rafmacalaba/datause-encoder-data huggingface dataset:
being budget leakages which also require calculating how much money was _supposed_ to reach a given health facility.<sup>8</sup> A country with high marks on all five of these governance performance measures has a functioning public health care service, while a country with low marks does not. Thus, independent of any effort to gather information on governance determinants across countries, these five governance performance measures could be used to benchmark the performance of public health care provision across countries and identify areas that are problematic. If worker absenteeism is high, then the personnel management system needs to be investigated. If counterfeit drugs are common, drug regulations and enforcement need analysis.

Additional governance performance measures that appear in different studies and which could also be considered include:

- share of births and deaths that are registered (HSM )

- disparities in health care service coverage between high and low income groups (HSM)

- share of health facilities that receive supervisory visits in accordance with national guidelines (HSM)

- share of workers who complete programmed training (GFATM)

- share of health facilities that are staffed according to national standards (GFATM, Tanzania)

- share of budgeted funds that are executed (PEFA, GFATM)

- share of positions that are vacant (GFATM SWEF)

- share of audited objections settled within expected time frame (Bangladesh)

- share of contracts awarded within initial bid validity period (Bangladesh)

- share of nationally distributed commodities that reach local facilities (Bangladesh)

- share of local governments submitting health information reports on time (Ethiopia, HSDP II, Mozambique and Tanzania)

- share of donor agency funds that are disbursed through the federal Ministry of Health without earmarking (Ethiopia, HSDPII and Ghana)

- share of maternal deaths that are audited (Ghana and Nicaragua)

- share of population aware of their rights to public health care services (Kyrgyz Republic)

- share of providers with satisfactory compliance of management agreements (Nicaragua)

- share of providers complying with medical protocols (WHO and IHME)

- share of health workers who leave service by cause (Mozambique)

# **_Measuring governance determinants_**

While the governance performance measures are useful for answering a range of questions, the suggested governance determinants address very specific approaches to governance. Each of the governance performance measures tells something about the

> 8 Surveys can reliably estimate the resources available at health care facilities, but it can be quite difficult to estimate the amount of resources that _should_ be at a given health care facility, particularly in countries with federated structures or in which facilities receive resources from a wide range of programs or funders.

______________________________________________________________________________________

-20-
# 3 Data

One of the major impediments to analysis of conict is the lack of conict data at suciently granular geographic scale. Most of the literature on the legacies of war therefore is focused on assessing the eects of conict intensity at relatively high geographical levels. For example, previous studies exploring the determinants of Nepal's civil conict (Murshed and Gates, 2005; Bohara et al., 2006; Do and Iyer, 2010; Macours, 2011) and the consequences of the conict (Valente, 2014; Pivovarova and Swee, 2015; Menon and van der Meulen Rodgers, 2015) exploit variation in intensity of the insurgency at district level or even higher geographical units. Dening conict variables at a broader geographical level measures individuals' exposure to conict less precisely and can create measurement error. Moreover, signicant heterogeneity in geography, socioeconomic status and development among areas within districts can create diculty in addressing the association between the determinants of war at teh district level and explained variables of interest, likely leading to omitted variable bias.

In contrast, this analysis uses detailed and geographically granular conict intensity data; i.e. village-level insurgency. As households within a village tend to be highly homogeneous in socioeconomic status and ethnicity and live in the same geographical terrain, this paper is able to avoid many of the concerns induced by the imprecise measurement of individuals' exposure to conict. Additionally, combining these high-resolution conict data with the 2016 Nepal Demographic Health Survey (NDHS 2016) allows me to explore the impacts of the conict on the children of individuals who were exposed to the war in their own childhoods.

## 3.1 Conict Data

I use the Informal Sector Service Center's (INSEC) records of the conict victims to create conict intensity variables. INSEC is an active Nepalese non-governmental human rights organization. Throughout the war, INSEC documented human rights violations and abuses extensively and its archive of the casualties provides detailed information on each victim's demographic, social and economic characteristics. The database is considered the most reliable data source on casualties of the conict. Numerous studies including Do and Iyer (2010); Nepal et al. (2011); Valente (2014, 2015) and Libois (2016) have used the database. I extract demographic, educational achievement, 8
51 Table III.3 Contraceptive Hethods by Source by Location for Selected Countries

|Government<br>Country<br>(Year)<br>(Percent)<br>-|Commercial<br> <br>(Percent)|a<br>_NGO_<br> <br>(Percent)|Otherb<br> <br>(Percent)|
|---|---|---|---|
|Africa<br>Zimbabve<br>(1984)||||
|Urban<br>47.9|10.8|39.2<br>|2.1<br>|
|Rural<br>37.8|0.7|53.6|1.4|
|Zaire (1984)||||
|Urban<br>56.5|37.1|2.4|4.0|
|Rural<br>86.0|4.7|7.0|2.3|
|Asia||||
|WTepal<br>(1981)||||
|Urban<br>64.3|7.3|28.4<br>|0.0<br>|
|Rural<br>75.4|1.0|21.9|1.7|
|Korea (1979)||||
|Urban<br>39.4|59.2|0.0|1.4|
|<br>Rural<br>73.9|22.2|0.0|3.8|
|Pakistan<br>(1985)||||
|Urban<br>63.9|30.2|--|6.0|
|Rural<br>82.8|11.4|--|5.8|
|Sri Lanka (1982)||||
|Urban<br>66.3|14.0|18.7|1.0|
|Rural<br>82.5|6.9|9.6|1.1|
|Thailand<br>(1984)c||||
|Urtan<br>65.9|30.7|0.7|2.6|
|Rural<br>80.9|17.1|0.7|1.3|
|Latin<br>America||||
|Colombia<br>(1 9 8 6)C||||
|Urban<br>27.2|45.4<br>|26.0<br>|1.4<br>|
|Rural<br>58.2|32.2|8.4|1.3|
|Ecuador (1987)||||
|Urban<br>37.6|51.6|8.4|1.9|
|Rural<br>42.4|51.0|4.3|2.3|
|Honduras<br>(1984)||||
|Urban<br>57.8|34.9|3.6|3.6|
|Rural<br>68.2|19.7|7.6|4.5|
|Mear East||||
|<br>Egypt (1984)c||||
|<br>Urban<br>24.8|72.4|1.8|1.1|
|Rural<br>34.2|64.1|0.5|1.1|
|Morocco (1984)c||||
|Urban<br>50.9|47.6|--|1.5|
|Rural<br>69.2|28.5|--|2.4|

Source: CPS; DHS.

- a. Includes private physicians, hospitals, pharmacies, and any other private, non-NGO.

- b. Unspecified source, may encompass NGOs when private, nonprofits are not a category, and may include commercial vhere it is not a separate category.

- c. IUD and male and female sterilization.
Figure 11: Testing with alternate cutoffs <!-- Start of picture text --> Working Age Population<br>one two three four five six seven eight<br>2017q1 2017q2 2017q3 2017q4 2018q1 2018q2 2018q3 2018q4<br>15<br>10<br>5<br>0<br>−5<br>−10<br><!-- End of picture text --> first quarter of 2018 is in fact a result of the influx of Venezuelans. In absence of perfect control state for Roraima, this paper estimates placebo effect on the working age population of all the states in the north of Brazil, as Roraima is in the north and the northern region lags behind many socio-economic indicators in Brazil. Table 11 shows that the only two states that experienced a significant increase in working age population are Roraima and Amazonas, the two states bordering Venezuela at the north. While Roraima sees a 1.7%-6.7% increase working age population, Amazonas sees a mere 0.3% or 11550 people increase in the working age population in the first quarter of 2018, providing evidence that it is indeed the influx of Venezuelans which led to the population shock that may affect the labor market and the fiscal outcomes of Roraima.

Table 11: Testing for placebo outcome

||(1)<br>AC|(2)<br>AP|(3)<br>AM|(4)<br>PA|(5)<br>RO|(6)<br>RR|(7)<br>TO|
|---|---|---|---|---|---|---|---|
|RD Estimate|4.028|-3.022|11.55<sup>_∗∗_</sup>|-31.88<sup>_∗∗_</sup>|-9.395<sup>_∗∗_</sup>|6.454<sup>_∗∗_</sup>|-1.218|
||(1.94)|(-0.87)|(2.81)|(-3.05)|(-3.16)|(2.366)|(-0.27)|
|_N_|36|36|36|36|36|36|36|

The standard errors are given in parentheses.<sup>+</sup> _p <_ 0 _._ 10,<sup>_∗_</sup> _p <_ 0 _._ 05. All estimations use the bandwidths proposed by Cattaneo et al. (2021) and assuming uniform kernel. Covariates include dummies for each quarter and value of the outcome variables in the previous quarter.

Further evidence of the increase in working age population being driven by the influx of displaced Venezuelans come from the demographics of the working age population from other sources. For example, Chaves and Echeverria (2020) sketches profiles of Venezuelan migrants and refugees across countries in Latin America and reports that 58% of the displaced Venezuelans are male and 41% are female, while 71% have below college education and 29% have college education and above. It also reports that within the working age population, 22% are between the age of 15 and 24 years, 71% are between the age of 25 and 49 years and 7% are above 50 years. Table 7 provides evidence that the gender balance in the increase in working age population observed is similar, with 59% (6,545) male and 41% (4,562) female. Similarly, table 6 reports that about 64% (11,250) has below college 29
33 displaced person to return or to stay, and his or her success in either endeavor, is what happens in between. In the next section, we evaluate the likelihood of voluntary return.

# <u>Is Voluntary Return Possible?</u>

The decision to return voluntarily is made after comparing information about conditions and prospects of countries of origin and the host countries. (Koser 1998, Faist 1999, King 2000) Policy in the host country with regard to incentives or disincentives to stay or return also plays a role, (Bloch & Atfield 2002) however, as pointed out by the survey research done by Black, Koser and Munk on Voluntary Return of refugees in the UK, the decision to return is made based more on the situation in the country of origin than policies of host countries. (2004) Three sets of factors determine the decision to return: structural, individual and policy interventions. Structural factors include both situations in the host country and country of origin. Individual factors include individual attributes like age and gender and social relations like spouse and children. Policy incentives also include policy interventions both from the host country and country of origin. Based on the above‐mentioned survey, the top three factors potential returnees consider is security, employment, and housing. Although the sample is small, it included multiple groups of countries of origin (Somalis, Tamils, Kurds, Afghans, Iranians Kosovans, and Chinese). Different nationalities emphasized different considerations. Not surprisingly, respondents from Afghanistan and Somalia reported that security is their greatest concern.

Ideally, the forcibly displaced person will have saved up some capital and acquired skills abroad that can be invested in the country of origin. (Sorensen et al. 2008) Asylum seekers or refugees that have resided in the host country for only a brief time will not have built up savings, and – unless they have access to higher paying jobs ‐ the human capital and skills they acquire while abroad will be limited or may not fit the needs of their country of origin.

If they return voluntarily, refugees tend to do so with caution and over time. A family will rely on its own network, and often send a scout, typically a young man, to verify that the ground is secure and that their land and house can be retrieved, and others will follow before women and children return. (Harild et al. 2015) If the decision is made to return, the aim of policymakers should be to achieve “sustainable return”. <mark>There are many definitions of sustainable return – here is a useful one from the IOM “Sustainable return should be understood either as a) successful reintegration in the country of origin, which includes the economic, social and psychological aspects and the capacity of the individual to cope with push factors, both old and new on the same level as the local population, or b) eventual legal remigration made possible by skills acquired during the reintegration process” (2015)</mark>
> .JTA [IfO

uLAT IlP FLOWF FLW O RDS gm tpeII _Redl_ *M cont'd

# al Ac

Ip, Ip,1-C 1 a$.gtcfl.(1 e e,.(r 1 ) where: kgr - refers to growth rate of real value added In the k sector: agriculture, mining, manufacturing. other industries, services.

- gtWfe real growth of iDPfc GDPsp 0P at market prices

TOTUACpS - ational acccnts: capital sxurce of funds TOTCI - natinal accosuts: capital ues of fus TOTNAurS nat onal accounts: current sources of ftun TOTNACuIrU national acosuts: awrrent uses of ud where: 19 - total pltlic Investment - Ip total private investment Sp - private sector savings Cp private censu ptfon mm:

1. Cp. private consumptfon, is the balancing Item In the national accounts.

2. ProJections of export and isport prices. including the maeufacturing Index, and LIBOR are Macroeconomic Indicators, international taken from the Plwning Assuspti4um for Kajor Economic Analysis and Prospests Division, International Economics Departent, irid Bank.

4/ includes increases in stocks.
The Mexican Pension Annuity Market 2005. The 2004 increase in premiums was due to an increase in IV and RT pension annuities approved by the congress in that year that required retroactive payments for insured individuals as of March 2004. Within the period between 1997 and 2005 the share of premiums for IV benefits decreased from 96 percent of total premiums in 1997 to 85 percent in 2005.

**Table 7: Pension Annuity Market Growth (1997–2005)**

||**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|---|---|---|---|---|
|Eligible Insured|6,068|23,905|24,782|27,384|30,778|14,879|5,998|6,365|7,917|
|Annuitants|4,410|23,457|24,599|27,188|30,824|15,798|6,153|6,187|7,927|
|GPI (MXN mln.)<br>1/|2,522|12,913|13,389|14,529|16,641|10,261|3,497|5,225|4,504|
|_o/w IV (percent)_|_96_|_90_|_87_|_86_|_86_|_86_|_86_|_85_|_85_|
|GPI/GDP (percent)|0.08|0.33|0.29|0.26|0.28|0.16|0.05|0.07|0.05|
|GPI/Pop.(MXN)|26.9|139.0|139.3|149.0|168.3|102.4|34.4|50.8|43.6|

**Notes** : 1/ Real 2005 Pesos. **Source** : CNSF, IFS, WDI.

Pension annuity market penetration increased from 0.1 percent to 0.3 percent of GDP between 1997 and 2001 while afterwards it decreased until reaching five basis points in December 2005. Again a similar pattern can be observed in terms of market density.

Section III of this paper provides a detailed discussion of the factors behind the mentioned major change in the trends in the pension annuity market after 2001/2002.

# **_II.B Pension Annuity Products, Pricing, and Competition_**

Pension annuities stemming from the 1997 reform are all immediate premium level annuities indexed to inflation (CNSF 2006g). The products have two components: (1) an annuity for retirement, unemployment at old age, disability, incapacity, worker’s compensation, orphans, widows and widowers; and (2) a survivor benefit for eventual beneficiaries existing when the right to a pension annuity is acquired. In calculating premiums, pension annuity companies calculate separately the premium related to the annuity and the premium related to the survivor benefit. The calculation of the survivor benefit considers the death conditional probability of the pensioner.

Two sets of gender specific mortality tables are used for insured members that are disabled or not disabled and a unique technical rate of 3.5 percent real has to be used. For male and female disabled pensioners (IV benefits), the two mortality tables used are the EMSSIM-97 and the EMSSIH-97, respectively. These tables were developed by the IMSS. For non-disabled male and female pensioners the mortality tables used are the EMSSAM-97 and EMSSAH-97, respectively. These tables were prepared by the CNSF on the basis of information provided by the Population National College (CONAPO). These are gender specific dynamic tables approved by the special committee formed

- 7 -
# **1.  Introduction**

The World Bank conducted a survey of 1,948 retail stores in 16 states (provinces) and 41 cities of India in 2006. The survey shows that 27% of the respondents find labor regulation to be an obstacle for their business with significant variation in the figure across states. Using this survey, we analyze the effect of labor regulation on employment at the store level. We find that stores hire fewer workers in states with more stringent (pro-worker) labor laws. Our estimates show that labor reforms are likely to increase employment by 22% of the current level for an average store.

A number of studies have looked at the impact of government policies on economic outcomes. Hall and Jones (1999), Acemoglu et al (2001) and Djankov et al (2002, 2003) show a strong negative effect of bad institutions and heavier regulations on economic performance. Labor regulations in particular have received considerable attention. For example, Botero et al (2004) look at labor regulations in a cross-section of 85 countries and find that rigid regulation of labor is associated with lower labor force participation and higher unemployment. Nickell (1997) attributes higher unemployment in Europe vis-à-vis North America to the rigid labor institutions in the former. Across Indian states, Besley and Burgess (2004) find that rigid labor laws resulted in lower levels of investment, employment, productivity and output in registered manufacturing businesses. Similar findings are reported by Holmes (1988) for manufacturing in the United States and Bertrand and Kramarz (2002) for the retail sector in France.

The present paper contributes to this rich literature in two important ways. First, we focus on a service sector in a developing country.<sup>1</sup> Existing work on labor regulation

> 1 Regulation of the labor market is quite rigid in India. For example, the World Bank’s Doing Business project ranks India at 112 of 175 countries on the rigidity of employment regulation (World Bank, 2006).

1
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1
We applied the same methodology as outlined above for the uniform climate scenarios. We first calculated the level of farm net revenue under current conditions and then the level of net revenue under each climate scenario. The net revenues were predicted using the estimated regression coefficients from Table 6. The change in net revenue was then multiplied by the hectares of cropland in that district. These impacts were then summed across all districts in Africa. We relied on the same underlying predictions of the quantity of cropland and irrigation as with the uniform scenarios. Of course, with the AOGCM scenarios the predicted change in net revenues will be different depending on the climate scenario.

In Table 10 we present the results of the nine scenarios: for the three models in three time periods. The PCM results suggest that with ample rainfall and only a small increase in temperature the net effect on all African farms would be a gain of from $87 to $97 billion per year. The CCSR results suggest that substantial drying and warming together would generate losses of from $19 to $27 billion beyond 2060 across Africa. The CCC results suggest that a large warming of 6C would lead to substantial losses across African farms equal to $48 billion by 2100. Irrigated farms are predicted to benefit across all but one of these scenarios, partly because they are climate insensitive and partly because they are located in relatively cool areas. Dryland farms are likely to be affected the most, whether it is a benefit of $72 billion or a loss of $44 billion.

Figures 9 to 11 illustrate the distribution of impacts given that each country will face its own climate scenario. Figures 9a, 9b, and 9c illustrate the impacts per hectare from the PCM climate regimes for 2020, 2060, and 2100. According to the PCM model, climate changes cause African net revenues to rise in all three time periods over the next century. Moderate temperature changes and favorable precipitation generally increase African productivity (although not necessarily as much as in other regions of the world). However, there are three bands of land stretching from east to west across Africa where net revenues fall: along the Mediterranean coast, from Kenya through West Africa, and across the southern tip of Africa. These three bands of land experience moderate to high losses in all three time periods.

Figures 10a, 10b, and 10c illustrate the impacts according to the CCSR climate scenarios. The 2020 CCSR scenarios are predicted to lead to significant losses per hectare in all of northern, western and southwestern Africa. By contrast, farms in the central humid region experience large benefits and farms in much of eastern and southeastern Africa experience moderate benefits. In 2060, the CCSR scenario limits benefits strictly to the humid region of 18