rafmacalaba/datause-encoder-data huggingface dataset:
in the case of the Doha scenarios, new Philippine tariff rates<sup>5</sup> . The following experiments are conducted and analyzed:

1. The core Doha scenario, with the indirect tax for revenue replacement

2. ROW free trade, full domestic liberalization<sup>6</sup> and indirect tax as replacement tax.

3. ROW free trade, full domestic liberalization and income tax as replacement tax.

4. ROW free trade, no domestic liberalization and indirect tax as replacement tax.

5. Full domestic liberalization, no ROW trade reform, and indirect tax as replacement tax.

Experiment (1) involves Doha-specified reductions in world and domestic tariff rates, export subsidies and domestic support. An indirect tax is introduced to compensate lost domestic tariff revenue. Scenarios (2) and (3) are the full (rest of world and domestic) liberalization scenarios, involving the elimination of all world and domestic import tariffs, under two alternative replacement tax schemes: indirect tax and income tax, respectively. Finally, scenarios (4) and (5) isolate the respective impacts of ROW and full domestic liberalization from scenario (2).

Table 2 summarizes the 2001 tariff rates for the Philippines, as well as the variations in world import and export prices, world export demand and Philippine import tariff rates as estimated by the GTAP world model. The export price and volume changes can be combined with a knowledge of the slope of the export demand schedule to compute the vertical shift in export demand that is used in the national model.

> 5 Tariff rate changes are derived from GTAP-estimated variations in the power of tariffs under Doha scenarios. If x is the tariff rate, the power of tariff is p_tm = (1+ x/100). GTAP generates results for p_tm, which in turn is used to compute the new tariff rate in the Philippine model.

> 6 All domestic tariffs are set to zero.

14
**Table 5A: Bias in Relative Mobility: Estimates from 13-50 Years Age Sample**

||**BAN**|**GLADESH**|||**INDIA**||
|---|---|---|---|---|---|---|
||**Rank Corr.**|**IGC**|**IGRC**|**Rank Corr.**|**IGC**|**IGRC**|
|**All Children Sample**|||||||
|**Father's Schooling**|**-8.27%**|**-10.30%**|**-29.22%**|**-9.76%**|**-11.10%**|**-15.73%**|
|**Mother's Schooling**|**-11.16%**|**-10.20%**|**-36.27%**|**-18.96%**|**-15.70%**|**-25.60%**|
|**Average Schooling**|**-5.14%**|**-5.09%**|**-21.86%**|**-11.65%**|**-13.37%**|**-20.58%**|
|**Father-Sons**|**-5.05%**|**-8.46%**|**-26.72%**|**-1.63%**|**-3.04%**|**-7.54%**|
|**Mother-Daughters**|**-14.16%**|**-8.05%**|**-41.44%**|**-14.28%**|**-8.68%**|**-20.37%**|

|**Table 5B: Bi**|**as in Relative**<br>**BAN**|**Mobility: E**<br>**GLADESH**|**stimates fro**<br>|**m 16-60 Year**|**s Age Sam**<br>**INDIA**|**ple**|
|---|---|---|---|---|---|---|
||**Rank Corr.**|**IGC**|**IGRC**|**Rank Corr.**|**IGC**|**IGRC**|
|**All Children Sample**|||||||
|**Father's Schooling**|**-6.14%**|**-7.7%**|**-21.23%**|**-6.72%**|**-9.8%**|**-13.47%**|
|**Mother's Schooling**|**-7.63%**|**-7.5%**|**-27.67%**|**-17.33%**|**-15.2%**|**-24.55%**|
|**Average Schooling**|**-1.4%**|**-1.26%**|**-11.13%**|**-9.09%**|**-12.54%**|**-19.09%**|
|**Father-Sons**|**-5.1%**|**-7.26%**|**-21.47%**|**-0.58%**|**-2.41%**|**-6.88%**|
|**Mother-Daughters**|**-8.14%**|**-4.54%**|**-30.84%**|**-7.3%**|**-5.34%**|**-13.17%**|

NOTES: (3) The numbers in the table are the percentage bias as defined in Table 3. (2) IGC provides estimate of Pearson correlation, IGRC is intergenerational Regression Coefficient. (3) Average schooling is the average of mother's and father's schooling.
- 27-

Table 5--Nutritional status determinants: First stage instrument equations for Model I **(N-1078)**

|Independent<br>Variables|Diarrhea Days|Illr<br>is**Days**|<br>Birth in<br>Hospital|Vaccination|txcLusive<br>Breast-feeding|Log<br>Calories<br>per capita|
|---|---|---|---|---|---|---|
|Ir .'cept<br>Predicted expenditures|1.064<br>(4.23)**<br>-0.158x10<sup>4</sup><br>(-0.32)|1.168<br>(3.51)**<br>-0.162x10 3<br>(-2.48)"*|-0.026<br>(-0.69)<br>0.148xlO 4<br>(1.69)*|-0.084<br>(-1.09)<br>0.287x104<br>(1.73)'|0.099<br>(1.22)<br>*0.130x10<sup>4</sup><br>(-0.81)|5.848<br>(37.38)**|
|Age, in months|0.788xlO0<sup>2</sup><br>(1.01)|0.323xlO 1<br>(3.07)"*|||||
|Age, squared|-0.301xlO0<sup>3</sup><br>(-2.81)*"|-0.507xlO0 <sup>3</sup><br>(-3.51)**|||||
|Sex of child|0.541xl0<br>(0.72)|0.155<br>(1.55)*||-0.286xlO0<sup>2</sup><br>(-0.11)|0.571<br>(2.32)**||
|Hother's education|-0.229<br>(-1.75)'|-0.160<br>(-1.22)|0.151xlO<sup>2</sup><br>(-0.06)|0.083<br>(1.68)*|0.097<br>(2.04)**|0.077<br>(5.62)**|
|Village prevalence of<br>diarrhea x mothers<br>education|0.293<br>(-2.35)**||||||
|Household size|-0.013<br>(-1.68)-|-0.045<br>(-4.13)"-|0.597xlO0<sup>3</sup><br>(0.42)|0.008<br>(3.04)*"|*0.486xl0<br>3<br>(-0.18)|-0.019<br>(-9.21)**|
|Village average<br>excluding self|0.247<br>(3.12)**|0.943xlO<sup>3</sup><br>(2.22)**|0.738<br>(9.36)**|0.849<br>(17.28)**|0.890<br>(17.86)"||
|Tapwater|-0.137<br>c-1.53)|-0.098<br>(-15.26)**|||||
|Distance to private<br>doctor|-0.701x10<sup>3</sup><br>(-0.61)|-0.981x10 1<br>(-0.08)|-0.625x10<sup>4</sup><br>(-0.28)|0.131xlO0<sup>3</sup><br>(0.33)|-0.264xlO0<sup>3</sup><br>(-0.67)||
|Distance to gover-wment<br>cLinic|-0.732xl0O<br>(-0.72)|*0.156x10 2<br>(-0.43)|0.321xlO0<sup>4</sup><br>(0.17)|-0.505xlO<sup>3</sup><br>(-1.32)|-0.439x10<sup>3</sup><br>(-1.24)||
|Log predicted<br>expenditure per capita||||||0.340<br>(25.36)**|
|Log predicted<br>expenditure x householdsize||||||-0.003<br>(-16.54)*"|
|Price of wheat||||||-0.273<br>(-7.46)**|
|Price of rice||||||-0.247<br>(-7.13)**|
|Percent children<br>belowsix years||||||-0.495<br>(-14.63)**|

Note: Figures in tartwitheses are t-values.

significance at 0l level. * Significance at 1% level.

a Predicted value
# **1. Introduction**

Banking in Africa has undergone dramatic changes over the past 20 years. While dominated by government-owned banks in the 1980s and subject to restrictive regulation – including interest rate ceilings and credit quotas – financial liberalization, institutional and regulatory upgrades and globalization have changed the face of financial systems across the region. Today, most countries have deeper and more stable financial systems, though challenges of concentration and limited competition, high costs, short maturities, and limited inclusion persist.

This paper takes stock of the current state of banking systems across Sub-Saharan Africa and discusses recent developments including innovations that might help Africa leapfrog more traditional banking models. We use an array of different data sources to document different dimensions of the development of African banking systems, highlighting variation within the region and changes over time. We compare Africa’s banking systems to those of comparable low- and lower-middle income countries outside the region, and gauge whether there is an “Africa-specific” element to banking development. We also discuss progress in policies and institutions underpinning financial deepening and the results of specific innovations, including innovative branch expansion programs, mobile banking and new financial products to reach out to previously unbanked population segments. Overall, we will show a picture of achievements and challenges, with progress along some fronts but other challenges persisting and new ones arising.

When talking about financial systems in Africa, one has to take into account the enormous variation within the region.<sup>i</sup> On the one hand, South Africa and Mauritius have fairly developed banking systems and capital markets. On the other hand, smaller and poorer countries, such as the Central African Republic or South Sudan have shallow banking systems offering only the most rudimentary financial services, with few if any non-bank 2
Table 8: Estimations for changes in urban spatial mismatch in manufacturing sector

||Total emp<br>1994-|loyment,<br>2005|Organize<br>1994-<br>DV: C|d sector,<br>2005<br>hange in urb|Unorganiz<br>1994-<br>an spatial mi|ed sector,<br>2005<br>smatch for d|Total em<br>1994-<br>istrict emplo|ployment,<br>2000<br>yment|Total em<br>2000|ployment,<br>-2005|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Initial urban spatial mismatch|-0.688+++|-0.738+++|-0.343+++|-0.404+++|-0.789+++|-0.819+++|-0.708+++|-0.764+++|-0.617+++|-0.698+++|
||(0.057)|(0.060)|(0.063)|(0.070)|(0.057)|(0.060)|(0.050)|(0.056)|(0.090)|(0.094)|
|Change in urban share|-0.172+++|-0.169+++|-0.186+++|-0.180+++|-0.174+++|-0.170+++|-0.199+++|-0.188+++|-0.216+++|-0.198+++|
||(0.036)|(0.035)|(0.033)|(0.028)|(0.035)|(0.033)|(0.032)|(0.032)|(0.027)|(0.031)|
|Education level|-0.024+++|-0.028+++|-0.009+|-0.008|-0.028+++|-0.033+++|-0.026+++|-0.026+++|-0.020+++|-0.027+++|
||(0.006)|(0.008)|(0.005)|(0.006)|(0.006)|(0.007)|(0.006)|(0.007)|(0.007)|(0.008)|
|Infrastructure level index|-0.009|-0.008|0.006|-0.018|-0.006|0.012|-0.025+++|-0.020+|-0.007|-0.009|
||(0.007)|(0.017)|(0.007)|(0.018)|(0.007)|(0.017)|(0.006)|(0.012)|(0.007)|(0.017)|
|Log average wage|-0.001|0.008|-0.005|0.002|-0.007|0.002|-0.002|0.003|-0.002|0.007|
||(0.010)|(0.010)|(0.011)|(0.010)|(0.012)|(0.012)|(0.010)|(0.011)|(0.011)|(0.011)|
|Urban/rural wage ratio|-0.004|-0.007|0.001|-0.001|0.003|0.000|-0.001|-0.002|-0.001|-0.003|
||(0.008)|(0.007)|(0.007)|(0.007)|(0.007)|(0.006)|(0.005)|(0.005)|(0.006)|(0.006)|
|Log district land area|-0.014+|-0.015|0.006|0.008|-0.022++|-0.018|-0.010|0.007|-0.008|-0.014|
||(0.009)|(0.013)|(0.008)|(0.013)|(0.009)|(0.012)|(0.008)|(0.011)|(0.008)|(0.012)|
|Change in urban population|0.038|0.033|-0.079|-0.051|-0.184|-0.196|-0.077|-0.101|0.060|0.107|
||(0.182)|(0.223)|(0.075)|(0.089)|(0.114)|(0.152)|(0.116)|(0.149)|(0.146)|(0.167)|
|State fixed effects|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|250|250|201|201|247|247|250|250|250|250|
|Adjusted R-squared|0.608|0.629|0.304|0.367|0.651|0.683|0.597|0.626|0.500|0.520|

Notes: Estimations consider changes in district spatial mismatch for 1994-2005. The spatial mismatch index compares the observed industry distribution of district employment across urban and rural locations to a counterfactual where plants in the district are allocated to urban and rural locations according to national propensities by industry to be in urban settings (holding constant for the district the total urban and rural activity). Negative values indicate that the industry distribution across the district shifted to be in closer alignment to the counterfactual distribution. Estimations report robust standard errors, include state fixed effects as indicated, and weight observations by log district size. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.
Table 6: **Industry Labor Demand for White Collar Workers in Mexico (1987-90)**

|||||**(stand**|**ard errors**in**p**|**arenthesis)**|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Ln L t-l|Ln WBt|Ln WBt-l|Ln WWt|Ln WWt-l|Ln Industry<br>Value<br>Added|Constant|Wald<br>Test: p-<br>value(b)|L.R.<br>Elast.(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|Base<br>Specification(c)|0.137<br> <br>(0.157)|0.043(**)<br>(0.021)|0.012<br>(0.014)|-0.180(*)<br>(0.021)|0.037(***)<br>(0.028)|0.034(**)<br>(0.020)|-0.011(**)<br>(0.005)|--|-0.166|0.344|0.254|9532<br>(2383)|
|Time Variant<br>Specification|0.085<br>(0.115)|0.077(**)<br>(0.036)|-0.006<br>(0.015)|-0.169(*)<br>(0.055)|0.052(***)<br>(0.030)|0.050(**)<br>(0.025)|-0.009<br>(0.005)|--|-0.128|0.620|0.687|9532<br>(2383)|
|Variable* T88|-0.007<br>(1.287)|-0.003<br>(0.070)|0.044<br>(0.090)|-0.022<br>(0.078)|_-0.054_<br>(0.184)|-0.042<br>(0.053)|0.023<br>(0.015)|0.681|-0.210||||
|Variable* T89|0.584<br>(0.806)|-0.117(**)<br>(0.060)|-0.015<br>(0.083)|-0.033<br>(0.075)|0.061<br>(0.146)|-0.067<br>(0.084)|0.071(*)<br>(0.019)|0.343|-0.268||||
|Variable* T90|0.486<br>(0.497)|-0.029<br>(0.057)|0.052<br>(0.039)|-0.019<br>(0.071)|0.071<br>(0.104)|0.026<br>(0.102)|-0.017<br>(0.030)|0.808|-0.150||||
|Wald Test: p-<br>value d)|0.606|0.169|0.577|0.979|0.871|0.002|0.002||||||

Notes: GMM estimates with first-differenced data. All variables are assumed exogenous except for lagged employment. Instruments are based on the second and further lags of employment and capital stocks. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Best point estimates calculated as [(Ln WBt + Ln WBt-1 )/ Ln Lt-l 1. (b) Wald Test of Joint Significance of the variables interacted with a given year dummy. (c) Time dummies were included but are here omitted (d) Wald Test of Joint Significance of year dummies interacted with a given variable.
was about D 126,000 or about U.S. $12, and since, as shown in Table 4, capital expenditures constituted about 20 per cent of the total, on average, local governments had little more than $2 (U.S.) per head lo invest. Although similar low figures are not un}known in other low-income countries - in Iran. Malawi, and Paraguay, for example, local government spending was even lower in 1991 - the contrast with countries such as the United States, where in 1991 local aovernments spent, on average, over $2,000 per capita and state and local governments combined spent about $3,000, or Korea, where over a decade ago, in the early 1980s, some cities spent as much as $200 (U.S.) per capita, is striking.<sup>7</sup> The absolute level of resources available to local governments in Viet Nam is so low as to make it difficult for those governments to provide even the most minimal level of service.

Moreover, the variations between apparently comparable units within countries are often almost as striking as those among countries.. In the United States, for example, per capita local expenditures in 1990 in the lowest state were less than one-seventh of those in the highest state, and in Indonesia in 1990/91, it was only 7 percent. A particularly striking feature in almost every country, developed or developing, is the difference between big cities and other local oovernments. In Colombia in the late 1970s, for example, per capita tax revenues were ren times higher in the capital, Bogota, than in the many small rural municipalities, while in Canada in the same period, the ratio of per capita local revenues among provinces was almost the same. with the more urbanized areas having ten times the "own-source" per capita revenues of the more rural areas. Such variation reflects two different factors in most countries: big cities are richer, and they tend to carry out a wider range of functions. Both of these factors are at work in Viet Nam as well, as noted below.

little information is available.

**7** Comparative data are from R.M. Bird, "Financing Local Services: Patterns, Problems, and Possibilities," Paper prepared for Global Report on Human Settlements, April 1994.

10
a categorization of employment as either formal or informal based on registration with the social security system. It also captures individuals who are not employed. Responses to the questions asked allow us to distinguish between workers who are informally employed and those who are unemployed.

To shed light on whether the decline in formal employment was accompanied by an increase in informal employment, unemployment or inactivity, we estimate the effects of the minimum-wage increase using data from seven annual waves of the Turkish Household Labor Force Survey, from 2010 to 2016. To estimate the causal effects of the minimum wage using the HLFS data, we use a grouping estimator in a similar way as Blundell et al. (1998) and Harasztozi and Lindner (2019). We assign working-age individuals in the survey to mutually exclusive groups formed from combinations of the 26 regions (NUTS 2), age in five categories (16-19, 20-24, 25-34, 45-54, 5560), gender, and education (low, medium and high). We measure group-level exposure to the 2015 minimum-wage increase in the same way as we did previously with the EIS registry data; we define exposure as the percentage change in the wage bill that is necessary to bring all workers in a given group to the new minimum wage, relative to the 2015 wage bill.<sup>26</sup> Formally, the grouplevel difference-in-difference equation that we are estimating is the following: P (5) ggii gg gg gg ii gg ii gg ggii yy = αα+ ββ P × EE + φφEE + γγXX + PP + RR + PP × RR + εε where i is the percentage change in the labor-market outcome of interest of group g between year t-1 and year t.<sup>27</sup> is the measure of minimum-wage exposure to the 2016 minimum-wage yy increase at the group level, measured in 2015. gg _POST_ is a dummy variable that takes the value one EE if the group is observed after the minimum-wage increase (i.e., after 2015) and zero otherwise. The coefficient β on P P is the difference-in-difference coefficient that we are interested in estimating. It measures the effect of the minimum wage on labor-market outcomes at the group gg × EE level. and are year and region fixed effects, respectively, and is the interaction between region and year effects to account for region-specific labor-market shocks. ii gg ii gg is a vector PP RR PP × RR of control variables at the group level that includes dummies for age groups, education and gender. gg XX It also includes an interaction between education groups and year effects, which account for labor

> 27 To avoid the influence of extreme observations, we trim the top and bottom 2.5 percent of each outcome when estimating the difference-in-difference equation.

27
country sample, the mean hypothesis has stronger explanatory power in the high-income country sample.

a strong and independent explanatory power that has not been uncovered by previous literature, especially in the context of developing economies. Our model estimates for the tail hypothesis parameter are also close to the male-to-female variance ratio in test scores estimated from international student assessments. In addition, our model parameter estimates are fairly robust across specifications. This provides further comfort on the validity of the tail hypothesis in accounting for the gender gap reversal in educational attainment.

The paper is organized as follows. Section 2 presents empirical facts on the gender gap reversal in educational attainment that motivate the paper. Section 3 lays out our conceptual framework. Section 4 formulates two main hypotheses for the gender gap reversal within this framework. Section 5 describes our empirical methodology to estimate the contribution of the two hypotheses to the gender gap reversal. Section 6 presents our empirical results and discusses their implications. Section 7 concludes.

# **2 Empirical Motivation**

## **2.1 Data**

The data used throughout the paper is from the Barro-Lee educational attainment database. The dataset consists of harmonized country-level data on educational attainment constructed from nationally representative surveys and census data. The data is reported at five-year intervals from 1950 and 2010. It is also disaggregated into five-year age groups. The availability of age-specific data is valuable in our context, as it allows to construct the dynamics of educational attainment of specific age cohorts over time. Educational attainment is also reported separately for males and females, which allows to follow the evolution of educational attainment across cohorts by gender. Time-series of educational attainment are available for a total of 146 countries in the database, 5
risk of death from other OECD countries to Mexico. In the case of morbidity we present estimates of avoided illness costs and productivity losses alone (i.e., without WTP) as conservative, lower bound estimates of the benefits of reduced morbidity.

To summarize, 3 sets of benefit estimates are provided for each of the four air quality scenarios analyzed. (See Table 6.1.) Health Benefit Estimate 1, the most comprehensive, includes WTP to avoid illness, as well as avoided illness costs (COI) and reduced losses in productivity, in valuing reduced morbidity. Avoided premature mortality is valued using WTP. Health Benefit Estimate 2 includes the same comprehensive measure of the value of reduced morbidity, but values avoided premature mortality using foregone earnings, a lower bound to WTP. Health Benefit Estimate 3, the most conservative, values morbidity using COI and productivity measures alone and premature mortality using foregone earnings. Health Benefit Estimates 1 and 2 vary depending on the income elasticity used to transfer WTP estimates for morbidity and mortality from other countries to Mexico.

For reasons described more fully below, we view Health Benefit Estimate 1, with an income elasticity of 1.0 used for benefits transfer, as a Central Estimate of the value of health benefits. Health Benefit Estimate 1, using an income elasticity of 1.0, is viewed as a High Estimate and Health Benefit Estimate 3 as a Low Estimate. We interpret Health Benefit Estimate 1, using an income elasticity of 1.0 for benefits transfer, as a "Central Estimate" of the health benefits of pollution reduction. This is motivated by the belief that the estimates of WTP for reduced morbidity used in the analysis are more reliable (and certainly less controversial) than the estimates of WTP for reduced risk of death. It is also the case that WTP for reduction in risk of death is based on small risk changes. Applying a marginal WTP estimate to the large risk changes in AQS 1 and AQS2 may yield implausibly large estimates of WTP. Health Benefit Estimate 3, which uses a lower bound estimate for morbidity (= Productivity Loss + COI) and mortality (Human Capital approach), is a conservative, lower bound estimate to benefits.

**Table 6.1 Overview of Health Benefit Estimates Presented in the Study**

||_IncomeelasticityofWTP_|
|---|---|
|_ComponentsofHealthBenefits_|1.0|
|1.Healthbenefitestimate**1**including morbidity<br>(Prod.Loss+COI+WTP) andWTPformortality|Highestimate|
|2.Healthbenefit estimate2includingmorbidity<br>(Prod.Loss+**COI**+WTP)and humancapital<br>lossesformortality|Centralestimate|
|3.Healthbenefit estimate3including morbidity<br>(Prod.Loss+COI) andhumancapital lossesfor<br>mortality|Lowestimate|

The following sections explain in more detail how productivity losses, COI, human capital losses and WTP are measured. For a more detailed discussion of these methods see Cesar and others (2000).

_30_
# Integrating Variable Renewable Energy in the Bangladesh Power System: A Planning Analysis<sup>1</sup>

Miklos Bankuti, Debabrata Chattopadhyay and Chong‐Suk Song JEL classification numbers: **C61,Q41, Q47** Key words: Electricity system. Renewable energy. Power system planning. Bangladesh.

> 1 The authors acknowledge with gratitude support for the project from the Korea Green Growth Trust Fund and the NDC Partnership.  The authors acknowledge the review of the paper by Silvia Martinez Romero (ESMAP VRE Integration) and Professor Jong‐bae Park (Konkuk University, South Korea). Responsibility for the content of the paper is the authors’ alone.