rafmacalaba/datause-encoder-data huggingface dataset:
- McKenzie, D., J. Gibson and S. Stillman. 2010. How Important is Selection? Experimental vs. Non-experimental Measures of the Income Gains from Migration. _Journal of the European Economic Association_ , 8(4): 913–45.

- McKenzie, D. and M. Sasin. 2007. Migration, remittances, poverty, and human capital: Conceptual and empirical challenges. World Bank Policy Research Working Paper 4272. The World Bank: Washington DC.

- McKenzie, D., C. Theoharides and D. Yang. 2014. Distortions in the international migrant labor market: Evidence from Filipino migration and wage responses to destination country economic shocks. _American Economic Journal: Applied Economics_ , 6(2): 49–75.

- Ministerio de Educación and SESAN, 2008. Tercer Censo Nacional de Talla en Escolares del Primer Grado de Educación Primaria del Sector Oficial de la República de Guatemala, 4 al 8 de agosto de 2008, Executive Summary, Final Report.

- Mohapatra, S., D. Ratha and A. Silwal. 2010. Migration and development brief no. 13. The World Bank: Washington DC.

- Nobles, J. 2007. Parental migration and child health in Mexico. Conference paper presented at the Population Association of America Meetings.

- Panjwani A. and R. Heidkamp. 2017. Complementary feeding interventions have a small but significant impact on linear and ponderal growth of children in low-and middle-income countries: a systematic review and meta-analysis. _Journal of Nutrition_ , 147: 2169S–78S.

- Papademetriou, G. and A. Terrazas. 2009. Immigrants and the current economic crisis: Research evidence, policy challenges, and implications. Migration Policy Institute: Washington DC.

- Prentice, A.M., K.A. Ward, G.R. Goldberg, L.M. Jarjou, S.E. Moore, A.J. Fulford and A. Prentice. 2013. Critical windows for nutritional interventions against stunting. _American Journal of Clinical Nutrition_ , 97(5): 911–18.

- Saenz de Tejada, S. 2009. Assessing the impact of the financial crisis in high-migration communities in the western highlands of Guatemala: A qualitative study. Report submitted to the World Bank: Guatemala City, Guatemala.

- Smith, J.P. and D. Thomas. 2003. Remembrances of things past: test-retest reliability of retrospective migration histories. _Journal of Royal Statistical Society Series A_ , 166(1): 23– 49.

- Stark, O. and D.E. Bloom. 1985. The new economics of labor migration. _American Economic Review_ , 75(2): 173–8.

- Stillman, S. and D. Thomas. 2008. Nutritional status during an economic crisis: Evidence from Russia. _Economic Journal_ , 118(August): 1385–1417.

- Taylor, J. E. and J. Mora. 2006. Does migration reshape expenditures in rural households? Evdence from Mexico. World Bank Policy Research Working paper 3842. The World Bank: Washington DC.

- Thomas, D., J. Strauss and M-H. Henriques. 1991. How does mother’s education affect child height. _Journal of Human Resources_ , 26(2): 183–211.
13 In the regressions we always include year dummies (<sup>δ</sup> _t_<sup>) and country fixed effects (μ</sup> _i_<sup>), the</sup> latter ones controlling for all unobserved and omitted country-specific features that might influence the probability of a current account reversal, the former ones capturing common (across countries) trends in current account adjustments and explanatory variables. Due to the presence of country fixed effects, we can only identify time-varying determinants of reversals, but also exclude spurious relationships due to correlation between observables and unobserved time-invariant country-specific factors.

Moreover, we estimate both a probit and a linear probability model so as to prove the robustness of our results to different distributional assumptions on the errors. The linear model turns out to be particularly useful when we add the migrants’ remittances to our base specification. Before getting to this, we need a brief logical detour.

In the absence of a theoretical model that links workers’ remittances to current account reversals, we rely upon a series of heuristic considerations, which takes us to expect a negative relationship between workers’ remittances and the occurrence of reversals.

A current account reversal in a given country is often due to sudden reversals of private capital inflows triggered by foreign investors’ confidence loss on the ability of that country to repay its liabilities; this loss of confidence is typically, though not always, induced by a worsening of the fundamentals. A reduction in the level of international reserves below a certain threshold and/or an increase in the external debt above a certain threshold can be seen as events triggering such a loss of confidence and hence inducing the current account reversal. In this context one might expect that a high level of workers’ remittances, whose mostly altruistic determinants are reflected in the “good” statistical properties outlined in section 3, makes given fundamentals appear to be better so reducing the probability of financial crisis<sup>18</sup> .

The properties of stability and a-cyclicality of workers’ remittances have also an important empirical implication: any effect of workers’ remittances can only be identified through the cross-country variance rather than the within-country across-time variance. This is to say that we

> 18 Incidentally, the IMF in its April 2005 World Economic Outlook (Chapter II) writes: _“[...] remittances display a significant, positive association with credit ratings for sovereign debt”_ .
<!-- Start of picture text --> o<br>rng5<br>a<br>©<br>Lt2<br>><br>Sa<br>on<br>-2 t1 t t+1 t+2 t+3<br>Year relative to privatization<br><!-- End of picture text --> <!-- Start of picture text --> a=2<br>E<br>3<br>&<br>aD<br>Ss<br>°<br>2<br>-2 t1 t t+1 t+2 t+3<br>Year relative to privatization<br><!-- End of picture text -->
Fisker, P. 2014. “Green Lights: quantifying the economic impact of drought”. IFRO working paper series, University of Copenhagen.

Hill, R.V. 2009. “Using stated preferences and beliefs to identify the impact of risk on poor households." The Journal of Development Studies, 45(2): 151-171.

Jacoby, Hanan G. and Emmanuel Skoufias. 1997. "Risk, Financial Markets, and Human Capital in a Developing Country," Review of Economic Studies, Wiley Blackwell, vol. 64(3), pages 311-35, July.

Ligon, Ethan and Laura Schechter, 2003. "Measuring Vulnerability," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 113(486), pages C95-C102, March.

Mu R, Zhang X. Why does the Great Chinese Famine affect the male and female survivors differently? Mortality selection versus son preference. Economics and Human Biology 2011; 9: 92-105. doi: 10.1016/j.ehb.2010.07.003 Neugebauer R, Hoek HW, Susser E. Prenatal exposure to wartime famine and development of antisocial personality disorder in early adulthood. JAMA 1999; 281: 455-62. doi: 10.1097/00006254-200001000-00005 Pritchett, L., Asep Suryahadi, Sudarno Sumarto. 2000. Quantifying Vulnerability to Poverty: A Proposed Measure, Applied to Indonesia. Policy Research Working Paper, World Bank. http://dx.doi.org/10.1596/1813-9450-2437 Ravelli AC, van der Meulen JH, Michels RP, Osmond C, Barker DJ, Hales CN, et al. Glucose tolerance in adults after prenatal exposure to famine. Lancet 1998; 351:173-7. doi: 10.1016/s0140-6736(97)07244-9 The Sahel precipitation index: doi:10.6069/H5MW2F2Q Stanner SA, Yudkin JS. Fetal programming and the Leningrad Siege study. Twin Research 2001; 4: 287-92. doi:10.1375/1369052012498 Tiwari, Sailesh, Hanan G. Jacoby and Emmanuel Skoufias, 2013. "Monsoon babies : rainfall shocks and child nutrition in Nepal," Policy Research Working Paper Series 6395, The World Bank.

Townsend, R.M. 1994. Risk and Insurance in Village India." Econometrica: Journal of the Econometric Society, 62(3): 539{591.Karlan et al. 2013; Udry, C. 1994. “Risk and insurance in a rural credit market: An empirical investigation in northern Nigeria." The Review of Economic Studies, 61(3): 495-526.

25
reshore activities.

By focusing on Mexico, this article also contributes to three strands of literature that have been largely concentrated on high-income countries. First, it relates to the emerging literature of the economic impacts of automation. Notable articles in this area include Acemoglu & Restrepo (2017), who find negative impacts of automation on LLM outcomes in the US, and Graetz & Michaels (2017) who find positive impacts of automation on labor productivity and TFP, and no effects on total employment using country-sector level data for a sample of 17 high-income countries. Chiacchio et al. (2018) apply Acemoglu and Restrepo’s (2017) methodology to six European countries and find strong negative impacts of automation on employment. Using a similar approach, Dauth et al. (2017) find that while automation in Germany had a negative impact on manufacturing jobs, the total effect was zero as jobs were created in the services sector. They also find negative impacts of automation on wages of middle-skill workers. There are two recent studies that examine the impacts of automation in Mexico. Faber (2018) and Pedemonte et al. (2018) examine the labor market impacts of exposure to US and domestic automation across Mexican municipalities. However, unlike this article, they do not use trade data at the local level and instead rely on a Bartik-style variable to allocate national-level exports to each locality. At the same time, municipal-level data do not necessarily provide an appropriate approximation of local labor markets. Thereby, the existence of labor migration across municipalities may introduce a bias to the estimated labor market impacts. Finally, they use data from Census samples instead of the full counts, which may lead to noisy labor market indicators for small municipalities.

Second, this article contributes to the literature on the technological drivers of offshoring (see, for example, Abramovsky & Griffith (2006); Bartel et al. (2014); Fort (2016)). Finally, this article is linked to a large body of literature on the impacts of offshoring (see, for example, Baumgarten et al. (2013); Ebenstein et al. (2014); Hummels et al. (2014)) and trade (see, for example, Autor et al. (2013); Hakobyan & McLaren (2016)) on the labor market.

7
# Annex 1. Data, Sources and Units

The data used in the regression exercises conducted in this paper comes from three proprietary sources; the ILS Data Base of Lane Financial LLC, Price Sheets of Access Re and the remodeled scenario set of AIR Worldwide. The secondary market ILS data is a compilation of Price Indication Sheets produced by various brokers at regular intervals. The indications are averaged at mid-market quotes. Similarly, ILW prices from Access Re are “indication only” prices. They represent neither actionable nor transacted prices. As such, statistical exercise results should be viewed with appropriate caution. Further, while every attempt has been made to be accurate, any errors that may have occurred are the responsibility of the author, not the source.

Where possible in the following, data on spreads and expected losses, etc., are quoted in decimal form. Thus a spread of 0.095 is the equivalent of 9.5% or 950 basis points. Unfortunately, not all sources use the same convention so attention should be paid to context. Also, while data is in that form, text may use the conventions interchangeably.

21
# UNITED REPUBLIC OF TANZANIA

# URBAN SECTOR REHABILITATION PROJECT

## **TABLE OF CONTENTS**

|**CRE**|**DIT A**|**ND PROJECT SUMMARY ...........................................**|PageNo.<br>**ill**|
|---|---|---|---|
|**1.**|**UR**|**BAN SECTOR**||
||A.|Country Background .......................................|1|
||B.|Urban Sector Overview ...................................... lI||
||C.|Sector Policy Framework ......................................|8|
||D.|Previous IDA Involvement and Lessons Learned ......................|................<br>9|
|2.|**TH**|**E PROJECT**||
||A.|Project Origin .10||
||B.|Rationale for IDA Involvement .1||
||C.|Project Objectives .12||
||D.|Project Description .12||
||E.|Project Costs and Financing .16||
|**3.**|**PR**|**OJECT IMPLEMENTATION**||
||A.|Institutional Arrangements .18||
||B.|Procurement .21||
||C.|Disbursement .24||
||D.|Accounting, Auditing, and Reporting .24||
||E.|Monitoring and Supervision .26||
|**4.**|**PR**|**OJECT**BENEFITS AND RISKS||
||A.|Financial Analysis and Management Improvement Plans .........|................... 28|
||B.|Economic Analysis ...........................................|37|
||C.|Risk and Sensitivity Analysis ...........................................|41|
||D.|Environmental Impact ...........................................|44|
|**5.**|**AG**|**REEMENTS REACHED AND RECOMMENDATION**||
||A.|Agreements Reached ...........................|46|
||B.|Conditions of Credit Effectiveness ...........................|47|
||C.|Conditions of Disbursement ...........................|48|
||D.|Recommendation .48||
**Figure 15:  Evolution of multi-polarity, alternative indices, 1968-2008**

<!-- Start of picture text --> Multipolarity<br>index<br>0.25<br>0.212<br>0.20<br>Herfindahl (real<br>0.15 index)<br>0.108<br>0.10<br>Herfindahl (HBS<br>0.05 index) Herfindahl (PPP 0.069<br>index)<br>0.00<br>1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br><!-- End of picture text --> Source: reproduced from World Bank, _Global Development Horizons_ , 2011, Figure 1.4. Note: These are Herfindahl concentration indexes of multi-polar growth measures that incorporate both GDP growth and connectedness via trade, financial flows and technology. The real index uses data based in real 2000 US dollars, the HBS adjusts exchange rates for Harrod-BalassaSamuelson effects, and the PPP index adjusts exchange rates based on purchasing power partiy. The Concentration is based on the share of the top 15 economies in the world economy, computed over five year moving averages.

Remarkably, even in the area of development assistance, more advanced developing countries are becoming somewhat important donors. Some upper middle income countries have joined the OECD and now report their overseas development assistance (ODA) to the Development Assistance Committee (DAC) of the OECD. There are also some non-OECD members that voluntarily report their data to the DAC. Finally, the OECD has conducted a study to estimate the ODA of the BRICS (including South Africa as the capital ―S‖).<sup>19</sup> The table below uses this data and includes only those ―emerging donors‖ that are classified as middle income countries under the World Bank country classification.<sup>20</sup> The total of an estimated $4.3 to $5.5 billion per year is a substantial amount – even if only about 3.5 to 4.5 percent of total net ODA from DAC member countries. One might speculate that the motivation for some of these aid flows is the recognition of the importance of multi-polarity: there are increasing economic links between middle income countries and low income countries. As a result, it may be in the

> 19 See OECD, 2010, for the data.

> 20 For example, Poland is now considered a high income country by the World Bank classification.  Its net ODA in 2008 was $372.4 million.  Similarly, the Czech Republic is now considered a high income country, and its net ODA was $249.2 million in 2008.  So, these two recently ―graduated‖ former MICs contributed nearly a half billion dollars in net ODA in 2008.

24
We set out, in general terms, how the impact of school management can be decomposed into learning gains that arise because given actors (teachers, students and parents) become more productive, and learning gains that arise because different actors join the school. To explore why these _incentive_ and _selection_ effects might arise, we turn to a specific model that captures key features of education systems in Latin America.

This model has two main building blocks. The first is the education production function: we assume that student learning depends on teacher ability, teacher effort, and household effort. The second is the impact of management practices where, considering the personnel policy restrictions the public sector faces, we distinguish between operations and people management. Good people management practices enable managers to observe and contract on the performance of their employees, as well as to cultivate the intrinsic motivation of their staff. Good operations management practices enable managers to use resources efficiently and hence offer a higher level of teacher compensation and a more stimulating environment for students.<sup>2</sup> Our framework predicts that good people management practices increase expected test scores through two channels. A teacher with a given ability and intrinsic motivation to teach exerts more effort because these practices provide extrinsic, and cultivate intrinsic, incentives. Compounding this, good people management practices improve selection: a teacher with high ability and high intrinsic motivation prefers a school with performance pay over alternative employments because she anticipates that she will work hard and be rewarded for producing student learning. We focus on Latin American countries and find support for both mechanisms in our PISA data. Principals in schools with higher PISA-based people management scores (predominantly private schools) are less likely to report experiencing teacher shortages and also report higher levels of teacher motivation and effort, compared to principals in schools with lower PISA-based people management scores.

Our framework also predicts that good operations management practices increase expected test scores through two channels. There is no teacher incentive effect but the selection effect remains, now driven by the _level_ rather than _structure_ of compensation. This is reinforced by a household

> 2This assumption echoes the observation made by Baker et al. [1988] that compensation plans featuring explicit rewards seldom account for _all_ of a worker’s rewards.

2
Appendix figures A.2 and A.3 show these trends for our four main outcomes of interest.

Figure 2: Trends in service sector employment before and after openings and closings for those close to and further away from mines <!-- Start of picture text --> Mine opening Mine closing<br>-10 -5 0 5 10 -10 -5 0 5 10<br>Year since mine opening Year since mine closing<br>Treatment Control Treatment Control<br>.15 .15<br>.10 .10<br>.05 .05<br>Mean of service per year Mean of service per year<br>0 0<br><!-- End of picture text -->

*Lowess smoothing. Negative values are before opening or closing **The treatment group is within 20 and the control group is 20-200 kilometers from a mine

The main results following the empirical strategy previously outlined are reported in Table 3, with Panel A showing women’s outcomes and Panel B the outcomes for these women’s partners. The first variable, _active (20 km)_ , captures the difference in outcomes between individuals living close to a producing mine and those living farther away. In Panel A, we see that the coefficient is positive and statistically significantly correlated with the woman working, working in the service sector, and working with unskilled manual work (significant only at the 10 percent level). The second variable, _inactive (20 km),_ shows the difference between women living close to future mines and women living further away. We see that women in mining areas before the mine starts producing are more likely to work, especially as self-employed agricultural workers.

Due to the possibility of non-random mine placement, we use a difference-in-difference strategy, whereby the effect of a mine opening can be read out as the difference between the coefficients for _active (20 km)_ and _inactive (20 km)_ . Test results are presented for this difference ( _β_ 1 _− β_ 2 = 0) henceforth. This difference shows that there is a decline of 5.4 percentage points in the probability that a woman is working when a mine opens in the area (which is calculated by the difference between _active_ and _inactive_ : 2.6 - 8.0 = 5.4). Investigating the sectoral composition of the effect, it emerges that the decline in overall employment is driven by a decline in agricultural self-employment, an effect which is partly offset by an increase in service sector employment. The increase in the likelihood of working in the service sector is substantial at 2 percentage points. The sample mean of engaging in service sector jobs is 3.6 %, so the increase in the likelihood is over 50 %. Trying to quantify the effect of mine opening on female service sector employment, we make a back-of-the-envelope calculation and estimate that 94,402 women have benefited from service sector jobs generated by the industrial mining sector, while 283,206 women left the labor market.<sup>10</sup>

> 10According to the World Bank Indicators for 2011, the Sub-Saharan African female population aged 15-65

11
**_Review of energy demand and system models: Policy implications_**

Table A1.4: Comparison of hybrid models

|Criteria|NEMS|POLES|WEM|SAGE|
|---|---|---|---|---|
|Approach|Optimisation|Accounting|Accounting|Optimisation|
|Geographical<br>coverage|Country|Global but regional<br>and<br>country<br>specific<br>studies<br>possible|Global<br>but<br>regional<br>or<br>country specific<br>studies possible|Global but regional<br>or country specific<br>study possible|
|Activity coverage|Energy system|Energy System|Energy System|Energy System and<br>energy trading|
|Level<br>of<br>disaggregation|Pre-defined|Pre-defined|Pre-defined|Pre-defined|
|Technology<br>coverage|Extensive<br>but<br>pre-defined|Extensive but pre-<br>defined|Extensive<br>but<br>pre-defined|Extensive and pre-<br>defined|
|Dataneed|Extensive|Extensive|Extensive|Extensive|
|Skill requirement|Very high|High to very high|High<br>to<br>very<br>high|Very high|
|Portability<br>to<br>anothercountry|Difficult|Difficult|Difficult|Difficult|
|Documentation|Extensive|Limited|Good|Extensive|
|Capability to analyse<br>price-induced<br>policies|Good|Good||Good|
|Capability to analyse<br>non-price policies|Good|Very good|Very good|Good|
|Rural energy|Possible<br>and<br>covered<br>in<br>a<br>limited way|Possible but not<br>included|Possible<br>and<br>included<br>in<br>a<br>limited way in<br>recent version|Possible but not<br>included|
|Informal sector|Difficult and not<br>included|Possible but not<br>included|Possible but not<br>included|Not included|
|New<br>technology<br>addition|Possible<br>but<br>difficult|Possible<br>but<br>difficult|Possible<br>but<br>difficult|Possible<br>but<br>difficult|
|Subsidies|Yes|Yes|Yes|Yes|
|Rural-urban divide|Possible<br>and<br>considered|Possible but not<br>considered|Possible<br>and<br>included in the<br>recent version|Possible but not<br>considered|
|Economic transition|Not applicable|Considered<br>implicitly|Considered<br>implicitly|Considered<br>implicitly|

149