rafmacalaba/datause-encoder-data huggingface dataset:
Table 6b. Impact of the Uruguay Round on Tenms-of-Trade in Agriculture of Least-Developed Countries

||Terms of Trae|ofTrade|Percent of|GDP|
|---|---|---|---|---|
|Countries|Scenario<br>I 1/<br>mil US$|Scenario<br>IV 2/<br>mit US $|Scenario<br>1 1/<br>Percent|Scenario<br>IV 2/<br>Percent|
|Bangladesh|1.47|-19.88|-0.02|-0.05|
|Benin|-1.94|-2.22|-0.10|-0.11|
|Bhutan<br>Botswana|-0.08<br>0.10|-0.49<br>1.35|-0.03<br>0.00|-0.19<br>0.04|
|BurkinaFaso|-1.82|-0.11|-0.07|0.00|
|Burnmdi|0.06|1.79|0.01|0.16|
|Cambodia|0.19|-0.30|0.02|-0.03|
|CentralA-ican Republic|-0.48|-0.54|-0.04|-0.04|
|<br>Chad|-1.96|1.09|_-0.15_|0.09|
|China|-46.89|78.24|-0.01|0.02|
|Comoros|-0.16|-0.73|-0.06|-0.29|
|Egypt|-39.09|-113.10|-0.11|-0.33|
|Equatorial Guinea|-0.08|-0.36|-0.06|-0.25|
|Ethiopia|-3.22|-3.08|-0.05|_-0.05_|
|<br>Gambia|-0.51|-4.01|-0.15|-1.19|
|Ghana|5.05|-7.01|-0.08|-0.10|
|Guinea|-0.99|<br>-11.12|<br>-0.03|-0.37|
|Guinea-Bissau|0.01|-0.80|0.01|-0.35|
|Guyana|2.12|12.63|0.78|4.66|
|Haiti|-1.13|-8.03|-0.06|-0.45|
|Honduras|-4.51|11.01|-0.15|0.36|
|India|8.90|78.44|0.00|0.03|
|Indonesia|10.80|12.45|0.01|0.01|
|Kenya|0.73|11.69|0.01|0.14|
|Lao PDR|-0.31|-0.44|-0.03|-0.04|
|Lesotho|0.15|-3.93|0.02|-0.62|
|Liberia|0.67|-1.83|0.06|-0.16|
|Madagascar|0.22|3.16|<br>0.01|0.11|
|Malawi|2.46|12.35|0.13|0.63|
|Maldives|0.45|-0.70|0.27|-0.42|
|Mali|-4.37|-0.38|-0.17|-0.01|
|Mauritania|-1.10|-5.74|-0.10|-0.52|
|Mozambique|-1.20|-5.44|-0.09|-0.39|
|Myanmar<br>|-0.20<br>|3.33<br>|0.00<br>|0.01<br>|
|Namibia|-1.18|-0.81|-0.05|-0.04|
|Nepal|-0.13|-1.41|0.00|-0.04|
|Nicaragua|0.56|6.84|0.03|0.40|
|<br>Niger|-1.41|-3.81|-0.06|-0.16|
|Nigeria<br>|-5.88<br>|-29.42<br>|-0.02|-0.09|
|Pakistan|-16.61|-19.16|-0.04|-0.04|
|Rwanda|0.30|1.35|0.02|0.07|
|Sao Tome & Principe|-0.07|-0.22|-0.16|-0.46|
|Sierra Leone|0.56|-2.89|0.07|-0.38|
|Solomonlslands|0.25|0.03|0.11|0.01|
|Somalia|-0.92|-3.16|-0.10|-0.34|
|Sri Lanka|-1.70|-4.69|-0.02|-0.05|
|Sudan|-578|092|-008|001|
|<br>Tanzania|.<br>-0.87|.<br>4.29|.<br>-0.03|.<br>0.15|
|T|166|114|1||
|ogo<br>Uganda|-.<br>-0.01|-.<br>4.38|-0.0<br>0.00|-0.07<br>_0.15_|
|<br>VietNam|-1.05|15.88|-0.01|0.18|
|Yemen|-8.34|-36.84|-0.10|-0.43|
|Zaire<br>Zbi|-1.61<br>|_-5.05_<br>|-0.02<br>|-0.06<br>|
|ama<br>Zimbabwe|-0.91<br>1.87|-0.57<br>15.55|-0.03<br>0.06|-0.02<br>0.47|

I/Based on price changes in Scenario II by Goldin, et **al.** (1995). 2/Based on **pnice** changes in scenario IV by Goldin, et al. _(1995)._ Source of basic data: FAO Comxmdity Balance Database.

27
is more skill-intensive than tradables sector 1, then the factor bias effect will increase the output of tradables sector 2.<sup>xiv</sup> The net effect of the factor bias and scale effects in the HOS model with a non-tradable health sector in which endowments are endogenous depends on the sign and relative size of the factor bias and scale effects. Table 1 shows that the net effects are in general indeterminate, depending on the factor intensity rankings and the ‘efficiency’ of the health sector in treating and curing sick workers.

INSERT Table 1 Here All cases shown in Table 1 are representative of the long-term, since skilled and unskilled workers are fully mobile. Introducing health-specific skilled workers complicates the analysis by introducing separate effective endowments, waiting lists and wages for health-specific and other skilled workers respectively. The added complexities obscure the derivation of the R theorem (where the health sector expansion would be driven by an increase in the use of domestic unskilled workers) and the derivation of the impacts of the importation of health-specific skilled workers on the proportionate changes in outputs of tradables.<sup>xv</sup> Combined with the absence of real-life complexities, such as more sectors, factors of production and households, a tax-charging and transfer- and public good-providing government, intermediate inputs, remittances of migrant workers and welfare gains from health sector provisioning, this provides a strong argument for the use of an applied model.

# **3. Model Simulations and Results: Reducing Rationing in UK Health Care**

The model used in this study is a comparative static CGE model of the UK economy. The SAM underlying the model has been constructed by augmenting the UK InputOutput Supply and Use Tables for 2000 with data from the General Household
European, and Latin American economies. The countries are in the data set are: Argentina, Brazil, Chile, Colombia, Indonesia, Korea (South), Malaysia, Mexico, Peru, Philippines, Poland, Russia, Taiwan, Thailand, Turkey, and Venezuela. The data set covers the period January 1990-June 2000. Appendix Table 1 displays the available data for each country and variable.

JP Morgan produces the EMBI and EMBI+ (henceforth EMBI) series for a group of emerging markets, but also on a country-by-country basis. The index by country is a total return index that tracks traded debt instruments denominated in foreign currency. The instruments used are Brady bonds, benchmark Eurobonds, loans, and Argentine domestic debt. The EMBI spreads mostly reflect the difference between each country’s sovereign bond yields relative to yields of benchmark instruments issued from developed countries. The spreads are commonly used as measures of country risk or default risk. When the probability of a sovereign default increases vis-à-vis the U.S., bond prices decrease and yield spreads increase. The other variables that we use in this paper, stock returns, interest rates, and credit ratings, were downloaded from Bloomberg. Stock market price indexes for each country are measured in U.S. dollars. We use ratings on sovereign debt issued in domestic and foreign currency. These ratings try to measure the ability of the issuer to pay back its debt. We work with ratings from three major international rating agencies: Moody’s, Standard and Poor’s, and Fitch-IBCA.

Table 1 provides some measures of financial market instability in our sample. Daily changes (in absolute values) in both markets are large and oscillate around 2.5 percent for sovereign spreads and around 1.6 percent for stocks. Our number of observations is high (about 11 thousand for bond spreads and 22 thousand for stock 11
# **1 Introduction**

_Mobile phones have changed the world, for better or worse._ _Alex Clark_ Mobile phones play an important role in modern life. Aside from the basic function of facilitating communication, mobile phones have become a tool for financial intermediation<sup>1</sup> and the source of broadband Internet to millions of people around the world. The significant increase in adoption of mobile phones across countries over the past two decades underscores the relevance of the technology to livelihoods.

The impact of mobile phones has become a topic of growing interest among researchers and policymakers (Aker and Mbiti, 2010; Manacorda and Tesei, 2020; Guriev et al., 2021). However, available studies have almost entirely focused on the microeconomic impacts of mobile phone uptake (Aker, 2010; Suri and Jack, 2016; Suri, 2017; Batista et al., 2018) and the effects on political economy (Manacorda and Tesei, 2020; Guriev et al., 2021; Gonzalez, 2021). Very little is known about the extent to which mobile phones stimulate economic development. While cross-country correlation between mobile phone penetration and GDP per capita as shown in Figure 1 suggests a positive relationship, evidence on the causal impact of mobile phones on economic development is scant.

In this paper, I present causal evidence on the extent to which mobile phone access spurs local economic growth. By linking a global dataset on the expansion in the second, third, and fourth generation (i.e., 2G, 3G, & 4G) mobile network coverage and nightlights (a proxy for economic activities) in over 34,000 subnational districts in 120 countries between 1998 and 2018, I provide new evidence on the development impact of mobile phones. The identification strategy of the paper is structured in two parts. First, I use a two-way fixed effects (TWFE) estimator that leverages the gradual expansion in mobile network connectivity across districts and time to estimate the effects of expansion in mobile connectivity on local economic growth. To minimize the bias associated with TWFE estimates resulting from heterogeneous treatment effects and the associated negative weighting, I complement the analysis with the De Chaisemartin and D’Haultfoeuille (2020a) TWFE estimator that is robust to heterogeneous treatment effects. In spite of this, the TWFE estimates are unlikely to generate causal impacts due to the plausibly endogenous nature of mobile network expansion.

> 1Mobile money platforms–a financial technology (FinTech) application using mobile phones has revolutionized financial markets in Africa enabling previously unbanked population to access financial services. See for instance, Suri and Jack (2016), Suri (2017) and Batista et al. (2018).

2
in local educational markets. We do find, however, that schools with a larger share of the local school market have higher fees (column 4 specification). These results hold even after controlling for the concentration of the local market by including both the school’s market power and the market concentration variables (column 5 specification). Although we do not have a full explanation for this result, it may reflect a segmented local market structure in which a leading school sets the price as a monopolist, and other schools in the market compete among themselves, charging lower fees.

# **6. Conclusions**

During the past century there has been a major expansion of the non-public school system in Haiti. With this growth, school attainment has advanced, and the large gender gap in schooling has closed. Nonetheless, severe gaps in school attainment persist between urban and rural areas and between the poor and the better off. Our analysis suggests that among the many obstacles to school attendance, poverty was a key barrier before the earthquake, a situation that has likely worsened since. The multivariate analysis of the determinants of school attendance suggests that among the most important determinants of schooling are the education of the parents, the structure of the household (single parents, number of siblings), and the socioeconomic level of the household. An important aspect of the Haitian educational system is that a large fraction of students are over the prescribed age for their grades. Investigating further on this issue, we find that the over-age phenomenon appears to be driven by late entrance to school rather than repetition or drop-out and reentrance to school.

Recognizing that fees are a major barrier to school attendance for many Haitians, we investigate their determinants. We find that factors associated with higher education quality and teacher quality (as measured by the proportion of qualified teachers) are associated with higher fees while recreational facilities and the student-teacher ratio do not have any statistically significant association with fees. We also find that licensed schools and those with management committees charge premiums, while schools managed by institutions that presumably have altruistic motives (religious and community groups) charge lower fees than secular and independent schools. While we do not find evidence of monopolistic competition in the schooling market, we do find that schools with a larger share of the market charge more.

15
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**Gender and Tax Incidence of Rural Land Use Fee and Agricultural Income Tax in Ethiopia**<sup>*****</sup>

Hitomi Komatsu<sup>a</sup> , <mark>Alemayehu A. Ambel</mark><sup>b</sup> <mark>,</mark> Gayatri Koolwal<sup>c</sup> , Manex Bule Yonis<sup>d</sup> JEL: H71, Q15, J16

**Keywords:** Agriculture, land ownership, taxation, gender, Ethiopia

* The study is part of the Ethiopia Technical Assistance to Support Data Collection and Policy Guidance on Taxation and Gender project.   We thank Daniel Ayalew Ali and Stephen Younger for valuable comments and discussions on earlier drafts. Any error or omission is our responsibility alone.

> a Development Data Group, World Bank, Washington, DC, hkomatsu@worldbank.org

> b Development Data Group, World Bank, Washington, DC <u>aambel@worldbank.org</u>

> c Development Data Group, World Bank, Washington, DC, gkoolwal@worldbank.org

> d Development Data Group, World Bank, Addis Ababa, myonis@worldbank.org

.
# **<u>Table A8: Previous union characteristics, by current marital status.</u>**

||Remarried<br>widows|Remarried<br>divorcees|Widows|Divorcees|
|---|---|---|---|---|
|Age at last dissolution|34.1|24.8|50.39***|32.53***|
|Number of dissolutions|1.19|1.23|1.32**|1.42***|
|Rural area at time of dissolution|0.63|0.51|0.5**|0.3***|
|Polygamous previous union|0.49|0.37|0.53|0.33|
|First rank spouse in previous union|0.31|0.16|0.47**|0.2|
|Number of children from previous union|4.1|1.6|4.58|2.02**|
|Had no children from previous union|0.06|0.22|0.1|0.16|
|Had a son at time of dissolution|0.53|0.24|0.69***|0.47***|
|At least one child from previous union is<br>living in the household|0.65|0.56|0.74**|0.78***|
|Ex-husband works in the agricultural<br>sector|0.39|0.37|0.35|0.21|
|Ex-husband works in the informal (non-<br>agri.) sector|0.36|0.40|0.23***|0.45***|
|Ex-husband works in the formal sector|0.15|0.15|0.21*|0.27***|
|N|155|247|430|170|

Note: All characteristics are expressed as shares of the marital status group except for ages, the number of dissolutions and the number of children. Divorced includes separated women. Significance tests are relative to remarried widows for non-remarried widows and to remarried divorcees for non-remarried divorcees, where *** p<0.01, ** p<0.05, * p<0.1.

Source: Authors’ calculations using PSF1 55
in urban areas and stronger for women as compared to men. First, with the substantial fall in schooling costs in grades 6 to 8 in rural areas, the improvement in school completion rates in rural areas vis-à-vis urban areas would be bigger for women than for men because the price elasticity of schooling demand is higher for women. Second, the sheepskin effects would be more important for urban men than for rural men whereas there would not be much difference in the sheepskin effects by rural and urban residence for women. Third, the selection effect would also favor rural women. Before the policy, the completion rate of grade 8 was roughly 80 percent for urban men, 60 percent for rural men, almost 70 percent for urban women, but just above 20 percent for rural women (figure 1). Thus, the rural women who are treated by the policy constitute a much less-marginal group (with presumably higher average ability) than urban women whereas there is not as much of a difference in this sense between urban and rural men.

Panel (D) of figure 2 indicates a substantial narrowing of the urban-rural gap in grades 6 to 8 for women. In fact, according to the various models considered (see table A10), the completion rate of grade 8 increases by 12–20 percentage points in urban areas, and by a striking 29–41 percentage points in rural areas. Consequently, the urban-rural gap in the completion rate of grade 8 narrows by 16–30 percentage points.

# **_Analysis of Completed Years of Schooling_**

In this section, we examine the effect of the policy on the completed years of schooling at age 15 and at age 17 for urban men, urban women, rural men, and rural women separately. This is equivalent to cumulating the effects presented by grade level in the previous subsections 32
frame for our estimation. This may have to do with the fact that many donors declared more openly around the year 2000 that their aid allocations will become more policy selective as described in Easterly (2003). Note that the difference in the time frame between Tables 4 and 5 is due to the fact that during the 1998-2015 period the CPIA was measured on a six-point scale, whereas during the 1991-1997 period the CPIA was measured on a five-point scale.

Table 6: Global Policy Selectivity, Democracy, and Resources (1998–2015)

|Dependent Variable: CPIA,|log||||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged GPS|0.067<sup>_∗_</sup>|0.061<sup>_∗_</sup>|0.016|0.003|0.045<sup>_∗∗∗_</sup>|0.040<sup>_∗∗_</sup>|
||(0.036)|(0.031)|(0.014)|(0.035)|(0.008)|(0.017)|
|Lagged IDA recipient|0.154<br>(0.139)|0.143<br>(0.169)|||||
|Lagged GPS* IDA recipient|-0.052<br>(0.036)|-0.039<br>(0.044)|||||
|Lagged Democracy (ANRR)|||-0.043<br>(0.051)|-0.091<br>(0.235)|||
|Lagged GPS*Democracy|||0.050<sup>_∗∗∗_</sup><br>(0.014)|0.057<br>(0.061)|||
|Rich in Natural Resources|||||0.045<br>(0.071)|-0.026<br>(0.129)|
|Lagged GPS* Rich|||||-0.047<sup>_∗∗_</sup><br>(0.019)|-0.014<br>(0.034)|
|Region-Year FE|No|Yes|No|Yes|No|Yes|
|GPS without outliers|No|No|No|No|No|No|
|N|2323|2323|2319|2319|2323|2323|
|R-squared|0.015|0.146|0.137|0.225|0.102|0.169|

Dependent variable is the log of the overall CPIA in recipient countries. GPS is the log of the share of policy-selective aid disbursed globally, where GPS is determined without removing outlier observations. IDA recipient is a recipient that is IDA eligible. Democracy measure taken from Acemoglu, Naidu, Restrepo, and Robinson (forthcoming) and updated in accordance with their methodology. A recipient is rich in natural resources if the initial value of total natural resources rent as a percent of GDP is above the 75th percentile. All regressions use recipientyear observations and include a constant term that is not reported. Regressions (2), (4), and (6) include region and region-year fixed effects that are not reported. Year level cluster-robust standard errors in (1), (3), and (5) and region-year level cluster-robust standard errors in (2), (4), and (6) reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent Tables 6 and 7 show results for regressions with interaction terms. The difference between the two tables is that Table 6 uses the GPS measure derived from the full sample and Table 7 uses the GPS measure derived from the sample without outliers. Here, we are investigating some additional insights 19
We use four data sources in our analysis. Our primary data source is the specialized household survey data from the randomized evaluation of _Pantawid_ that contains information on consumption, select food prices, child anthropometry, health care utilization, labor supply, demographic composition and other behavior. We also use village-level administrative data on program saturation, data on unit values of food prices from the national household budget survey, as well as the Household Assessment Form that determined program-eligible households in 2008.

# <u>Household Data from the 2011</u> _<u>Pantawid</u>_ <u>Impact Evaluation</u>

2,555 households were surveyed in the eight study municipalities during the follow-up survey.<sup>14</sup> With an eye toward investigating potential spillovers on nonbeneficiary households, the study population included 1,418 households that were eligible for the transfers, as well as 1,137 households that had proxy means test scores _above_ the provincial threshold but had age-eligible children or pregnant women. For anthropometric outcomes, we focus on children ages 6 to 36 months at the time of the follow-up survey, while for food intake, we consider children aged 6 to 60 months. Further details on sample sizes, program take up, anthropometric measures, and the robustness of our results to trimming cutoffs for the anthropometric measures are presented in Annex 1.

# <u>Program Saturation</u>

We have comprehensive village-level data on the number of households enrolled in _Pantawid_ each year from 2009 to 2015 as reported by the provincial office of the Department of Social Welfare. These data allow us to calculate annual village level program saturation, defined as the number of beneficiary households for that year divided by census bureau estimates for the total number of households in the village in 2015. We use this measure in conjunction with the food goods unit value information from the household budget survey.

We also calculate potential program saturation for the villages in our experimental survey sample, defined as the number of households with a proxy-means test score below the provincespecific poverty threshold divided by the village population. As the municipalities selected at this stage of the program were among the poorest in the Philippines, it is no surprise that in many of

> 14 Household-level attrition from the baseline sample was 11.4 percent (80 of 624 households) in treated villages and 11.2 percent (80 of 634 households) in control villages, with no evidence of a significant difference by treatment status (Chaudhury, Friedman, and Onishi 2013).

12
ally do not increase GVC-exports from developed countries either to their developed counterparts (NN, baseline) or to developing countries (NS).

Table 4: Heterogeneous impact of services RTAs by income group

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||||GVC-exports||
||Gross exports<br>(total)|Intermediate<br>goods|Backward<br>(sourcing)|Forward<br>(selling)|
|RTA|0.134<sup>_∗∗∗_</sup>|0.135<sup>_∗∗_</sup>|0.128<sup>_∗∗_</sup>|0.144<sup>_∗∗_</sup>|
||(0.0386)|(0.0448)|(0.0441)|(0.0499)|
|SERV|-0.119|-0.122|-0.159<sup>_∗_</sup>|-0.0711|
||(0.0620)|(0.0708)|(0.0762)|(0.0728)|
|SERV*SS|0.233<sup>_∗∗_</sup>|0.237<sup>_∗_</sup>|0.363<sup>_∗∗_</sup>|0.145|
||(0.0888)|(0.0991)|(0.114)|(0.0983)|
|SERV*NS|0.0169|0.0224|0.0589|-0.0398|
||(0.0751)|(0.0826)|(0.0901)|(0.0866)|
|SERV*SN|0.238<sup>_∗∗_</sup>|0.265<sup>_∗∗_</sup>|0.296<sup>_∗∗∗_</sup>|0.298<sup>_∗∗_</sup>|
||(0.0749)|(0.0842)|(0.0870)|(0.0931)|
|Obs.|12320|12320|12320|12320|

Standard errors clustered by country pairs in parentheses. All specifications include exporter-year, importer-year, and country pair fixed effects, and are estimated using PPML. The baseline (SERV) is between developed countries (NN). _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 The asymmetric effect of services trade agreements is evident: a services agreement between a developing (South) and developed country (North) increases gross and GVC-exports from South to North, but not the other way around. Furthermore, the increase is proportionately larger for GVCtrade than for gross trade.

The average effect of signing an RTA is a 14.3 percent increase in gross bilateral trade.<sup>36</sup> When a North-South country pair further concludes a services agreement (or the services agreement enters into force at a later stage), this increases total gross exports from South to North by an additional 12.6 percent, making the total effect of their RTA (in goods and services) a 27 percent increase in total gross trade.<sup>37</sup> When looking only at intermediate goods, the effect of a goods RTA is almost the same but the additional effect of the services agreement is larger at 15.4 percent, suggesting that

> 36The coefficients ( _β_ ) in the PPML regressions are interpreted as ( _eβ −_ 1) percent change in trade flows.

> 37Note that baseline _SERV_ is the effect of services trade agreements on developed country pairs (NN). Hence, the interpretation of the coefficients on interaction variables are always relative to North-North services agreements. To compute the effect of services agreements on South-North trade, for example, one needs to add the coefficients of _SERV_ and _SERV_ * _SN_ . Table A.2 reports coefficients where _SERV_ * _NN_ is introduced instead of _SERV_ .

20