analysis**

Hai-Anh H. Dang, Minh Cong Nguyen, and Trong-Anh Trinh<sup>*</sup> Originally published in the <u>Policy Research Working Paper Series on</u> _June 2023._ This version is updated on _October 2023._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u>

# **JEL Classification:** Q54; I32; O1

**Key words** : Climate change; temperature; poverty; inequality; subnational data

* Dang (hdang@worldbank.org; corresponding author) is a senior economist with the Living Standards Measurement Unit, Development Data Group, World Bank and is also affiliated with GLO, IZA, Indiana University, and London School of Economics and Political Science; Nguyen (mnguyen3@worldbank.org) is a senior economist with the Global Poverty Practice, World Bank; Trinh (trong-anh.trinh@monash.edu) is a consultant with the Living Standards Measurement Unit, Development Data Group, World Bank, and a research fellow at the Centre for Health Economics, Monash University, Australia. We would like to thank Carlo Azzarri, Edward Barbier, Romina Catavassi, Andrew Dabalen, Benjamin Davis, Stephane Hallegatte, David Johnston, Wojciech Kopczuk, Anke Leroux, Paulina Oliva, Paul Raschky, Russell Smyth, Jevgenijs Steinbuks, and participants at the Frontiers in Development Policy Conference (KDI) and seminars at Food and Agriculture Organization and Monash University for useful comments on earlier versions. We would like to thank Matthias Kalkuhl for helpful advice on data and Brenan Andre for assistance with the global maps. We would also like to thank the UK Foreign Commonwealth and Development Office (FCDO) for funding assistance through a Knowledge for Change (KCP) grant for the World Development Report 2021 “Data for Better Lives” and the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program.
# **_Scenario 3. Reform of civil service wages_**

**The third alternative scenario considers the implications of the reform of civil service wages currently under discussion.** Wages are the biggest component of government expenses (36 percent) costing over half of its own revenues. They cause a significant rigidity in government spending and exacerbate the economic impact of external shocks. In case of negative revenue shocks, spending cuts tend to be borne by other expenditures while wages tend to be protected and are sometimes allowed to grow.

**The government, with the help of International Labor Organization (ILO), has been developing a plan to reform civil service pay.** Under the current proposal, four policy options are identified, all of which call for a revised pay structure for 23 pay grades, while eliminating disparities within each pay grade.<sup>28</sup>

**Table 4. Salary Scale Adjustment Options**

|**Options**|**Principle**|**Estimated one-off cost**<br>**(billion BIF)**|
|---|---|---|
|1. Maximum salary<br>adjustment|Salaries within each grade to be raised to the highest observed<br>for thegrade|273|
|2.<br>Average<br>salary<br>adjustment|Salaries of those below the current average to be raised to the<br>average for thegrade|17|
|3. Reference salary<br>adjustment|Salaries to be adjusted to the reference wage for each grade<br>proposed bythe union|87|
|4. Combination of<br>2 & 3|Salaries to be adjusted to levels that lie somewhere between the<br>average (Option 2) and the reference salaries (Option 3)|18|

_Source_ : Government of Burundi Ministry of Public Service, Work, and Social Security (2012).

_Note_ : Estimates costs associated with these options are one-off costs of adjusting the current salaries to the respective reference salaries under each option. See Government of Burundi Ministry of Public Service, Work, and Social Security (2012) for more detail.

**The reform proposal aims primarily to reduce inequality within the pay structure and not at controlling short-run growth in the wage bill.** All options currently under discussion are going to increase wage bill in the short run, but are expected to generate savings and additional tax revenues in the long run (the rationalization of the pay scale is expected to bring about more orderly management of wage bill eventually resulting in lower shares in terms of revenue, expenditure, and GDP).

**Dynamic implication must be carefully studied once the details have been worked out.** Apart from the initial costs of adjustment, the different wage structures would have different dynamic implications for the medium-term fiscal outcomes. The dynamic costs will depend on, _inter alia_ , growth of new hiring in the priority sectors, their distributions across the pay scales,

> 28 See Government of Burundi Ministry of Public Service, Work, and Social Security (2012) for detail.

22