rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

- [1] ABAGISA, J. (2014) “Challenges and Prospects of Balanced scorecard Implementation in the Development Bank of Ethiopia,” Mimeo.

- [2] ACEMOGLU, D., P.AGHION, C.LELARGE, J.VAN REENEN AND F.ZILIBOTI (2007) “Technology, Information, and the Decentralization of the Firm,” _Quarterly Journal of Economics_ 122 (4): 1759-1799.

- [3] ACEMOGLU, D. AND J. A.ROBNSON (2012) “Why Nations Fail: The Origins of Power, Prosperity and Poverty,” New York: Crown Business

- [4] AGHION, P. AND J.TIROLE (1997) “Formal and Real Authority in Organizations,” _Journal of Political Economy_ 105 (1): 1–29.

- [5] AGHION, P., N.BLOOM, B.LUCKING, R.SADUN AND J.VAN REENEN (2017) “Turbulence, Firm Decentralization and Growth in Bad Times,” National Bureau of Economic Research Working Paper No. 23354.

- [6] ANDRADE, P. AND H.LE BIHANA (2013) “Inattentive Professional Forecasters,” _Journal of Monetary Economics_ 60 (8): 967-982.

- [7] ARGENZIANO, R., S.SEVERINOV AND F.SQUINTANI (2016) “Strategic Information Acquisition and Transmission,” _American Economic Journal: Microeconomics_ 8 (3): 119-55.

- [8] AGARWAL, S. AND I.BEN-DAVID (2018) “Loan Prospecting and the Loss of Soft Information,” _Journal of Financial Economics_ 129 (3): 608-628.

- [9] AGARWAL, S. AND R.HAUSWALD (2010) “Distance and Private Information in Lending,” _Review of Financial Studies_ 23 (7): 2757-2788.

- [10] AGARWAL, S. AND R.HAUSWALD (2009) “Authority and Information,” Mimeo.

- [11] BANDIERA, O., M.BEST, A.KHAN AND A.PRAT (2018) “Incentives and the Allocation of Authority in Organizations: A Field Experiment with Bureaucrats,” Mimeo: London School of Economics.

- [12] BANERJEE, A., R.HANNA, J.KYLE, B. A.OLKEN AND S.SUMARTO (2018) “Tangible Information and Citizen Empowerment: Identification Cards and Food Subsidy Programs in Indonesia,” _Journal of Political Economy_ 126 (2): 451-491.

- [13] BARDHAN, P. (2002) “Decentralization of Governance and Development,” _Journal of Economic Perspectives_ 16 (4): 185-205.

- [14] BARDHAN, P. AND D.MOOKHERJEE (2006). “Decentralisation and Accountability in Infrastructure Delivery in Developing Countries,” _Economic Journal_ , 116 (508): 101-127.

- [15] BENDER, S., N.BLOOM, D.CARD, J.VAN REENEN AND S.WOLTER (2018) “Management Practices, Workforce Selection, and Productivity,” _Journal of Labor Economics_ 36 (S1): S371-S409.

- [16] BERTRAND, M., R.BURGESS, A.CHAWLA AND G.XU (2016) “The Costs of Bureaucratic Rigidity: Evidence from the Indian Administrative Service,” University of Chicago Working Paper.

- [17] BESLEY, T. AND T. PERSSON (2011) “Pillars of Prosperity: The Political Economics of Development Clusters,” Princeton: Princeton University Press.

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- D. Using LASSO instead of XGboost for the geospatial and census model

This exercise provides a useful opportunity to compare predictions across two different machine learning approaches: Extreme Gradient Boosting, and post-LASSO, which uses LASSO-selected variables in an OLS regression model. The main difference between these two approaches is that the former, as applied here, is a tree-based classification method that uses sequential random forests to predict the dependent variable. Extreme gradient boosting can therefore accommodate highly non-linear relationships. In contrast, post-lasso imposes a linear functional form. An open question in this context is how much this linearity assumption affects the accuracy of the predictions.

The results when using post-LASSO to generate the geospatial predictions based on the partial registry – the second step of the partial registry procedure -- are displayed in Figure 8. These are applied to predictions from the baseline census model, which uses data from all districts but predicts the average welfare of the bottom half of households. Overall, the post-LASSO model performs slightly better in terms of rank correlation, while extreme gradient boosting performs a bit better when looking at AUC and R-squared. Thus, in this context, whether one uses postLASSO or extreme gradient boosting in the second stage of the partial registry method makes little difference. However, the estimation method for the benchmark welfare seems to affect the results considerably. When the geospatial models are trained using a benchmark welfare that was predicted using LASSO, the metrics for the partial registry methods decrease significantly: rank correlations go from 0.75 to 0.27, AUC from 0.89 to 0.66 and R-squared from 0.57 to 0.08. This could indicate potential measurement errors in the census that affects the performance of LASSO models since they are more sensitive to outliers.

**Figure 8.** Partial registry predictions using LASSO vs. XGBoost

<!-- Start of picture text -->
1.00<br>0.89<br> 0.90 0.85<br> 0.80 0.76  0.75<br>0.66<br> 0.70 0.63<br>0.56  0.57<br> 0.60<br> 0.50<br> 0.40<br>0.28  0.27<br> 0.30<br> 0.20<br>0.08<br> 0.10 0.03<br> -<br> Rank correlations  AUC R2  Rank correlations  AUC R2<br>LASSO-benchmark XGBoost-benchmark<br>LASSO-predictions XGBoost-predictions<br><!-- End of picture text -->

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approaches may impose the cost of periodically relieving expert teachers from teaching responsibilities.

29. The strength of the present study is that it provides summary evidence on the relative efficacy of both inservice education and supervision across a large population of small rural primary schools in an entire nation. The weaknesses are that the study is correlational, not experimental, so that causal inferences are made with caution; and that the evidence does not point to the types of programs that are particularly effective.

30. We shall consider as a key predictor variable the intensity of teachers' exposure to inservice training, as indicated by the duration of their training experiences and the content coverage of those courses. Similarly, we shall examine exposure to supervision, both internal and external. Outcome variables are the perceived quality of instruction and students' total achievement. We control a variety of covariates measured at the child, teacher and school levels. Effects of other policy options, including specialization and instructional materials, will be reported later.

# Sample

31. Early in 1988, the Thai government, in collaboration of Project BRIDGES (Basic Research in Developing Educational Systems), conducted a representative national survey of approximately 400 primary schools, 4000 teachers, and 10,000 sixth-grade students. This 12
emphasis on specifically defined socially vulnerable groups (although it was recognized that some of these groups could be captured in the Phase 2 sample groups as described above), and in a limitation of the Phase 2 selection to working age respondents who are actual or likely participants in the labor market. Other factors underpinning this articulation of the Phase 2 sample included the recognition that (i) members of socially vulnerable groups are generally recipients of some sort of benefits through nationally defined programs (pensions, disability, child support, etc.) and would consequently be less directly impacted by mine closure than individuals competing on the local job market; and (ii) particular aspects of social vulnerability or manifestations of relatively new problems such as the migration of the working-age population, or the emergence of drug abuse as a local problem were captured during the Phase 1 interviews and (in Ukraine) the quantitative survey.

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Refugee influx Refugee influxI: Period October 1993. January 1997 December 1996 “Fuly 200! SSR| mm 1 [irfs |e [a Pete[om |][veel][|s |e}| [|Pets i |e}[oaste| metitoa [me lalatal}s+[s]«] | |reaom | ae [ al pmo | owaxe | ET | awe TT Poe a a CONTROL MIXED COHORT MAIN TREATMENT COHORT Not COHORT Some early childhood (0Early childhood (0-5) during the in early childhood during the 5) during and before the refugees’ influx refugees’ influx period I and I refugees’ influx period I and II Parents period I and I Parents (Mothers and Fathers) who NOT (Mothers and Fathers) who were Parents who spent some were in their early childhood 5 in their early childhood (more than years oftheir early between October 1993 and July years old) and born after 1978 childhood during or before 2001 (date of birth of the youngest the refugees’ influx (1988 mother in the sample) to October 1993)
Gini, which can occur because the Gini is a broader measure of inequality that takes into account the overall consumption distribution.<sup>5</sup>

**Figure 3: Shared Prosperity has improved over time** Average annual growth in per capita consumption (%)

**Figure 4: Consumption inequality (Gini) has remained stable since 2005**

<!-- Start of picture text --> 6.0 5.6 0.40<br>5.2<br>5.0<br>0.30<br>4.0<br>3.0 0.20<br>2.1<br>2.0 1.7<br>1.3<br>0.10<br>0.8<br>1.0<br>0.0 0.00<br>1994 to 2005 2005 to 2010 2010 to 2012 1993‐94 2004‐05 2009‐10 2011‐12<br>Poorest 40% Average Rural Urban<br>0.36 0.37 0.37<br>0.31<br>0.25 0.27 0.27 0.27<br><!-- End of picture text --> _Note:_ Consumption expressed in constant 2004‐05 All India Rural Rupees, corrected for cost‐of‐living differences between states and rural and urban areas using the official poverty lines. _Source:_ Authors’ estimates from respective NSS rounds.

# High degree of mobility, where movements upward dominate

The true extent of mobility is not evident in trends in the shared prosperity indicator which refers to consumption growth of the “anonymous” bottom 40%, computed from the difference in (per capita) consumption of the bottom 40% of two different distributions at two points of time and _not_ of the same group of households over time. In contrast, consumption growth of the “non‐anonymous” bottom 40% of the distribution would measure the difference in consumption of the same group of households (those in the bottom 40% of the initial year) over time. The implications of anonymity are important to recognize for the correct interpretation of Figure 3.

How much difference does “anonymity” make in measuring shared prosperity in India? Although NSS does not allow for a tracking of expenditures by households over time, the India Human Development Surveys (IHDS) for 2004‐05 and 2011‐12, which include a panel component, offer the opportunity for a test. Between 2005 and 2012, annual per capita consumption growth among households who were in the bottom 40% of the consumption distribution in 2005 (9 percent) was much higher than that of the total population (4.7 percent); whereas consumption growth of the anonymous bottom 40% (4.4 percent) was on par with that of the overall population.<sup>6</sup> In other words, the least well‐off in 2005

> 5 It is possible, for example, for the consumption Gini to remain constant if inequality among top three quintiles falls, even as the share of the bottom 40% in total consumption declines.

> 6 See Balcazar et al (2016) for more details on the incidence of consumption growth across the distribution, using IHDS data for 2005 and 2012.

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# **I - INTRODUCTION**

Numerous studies have documented the rising value of human capital in the global economy. Today, most employers require workers to learn skills throughout life. Good formal education is a requirement for being able to continuously learn new skills and adapt to new demands. This paper focuses on estimating the potential value of one of the suggested policy responses to the increased value of human capital, namely lifelong learning. Policymakers in Latin America are increasingly looking to lifelong learning policies to promote continued improvement in a country’s human capital stock.

Lifelong learning (LLL) is a concept that stresses the importance of learning throughout life. It encompasses the traditional formal education system (schools, training institutions, universities etc.), non-formal learning (structured on-the-job training) and informal learning (skills learned from family members or people in the community). It advocates that the formal education system should recognize skills acquired outside the system. The focus on learning by the adult population is crucial, because the aging population of most high-and middle-income countries implies that future improvements to the stock of human capital will rather come from continued adult learning than from entry into the labor force of new generations.

Although LLL is still in its concept phase, a growing number of reports examine policies to foster lifelong learning. LLL policies range from curriculum changes towards competency based curricula, participatory teaching, certification mechanisms that recognize learning outside of the formal education system, integration of job training into the education system, regulation of credit transfer between education institutions, and increased educational opportunities for adults. However, there exists little economic investigation on whether such new opportunities really bring value to the individual, firm and country. OECD (2002) and World Bank (2002) summarize the scant research that exists.

This paper contributes to address this shortfall of economic investigation of lifelong learning by examining the value of one element of lifelong learning, formal adult education. We estimate rates of return to formal education for (re-)entry into the education system by age-cohort. This reveals the age at which formal education remains an economically rewarding investment for the individual. We use the methodology of 2
relevant PPP benchmark year (e.g. 2005 or 2011) by the national consumer price index (CPI) and then the benchmark year PPP conversion factor is applied to obtain the PPP US dollar value of consumption. The poor are then identified as those whose consumption (or income for some countries) in PPP US dollars is less than the global extreme poverty line, currently set at 1.25 in 2005 PPP US dollars.

This approach of using the national CPI to bring all consumption values to a common date is considered to be an approximate method for capturing changing relative price levels across countries. If it performs well, there should not be sharp changes in poverty rates with the release of new PPP data, but historically this has not been the case. For example, when the 2005 PPP data were released, Chen and Ravallion (2010) re-estimated the global poverty line and headcount based on adjusted 2005 PPP data and observed significant changes in the poverty line and average value of consumption (relative to expectations based on the extrapolated PPP adjustment factors from the 1993 PPPs). Both of these changes resulted in Chen and Ravallion’s assessment that the new price data indicated that past estimates of global poverty needed to be adjusted upwards by 500 million persons. Nothing had changed to the standard of living of the poor overnight, but the belief was that the 2005 PPP data provided a better assessment of relative prices than the previous PPP factors (scaled annually by the ratio of inflation rates).

Table 1 replicates a table from Deaton (2005) that presents three very different profiles of global poverty in 1993, each produced from different rounds of PPP data. For example, shifting from the 1985 PPP data to the 1993 PPP data led to a 35 percent decline in poverty in Latin America and a 27 percent increase in poverty in Sub-Saharan Africa for 1993. Similarly, shifting from the 1993 PPP data to the 2005 PPP conversion factors doubled the estimated rate of poverty in East Asia and the Pacific, and increased the headcount by half a billion people for 1993. In 4
<!-- Start of picture text --> 8 (a) Purestand Yield source 8 {b) All elds Yield source<br>6 : 6 ;<br>e 4 e4<br>2 2<br>oO o<br>33<br>&2 8 2<br>ci<br><!-- End of picture text --> <!-- Start of picture text --> 3° 2<br>3 {ytaes|F% Vig<br>= oO & 0<br>e<br>2 -2<br>a4 4<br>Plot area Soil Quality Fertilizer Plot area Soil Quality Fertilizer<br><!-- End of picture text -->
government officials shape the relationship between mining and the assessment of government performance in resource-rich communities. Third, we explore whether the legal rights or the discretionary power of subnational governments over the tax and revenue system matters in the analysis of the effects of natural resources on the provision of local public goods and services. Although royalties from mining industries are mostly controlled by central government authorities, mining-related or induced business generate rents and revenue opportunities that can be exploited by local authorities.

To date, and to the best of our knowledge, there is little evidence on the mediating role of institutions, local governance, and decentralization in the relationship between natural resources and development at the local level. Most of the existing evidence on such confoundedness is provided in macro-level studies where it has been shown that the quality of institutions and governance are key explanatory factors of why natural resources might be a curse for some countries and a blessing for others (van der Ploeg, 2011). Our paper, then, makes a significant contribution to the growing literature on the local effects of mining in Africa.

For our analysis, we match the Afrobarometer survey geo-referenced data with the SNL Metals and Mining by the S&P that provide time-series information on industrial mines in countries in Africa. The SNL Metal and Mining data are provided annually, which facilitates the merging with multiple rounds of Afrobarometer surveys and the identification of residents who live within a certain distance to a mine, be it active or non-active at the time of the survey. We follow existing publications by considering a 50 km radius to a mine as the reference value.<sup>1</sup> We exploit the availability of a new dataset on decentralization – mainly the decision-making power of subnational governments over tax and revenue instruments, which, unlike existing works, covers a range of African countries as well as those that have been implementing decentralization since the early 2000s.

We employ a difference-in-differences strategy, similar to that used in recent studies (Knutsen et al. (2017); Goltz and Barnwal, 2019) to study the gap between the effects of living within a 50 km radius of an active mine versus a non-active mine, and the gap between each of these categories and living afar a mining zone.<sup>2</sup> Furthermore, the use of multiple rounds of the Afrobarometer surveys allows us to control for both time (shocks) and country-level heterogeneity. The results show a negative impact of living near active mining areas on the perceived performance of government authorities and the assessment of how _well_ or _badly_ they handle living standards of

> 1 See Knutsen et al. (2017).

> 2 The non-active mines also include the ones define as “yet to open”, “under mitigation”, “on care and maintenance”, “under rehabilitation”, and “on hold”.

3
**Table 4. Robustness check: First-stage fixed effects panel regression**

||I|II<br>|III<br>Aggregate EW|IV<br>AP<br>All EMDEs|V<br>Baseline with|VI<br>Women|VII<br>'s EWAP|
|---|---|---|---|---|---|---|---|
|Dependent variable: Change in<br>EWAP|Baseline|All countries,<br>1960-2019|All EMDEs|with trimmed<br>sample|10-year<br>averages|Baseline (all<br>women)|Women in non-<br>agriculture|
|Labor productivity growth|-0.1192***|-0.0527***|-0.1069***|-0.1671***|-0.0503***|-0.1043***|-0.0387***|
||[0.0218]|[0.0114]|[0.0211]|[0.0151]|[0.0191]|[0.0192]|[0.0113]|
|Working-age population<br>|-0.1947***|-0.2726***|-0.1882***|-0.2365***|-0.0896***|-0.1879***|-0.1038***|
|growth|[0.0368]|[0.0800]|[0.0353]|[0.0302]|[0.0331]|[0.0355]|[0.0342]|
|Lagged EWAP|-0.0622***|-0.0437***|-0.0696***|-0.0598***|-0.0783***|-0.0599***|-0.0481**|
||[0.0124]|[0.0086]|[0.0116]|[0.0108]|[0.0102]|[0.0101]|[0.0189]|
|Constant|4.4334***|3.5284***|4.8639***|4.4522***|5.2580***|3.4728***|1.6094***|
||[0.7700]|[0.6423]|[0.7126]|[0.6673]|[0.6820]|[0.5260]|[0.4880]|
|Observations|2058|6799|2447|2378|206|2058|2058|
|Number of countries|103|160|124|124|103|103|103|
|Adjusted R-squared|0.234|0.152|0.222|0.303|0.54|0.192|0.069|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Countryfixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Authors’ estimation.

Note: *** indicates significance at the 1 percent level, ** at the 5 percent level, and * at the 10 percent level. Robust standard errors reported in brackets. Small states are excluded from the sample. Country fixed effects and year dummies are included. Columns show the results from regressions of the change in employment ratios using different samples. Column I, for the baseline sample, includes all EMDEs that are not small states during 2000–19. Column II includes all countries including advanced economies and EMDEs during 1960–2019. Column III includes all EMDEs during 2000–19. Column IV includes all EMDE during 2000– 2019, excluding those whose real output growth or employment ratio growth is in the top or bottom 1 percent. Column V uses the baseline sample of Column I but using the 10-year average annual change in employment-to-working-age-population ratio as dependent variable.

26
and launching a business website on TFPR and factor demand. The data and methodology appear to be consistent with the existing literature that focuses only on learning by exporting effects. The empirical strategy builds on the Control Function approach and thus controls for the endogeneity of input choices. In addition, we assume an endogenous productivity process that is a function of firm digitization, learning-by-exporting, and managerial experience. At the time of writing, this paper is the only study that utilizes a large sample of enterprises from across the developing world and simultaneously studies the impact of more than one choice variable on both TFPR and factor demand.

The resulting evidence suggests that digital-technology adoption affects manufacturing firm performance in developing countries. However, the productivity-premium from email and website adoption varies across firms, as do the effects of exporting and managerial experience. Nonetheless, estimates of the median effect of digital technology adoption on TFPR indicate that the expected economic magnitudes (probability-adjusted) of these effects are potentially larger for digital-technology adoption than for exporting or enhancing managerial capabilities. Moreover, there is evidence of complementarities among these choice variables when it comes to their impact on TFPR. Finally, on average and contrary to most of the evidence found in the literature, we do not find a digitization-driven displacement effect on jobs or capital. By contrast, digital technology adoption seems to increase firms’ demand for labor and capital. Last but not least, the evidence from the rich set of interactions suggests that program targeting in developing economies can yield substantial aggregate TFPR gains relative to random treatment selection.

# **References**

Acemoglu, Daron, and David H Autor. 2011. “Skills, tasks and technologies: implications for employment and earnings.” _Handbook of labor economics_ 4:1043–1171.

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