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

Ahimbisibwe, P. (2019, May 21) No money for teachers’ salary raise – government _. The Daily Monitor._ Retrieved from <u>https://www.monitor.co.ug/News/National/No-money-teachers--salary-raisegovernment/688334-5124224-1iat17/</u>

- Araujo, M. C., Carneiro, P., Cruz-Aguayo, Y., & Schady, N. (2016). Teacher quality and learning outcomes in kindergarten. _The Quarterly Journal of Economics, 131_ (3), 1415-1453.

- <mark>Barrera-Osorio, F., & Raju, D. (2017). Teacher performance pay: Experimental evidence from Pakistan.</mark> _<mark>Journal of Public Economics</mark>_ <mark>,</mark> _<mark>148</mark>_ <mark>, 75-91.</mark>

- <mark>Bashir, S., Lockheed, M., Ninan, E., & Tan, J. P. (2018).</mark> _<mark>Facing forward: Schooling for learning in Africa</mark>_ <mark>. The World Bank.</mark>

- <mark>Bau, N., & Das, J. (2020). Teacher Value Added in a Low-Income Country.</mark> _<mark>American Economic Journal: Economic Policy, 12(1), 62-96.</mark>_

<mark>Blanc, E. (2019).</mark> _<mark>Red State Revolt: The Teachers' Strike Wave and Working-class Politics</mark>_ <mark>. Verso Books.</mark>

- <mark>Bobba, M., Ederer, T., Leon-Ciliotta, G., Neilson, C., & Nieddu, M. (2021). Teacher Compensation and Structural Inequality: Evidence from Centralized Teacher School Choice in Peru.</mark>

- <mark>Bold, T., Filmer, D., Martin, G., Molina, E., Stacy, B., Rockmore, C., Svensson, J., & Wane, W. (2017). Enrollment without learning: Teacher effort, knowledge, and skill in primary schools in Africa.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>31</mark>_ <mark>(4), 185-204.</mark>

- <mark>Bold, T., Kimenyi, M., Mwabu, G., & Sandefur, J. (2018). Experimental evidence on scaling up education reforms in Kenya.</mark> _<mark>Journal of Public Economics</mark>_ <mark>,</mark> _<mark>168</mark>_ <mark>, 1-20.</mark>

- Cabrera, J. M., & Webbink, D. (2020). Do higher salaries yield better teachers and better student outcomes? _Journal of Human Resources. 55(4), 1222-1257_

- Chelwa, G., Pellicer, M., & Maboshe, M. (2019). Teacher pay and educational outcomes: Evidence from the rural hardship allowance in Zambia. South African Journal of Economics, 87(3), 255-282.

- Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review, 104(9), 2633-2679.

- <mark>Cilliers, J., Kasirye, I., Leaver, C., Serneels, P., & Zeitlin, A. (2018). Pay for locally monitored performance? A welfare analysis for teacher attendance in Ugandan primary schools.</mark> _<mark>Journal of Public Economics</mark>_ <mark>,</mark> _<mark>167</mark>_ <mark>, 69-90.</mark>

- <mark>Chudgar, A. (2015). Association between contract teachers and student learning in five Francophone African countries.</mark> _<mark>Comparative Education Review</mark>_ <mark>,</mark> _<mark>59</mark>_ <mark>(2), 261-288.</mark>

24
controls, we also include a dummy for gender of the child in the regression specification.<sup>18</sup> This implies that any common factors (such as cultural norms) that might affect the average schooling attainment of girls irrespective of parental socioeconomic status are absorbed as a shift in the intercept.

The estimates in the top panel of Table 1 provide strong evidence that truncation bias in the IGRC estimates is substantial for all three definitions of parental education. Consistent with the expectation based on the graphs discussed above, the IGRC estimate in the coresident sample is significantly biased _downward_ . The null hypothesis that the estimate from the coresident sample is equal to the estimate from the full sample is rejected unambiguously with P-values equal to 0.00 in all of the different cases.<sup>19</sup> The pattern is remarkably consistent, and justifies the widespread opinion that there are good reasons to expect the IGRC estimates to be biased downward due to non-random sample selection bias because of the coresidency requirement used in the household surveys.

To get a better sense of the implied magnitudes, we report bias defined as follows (using IGRC as an example), where _IGRCCR_ denotes the estimate from a coresident sample, while _IGRCF_ is the estimate from the corresponding full sample including non-resident household members.

The first column in the bottom panel of Table 1 reports the bias in the IGRC estimates from the coresident sample. The evidence is clear: the estimate from coresident sample is biased downward, and the magnitude of bias is substantial across all three indicators of parental education. The bias is the highest when mother’s schooling is the indicator of

> 18The estimates and the conclusions do not depend on the inclusion of the gender dummy.

> 19We, however, note here that the formal test of equality of estimates may not be very useful in our context. Even with very small numerical difference between the estimates from the full and coresident samples, one can reject the null hypothesis of equality simply because the standard errors are extremely small (see, for example, the IGC estimates). So the focus should be on the magnitude of the bias not the statistical test of equality of estimates.

13
**Aggregation** In general, precision will be comparatively low at the level of geographical units, while predicting poverty over larger areas increases precision as idiosyncratic errors are averaged out. Aggregation over groups of units (‘areas’) can be represented by a linear mapping _B_ . The estimator for the area-level poverty rate is seen to equal _Bq_ ˇ _O_ , while its variance-covariance matrix solves:

The standard errors of the area-level poverty estimates reported in Table 5 (in the column headed ‘SEM’) are obtained by evaluating the square root of the diagonal elements of _BHB_<sup>_T_</sup> .

**Lemma 1** _Let q, u, W, X,_ Σ _, ε; ω_ = ( _ρ, θ, σ_<sup>2</sup> _, τ_ ) _and index sets S, O be as defined and discussed above; assume further that observation errors eS are independent of ε and normally distributed with zero mean and given variance matrix DS_ = _EeSe_<sup>_T_</sup> _S_<sup>_._</sup> _Let_ ˇ _ω_ = (ˇ _ρ,_ Σ<sup>ˇ2</sup> _, θ,_<sup>ˇ</sup> ˇ _τ_ ) _be maximum-likelihood estimators of the corresponding parameters with_ ˇ _variance matrix V_ (ˇ _ω_ ) _, and let qO_ = _XOθ_<sup>ˇ</sup> +Σ<sup>ˇ</sup> _OS_ [Σ<sup>ˇ</sup> _SS_ + _DS_ ]<sup>_−_1</sup> (ˆ _qS−XSθ_<sup>ˇ</sup> ) _denote predictions of qO. Denote by SOS the matrix:_

_Then: (i)_ _with:_ _And: (ii) The three terms on the right side are asymptotically independent and their covariance matrices can be consistently estimated by:_

**Proof** Here we provide an outline of the proof. The detailed proof is given in Ap-

11
10 on the two sources might correspond to quite different levels of governance quality. As discussed in detail in Kaufmann, Kraay, and Mastruzzi (2004) and also in Appendix D, our aggregation procedure provides a way of placing such different sources in common units that allows for meaningful aggregation across sources.

The only data sources we have not been able to obtain permission to publicize fully are the World Bank's Country Policy and Institutional Assessment, and the corresponding internal assessments produced by the African Development Bank and the Asian Development Bank. We do note however that starting in 2002 the World Bank began publishing limited information on its CPIA assessments on its external website. For the years 2002-2004 the overall CPIA ratings are reported by quintile for countries eligible to borrow from the International Development Association (IDA), the concessional lending window of the World Bank. Starting in 2005, the individual country scores for the IDA allocation factor, a rating that reflects the CPIA as well as other considerations, is now publicly available. The African Development Bank's CPIA ratings are also publicly available by quintile only since 2004, and are fully public since 2005, and the Asian Development Bank's scores have been fully public for its concessional borrowers since 2005.

Finally, as a new feature this year, we are also providing downloadable spreadsheets reporting further details of the available underlying source data for each indicator. These can be found on the Resources page of www.govindicators.org.

# **2.2  Revisions to Underlying Data Sources**

In this round of the governance indicators we have added two new data sources. The Institutional Profiles database is a relatively recent effort by the Treasury and Economic Policy Directorate General of the French Ministry of the Economy, Industry, and Employment and the French bilateral aid agency Agence Française de Développement. This data source covers 85 countries in 2006, with an earlier round covering 51 countries in 2001, and the latest version is documented in Meisel and OuldAoudia (2007). We have not used this source previously because of the long lag between the first and second rounds -- in the WGI we use only data sources that are updated every three years or more frequently. However, the Institutional Profiles 11
Switzerland and how immigration flows have been influenced by both the new provisions in the Swiss immigration policy and political turmoil in the Balkans.

# **3. Immigration from Yugoslavia to Switzerland**

Immigration from the former Republic of Yugoslavia to Switzerland is not a recent phenomenon; however, the evolution of the resident Yugoslav population since the 1980s is quite unique compared to that of immigrants from other European countries. From 1960 to 2003, the share of Yugoslav citizens in the foreign population rose hundred fold, from 0.2% to 23.8% and in 2003, for the first time, it overtook Italy as the largest foreign community in Switzerland. However, the community is heterogeneous and at the beginning of the 21<sup>st</sup> century, it is issued from five distinct countries (Bosnia-Herzegovina, Croatia, FYR of Macedonia, Serbia-Montenegro, and Slovenia).

In the 1950s and 1960s, citizens from France, Germany, Italy, and Spain made up more than 3⁄4 of the foreign population<sup>4</sup> (see Table 2). In 1960, the Yugoslav community was extremely small and similar in size to that of Turkey or Portugal. They each represented less than 0.2% of the foreign population. During the following 20 years, Yugoslavia’s and Turkey’s shares increased quite rapidly and at almost the same rate to reach, respectively, 4.9% and 4.3% of the foreign population in 1980. Portugal’s, however, lagged behind and reached only 2% (columns 3 and 4). Part of the explanation lies in the fact that the governments of Yugoslavia and Portugal, though both dictatorial at the time, had very different positions toward emigration of their workers. Until the democratic Carnation Revolution in April 1974, emigration from Portugal was very strictly controlled. Also, because of its comparative weak

> 4  In 1968, the immigration of permanent residents from Germany, France, Italy and Spain was 15,588, 8,702, 57,489 and 18,035 respectively for a total inflow of 137,901 of which 75% were workers (OFE, 2001a, Tables 11.2R and 11.21R).

9
illustrates the contribution that bilateral data can make to the analysis of international financial integration.

The South has become more integrated into global finance across all investment types, resulting in significant changes in the composition of international investment. In 2001, the South’s participation in international markets occurred mainly through FDI and loans and deposits, in roughly equal parts, and to a lesser extent through international reserve holdings in the North (table 1). By 2018, the composition of South investment had changed along several dimensions. First, FDI remained the largest investment type for the South, more than doubling in value as a share of world GDP (from 9 to 20 percent). Whereas the bulk of FDI remained North-to-North, growth was strongest for the South as a source of FDI. South-to-North FDI came to rival North-to-South FDI.

Second, portfolio investment expanded rapidly over the sample period. By 2018, portfolio investment was larger than loans and deposits and international reserves. The South’s loans and deposits were only slightly higher (as a share of world GDP) in 2018 than in 2001, whereas portfolio investment grew rapidly, from a much lower level in 2001. In particular, holdings of debt and equity more than tripled for North-to-South, South-to-North, and South-to-South investments as ratios to world GDP (table 1).

Third, international reserves of South countries also gained relative importance during the sample period. Reserve holdings in the North became the largest of all investment types involving the South, amounting to 10 percent of world GDP in 2018. This fact is not surprising as many South countries have run large current account surpluses and accumulated reserve assets issued by the North since the early 2000s. But the South has also become a destination for reserve holdings, in particular, with increases in reserves denominated in Australian dollars and Chinese renminbi, among others (appendix, section A.4). Between 2001 and 2018, the combined value of South-to-South and Northto-South international reserves, as a share of world GDP, expanded at an even faster pace than South13
# _Pollution and City Competitiveness: a Descriptive Analysis_<sup>_1_</sup>

## **Nancy Lozano‐Gracia and Maria E. Soppelsa**

**JEL** : Q53, R11, O44

> 1 This work is part of the Pollution Management and Environmental Health program (PMEH) supported under TF0A3415. The PMEH program focuses on providing technical assistance to these countries, facilitating pollution management, promoting public awareness, and helping them reduce the negative effects of pollution on health. The authors are grateful to Somik V. Lall and Richard Damania for their comments. 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.
# 3.3. Sample Balancing

In this section, we report the mean values for the variables used in the propensity-score matching and the balancing of these features. Table 7 reports the differences in treatment and control groups, for several observable characteristics before the matching. Table 8 reports the same results after the matching. T tests were performed to detect the mean differences of the variables between the two groups (control and treatment) before and after matching. If the quality of the matching is good, we expect a lack of statistically significant differences after matching. This is reported in table 8 suggesting that the matching method presents robust results. At the end of Tables 7 and 8, we include the number of observations that were effectively used in the matching.

Table 7 – Balancing results of the sample _before_ matching - mean values

|||TEC-gera|l|TEC|- after ge<br>educatio|neral<br>n|TEC - c<br>gen|oncurren<br>eral educa|tly with<br>tion|Techn<br>the|ical educ<br>tertiary|ation at<br>level||FIC||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||(1)|||(2)|||(3)|||(4)|||(5)||
||treat.|contr.|Mean<br>dif.|treat.|contr.|Mean<br>dif.|treat.|contr.|Mean<br>dif.|treat.|contr.|Mean<br>dif.|treat.|contr.|Mean<br>dif.|
|Experience|19.9|18.5|***|19.0|18.5|**|20.7|18.5|***|21.6|22.0||20.0|23.1|***|
|Potential experience|16.9|15.6|***|16.5|15.6|***|17.3|15.6|***|16.1|17.5||18.3|23.3|***|
|Self-employed / employers|0.20|0.21|***|0.17|0.21|***|0.22|0.21||0.22|0.22||0.26|0.30|***|
|Agriculture job|0.02|0.02|**|0.01|0.02|***|0.02|0.02||0.02|0.01||0.03|0.14|***|
|Activity - extractive industries|0.01|0.00|***|0.01|0.00|***|0.01|0.00|***|0.01|0.00||0.00|0.00||
|Activity - manufacturing|0.15|0.12|***|0.16|0.12|***|0.15|0.12|***|0.17|0.07|***|0.17|0.14|***|
|Activity - construction|0.03|0.03||0.02|0.03||0.03|0.03||0.04|0.02|**|0.05|0.09|***|
|Activity - commerce and repair|0.16|0.22|***|0.16|0.22|***|0.17|0.22|***|0.13|0.10|*|0.21|0.19|***|
|Activity - lodging and food|0.02|0.03|***|0.02|0.03|**|0.02|0.03|***|0.02|0.01||0.04|0.04||
|Activity - transportation, warehousing<br>and communications|0.04|0.05|***|0.05|0.05||0.04|0.05|***|0.03|0.03||0.06|0.05|**|
|Activity - financial services|0.03|0.03||0.02|0.03|***|0.03|0.03||0.05|0.05||0.02|0.01|***|
|Activity - real estate activities and<br>service activities|0.12|0.10|***|0.12|0.10|***|0.11|0.10|**|0.15|0.13||0.10|0.06|***|
|Activity - public administration|0.10|0.11|***|0.09|0.11|***|0.12|0.11||0.09|0.15|**|0.07|0.05|***|
|Activity - education|0.13|0.14|**|0.07|0.14|***|0.17|0.14|***|0.17|0.27|***|0.07|0.05|***|
# **Table 2. Change in censored headcount ratio models results with restricted and unbalanced panel data.**

||Change<br>|_Depe_<br>in censor<br>|_ndent vari_<br>ed headco<br>~~(~~<br>ΔHci(t)<br>Δt<br>~~)~~<br>|_able:_<br>unt ratio p<br>|er year<br>|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Initial poverty centrality (Sc(t))<br>||-0.010<sup>***</sup>|-0.009<sup>***</sup>|-0.009<sup>***</sup>|-0.009<sup>***</sup>|
|||(0.002)|(0.003)|(0.003)|(0.003)|
|Initial censored headcount ratio (Hc(t))|||-0.003||-0.001|
||||(0.006)||(0.016)|
|Initial headcount ratios of all other indicators<br>(∑<br>H<br>i≠c<br>ci<sup>(t)) </sup>||||0.003|0.002|
|||||(0.005)|(0.014)|
|Constant|-0.002<sup>***</sup>|-0.001<sup>*</sup>|0.001<sup>*</sup>|-0.002|0.002|
||(0.000)|(0.001)|(0.000)|(0.003)|(0.008)|
|Observations|296|296|296|296|296|
|R<sup>2</sup><br>|0.198|0.318|0.318|0.318|0.318|
|Adjusted R<sup>2</sup><br><br>|0.097|0.229|0.226|0.227|0.224|
|Country dummy (αc)<br>|Yes|Yes|Yes|Yes|Yes|
|Indicator dummy (βc)|Yes|Yes|Yes|Yes|Yes|
|Year dummy<br> <sup>*</sup>|Yes<br> <sup>**</sup>|Yes<br> <sup>***</sup>|Yes<br>|Yes|Yes|

_Note: Robust_ Standard errors in parentheses.<sup>*</sup> p<0.1,<sup>**</sup> p<0.05,<sup>***</sup> p<0.01.

Second, to address potential endogeneity concerns, we implement an instrumental variable (IV) approach. Endogeneity might arise because of unobserved factors influencing both Poverty Centrality and the changes in the censored headcount ratio, leading to biased coefficient estimates.

The IV approach helps us tackle this issue by using an additional variable as an instrument. This variable should be related with Poverty Centrality, but uncorrelated with the error term of our model. Here, for each indicator in a country we define the instrumental variable as the respective Poverty Centrality measure of the same indicator in the country with the most similar Poverty Space in the initial time period (estimated through the coefficient of correlation between the edges of the Poverty Space). The logic behind this instrument is that countries with similar Poverty Spaces should also exhibit similar Poverty Centrality, but the exact circumstances (such as policy environment, 23
relationship intensifies with distance, and the farthest facilities are estimated to have 4.9 and 4 percentage points lower diagnostic accuracy than those closest to fair or good quality roads.

Our results may indicate that, despite the country's efforts to make hard-to-reach areas more attractive to health workers (Ministry of Health, 2009; Ministry of Health, 2015), health centers in remote areas still face transportation barriers for the recruitment and retention of top-performance medical providers, as documented in Ojakaa, Olango, & Jarvis, 2014 for Kenya. The findings on the relationship between providers’ knowledge and the access to electricity and distance to the nearest fair or good quality road highlight the importance of facilities' infrastructure quality for health provider retention (Ojakaa, Olango, & Jarvis, 2014; Rockers, et al., 2012; Rowe, de Savigny, FLanata, & Victora, 2005; WHO, 2010; Lenz, Munyhirwe, Peters, & Sievert, 2016).

_Table 2. Linear regression for diagnostic accuracy_

||(1)<br>|(2)<br>|
|---|---|---|
||Diagnostic|Children and mother illness|
||Accuracy|Diag. Accuracy|
|Stable Electricity vs No Access|4.20***|5.03***|
||(1.44)|(1.50)|
|Unstable Electricity vs No Access|2.04<br>|1.88<br>|
||(1.43)|(1.47)|
|2G vs No Access|2.66|-2.08|
||(1.83)|(1.86)|
|3G vs No Access|2.19|-2.22|
||(2.42)|(2.45)|
|Second quantile road distance|-1.76|-0.89|
||(1.13)|(1.15)|
|Third quantile road distance|-3.71***|-2.51**|
||(1.16)|(1.21)|
|Fourth quantile road distance|-4.93***|-4.05***|
||(1.20)|(1.24)|
|Public|3.70***|5.46***|
||(1.01)|(1.00)|
|Rural|-2.14*|-1.69|
||(1.28)|(1.28)|
|Deprivation Score|-0.09*|-0.06|
||(0.05)|(0.05)|
|Catchment pop.|-0.03|0.01|
||(0.08)|(0.07)|
|Hospital vs Dispensary|2.94**|0.62|
||(1.28)|(1.45)|
|Health Center vs Dispensary|2.49**|-0.97|
||(0.97)|(1.07)|
|% of Male-Facility|2.33*|1.41|
||(1.32)|(1.34)|
|Average providers age -Facility|-0.14***|-0.23***|
||(0.05)|(0.05)|
|% Post-secondary education-Facility|10.63***|7.37***|
||(1.62)|(1.47)|
|Constant|58.00***|66.09***|
||(4.42)|(4.36)|
|Observations|2,548|2,545|
|R-squared|0.17|0.15|
|Controls|Yes|Yes|
|Fixed Effects|County|County|
|Mean|65.74|67.47|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 13
Table 2. Determinants of Economic Growth, 1992-2004

||OLS<br>(1)|Fixed Effects<br>(2)|Random Effects<br>(3)|OLS<br>(4)|Fixed Effects<br>(5)|Random Effects<br>(6)|
|---|---|---|---|---|---|---|
|War dummy|-4.11|-5.34***|-4.51**|-4.51|-5.17***|-4.73***|
||(1.52)|(2.83)|(2.50)|(1.60)|(2.64)|(2.58)|
|Ln(inflation) (IMF)|-1.95***|-1.12***|-1.72***|-2.33***|-1.60***|-2.20***|
||(5.56)|(3.35)|(5.81)|(6.97)|(4.14)|(7.24)|
|Fiscal balance (%GDP) (IMF)|0.37**|0.39***|0.40***|0.38**|0.46***|0.43***|
||(2.54)|(5.03)|(5.57)|(2.55)|(5.89)|(5.88)|
|EDRD Index of small-scale privatization|1.38*|4.83***|2.42***||||
||(1.82)|(5.11)|(3.22)||||
|EDRD Index of price liberalization|-0.58|-0.34|-0.62||||
||(0.41)|(0.34)|(0.65)||||
|Index of Reform Progress||||-0.04|0.39**|0.04|
|||||(0.34)|(2.19)|(0.34)|
|Constant|6.85|-8.42|2.95|11.60***|-0.47|9.57***|
||(1.11)|(1.57)|(0.67)|(3.00)|(0.09)|(2.76)|
|Observations|305|305|305|305|305|305|
|R-squared|0.51|0.59||0.5|0.56||
|Number of countries||25|25||25|25|

Robust t statistics in parentheses. Dependent variable is Economic Growth (WB) * significant at 10%; ** significant at 5%; *** significant at 1% The baseline results appear to be robust to various specifications. For example, the IRP reform measure can be used in place of the EBRD reform indexes. The results, based on equation (2) and presented in columns 4 to 6 of Table 2 are similar to those in columns 1 to 3, although IRP itself does not appear to be consistently significant. IRP has a significant effect using a fixed effects estimator, but not for OLS nor for random effects. The impact of fiscal balance, however, is consistent and robust, and so are the war dummy and inflation.

We also test the results for the inclusion of an alternative measure of initial conditions—initial income per capita in 1992, a measure of initial conditions typically used in the broader literature on growth but not in literature on transition economies, possibly because data for early transition period are more likely to be measured with error. The results (not shown) using OLS and Random Effects regression method remain consistent with the baseline results. With respect to initial income, the results suggest some convergence over time: economies with higher per capita income in 1992 grew more slowly thereafter.

# B. Time-Varying Effects

We begin this section with two observations from the previous section. First, while the impact of fiscal balance has been mixed in the previous literature, we are able to provide relatively more robust evidence that fiscal balance matters for growth. Second, Sachs (1996) provides evidence that IRP is a strongly significant determinant of economic growth although we are unable to provide robust confirmation. Consistent with the discussion in Section 2, these two observations may be explained in part by the continually evolving relative importance of the main determinants of growth in transition economies. That is, overall reform progress may have been important during the first 15