rafmacalaba/datause-encoder-data huggingface dataset:
Related to our focus on vulnerability, the high forest loss in areas where the _income & asset poor_ – the most vulnerable group - in dry and in African sites is a matter of concern. Given the strong correlation between forest cover and forest income and reliance,<sup>37</sup> this suggests that a major source of income and a buffer against climate and other shocks is under threat.

Finally, the higher tree loss in poor villages in dry zones could also be linked to higher exploitation of forest resources due to higher exposure to climate variability. To check whether tree cover loss might be driven by high climate variability, we regress tree cover loss during the period 2000-2010 as a function of climate parameters, i.e. the mean and standard deviation of the precipitation and temperature over the period 1980-2010. We control for tree cover and regions. The results are presented in Table 9.

**Table 9: Regression model for tree cover loss and climate**<sup>**38**</sup>

||_Dry ar_<br>|_eas_<br>|_Wet ar_<br>|_eas_<br>|
|---|---|---|---|---|
||_Coefficient_|_Std.errors_|_Coefficient_|_Std.errors_|
|Precip mean (mm)|0.004|0.003|- 0.000|0.002|
|Precip std.dev.|0.030*|0.017|0.002|0.007|
|Temp mean (C)|-0.323***|0.088|1.548***|0.281|
|Temp std.dev.|17.418***|3.732|60.944***|13.549|
|Tree cover (share)|-0.632***|0.152|0.409***|0.142|
|Tree cover squared|0.012***|0.003|-0.004***|0.001|
|Latin America<sup>1</sup>|(omitt|ed)|-4.213*|2.421|
|South Asia<sup>1</sup>|-7.680**|3.143|- 3.054|2.637|
|East Asia<sup>1</sup>|-5.180***|1.920|- 0.120|2.562|
|Constant|- 2.917|4.229|-63.824***|11.860|
|R-square|0.276||0.252||
|N|164||152||

1 Regional effect as compared with Africa (default region).

For dry areas, both higher rainfall and temperature variability is correlated positively with more forest loss. For wet areas, only rainfall variability is significant. While such correlations does not give any proof of causality, the results are consistent with an hypothesis of higher climate variability causing more fluctuations in incomes, and exploitation of forest – both land clearing for agriculture and harvesting of trees – is one strategy to deal with the income shocks generated by climate extremes.

# **6 Summary and policy implications**

## **6.1 Exposure to climate conditions and weather anomalies**

The income poor households tend to live in relatively more extreme climate conditions, as measured by the mean precipitation and temperature over the 30-year period (1981-2010). In the dry areas the income poor households on average live in (even) dryer and warmer villages, while in the wet areas they tend to live in (even) wetter and colder sites. In terms of climate variability over the 1981-2010 period, in both wet and dry areas the income poor households tend to live in villages that

> 37 For the African sub-sample, the correlation between forest reliance and forest cover is 0.36.

> 38 Three cover loss is the reduction in the tree cover as share of total land area for the period 2000-2010. The model is run at village level, using standard OLS.

29
and come from information provided by the household interviews in the preceding wave of the panel. Our final (unbalanced) panel dataset includes 5,461 observations of 2,736 agricultural households from waves 2, 3, and 4 of the Nigeria GHS Uniform Panel Data that were also interviewed in the preceding wave. Descriptive statistics for the key variables of interest are reported in Table A1 in the Appendix. Note that the average value of the reversed GS-SPEI is 0.23, suggesting that in our sample drier-than-normal conditions represent the prevalent type of weather anomaly experienced by farming households, and that growing season weather throughout the survey period has been overall less favorable compared to the historical (1900-2020) long-run sample mean over which the SPEI is computed.

# **4. Methods**

Our identification strategy is based on the use of causal forests. Causal forests were specifically developed for the estimation of Conditional Average Treatment Effects (CATEs), which constitute non-parametric estimates of treatment effect heterogeneity based on a set of chosen covariates (Athey & Imbens, 2016; Athey et al., 2019; Wager & Athey, 2018). Causal forests are a causal inference adaptation of random forests—a powerful supervised machine learning method aimed at predicting a given outcome on the basis of a set of inputs (Breiman, 2001; Hastie et al., 2009)— to the task of predicting heterogeneity in causal effects.

The causal forest algorithm is an ensemble of causal trees. Each of these trees is defined by datadriven sample splits that generate leaves, which are followed by a prediction of the causal effect over a set of conditioning characteristics _X_ . The aim of a causal forest is to split the data so as to maximize treatment effect heterogeneity across leaves. To prevent the risk of overfitting, the algorithm is based on the so-called "honest approach" (Athey & Imbens, 2016), which randomly splits the sample in two equal parts: half of the sample (the _prediction_ sample) is employed to define the sample splits (leaves), while the other, the _estimation_ sample, is used for estimating the predicted CATE. Such a procedure is repeated as many times as there are trees in the forest (2,000 trees, in our case). <mark>Each individual tree explicitly searches for the subgroups where the treatment effects differ most, and th</mark> e final causal forest prediction is a weighted average over the predictions across trees, which, as Wager and Athey (2018) show, is consistent and asymptotically normal. In order to capture group-level effects, we cluster standard errors of the estimated effects at the 16
policy-relevant unit than pixels.

For continuous outcome variables (nighttime lights at the 1km grid and Kebele level, and cropland and urban land at the Kebele level), we transform the variables using the inverse hyperbolic sine (IHS) transformation, which is defined as _Yi_ = _log_ ( _yi_ + � _yi_<sup>2+ 1).Theinterpretationisequivalenttothatof</sup> logarithms. The transformation is defined at zero and provides a way to use a transformation that is suited for cases where there are a large number of zeros (Mitnik et al., 2018).

# **3.3 Separating Units by Baseline Economic Activity**

Across analyses, we explore heterogeneity of results according to baseline levels of economic activity. We group units (1x1km pixels and Kebeles) according to the baseline level of nighttime lights of the Woreda in which the unit is located. We divide Woredas into four groups. The first group consists of Woredas that registered no nighttime lights at baseline. We then divide Woredas that registered nighttime lights into three similarly sized groups using 33<sup>_rd_</sup> and 66<sup>_th_</sup> percentile cut-offs, based on the maximum nighttime lights value recorded within the Woreda. There are 557 dark Woredas, 74 Woredas with low light (max. 1–5 NTL value), 78 Woredas with medium light (max. 6–8 NTL value), and 71 Woredas with high light (max. _>_ 8 NTL value). Appendix S4, which presents trends in dependent variables for Woredas in each category, shows that Woredas in each category experienced growth in nighttime lights and urbanization. Using the baseline value of nighttime lights at the Woreda level—not the level of the individual unit (e.g., Kebele)—ensures that two nearby units from the same town or city are not separated into different analyses. For example, the approach ensures that a brightly lit pixel in a city’s center and a dimly lit pixel in the same city’s periphery are included in the same group. To test the sensitivity of these groupings, we also divide Woredas into two groups: those that had zero baseline nighttime lights, and those that had some positive baseline lights.

# **3.4 Descriptive Statistics**

Table 1 shows descriptive statistics of outcome variables at the Kebele level, and Figure 2 shows maps of dependent variables and road upgrades. Nighttime lights and urban land are heavily concentrated in Ethiopia; most of the country is neither lit nor settled. However, during the study period, Ethiopia experienced significant growth in both urban area and the intensity of lit pixels. The number of Kebeles with some positive nighttime lights increased 103% from 1996 to 2013 and further increased by 414% from 2013 to 2016.<sup>7</sup> In addition, the number of Kebeles with some urban land grew by 96% (from 542 to 1,062 out of 15,714). In contrast to nighttime lights and urban area, much of Ethiopia is classified as cropland, and

> 7Much of the increase from 2013 to 2016 is likely driven by 2013 using DMSP-OLS data and 2016 data using DMSP-OLS-like data, which is derived from VIIRS and better captures low-level light.

11
**Table 1: Tax Expenditures as a Percentage of GDP, the DR and Comparator Countries**

|Country|Tax Expenditure (% of GDP)|Most Recent Year|
|---|---|---|
|Dominican Republic|6.5|2016|
|Uruguay|6,3|2014|
|Ecuador|4,6|2016|
|Chile|4,2|2016|
|Brazil|4.2|2016|
|Argentina|2.8|2016|
|Guatemala|2.5|2015|

Source: Authors’ estimates based on national authorities’ data.

The largest category of tax expenditures are exemptions and deductions from the DR’s value-added tax (VAT), which is known as the Tax on the Transfer of Industrial Goods and Services ( _Impuesto sobre Transferencias de Bienes Industrializados y Servicios_ , ITBIS). Other major tax-expenditure categories include preferential rates for fuel products, estate tax deductions, and CIT incentives. Most of the latter accrue to firms located in SEZs.

The CIT is subject to a complex and generous array of exemptions and tax credits, as well as long periods during which these exemptions can be claimed. In total, the DR’s CIT expenditures equal close to 1 percent of GDP. Special CIT regimes apply to firms located in SEZs, tourism-development clusters, and specially designated regions, and many individual firms can claim additional exceptions to the standard regime. Under Law 8-1990, firms established in SEZs are fully exempt from the CIT, ITBIS, and all local taxes. Law 158-2001 exempts firms working in several tourism-development clusters from the CIT, ITBIS, and other taxes and fees. Law 195-2013 extended the tax exemption for newly established firms from 10 to 15 years. Law 28-2001 exempts firms located in border regions from the CIT and ITBIS, while Law 108-2010 provides tax incentives to firms related to the film industry, and Law 66-1997 provides 7
1

# I. **INTRODUCTION**

How do China’s health care providers perform? What determines their performance? How can the government improve it? To answer these questions, we undertook a review of the Chinese-language and English-language literatures on service delivery in China, as well as looking at lessons from abroad. The review comes at a key moment in China’s health reform process. There is broad agreement that the health service delivery system is not functioning well, but there is considerable disagreement about how to fix it. Some argue for turning over the entire system to a free market. Others recall the benefits of a centrally planned health system. The truth—this paper argues—lies somewhere between these two extreme views.

# II. **PROVIDER PERFORMANCE IN CHINA’S HEALTH SECTOR**

_Quality_ is a key dimension of any provider’s performance. Like many countries, China does not have a strong system for monitoring the quality of care. But several indicators suggest quality could be better. The skill of providers is low, especially at the village level. A large-scale study of 46 counties and 781 village doctors in 9 western provinces conducted in 2001 found that 70% of village doctors had no more than a high school education and had received an average of only 20 months of medical training.<sup>1</sup> There is widespread evidence of unnecessary care being provided in China, especially drugs. In 1998-99, a study conducted in 4 township health centers and 8 village clinics in Wuxi County of Chongqing and Min County of Gansu concluded that less than 2% of drug prescriptions were ‘rational’. In the case of village clinics, only 0.06% of drug prescriptions were deemed reasonable.<sup>2</sup> Unnecessary care makes for costs that are higher than necessary. For
30

- Yd Real disposable income, calculated as (8m1+Ov+Oj+cP+iP-6lP-e8D$P/P) eTB Trade balance, IFS line 77acd

- cp Real private consumption, IFS line 96f Omld Real money demand (flow), IFS line 34 (changes) ov Real bank deposits demand (flow), IFS line 35 (changes) Obd Real public bonds demand (flow) ip Real private investment, IFS lines 93e and 93i dLp Variation in domestic credit to the private sector, IFS line 32d (changes)

- eOR<sup>3</sup> Variation in international reserves, IFS line 79cd arrears Interest arrears on foreign debt, World Debt Tables (1990) OVS Flow supply of bank deposits, IFS line 35 (changes)

- im Nominal interest rate on deposits, CB OLg Variation in domestic credits to the public sector, WB and FM ig Real public investment, WB and FIEL OBs Flow supply of public bonds, WB and FM ib Nominal interest rate on bonds yS Real aggregate supply, IFS line 99b k Real stock of capital, FM **iT** Domestic inflation rate, WB P Domestic price level (WPI index), WB Ml Stock of money, IFS line 34 B Stock of public bonds eDt Stock of external debt, World Debt Tables and CB ebD$p Variation in external credit to the private sector, calculates as OeD$bOeD$, ebD$, Variation in external credit to the public sector, WB e Nominal exchange rate, IFS line wf W* Imported foreign inflation rate, WB G Public consumption t Fiscal Revenues x Exports, WB im Imports, WB it external debt interest rate

- fdsp Interest paid on foreign debt by the private sector fdsg Interest paid on foreign debt by the public sector P* foreign GDP deflator (US)

- oM1 Flow supply of money (Ml) p Rate of growth of money (/M/M) A t + (im - w)(V- 1/P) + (ib - Ir)(B-1<sup>/P)-</sup> (ij - ir)(L'p_./P) - fdsp, calculated as y yd

- Z (i, - ir)V-l + (iL - r)L-1 + (if - ff - eA)eR$-i + i(B-1), calculated as OM18 + OVs OB - eOR$ -L4 - OLp u im + fdsp **+** fdsg - (if - **i** - eA)eRS l, calculated as [eOD$P + eOD$8 - eOJ$]/P + x - eOR$/P

- oi g + (is - iT)L8_/P - eODD, - t, calculated as OL./P + OB6/P - ig - (ib - ir)B-./P - fds 5

- pdebt Ex-post price of debt on the secondary market q Buyback price

- f Loan make available by the International Financial Institutions n **New** money
sectoral allocation trends are based more on household roles than gender. Several hypotheses will be tested. First, there are patterns that we expect to observe regardless of the status of the economy: i) married women are less likely to enter work than are women who do not have another breadwinner in the household, suggesting that household role matters more than sex in determining labor force participation; ii) women who head households similar to those of men have labor force entry patterns that are similar to men, i.e. breadwinners behave similarly regardless of sex; and iii) if gender discrimination is the primary explanation for low female labor force participation or higher female informal sector participation, then labor force entry rates for all women should be lower than men's, and rates in the informal sector should be higher than men's.

Second, a recessionary period will induce changes in labor force behavior such that: i) wives are more likely to enter the labor force when there is an expected or real shock to household income, but all other types of household heads should be less likely to enter since their non-work status in period one implies sources of non-labor income, and ii) wives are more likely to enter the flexible informal sector if there are children in the household, but the primary breadwinners should be unaffected by household structure, regardless of sex.

# **III. Data, Terminology, and an Historical Context**

# _3.1_ **_Sampling_**

The national Urban Employment Survey (ENEU) collected by the Mexican Statistical Institute (INEGI) for the period 1987-1993 is used. It is arranged such that an observation is selected into the data set, surveyed every three months, and then dropped from the sample after the fifth interview, thereby following individuals and their 9
**<u>Figure F1: Unconditional quantile regression of height-for-age z-scores on food security measures, limited controls (Nepal)</u>**

<!-- Start of picture text --> Per Capita Caloric Availability 1-SSR<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile .05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>RIF-OLS 95% Confidence Interval OLS<br>RIF-OLS<br>1-SSEXR FCS<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95 Quintile<br>Quintile<br>95% Confidence Interval OLS<br>95% Confidence Interval OLS RIF-OLS<br>RIF-OLS<br>.6<br>.5<br>.4 .4<br>.3<br>.2<br>.2<br>0<br>.1<br>-.2<br>0<br>.5 .6<br>.4<br>.4<br>.3<br>.2<br>.2<br>.1<br>0<br>0<br><!-- End of picture text --> 69
**Table 2.** Sectoral structure of illustrative SSA country economy (percent).

|Sector|Value<br>added|Production|Employment|Exports|Imports|Exports‐<br>Output<br>ratio|<br>Imports‐<br>Demand<br>ratio|
|---|---|---|---|---|---|---|---|
|Agriculture|24.6|15.9|59.7|21.3|4.6|10.2|4.8|
|Forestry|4.6|3.1|5.4|0.0|0.0|0.1|0.1|
|Fishing|1.3|0.8|0.9|3.9|0.0|33.0|0.4|
|Petroleum and gas|0.1|0.1|0.0|0.0|0.0|0.1|0.1|
|Mining|1.1|1.1|0.2|0.6|1.4|3.5|16.8|
|Food|7.2|11.3|2.9|12.6|7.0|9.0|10.5|
|Beverages|0.9|1.7|0.1|2.9|1.1|13.3|14.3|
|Tobbaco products|0.1|0.2|0.0|0.3|0.2|15.1|39.3|
|Textiles and leather|1.0|1.0|0.2|8.4|4.9|56.4|64.4|
|Wood|0.0|0.5|0.0|0.4|0.2|6.5|5.7|
|Paper|0.1|0.1|0.0|0.5|1.9|30.9|63.7|
|Refined petroleum products|0.0|0.1|0.0|1.0|16.2|97.7|84.7|
|Chemical products|1.1|1.3|0.1|3.1|11.1|17.2|59.2|
|Rubber and plastic|0.2|0.2|0.0|1.0|2.1|33.3|68.8|
|Non‐metalic mineral products|0.8|1.2|0.1|3.1|2.6|20.3|29.4|
|Metal products|0.8|1.3|0.1|5.4|8.2|34.7|59.0|
|Machinery and vehicles|0.1|0.2|0.0|2.7|27.4|98.5|99.9|
|Other manufactures|0.6|1.0|0.1|0.9|1.9|7.3|23.4|
|Electricity and gas|0.5|0.4|0.1|0.5|0.1|10.4|3.8|
|Construction|6.6|11.3|2.0|0.0|0.0|0.0|0.0|
|Trade, hotels and resturants|14.9|17.2|8.2|15.8|2.1|8.3|1.9|
|Transport|2.5|2.3|1.3|2.8|3.9|10.9|21.4|
|Communications|3.7|3.6|0.8|0.0|0.0|0.0|0.0|
|Government|3.5|3.8|3.5|8.1|0.5|19.3|2.2|
|Other services|23.8|20.3|14.1|4.6|2.4|2.1|1.7|
|Total|100.0|100.0|100.0|100.0|100.0|8.1|15.8|

_Note_ : The columns Value added, Production, Employment, Exports, and Imports show the shares (in percent) of each sector in the country totals. The columns Exports-output ratio and Importsdemand show exports and imports as shares (in percent) of total sector output and total domestic demand, respectively.

Table 3 shows the factor shares in total sectoral value added. For example, the table shows that agriculture is relatively intensive in the use of labor; this information will be useful to analyze the results from the CGE simulations.

13
The role of the non-communicable disease as a population health challenge is central because, among other reasons, these diseases exacerbate social inequalities and reduce the well-being of populations living in poverty. For instance, individuals in the top income quintile are more likely to be diagnosed with diabetes, the leading cause of DALYs nationally, due to better health care access. However, populations living in poverty have a greater risk for diabetes, and once the disease is diagnosed the probability of experiencing complications increases up to 18 percentage points, in comparison to the population with the highest income quintile (Gutiérrez... [ _et al_ .], 2016b).

Similarly, national health surveys indicate that the presence of at least one person with a diagnosis of diabetes and/or hypertension increases the resources allocated to health as a percentage of total household expenditure. This increase is greater among households in the lowest income quintile (Gutierrez, Garcia-Saiso and Aracena, 2018), exacerbating social inequalities and contributing to a decrease in well-being among populations living in poverty.

Two events have progressed in tandem with these national trends that are relevant for the accumulation of human capital: the overweight and obesity epidemic and trends in teenage pregnancy. The following subsections discuss these subjects in further detail.

# _A. Overweight and obesity_

The trends of increasing prevalence of overweight and obesity are related to the challenge of combatting the diabetes epidemic in Mexico. Between 1999 and 2016, the combined prevalence of overweight and obesity among Mexican adult women increased from 61.0% to 73.8%; among girls ages 5 to 11 it increased from 22.8% to 32.8%; and for adolescent women ages 12 to 19 it increased from 28.3% to 39.2% (Shamah-Levy et al., 2018; Uribe-Carvajal et al., 2018). Despite the high prevalence of overweight and obesity in 1999, the situation has worsened over time. In the 2006, 2012, and 2016 National Health and Nutrition Survey (ENSANUT in Spanish), datasets in which the PROSPERA population could be identified, the difference between the prevalence of overweight and obesity among the program's population and among the general population with an equivalent socioeconomic status who did not participate in the program are not significant.

We are able to identify important trends in the health conditions of populations with lower socioeconomic levels by analyzing the 2000 National Health Survey (ENSA) and the 2006 and 2012 ENSANUT, and by narrowing down our analysis to the 20 to 24 age group and the villages with relatively greater presence of the program. (Valdespino et al., 2003; Olaiz-Fernández et al., 2006; Gutiérrez et al., 2012).

Figure 1 shows that between 2000 and 2012, there is a decrease in the percentage of the population 20-24 years of age with a healthy (normal) body mass index (BMI between 18.5 and 24.9) for both males and females. At the same time, there is an increase in the proportion of the population categorized as obese. Over this time period, the overweight category remains relatively constant.

5
is extracted from Lesotho’s 2017/18 Continuous Multipurpose Household Survey and listed in Annex 6.

The availability of the three common modules containing a group of consecutive questions in household questionnaires is summarized by Table 3. The details of the subcategories of transport expenditure vary dramatically by country, which can be found in Annex 1 and Annex 2.

**Table 3. Availability of three common sections in household questionnaires**

|**Country**|**Expenditure**<br>(recallperiods if available)|**Durable goods**|**Access to facilities**|
|---|---|---|---|
|Albania|1 month|X|X|
|Burkina Faso|7 days,3 months|X|X|
|Cambodia|1 month, 3 months,<br>6 months,1year|X|X|
|Ethiopia|7 days|X|X|
|Iraq|1 month,1year|X|X|
|Malawi|7 days,1 month|X|X|
|Mali|Not available|X||
|Nepal|1 month,1year|X|X|
|Niger|7 days, 1 month,<br>1year|X|X|
|Nigeria|7 days,1 month|X|X|
|Tanzania|7 days,1 month|X|X|
|Uganda|1 month,1year|X|X|
|Afghanistan|1 month,1year|X|X|
|Armenia|1year||X|
|Benin|3 months,6 months|X||
|Bhutan|1 month,1year|X|X|
|Burundi|3 months,1year|X|X|
|Cameroon|3 months,6 months|X|X|
|Colombia|Not available|X||
|Gambia|1 month,1year|X|X|
|Ghana|1 year|||
|Kenya|1 month,1year||X|
|Kyrgyz Republic|1 month,3 months|||
|Lesotho|3 months,1year|X|X|
|Mauritius|1 month|||
|Mongolia|1year|X||
|Nicaragua|7 days, 6 months,<br>1year|X||
|Pakistan|1 month,1year|X||
|Sierra Leone|1year|X|X|
|South Africa|1year|X|X|

Household surveys contain richer information on transport in other modules than the expenditure and ownership modules. The following paragraphs provide an overview of the presence of transport-related questions in various modules. We then identify a set of recurrent questions for each thematic across surveys. Table 4 lists the presence of such questions per module per country-survey. More details per country can be found in Annex 1.

9