rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7 Renegotiations (as fraction of investment) in Chile: Before and after the 2010 reform**

||**No. of**|**Rene**|**gotiations as fracti**|**on of Investment**<br>**Construction + First 6**|
|---|---|---|---|---|
||**Renegotiations**|**Construction**|**First 6years**|**years**|
|**Highways**|||||
|Before 2010 Reform|31|31.5%|16.5%|47.9%|
|After 2010 Reform|17|0.5%|1.2%|1.7%|
|**Transport**|||||
|Before 2010 Reform|50|28.9%|15.4%|44.2%|
|After 2010 Reform|28|0.4%|1.1%|1.5%|
|**All PPPs**|||||
|Before 2010 Reform|62|26.0%|14.0%|40.0%|
|After 2010 Reform|35|0.5%|0.9%|1.5%|

Note: Investment is defined as the estimated MOP budget (in UF) available in the bidding terms.

There are several institutional reasons for these comparatively good results. First, since the mid1980s and until recently, the finance minister (seconded by strict fiscal budget directors) has been extremely powerful, both by institutional design and by empowerment. For example, all renegotiations had to be approved by the finance minister. Second, projects –even in the case of self-financing PPPs-- had to be vetted by the social cost-benefit analysis unit, which ensured that approved projects had passed a hurdle rate of social profitability using a standard methodology. Third, the strategy of starting with a test run of only a few small projects and then improving the procedures and legislation was successful. Fourth, the notion was always that some projects could lose money, and that debts were not guaranteed, though they had liens on the flows of the project or on the results of a new auction of a failed PPP. More recently, the reform of the PPP Law improved the balance between the interests of the state and the private parties (Technical Panel, no supervening clauses) and the increased use of PVR contracts have reduced the extent of contract renegotiations.<sup>56</sup>

# **4. The ex post internal rate of return on PPP projects**

# <u>4.1 Overview</u>

In this section we estimate ex post internal rates of return (IRR) for Chile ́s PPPs.<sup>57</sup> The estimates we obtain can be interpreted as representing the cost to society of PPP provision of infrastructure. They also provide some indication on the profitability of individual projects and of the entire PPP program. We relate the estimated IRRs to key elements of Chilean PPPs discussed above.

> 56 Other important features that increased the confidence of international investors and thus decreased the costs and increased competition were agreements for investment protection, national treatment principles, free capital transfers and the possibility of appeals to international arbitration panels.

> 57 We also refer to these rates as “realized” or “actual” rates.

31
guidelines and the absence of restrictions on making loans abroad). There is a negative relationship between the diversification index and the likelihood of suffering a major crisis in small economies. Specifically, we include the diversification index and an interaction term. The interaction term equals the diversification index multiplied by the logarithm of real per capita GDP in 1995 (these are Purchasing Power Parity adjusted figures from the Penn World Tables.). As shown in Table 8, diversification is negatively associated with the likelihood of a crisis but diversification guidelines have less of a stabilizing effect in bigger countries. The cut-off is high; diversification guidelines have stabilizing effects in all but the nine largest countries.

One may, of course, argue that we do not have sufficiently detailed information on (a) regulatory and supervisory policies, (b) the actual implementation of those policies (except as noted the possibility that independence may proxy for the vigor with which policies are implemented), or (c) the transparency and accountability of the supervisory process to evaluate cross-country differences in regulatory and supervisory regimes. This argument, however, still leads to the conclusion that even very extensive checklists of regulatory and supervisory practices will be insufficient to boost bank performance and stability. Moreover, these arguments - in conjunction with this paper's results - strongly imply that the designers of regulatory/supervisory systems must pay close attention to how the individual components of the regulatory and supervisory regime influence both the incentives and the ability of the private sector to exert sound corporate control on banks.

# 6. Regulations on easing private-sector monitoring of banks

Private monitoring is strongly positively linked with bank development and negatively associated with net interest margins and the level of nonperforming loans (Table 7). The relationship is economically large. For instance, a one standard increase in the Private Monitoring Index in a country like Bangladesh with both weak private monitoring and low bank development (0.28), would increase bank development by about 32% (= (0.09* 1/0.28)* 100).

In terms of crises, there is not much of a link between private-sector monitoring and the likelihood of a banking crisis when controlling for other variables (Table 8). Since capital Country Risk Guide and is averaged over 1990-1999.

37
# **SI-4 Supplementary References**

1. Arrow, K. _et al._ Economic growth, carrying capacity, and the environment. _Ecological economics_ **15,** 91–95 (1995).

2. Fuentes, L., Mac-Clure, O., Moya, C. & Olivos, C. Santiago Chile: city of cities? Social inequalities in local labour market zones. _CEPAL review_ (2017).

3. Acemoglu, D., Johnson, S. & Robinson, J. A. The colonial origins of comparative development: An empirical investigation. _American economic review_ **91,** 1369–1401 (2001).

4. Dell, M. & Olken, B. A. The development effects of the extractive colonial economy: The dutch cultivation system in java. _The Review of Economic Studies_ **87,** 164–203 (2020).

5. Hill, F. & Gaddy, C. G. _The Siberian curse: How communist planners left Russia out in the cold_ (Brookings Institution Press, 2003).

6. Kumo, K. & Shadrina, E. On the Evolution of Hierarchical Urban Systems in Soviet Russia, 1897–1989. _Sustainability_ **13,** 11389 (2021).

7. Gentile, M. The rise and demise of the Soviet-made housing shortage in the baltic countries. _Housing Estates in the Baltic Countries,_ 51–70 (2019).

8. Kahn, M. E. Economic Geography and Pollution: A Comment on Joseph S. Shapiro’s “Pollution Trends and US Environmental Policy: Lessons from the Past Half Century”. _Review of Environmental Economics and Policy_ **16,** 357–360 (2022).

9. Jbaily, A. _et al._ Air pollution exposure disparities across US population and income groups. _Nature_ **601,** 228–233. issn: 1476-4687. `https://doi.org/10.1038/s41586-021-04190-y` (Jan. 2022).

10. Chi, G., Fang, H., Chatterjee, S. & Blumenstock, J. E. Microestimates of wealth for all low-and middle-income countries. _Proceedings of the National Academy of Sciences_ **119** (2022).

44
# **50 Years of Urbanization in Africa: Examining the Role of Climate Change**

- J. Vernon Henderson, London School of Economics Adam Storeygard, Tufts University Uwe Deichmann, World Bank

JEL Codes: O10, O55, R12 Key words: Africa, Urbanization, Climate Change Sector: Urban The authors thank Nat Tharnpanich, Marine Gassier, Kofi Acquah, Kevin Proulx, and Louise Bernard for research assistance, and Mark Roberts and seminar participants at Columbia, CUNY Institute for Demographic Research, GWU, LSE, LSU, Tufts, Williams, and the Population Association of America and Urban Economic Association meetings for helpful comments and suggestions. Financial support from the World Bank’s Knowledge for Change Program is gratefully acknowledged.
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based on patterns of economic activity. Therefore, for any area of analysis, the total sum of jobs adds up to 100% instead of representing the actual number of jobs. This has the advantage of being consistent between cities and being specifically geared to estimating informal as well as formal employment where highly localized data is not available or is inadequate.

# **Methodology**

For each residential location, we first compute the total number of locations that are reachable within 60 minutes using only public transportation or walking. For each of these reachable locations, we compute the share of total jobs that are in that location compared to the entire region. By combining the two previous steps, we are able to compute, for each residential location, the percentage of total jobs that are reachable under 60 minutes out of all jobs in the metropolitan area.

The accessibility analysis is not run at a single point in time. It is run multiple times over the course of a given window – in this case, from 8:00 AM to 9:00 AM on a Monday. The analysis tests the travel time 60 times over the window, with each minute used as a possible departure time. Destination locations will then have a different travel time at each minute-break. The proportion of times a given cell falls within the set travel time threshold (60 minutes) is calculated (in this case, using five Monte-Carlo draws per minute), and a frequency threshold is set to 50%.

This analysis requires to rely on a few assumptions that are consistent across cities. First, the walking speed is assumed to be 3.6 km/h. This speed is chosen as representing a moderate, comfortable walking pace in an urban setting. The speed of public transport does not rely on any assumptions as it is built in the transit network data that was collected.

Second, the threshold of 20 minutes is used as the maximum walking time _per leg_ of the trip. For example, an entire trip may include a 10-minute walk from home to a bus stop, and then a further 5-minute walk to connect between two buses, and another 15-minute walk to arrive at the final destination of the trip. But each of these walking times are limited to 20 minutes. This restriction is chosen to reflect particularly vulnerable and time constrained mobility. A trip could hypothetically involve walking any distance, and in networks with poor transit coverage, long walking distances may provide substantial added accessibility. However, for the purpose of this analysis focusing on women – whose movements are more time constrained, often physically constrained, and who are more vulnerable in waking long distances – a relatively 43
||**Pooled**|**ELMPS**<br>**2012 in est.**|**ELMPS**<br>**2018 in est.**|**Economic**<br>**Census**|**Establishm**<br>**ent Census**|
|---|---|---|---|---|---|
||(0.239)|(0.805)|(1.360)|(0.321)|(0.026)|
|7|1.907***|2.162|2.875|1.976***|1.725***|
||(0.162)|(0.870)|(1.590)|(0.341)|(0.020)|
|8-17|2.426***|2.401**|1.931|2.780***|2.449***|
||(0.196)|(0.734)|(0.712)|(0.314)|(0.028)|
|18-27|3.190***|4.597***|1.299|4.899***|3.223***|
||(0.326)|(1.601)|(0.514)|(0.490)|(0.044)|
|28+|4.302***|4.414***|2.869*|5.045***|4.795***|
||(0.495)|(1.677)|(1.222)|(0.542)|(0.074)|
|Don't know|3.313***|2.166|3.590**|1.859***||
||(0.842)|(1.021)|(1.685)|(0.287)||
|**Industry (wholesale/retail omit.)**||||||
|Manufacturing and related trades|0.661***|0.442***|0.972|0.757***|0.639***|
||(0.049)|(0.096)|(0.256)|(0.054)|(0.005)|
|Construction|0.362***|0.147***|0.491|7.319**|0.523***|
||(0.085)|(0.080)|(0.480)|(4.675)|(0.009)|
|Transportation and storage|0.526***|0.464|0.563|1.903|0.514***|
||(0.020)|(0.469)|(0.430)|(0.714)|(0.006)|
|Accommodation and food service|0.842|0.816|1.574|0.794**|0.759***|
||(0.075)|(0.249)|(0.532)|(0.068)|(0.009)|
|Various professional acts.|1.074|0.756|1.552|0.530***|1.562***|
||(0.075)|(0.183)|(0.402)|(0.041)|(0.013)|
|Other service|0.620***|0.488**|0.922|0.715***|0.537***|
||(0.046)|(0.130)|(0.251)|(0.051)|(0.005)|
|**Region (Greater Cairo omit.)**||||||
|Alexandria and Suez Canal|1.667***|1.227|1.086|2.139***|2.048***|
||(0.130)|(0.291)|(0.356)|(0.162)|(0.019)|

23
**Table 6. Income and the choice of migration destination -- using migrants from the NLSS 2002/3**

|**District difference in:**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|
|---|---|---|---|---|---|---|---|---|
|Average log income|-0.888**|-2.011|-0.888**|-2.140|||||
|Differential in log income due to education and high caste|||-0.002|-0.007|||||
|Relative log income controlling for education and ethnicity|||||0.860|0.283|||
|Average consumption adequacy index: total income|||||||-2.177***|-2.613|
|Differential due to education and caste: total income|||||||-0.384|-0.636|
|**Prices and amenities**|||||||||
|Log of rice price|2.160*|1.923|2.160*|1.907|1.873*|1.706|1.711|1.550|
|Housing price premium (log)|0.353***|3.056|0.353***|3.068|0.329***|3.048|0.383***|3.483|
|Time travel to nearest paved road|-1.658***|-3.327|-1.658***|-3.245|-1.386**|-2.541|-1.464***|-2.973|
|Time travel to nearest bank|0.939|1.356|0.940|1.284|0.979|1.385|0.744|1.123|
|Elevation in meters|0.105|0.273|0.105|0.278|-0.065|-0.156|0.231|0.603|
|**Population**|||||||||
|Population density|-0.384|-0.859|-0.384|-0.854|-0.531|-1.109|-0.571|-1.254|
|Log(population)|2.776***|4.738|2.776***|4.804|2.744***|4.325|2.898***|4.918|
|Ethnicity similarity index|0.915*|1.731|0.915*|1.698|0.925*|1.691|1.017*|1.794|
|Language similarity index|1.832***|2.812|1.833***|2.795|1.855***|2.711|1.380*|1.859|
|Religion similarity index|-0.743|-1.120|-0.743|-1.117|-1.066|-1.562|-0.392|-0.492|
|**Distance**|||||||||
|Distance above 100 Km|-11.355***|-6.698|-11.355***|-6.724 -|11.482***|-6.805|-11.697***|-6.963|
|Log-Likelihood|-620.|47|-620.|47|-623.|10|-617|.00|
|Number of observations|16,21|4|16,21|4|16,21|4|16,2|14|
|Pseudo R2|0.39|0|0.39|0|0.38|8|0.39|4|

The estimator is Fixed Effect Conditional Logit. Standard errors are corrected for clustering across district of origin. *** p<0.01, ** p<0.05, * p<0.1
# I. INTRODUCTION

Prior research on the effects of job-training among Peruvian males in the wage and nonwage (non-agricultural) sectors (Stelcner et al. 1988, Arriagada, 1989) showed no effect of training on the choice of sector of employment, but found a positive rela,.tionship between "occupational training" and earnings in the wage sector. Training was found to increase the earnings of male workers in the private sector by over 10 percent. This relationship remained positive and significant after selection into training programs was taken into account. In the nonwage sector, no relationship was found between occupational training and earnings.

Using data from the Peruvian Living Standards Survey (PLSS) carried out by The World Bank and tha Peruvian National Statistics Institute (INE) during 1985-86, this report analyses the effects of job-trai-iing for Peruvian women. In particular this paper examines the following issues: Which women receive job-training? What are the determinants of women's participation in training courses? Does training have an impact on women's labor force participation? What is the impact of training on the earnings of women? Given that job-training activities are very limited in rural areas, the analysis focuseT on women living in the urban areas of Peru.

The information gathered by the PLSS reveals that post-school training among women is as extensive as post-school training among men. About 2 percent of all women ages 15 to 65 reported participation in some occupational training course outside the formal education system. There are large variations in the percentage of women with training attendance by labor force participation status and sector of employment. About one half of the women in wage employment (public and private sectors) report tra_ning compared ! The autho'i wishes to acknowledge the comments of Rosemary Bellew, Avi Dor, Elizab-th King, Peter Moock and Philip Musgrove.
the IMF dummy is negative as expected, it is not significant in any of the D-GMM specifications except for Western European countries (reflecting the experiences of Greece, Iceland, and Portugal). Regarding the specification tests, the first-order serial correlation is not rejected as expected, while the second-order serial correlation is rejected. The Hansen J test does not reject over-identifying restrictions. We can thus conclude that D-GMM-2 is an internally consistent estimator. These results are consistent when the output gap is replaced with GDP growth to measure the business cycle (see table A3 in the annex).

**Table** **<u>3: Expenditure Growth and Cyclicality</u>**

|Dependent Variable:<br>|Expenditure<br>|_Growthit(Gr<br>|owth Rate of<br>|Real Expendit<br>|ure, %)<br>|
|---|---|---|---|---|---|
|Model|OLS<br>(1)|FE<br>(2)|D-GMM-1<br>(3)|D-GMM-2<br>(4)|D-GMM-3<br>(5)|
|Expenditure_Growthit-1|0.357***|0.122|0.230*|0.251**|-0.308**|
||(0.066)|(0.091)|(0.113)|(0.115)|(0.136)|
|IMF_Programit|-0.219|-1.726|-2.443|-2.452|-1.283|
||(1.132)|(1.600)|(1.624)|(1.927)|(12.091)|
|Revenue_GDPit|-0.046|0.226|0.727**|0.669**|0.509*|
||(0.112)|(0.283)|(0.263)|(0.270)|(0.266)|
|Output_Gapit|0.300|0.462|0.555|0.297|0.910*|
||(0.303)|(0.392)|(0.379)|(0.310)|(0.500)|
|Electionsit|1.210|1.643|1.073|0.749|0.671|
||(1.010)|(1.060)|(1.047)|(1.187)|(0.910)|
|Year Effects|Yes|Yes|Yes|Yes|Yes|
|Internal Instruments|No|No|Yes|Yes|Yes|
|# of Instruments|--|--|22|22|22|
|AR(1) Test p-val.|--|--|0.000|0.003|0.130|
|AR(2) Test p-val.|--|--|0.980|0.852|0.918|
|Hansen J Test p-val.|--|--|0.371|0.371|0.833|
|Sample Period|2000-2011|2000-2011|2000-2011|2000-2011|2000-2011|
|Countries|26|26|26|26|19|
|Observations|230|230|204|204|166|

Notes: The unit of observation is a country-year from the sample described in the Data Appendix. Columns (1) and (2) report standard errors clustered at the country level. Column (3) reports one-step difference GMM with clustered standard errors, while column (4) reports two-step GMM whose standard errors have had the Windmeijer correction. Column (5) reports only Western European countries. The internal instrument is the second lag of Wage_Bill_Growthit, and internal instruments are collapsed. ***, **, * indicate statistical significance at the 1%, 5%, 10% levels, respectively.

To assess whether wage bill spending behaves differently from other public expenditure, we repeat this estimation with real growth rate of total public spending. The results are reported in table 3 and are markedly different. Elections is not significant in any of the specifications, while OutputGap is only significant for Western European countries. The revenue to GDP ratio, however, is positive and significant across all D-GMM specifications, suggesting that expanding 10
World Bank (2021b). Stress Testing Social Protection. Washington, D.C.: The World Bank.

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World Bank (2022b). From the Last Mile to the Next Mile – 2022 Vietnam Poverty & Equity Assessment. Washington, D.C.: The World Bank.

World Bank. (2022c). Poverty and Shared Prosperity 2022: Correcting Course. Washington, D.C.: The World Bank.

World Bank (2022d). Progress, Setbacks, and Uncertainty: Effects of COVID-19 and Coup on Poverty in Myanmar. Myanmar Poverty Synthesis Note. Washington, D.C.: The World Bank.

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World Bank (2023b). Poverty and Inequality Platform Methodology Handbook. Edition 2023-09. Available at https://datanalytics.worldbank.org/PIP-Methodology/ Wouterse, F., & Taylor, J. E. (2008). Migration and Income Diversification: Evidence from Burkina Faso. _World Development_ , _36_ (4), 625-640. https://doi.org/10.1016/j.worlddev.2007.03.009

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# – APPENDIX A1 CHECKING THE ASSUMPTION OF JOINT DISTRIBUTION

As we mention above, the main data source for poverty and education comes from the same surveys in the GMD, and the social protection data comes from the ASPIRE, which is also based on household surveys collected in various years. For a small set of 42 countries, we can identify the same household surveys used for the ASPIRE indicator and for poverty and education indicators. For these 42 countries, we construct the joint distribution of poverty and social protection at the household level and aggregate this joint estimate to the subnational level. The subnational joint estimates are compared with the above estimates resulting from the random assignment at the household level due to lack of having indicators in the same survey data.

Figure A1.1 shows the scatter plot at the subnational level of no access to social protection from survey estimates and ones from simulation. On average, the graph shows the correlation of 80%. Simple average from both sources is similar for 557 subnational areas in 42 countries: with 60% from the simulation and 56.4% from the surveys.

Figure A1.2 shows the scatter plot of joint distribution of poverty and no access to social protection. On average, both survey and simulated estimates show similar numbers, with 25% from the simulation and 21.3% from the surveys.

40