rafmacalaba/datause-encoder-data huggingface dataset:
It is helpful to summarize the LFWP by benchmarking its magnitude to other persistent wage differentials observed in the literature: the gender wage gap (Goldin, 1992, <mark>Altonji and Blank, 1999, B</mark> lau and Kahn, 2017), and the urban‐to‐rural wage gap (Harris and Todaro, 1970, Glaeser and Mare, 2001, Combes et. al., 2008, Young, 2013, <mark>Jones, D’Aoust, and Bernard,</mark> 2017).<sup>5</sup> Column 6 adds dummies for whether the worker is female and whether the worker lives in an urban, rather than rural, area. In this specification, the LFWP is almost identical in absolute value to the average discount of female wages to male wages, once controlling for occupation, industry, education and experience. The urban wage premium however remains larger by approximately 50 percent. Column 7 interacts the urban and female dummies with the large firm dummy, to test for heterogeneity in the LFWP by location and worker. The interaction with urban is positive and significant, indicating that the large firm wage premium is even higher in urban areas. The interaction with female is positive but not significant, and also smaller than the urban premium in magnitude.

# The LFWP across developing economies and sectors

An unanswered question in the LFWP literature has been whether the premium varies across countries and levels of development. To investigate the question systematically, we estimate the wage premium separately for each survey, allowing more flexibility in parameters, for instance the returns to human capital as well as occupation and industry wage differentials. Table 2 shows the results of specifications identical to columns 3 and 4 of Table 1 estimated at the country level with year fixed effects, as well as the Nunn and Wantchekon (2011) ratio introduced above. This analysis reveals that across developing economies, there is substantial variation in the importance of human capital sorting in determining the LFWP.

This variation emerges among countries with similar GDP per capita: In Chad for instance (real GDP per capita: $863), the ratio of selection on unobservables required to explain the LFWP (conditional on occupation and industry) must be as large as 2.1 times the observed selection on education and experience. In the Kyrgyz Republic (real GDP per capita: $921), this ratio must be 25.1 times. Further, in Moldova and Nicaragua, the LFWP actually rises when conditioning education and experience,

> 5 Oi and Idson (1999) take this approach in U.S. data and find an LFWP of 35%: comparable to a gender gap of 36% of men over women, and greater than the wage gap of 14% for white over black employees. They also find that the premium is larger for men and larger in the United States than in France, Germany, Italy, and Japan.

5
31 The 2SLS estimates of the effects of high school completion on mortality between age 25 and 35 implied by these results suggest that raising high school completion by ten percentage points would reduce subsequent ten-year mortality rates by 1 to 2 percentage points. While the data do not allow us to thoroughly explore the determinants of this causal link, several checks confirm that the estimated effect is unlikely to be due to differences in the degree of labor market attachment induced by higher schooling: employment rates 5 and 10 years after exemption are found to be broadly similar for exempt and comparable non-exempt youths; moreover, conditioning 2SLS estimates also on these intermediate outcomes does not alter the estimated effect of high school completion on subsequent mortality.

In terms of individual probabilities of death, these estimates would imply that completing high school reduces the probability of dying between the mid-20s and mid-30s by between 10 and 20 percentage points. The magnitude of the effect, while at first surprisingly high, is only slightly stronger than that obtained by Lleras-Muney (2005) for older individuals with an additional year of compulsory schooling and roughly in line with that implied by results in Deschenes (2010). The size of the effect can be partly traced to the particular population affected by the specific instrumental variable strategy, namely youths at risk of dropping out, an interpretation consistent with the substantial difference between 2SLS and OLS estimates of the effect of schooling. Additionally, the research design does not allow us to rule out the presence of spillover effects in health behavior which, if present and assumed away, mechanically amplify the estimated individual direct effect. The estimated effect is driven almost exclusively by a decline in the incidence of natural causes. We argue this may reflect a mismeasured decline in drugs and alcohol related deaths, possibly amplified by the presence of peer
31

**Table 3**

**Testing Bequest Motives and Tax-Smoothing Approaches Fixed (Country & Time) Effects Panel Regressions Dependent Variable: Central Government Budget Surplus Share**

||Developed|Developing|Developed|Developing|
|---|---|---|---|---|
|Years : 1975 -1992|Reg5a|Reg5b|Reg6a|Reg6b|
|Control Variables|||||
|1. Dependent Variable Lag 1|0.4124***<br>(0.134)|0.7121***<br>(0.083)|0.4005***<br>(0.135)|0.7086***<br>(0.083)|
|2. Index of Political Rights|0.0222**|-0.0002|0.0232**|-0.0007|
||(0.009)|(0.002)|(0.009)|(0.002)|
|3. Agricultural Share (-)|-0.1222|-0.0906|-0.1072|-0.0883|
||(0.281)|(0.073)|(0.28)|(0.073)|
|4. Manufacturing Share (+)|0.2595|-0.2317|0.2525|-0.2416|
||(0.22)|(0.216)|(0.215)|(0.216)|
|5. Trade Share (+)|-0.0054<br>(0.053)|0.049**<br>(0.024)|-0.0038<br>(0.053)|0.0502**<br>(0.024)|
|6. Urban Population Share (+)|-0.0149***<br>(0.004)|-0.0035***<br>(0.001)|-0.0149***<br>(0.004)|-0.0039***<br>(0.001)|
|7. Log of per capita Real GDP|0.221***<br>(0.048)|0.0532***<br>(0.018)|0.2126***<br>(0.048)|0.0549***<br>(0.018)|
|8. Government Crises (-)|-0.0007|-0.0009|-0.00003|-0.0014|
||(0.001)|(0.002)|(0.001)|(0.002)|
|9. Cost of Debt Servicing (-)|0.0142|0.0125|0.0241|0.012|
||(0.065)|(0.028)|(0.065)|(0.028)|
|10. Seigniorage (-)|-0.1102|-0.1248|-0.0877|-0.1269|
||(0.104)|(0.079)|(0.103)|(0.079)|
|Tax-Smoothing Variables|||||
|11. Unanticipated changes|-0.0461|-0.0913***|-0.0452*|-0.0914***|
|in real per capita govt exp (-)<br>(4 year, simple average)|(0.028)|(0.021)|(0.027)|(0.02)|
|12. Unanticipated changes|-0.077|0.1073***|-0.0749|0.107***|
|in per capita real GDP (+)<br> (4year,simple average)|(0.053)|(0.026)|(0.052)|(0.026)|
|Bequest-Motives Variables|||||
|13. Expected per capita real<br>GDP Growth Rate (-)<br>(25 year construction)|-0.7784**<br>(0.319)|-0.2956<br>(0.451)|-0.6116**<br>(0.297)|-0.2293<br>(0.478)|
|14. Gini Coefficient (-)|-0.0008|-0.0008|0.0009|-0.0013|
|(adjusted, smoothed)|(0.0007)|(0.0032)|(0.0007)|(0.0033)|
|15. Life Expectancy at age 65 (-)|-0.0122<br>(0.009)|-0.0218**<br>(0.011)|-0.0115<br>(0.008)|-0.0206*<br>(0.011)|
|16. Total Dependency Ratio (-)|0.0627<br>(0.082)|-0.0457<br>(0.057)|||
|17. Youth Dependency Ratio (-)|||-0.0012<br>(0.092)|-0.0348<br>(0.065)|
|18. Elderly Dependency Ratio (-)|||0.266*<br>(0.153)|-0.4966<br>(0.578)|
|19. constant|-1.1082***|0.1958|-1.0647***|0.2461|
||(0.406)|(0.183)|(0.41)|(0.183)|
|R-squared|0.8703|0.8305|0.8724|0.8344|
|Adjusted R-squared<br>FSttiti|0.8389<br>5099|0.7859<br>3568|0.8408<br>4694|0.7900<br>3616|
|-asc<br>F-Stat df 1|.<br>51|.<br>65|.<br>52|.<br>66|
|F-Stat df 2|211|251|210|250|
|<br>Number of Countries|20|35|20|35|
|Number of Observations<br>Standard Errors are in parentheses.<br>*, **, *** denotes significance at the 90, 95 and 9<br>Robust standard errors obtained using White's c|263<br>9 percent confidence leve<br>orrection for heteroscedas|318<br>l respectively<br>ticity.|263|318|
_Table 3: Selected food prices (ln) and program saturation in nationally representative data_

||Rice, raw<br>regular<br>milled|Eggs<br>(fresh)|Fish<br>(fresh)|Meats<br>(fresh)|Vegetables<br>(fresh)|Milk<br>Products|
|---|---|---|---|---|---|---|
|_Panel A: Model with cutoff_|_at saturation_|<br>_of 50 percen_|<br>_t_||||
|Saturation|0.061***|0.083***|-0.016|0.002|0.030|0.140**|
||(0.014)|(0.018)|(0.015)|(0.013)|(0.018)|(0.063)|
|Saturation*Above cutoff|0.088*<br>(0.049)|0.185***<br>(0.062)|0.090*<br>(0.054)|0.053<br>(0.044)|0.180***<br>(0.062)|-0.008<br>(0.237)|
|Above cutoff|-0.022<br>(0.030)|-0.094**<br>(0.038)|-0.042<br>(0.033)|-0.007<br>(0.027)|-0.061<br>(0.038)|-0.095<br>(0.144)|
|Saturation +|||||||
|Saturation*Above cutoff|0.149***<br>(0.047)|0.268***<br>(0.059)|0.074<br>(0.051)|0.055<br>(0.042)|0.210***<br>(0.058)|0.132<br>(0.227)|
|_Panel B: Model with cutoff _|_at saturation _|_of 65 percen_|_t_||||
|Saturation|0.078***|0.089***|-0.009|0.019|0.054***|0.071|
||(0.013)|(0.016)|(0.014)|(0.012)|(0.017)|(0.058)|
|Saturation*Above cutoff<br>Above cutoff|-0.252***<br>(0.088)<br>0.245***|0.154<br>(0.110)<br>-0.070|0.332***<br>(0.095)<br>-0.249***|0.029<br>(0.080)<br>-0.004|0.160<br>(0.109)<br>-0.060|0.527<br>(0.429)<br>-0.456|
||(0.067)|(0.085)|(0.072)|(0.062)|(0.085)|(0.328)|
|Saturation +|||||||
|Saturation*Above cutoff|-0.174**<br>(0.086)|0.242**<br>(0.107)|0.323***<br>(0.093)|0.048<br>(0.079)|0.214**<br>(0.107)|0.598<br>(0.422)|
|_Panel C: Model with cutoff_|_at saturation_|_of 75 percen_|_t_||||
|Saturation|0.095***|0.097***|-0.014|0.023**|0.064***|0.055|
||(0.012)|(0.016)|(0.013)|(0.011)|(0.016)|(0.056)|
|Saturation*Above cutoff|-0.489***|0.220|0.194|0.070|0.154|0.309|
||(0.147)|(0.185)|(0.158)|(0.132)|(0.184)|(0.743)|
|Above cutoff|0.440***|-0.139|-0.116|-0.047|-0.058|-0.250|
||(0.127)|(0.160)|(0.137)|(0.114)|(0.161)|(0.643)|
|Saturation +|||||||
|Saturation*Above cutoff|-0.394***|0.317*|0.180|0.093|0.217|0.364|
||(0.146)|(0.183)|(0.157)|(0.131)|(0.183)|(0.737)|
|_Panel D: Linear model_|||||||
|Saturation|0.095***|0.114***|0.001|0.028***|0.087***|0.062|
||(0.011)|(0.015)|(0.012)|(0.010)|(0.015)|(0.053)|

note: *** p<0.01, ** p<0.05, * p<.1. Standard errors in parenthesis. Each row corresponds to a separate regression. Observations are at the village-food item-year level and pooled across the years 2009, 2012, and 2015. All models include year and village fixed effects. Prices are calculated as unit values; see text for details. Source: 2009, 2012, and 2015 Family Income and Expenditure Surveys and Department of Social Welfare Administrative Data on _Pantawid_ Program Enrollment 46
Policy ReseaRch WoRking PaPeR 4541

# **Abstract**

The inflow of large quantities of foreign aid into Rwanda since 1994 can have potential adverse effects such as aid dependency via a significant negative effect on tax efforts and on public investments. This paper carries out a theoretical and empirical study to examine these issues. The theoretical part develops a model in which the recipient government decides on the optimal level of tax and optimally allocates total government revenue between current expenditure and public investment. The theoretical model makes it possible to empirically test whether an increase in aid is likely to reduce the optimal tax rate and the proportion of public expenditure allocated to public investment. The econometric analysis uses time series data on Rwanda to show, in line with other studies in the literature, a negative relationship between increased aid and the tax rate; but the magnitude of the effects are extremely small. In the case of Rwanda, reforms to the tax administration and expansion of the tax base have had mitigating effects. As far as the effect on public investment, the overall effect was negative in the past; however, since 1995 the direction of this effect has changed.

This paper—a product of the Poverty Reduction and Economic Management 3 Division, Eastern Africa 2 Country Department—is part of the series of background papers that informed the Country Economic Memorandum, "Rwanda-Toward Sustained Growth and Competitiveness. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at Kezemenari@worldbank.org, ekebede@worldbank.org and lahiri@ siu.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
balances are higher than 10 percent of GDP per capita in 14 countries. While one document is needed to open an account in five countries, four documents, including ID, payment slip, proof of domicile and reference letter, are required in six countries, effectively preventing large parts of the population from accessing these services. While it is possible to apply for a loan over the phone or the Internet in six countries, customers can only submit loan applications at bank headquarters or at branches in five countries.

We conduct consistency checks on our data and show that, in general, banks in more economically and financially developed economies impose lower barriers. Also, we show that barriers are negatively correlated with financial outreach – measured by branches, loans and deposits per capita – and with lower financing obstacles as reported by firms. Our indicators thus capture an important aspect of banking sector outreach across countries.

While double-digit ratios of minimum balances, fees and minimum loan amounts to GDP per capita already give a first impression of the limited affordability of many of these services for large parts of the population in a number of countries, we offer back-of-the-envelope calculations using data on income distribution. We find, for example, that fees to maintain checking accounts effectively prevent more than 30 percent of the population from using such services in ten of the 58 countries in our sample.

We also explore which bank- and country-level characteristics are associated with barriers to banking. Consistent with the argument of scale economies in banking, we find that larger banks consistently impose lower barriers. Better physical infrastructure is also robustly associated with lower barriers. In more competitive, open and transparent economies bank customers also face lower barriers. Further, we find evidence that banks in countries with a more efficient contractual and informational framework impose lower barriers on customers. Finally, the relationship between barriers and bank ownership is not a simple one. Though foreign banks 3
specific tariffs are generally considered inefficient compared to ad valorem tariffs for two reasons: ad valorem tariffs are more transparent; and specific tariffs distort the choice of product within a category toward higher priced products. For example, a specific tariff of one euro per liter of wine is a very high percentage of the price of inexpensive wine, but a low percentage of the price of expensive wine. Consequently, specific tariffs would have the effect of switching the purchases of some consumers toward more expensive wines.

It is, however, the explicit policy of the Russian government to employ specific tariffs for this purpose. That is, specific tariffs are employed with the purpose of applying greater tariff protection against low priced imports.

## **9. Where specific tariffs apply, they tend to be considerably higher than the ad valorem component of the tariff.**

In table 7, we list the ad valorem equivalent of the specific tariffs by sector. We only consider tariff lines that are subject to specific tariffs in the calculations. In 2005, there were three aggregate sectors with an average specific tariff of 30% or higher: food; timber, wood, pulp and paper; and other goods producing industries.

## **The ad valorem component of the tariff is usually between 5 and 20 percent.**

If we examine only the ad valorem part of the tariff rates, we see that about 98 percent fall in the range of 5 to 20 percent. The government abandoned 40% ad valorem tariff rates on sugar in 2003, but replaced it with a specific tariff.<sup>10</sup> However, in 2003, the government introduced 60% and 80% tariff rates on meat and meat products. The highest rate of 100% is set on the import of some types of alcohol and this rate has remained unchanged throughout the period our sample period. Table 8 and Table 1 show that there was no significant change in ad valorem rates in 2001-2005. The average unweighted valorem rates in 2001-2005 were either 10.9% or 10.8%. That is, the ad valorem rates have not increased. The increase in the average tariff rates and the variance in the tariff rates over this period are due to the specific tariff component of the tariff structure.

# **III. Data sources**

> 10 The specific tariff on sugar ranges from $194 to $270 per ton.

9
_ft|y_ 1 _, . . . , ys_ ; _ψ_<sup>_MLE_</sup> ) for _t, s_ = 1 _, . . . , T_ , where _ψ_<sup>_MLE_</sup> = _{α, ση_<sup>2</sup><sup>_}_containstheparameters</sup> that pertain to the distribution of the factor. Notice that Λ and Ωare estimated in the first stage. Moreover, the initial factor has density _N_ (0 _, P_ 1) where _P_ 1 = inv(1 _− αα_<sup>_′_</sup> ) and _εt ∼ NID_ (0 _,_ Ω) is the _N ×_ 1 disturbance term.

The estimation of the parameter vector _ψ_<sup>_MLE_</sup> is based upon maximizing the log-likelihood function associated with (1) and (4). Meanwhile the estimated factor, _ft_<sup>_MLE_</sup> , is obtained through a recursive procedure. Specifically, the log-likelihood function associated to the Gaussian density is given by where the quantities _υt_ and _Ft_ represent the prediction residuals ( _yt −_ Λ _at|s_ ) and the predicted variance (Λ _Pt|s_ Λ<sup>_′_</sup> + Ω), which are evaluated by the Kalman filter.

# **4.2 Expanding the series using air quality information**

In this section we propose a simple methodology for expanding the “Google Mobility Index”obtained in the previous section by using air quality-related information. Specifically, we use the Air Quality Open Data Platform for extracting temperature and fine particulate matter (PM2.5) information per city in each country worldwide. Then, the information is averaged per country such as it can be easily associated with the Google Mobility Index over time.

Let’s consider _pj_ and _qj_ for _j_ = _t−J, . . . , t, . . . , T_ the normalized temperature and PM2.5 information at time _j_ , while _fj_<sup>_MLE_</sup> for _j_ = _t, . . . , T_ is the Google Mobility Index. Notice that _pj_ and _qj_ contain _J_ more data points than _fj_<sup>_MLE_</sup> . Therefore, we recover backwards the information as follows 7
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44