rafmacalaba/datause-encoder-data huggingface dataset:
Dang, H. A. (2012). “Vietnam: A widening poverty gap for ethnic minorities. Indigenous Peoples, Poverty and Development”. In Hall, G. and Patrinos, H. (Eds.) _Indigenous Peoples, Poverty and Development_ . Cambridge University Press.

Dang, H. A., Dhongde, S., Do, M., Nguyen, C. V., & Pimhidzai, O. (2023). Rapid Economic Growth but Rising Poverty Segregation: Will Vietnam Meet the SDGs for Equitable Development? IZA Discussion Paper no. 15916.

- Government of Vietnam (2008), Resolution on the program to support rapid and sustainable poverty reduction for 61 poor districts, The government’s resolution No. 30a/2008/NQCP dated on 27/12/2008. Available at: https://thuvienphapluat.vn/van-ban/Van-hoa-Xa- <u>hoi/Nghi-quyet-30a-2008-NQ-CP-chuong-trinh-ho-tro-giam-ngheo-nhanh-va-benvung-doi-voi-61-huyen-ngheo-83914.aspx</u>

- Government of Vietnam (2012), Decision No. 1489/QĐ-TTg of the Government of Vietnam on National Target Program on Sustainable Poverty Reduction in the 2012-2015 period, the Government of Vietnam. Available at: <u>https://chinhphu.vn/?pageid=27160&docid=163977&tagid=4&type=1</u>

- Government of Vietnam (2013), Decision <mark>218/2013/NĐ-CP on detailed provisions and implementation guidelines of corporate income tax, the G</mark> overnment of Vietnam. Available at: <u>https://thuvienphapluat.vn/van-ban/Doanh-nghiep/Nghi-dinh-218-2013ND-CP-huong-dan-thi-hanh-Luat-thue-thu-nhap-doanh-nghiep-217811.aspx</u>

- Government of Vietnam (2016), Decision No. 1722/QĐ-TTg of the Government of Vietnam on National Target Program on Sustainable Poverty Reduction in the 2016-2020 period, the Government of Vietnam. Available at: <u>https://chinhphu.vn/?pageid=27160&docid=186132&tagid=4&type=1</u>

- Government of Vietnam (2021), Báo cáo đánh giá kết quả thực hiện chương trình mục tiêu quốc gia giảm nghèo và các chương trình mục tiêu quốc gia giai đoạn 2016-2020 (Report on the evaluation of the implementation results of the national target program on poverty reduction and the national target programs for the period 2016-2020), Government of Vietnam.

- <mark>Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation.</mark> _<mark>Handbook of econometrics</mark>_ <mark>,</mark> _<mark>6</mark>_ <mark>, 4779-4874.</mark>

- Henderson, V., Storeygard, A., & Weil, D. N. (2011). A bright idea for measuring economic growth. _American Economic Review_ , 101(3), 194-199.

- Hu, Y., & Yao, J. (2022). Illuminating economic growth. _Journal of Econometrics_ , 228(2), 359-378.

- Jalan, J., & Ravallion, M. (2002). Geographic poverty traps? A micro model of consumption growth in rural China. _Journal of Applied Econometrics_ , 17(4), 329-346.

30
summarizes survey evidence from health workers in Africa who similarly indicate professional advancement, better facilities to work in, and safety for their families as prime motives for migrating, in addition to the higher incomes they can earn.

The evidence is consistent with the high-skilled wanting to be in places in which they can live well, without fear of violence or instability, and in which they have sufficient opportunities to advance their careers and work with like-minded people. It is thus not surprising that India, with a large population, rapidly growing economy, and high-tech centers, sees brain drain now as much less of a problem that small, remote or politically unstable countries do.

# **Question 6: Does Brain Gain Exist?**

A series of theoretical papers over the past 15 years, summarized in Docquier and Rapoport (2011), have shown that it is possible that high-skilled emigration can lead to a rise in human capital levels in the home country. The basic idea of such ―brain gain‖ theories is that decisions of individuals to invest in education react to the prospect of future migration, and that not all those who choose to increase their education because of the chance they may migrate actually end up migrating. Providing more opportunities to work in the U.S. for doctors and nurses from poor countries may then not deplete supplies of medical workers at home, but could in principle raise them by increasing the incentives for people in poor countries to train as doctors and nurses.

In the last few years, some case study microeconomic evidence has started to emerge on whether such a mechanism operates in practice. The key challenge for empirical work is identifying a plausible source of exogenous variation in either migration opportunities or returns to skill abroad, which can then be used to examine the responsiveness of human capital acquisitions. Shrestha (2010) uses a change in the educational requirements of British Gurkha Army recruiters in 1993, which increased the returns to education abroad for Gurkha men in Nepal, to show that this led to increases in the probabilities of completing primary and secondary education for males affected by this change. Chand and Clemens (2008) look at the response of Indo-Fijians to a 1987 coup in Fiji, which they argue affected the incentive to acquire education in order to emigrate for Indo-Fijians, but not for ethnic Fijians. They find increases in levels of Form 7 (13<sup>th</sup> grade) education and in bachelor degrees. Finally, Docquier et al. (2010) show that 12
<!-- Start of picture text --> T*=1.7 °C T*=2°C<br>10 a b<br>x<<br>()<br>28<br>><br><<br>8<br>v6<br>Cc<br>a<br>So<br>i)<br>[o}<br>o4<br>©<br>Cc<br>2<br>#<br>To<br>~ ? ee<br>—<br>re ee ee ee ee es | re ee ee ee ee es |<br>2020 2025 2030 2035 2040 2045 £2050 2020 2025 2030 2035 2040 2045 £2050<br>Year information is revealed Year information is revealed<br>— Pre-2030 Spending —— Post-2030 Spending<br><!-- End of picture text -->
prices predicted by income. While estimated<sup>price distortions in Paraguay may be comparable</sup> with estimated price distortions in Peru,<sup>caution should be used in comparing these estimated</sup> price distortions with those of Denmark.<sup>For this reason, comparisons are made within three broad</sup> income categories. As a further precaution,<sup>price dispersion was measured both over the entire</sup> sample of countries, and over a restricted<sup>sample which only considers countries in the high and</sup> middle income groups (eliminating India<sup>and most of Africa).</sup>

# 3.3 estimation

Since population densitv and urbanization<sup>are likely inputs in the distribution of goods and may</sup> affect relative prices. I include these variables<sup>in the price regressions. As indicated in equation 10,</sup> changes in the exchange rate can influence<sup>relative prices if nontraded prices are sticky. To control</sup> for this I include the log of the exchange<sup>rate in the year 1980 (the year of the ICP sample) minus</sup> the log of the average exchange rate over<sup>the previous ten years (defined as exzrate.).</sup> Deviations from world production prices<sup>are estimated using the following equation for each</sup> good i _P,J =_ A, + 131rent-p, + _32service&pj_ + /33<sup>log urban, +</sup><sup>_03_</sup> _4_<sup>_logdensity, + )3_</sup> _5_<sup>_ex.ratej +_tiij</sup> (12) where _rent-P,_ is the log rent price predicted<sup>by log income per worker, service4Pj is the log service</sup> and :onstruction price predicted by<sup>log income per worker, and ex-ratej is described above. I use</sup> the residual ,ij from the estimation of equation 12<sup>as an estimate ofE,j--,, the deviation of the</sup> relative price of good i in country j from<sup>the world relative price.</sup> 14
Piketty, T. (2017) _Capital in the Twenty-First Century_ , the Belknap Press of Harvard University Press, Cambridge MA.

Piketty, T. and Saez, E. (2003) “Income Inequality in the United States, 1913-1998,” _The Quarterly Journal of Economics_ 118(1): 1-39.

Quinn, S. (2016) “Europe: A Survey of Gender Budgeting Efforts,” Working Paper 16/155, International Monetary Fund, Washington DC.

Revenga, A. and Shetty, S. (2012) “Empowering Women is Smart Economics,” _Finance and Development_ 49(1), March.

Roncolato, L., Reksten, N. and Grown, C. (2017) “Engendering Growth Diagnostics: Examining Constraints to Private Investment and Entrepreneurship,” _Development Policy Review_ 35(2): 263-287.

Rodrik, D. (2010) “Diagnostics before Prescription,” Journal of Economic Perspectives 24(3): 33-44.

Stiglitz, J. (1999) “Reforming the Global Economic Architecture: Lessons from Recent Crises” _The Journal of Finance_ 54(4): 1508-21.

Stiglitz, J. (2012) _The Price of Inequality: How Today’s Divided Society Endangers Our Future._ W. W. Norton & Company.

Wodon, Q. and De La Briere, D. (2018) _Unrealized Potential: The High Cost of Gender Inequality in Earnings_ , World Bank Group, Washington DC.

World Bank (2006) Gender Equality as Smart Economics: A World Bank Group Gender Action Plan.

World Bank (2009) _Female Labor Force Participation in Turkey: Trends, Determinants and Policy Framework_ , Report No. 48508-TR, World Bank Group.

World Bank (2012) _Gender Equality and Economic Development_ , World Development Report, World Bank Group, Washington DC.

World Bank (2014) Voice and Agency: Empowering Women and Girls for Shared Prosperity, World Bank Group.

World Bank (2015) _Next Generation Albania: A Systematic Country Diagnostic_ , Report No. 95980, World Bank Group, Washington DC.

19
# **Table Sup 6: Estimating equation 1 using original data used as inputs in Kummu, Taka and Guillaume**

# **(2018)**

This table presents robustness checks for our main result presented in Table 1, column 2. Instead of using filledin disaggregated data provided by Kummu, Taka and Guillaume (2018), we use the inputs data coming from Genaioli et al. (2013). Column 1 uses the sub-sample of observed data available at the sub-national level (no national data, no extrapolated or interpolated data). Column 2 uses sub-national and nationally observed GDP data (no extrapolated or interpolated data). Column 3 uses sub-national data with both observed, extrapolated and interpolated data (no national data). Column 4 uses sub-national and national data, including interpolated data. Results for precipitation remain robust.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Rainfall|0.571**|0.555**|0.462***|0.430***|
||(0.211)|(0.195)|(0.112)|(0.114)|
|Rainfall<sup>�</sup>|-0.166*|-0.161*|-0.095**|-0.091**|
||(0.073)|(0.065)|(0.030)|(0.030)|
|Temperature|0.017|0.046|0.051|0.076*|
||(0.043)|(0.044)|(0.034)|(0.035)|
|Temperature<sup>�</sup>|-0.000|-0.001|-0.001|-0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Include regional data|Yes|Yes|Yes|Yes|
|Include national data|No|Yes|No|Yes|
|Include filled-in data|No|No|Yes|Yes|
|Observations|18,458|20,434|31,665|33,745|
|R-squared|0.379|0.377|0.248|0.254|

Clustered standard errors at the administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1.

20
# **Affordability and Subsidies in Public Urban Transport: What Do We Mean, What Can Be Done?**<sup>**1**</sup>

# **Nicolás Estupiñán The World Bank**

**Andrés Gómez-Lobo Departamento de Economía Universidad de Chile**

**Ramón Muñoz-Raskin The World Bank Tomás Serebrisky The World Bank**

> 1 The authors would like to express their gratitude to Antonio Estache for his guidance and suggestions, and Arturo Ardila, Marianne Fay, José Luis Irigoyen, Andreas Kopp, Jorge Rebelo and Lucía Spinelli for their comments. This paper was financed by the Transport and Rural Infrastructure Services Partnership (TRISP), a partnership between the UK Department for International Development and the World Bank, for learning and sharing of knowledge in the fields of transport and rural infrastructure services.
# **3.5 Additional geospatial predictors**

Previous sections reported results on the accuracy of point and uncertainty estimates generated using an identical set of publicly available geospatial indicators, as described in section 2.2. However, these are not the only freely available geospatial features. In addition, the predictor variables used were never interacted with any other variables; this may be particularly problematic for EBP, which assumes a linear functional form, while the ML methods are flexible enough to implicitly take into account possible interactions. This section therefore extends the previous analysis by examining the extent to which adding additional features and interactions improves the accuracy of predictions. For additional features, we use the MOSAIKS features (Rolf et al., 2021), which are derived from raw satellite imagery. In addition, we experimented by interacting all variables with the share of land classified as urban (at the sub-area level).

Table 12 shows the results comparing EBP (which is most likely to change when including interactions) and XGBoost, our overall preferred method in terms of accuracy.<sup>17</sup> Somewhat surprisingly, adding MOSAIKS features to the set of candidate predictors reduces the accuracy of EBP predictions on average, for both assets and poverty. For XGBoost, adding MOSAIKS features reduces correlation for assets on average by 1.6 percentage points and increases correlations for poverty by 0.3 percentage points. Adding urbanity interactions slightly increases the accuracy of EBP estimates for assets and slightly decreases it for poverty. For XGBoost, adding interactions with urbanity as candidate variables increases the accuracy of the average asset estimates by 0.9 pp and decreases the accuracy of the average poverty estimates by 0.8 pp. In short, neither adding MOSAIKS features nor interactions has a consistently positive impact on accuracy. In general, impacts on accuracy are small, although adding MOSAIKS leads to a sizeable decline of approximately 5 percent points in accuracy when predicting wealth in Burkina Faso, and a sizable increase of about the same amount in Vietnam when using EBP.

Finally, Table 13 reports uncertainty measures when MOSAIKS features plus interactions are added. Compared with Table 6, average coverage rates for assets fall 3 pp for EBP, increase 3 pp for Cubist regression, and 1 pp for XGBoost and BRF. For poverty, average coverage rates hardly change for EBP and BRF, decline about 1 pp for Cubist, and increase nearly 3 pp for XGBoost. For XGBoost and BRF, including additional candidate variables appears to have modestly positive impacts on coverage rates, despite the limited impacts on the accuracy of the point estimates. However, this increase seems to come from larger confidence intervals; the width of the confidence interval in the last four columns is markedly larger than those in Table 6.

> 17We choose to present only these two methods for parsimony in the table.

38
post-reform, the incidence of both reallocations and expropriations was higher outside as compared to inside the prefecture boundary (4.7% vs. 3.4% for reallocations and 6.3% vs. 5.6% for expropriations).

Informal land transactions were observed in 22% of villages (23% outside and 20% inside) and, where they occurred, transferred some 11% of arable village land (10% and 13% outside and inside), in 25% and 33% involving outsiders or contracts longer than 5 years, respectively, and contributing to some consolidation. Formal transfers were observed in 4% of villages (1.3% and 6.9% outside and inside, respectively) mostly with one partner only and in about 30% of cases (8% outside and 33% inside) mediated by local government. If they took place, they affected 7% of arable area, involved longer contract periods (77% between 6 and 30 years and 23% above 30 years) and involved a larger share of households (16% outside and 14% inside Chengdu prefecture) than informal transfers. The incidence of both types of transfers increased over time; informal transfers to 31.2% after the reforms although the area affected changed much less (12% vs. 16% for informal and 6% vs. 10% for formal transactions).

Before the reform, transfers of construction were observed only in 1.4% of villages; 1.8% and 1.0% outside and inside the prefecture boundary, respectively. Where observed, they involved some 7% of construction land (2.3% outside and 10% inside). After reform, the incidence of transfers increased to 3.2% of villages (2.6% and 3.6% outside and inside the boundary), affecting 12.8% of construction land (7.8% and 15.3% outside and inside). Presence of a formal enterprise is observed only in about 4.4% of villages (4% outside and 5% inside the boundary). With most villages having one enterprise only, 48%, 29%, and 24% of these were in the secondary, tertiary, and primary sector, respectively. After reform, the incidence of enterprises increased to 5% and 8%, respectively, with a significant increase in the share of agricultural ones, especially inside the Chengdu prefecture boundary.

Results from tests for parallel trends pre-reform (table 1 col. 7) suggest that this hypothesis can be rejected at 10% or above for only 2 variables -incidence of expropriations and formal land transfers- out of 45. In both cases, trends for villages inside Chengdu are higher than for those outside. However, for the latter, the significance disappears if we consider only formal transfers that were not initiated by local government. Data on other time varying variables controlled for in our regressions is in appendix table 1.

10
command and control approach (Scenario 1). The results of our study further strengthen the findings of the literature as summarized in a review article by Timilsina (2022). Based on carbon tax studies published since 1990, Timilsina (2022) presents detailed discussions on the economic impacts of a carbon tax under alternative schemes of revenue recycling. It concludes that recycling carbon tax revenue to households through a lump-sum transfer would be more economical (i.e., causes lower loss in welfare and GDP) than when the government uses revenue like in the status quo. Recycling carbon tax revenues to cut labor and capital tax would be further economic. Recycling carbon tax revenues to cut corporate income taxes or investing on productive sectors would be most economical as it would cause the lowest GDP loss or even cause GDP gain (McKibbin et al. 2015). A carbon tax with tax revenue recycled for subsidizing clean or green technologies to encourage a further reduction of GHGs and local air pollutants would cause higher economic losses.

Our study brings additional insights to the existing literature as it goes one step further to investigate the sensitivity of criteria used for recycling carbon tax revenue to a given purpose. For example, many studies consider recycling tax revenues to cut corporate income taxes, but they do not specify how individual sectors receive back the carbon tax revenues. As specified in the preceding section (Table 2), we consider four criteria to recycle the tax revenues to cut corporate income taxes. One critical insight is that using the carbon tax revenues to cut corporate income taxes does not always cause the carbon tax efficient. We find that when the carbon tax revenues are reallocated to non-energy firms based on their CO2 emission intensities to cut their corporate income taxes, it will cause higher economic loss (Scenario 6c). When carbon tax revenue is recycled to non-energy sectors based on their corporate income tax rates (Scenario 6b), the GDP loss caused by the carbon tax would be higher than that when carbon tax revenues are used to cut existing labor and capital taxes (Scenario 4a and 4b). Therefore, while designing a carbon tax, policy makers should consider not only where in the economy the carbon tax revenue is utilized, but also how the tax revenue is relocated for the targeted purpose.

18
such as oil, petroleum, natural gas, coal, etc.), and manufacturing sectors and provides information at the exporter-product-destination-year level for seven variables: country of origin, exporting firm identifier, country of destination, HS 6-digit product, export value, export quantity, and year.<sup>22</sup> Additional details on the data are provided in the Appendix.

Although for each firm we have information on its exports in all sectors, we define the key outcome variables capturing firm export decisions—firm export value, export quantity, export participation, entry and exit variables—at the exporting country-firm-product-destinationyear level _focusing exclusively on agricultural and agro-food products_ , i.e., those belonging to HS Chapters 06-24 (with the exception of HS Chapters 15 and 16), which are the products for which our measures of pesticide standards imposed by importing countries are available.

Regarding the intensive margin of trade at the firm level, the outcome variables that we consider are the value and quantity exported by firm _i_ from country _c_ of product _k_ to importing/destination country _d_ in year _t_ which are given by, respectively, ��,�,�,�,� expressed in current U.S. dollars and ��,�,�,�,� expressed in kilograms. We also calculate unit values as value divided by quantity expressed in U.S. dollars per kilogram.<sup>23</sup> Regarding the extensive margin of trade at the firm level in terms of firms’ decisions to export or to enter or exit a product-destination market, we need to expand (or fill in) the initial dataset described above with only positive exports by adding zeros on some dimensions to be able to define the corresponding outcome variables. If we were to follow the gravity equation literature (particularly studies employing an export participation equation to account for biases in gravity equation estimation) we would expand the initial dataset so as to make it a ‘square’ matrix where every firm in an exporting country would have an observation (a row) for every product-destination-year combination possible.

Given the large number of exporting firms in our sample of 42 developing countries, such an expanded data set would be computationally impossible to handle and would be highly cluttered by zeros as most firms tend to export a single product to a single destination. Moreover, our objective in constructing an expanded dataset is to have observations (rows) that make

> 22 Cebeci et al. (2012) show the quality of the data by comparing the total exports obtained from aggregating the transaction-level customs data at the country level with the total exports obtained at the country level from COMTRADE/WITS (World Integrated Trade Solution). 23 The number of observations in specifications that explain quantities or unit values will be smaller than those in the specifications that explain value given that 8 countries in our sample (Botswana, Chile, the Dominican Republic, Guatemala, Mexico, Macedonia, Pakistan and El Salvador) do not include quantity information in their exporter-level customs datasets.

12