rafmacalaba/datause-encoder-data huggingface dataset:
32 Incentives and CaRital SRending, G. Fromm (ed), 1971 Cohen D.,"Slow Growth and Large Debt in the Eighties: an Empirical Analysis", CEPREMAP working Raper, 1989 Corden M., "Debt Relief and Adjustment Incentives", in J. Frenkel et al. (eds), Analvtical Issues in Debt, IMF, 1989 Cuddington J.T., "Macroeconomic Determinants of Capital Flight:An Econometric Investigation", in Capital Flight and Third World, D. Lessard and J. Williamson (eds), Institute for International Economics, Washington D.C., 1987.

Dadkah K. and Kahedi F., " Simultaneous Estimation of Production Functions and Capital Stock for Developing Countries", Review of Economics and Statistics, vol.LKViii, n.3, 1986

**de** Melo **J.** & Faini R,"Adjustment, Investment in Developing Countries: An empirical Analysis", Economic Policy, 1990 Diwan I., "Linking Trade and External Debt Strategies", Journal of International Economics, 29, 1990 Dooley M. et al., "Debt Reduction and Economic Activity", Occasionnal Paper N.68, IMF, march 1990 Dornbusch R. , "Mexico : Stabilization, Debt and Growth", Economic Policy, october 1988 Dornbush R. & de Pablo J., "Argentina: Debt and Macroeconomic Instability", The University of Chicago Press, 1988 Easterly W., "Fiscal Adjustment and Deficit Financing during Debt Crisis", in Dealing with the Debt Crisis, 1989

Fischer S.,"Anticipations and Nonneutrality of Money", Journal of Political Economy, vol.87,n.2, april 1979 Fischer S, "Keynes-Wiksell and Neoclassical Models of Money and Growth", American Economic Review, december 1972 Fry M., "Money. Interest, and Bankina in Economic Development", The John Hopkins University Press, 1988 Fry M., "Foreign Debt Addiction in Some Developing Countries", Journal of Money and International Finance, 1989 Greene J & D. Villanueva, "Private Investment in Developing Countries: An Empirical Analysis", mimeo, IMF, 1990 Haque N., K. Lahiri & P. Montiel, "A Macroeconometric Model for Developing Countries, IMF Staff Papers, september 1990 Heller P.S., "Model of Public Fiscal Behavior in Developing Countries : Aid, Investment, and Taxation", American Economic Review, vol.65, n.3, june 1975 Hofman and Reisen, "Debt Overhang, Liquidity Constraints, and Ajustment Incentives", OECD technical Papers, october 1989 IMF, World Economic Outlook, supplementary Note, 1, 1989 Khan M.S. and P. Montiel, "Growth-Oriented Adjustment Programs : a Conceptual Framework", IMF Staff Papers, june 1989 Koen V., "Testing the Debt Overhang Hypothesis", MIT, july 1989 Krugman P., " Financing vs Forgiving : a Debt Overhang", NBER Working PaRer, January 1988 Langhor H. & A. Santomero, "The Impact of Equity in Bank Portfolios", Finance, vol.7, 1986.

Lucas R.E., "Adjustment Costs and the Theory of Supply", Journal of Political Economy, vol.75, 1967 Marquez J., "Foreign Exchange Constraints and Growth Possibilities in LDCs", Journal of Develolment Economics 19, 1985
<!-- Start of picture text --> Employed baseline: employed afterwards<br>05<br>@ -.067<br>@-.09<br>-1 1<br>=: bo * 11<br>: bess<br>3 15 .<br>2<br>a<br>E<br>Ww<br>-2<br>: 23 ® Pre-covid cohort<br>725 @ Covid cohort<br>4 months later*** 8 months later"** 12 months later*** 16 months later"**<br>EachT-tast coeff on d i cientfferances estimatesbetweenthe differencetwo coefficients from baselinefor aach periperi od forreportad each cohorton the atx-axis Bachlabp e riod.ls.<br>Significance levele: " pefl1 * paf.05 "** pef.04<br><!-- End of picture text -->
We need to limit our analysis to the electoral units in the vicinity of the weather stations based on two reasons: First, to ensure that we are studying units that in fact contained insurance beneficiaries and, second, to minimize measurement error given that as the distance from the electoral section to the weather station increases, the probability of difference between the rainfall measure and the real conditions in the field increases (spatial basis risk). To define the benchmark distance for selection we identify the distance at which two criteria are simultaneously met: a) There is no overlap of weather stations in order to avoid a case in which the same unit would be matched with rainfall data twice, thus duplicating one observation and, b) Each municipality covered by the WII program would have at least one electoral section included in our dataset.

Our dataset contains 1,198 electoral sections located at a maximum distance of the defined benchmark distance of 2,131 meters from the corresponding weather station. For approximately 10% of these observations we are not able to match the results of the 2000 Presidential elections since the map of electoral sections was modified between 2000 and 2006. We therefore use for the analysis 1,038 units comparable for the two elections. Summary statistics are described in Table 1. We observe that 30% of the observations received monetary compensation for drought during the 2005 agricultural season.

The share of votes for the incumbent party is the key dependent variable; it is calculated as the number of votes obtained by the incumbent party relative to the total number of valid votes casted in each electoral section.

The measure of rainfall is normalized using the threshold established for insurance payments. Figure 3 shows each unit’s rainfall deviation from the threshold and whether or not drought relief compensation was received in 2005. As we can observe, all electoral sections covered by the government program did receive the payment when accumulated rainfall fell below the established threshold. Conversely, those units that were covered by the program but had rainfall levels above the threshold did not receive any payment.

# **4. Empirical Strategy and Results**

Page **9** of **29**
on the public sector. Yet, increasingly, parents face choice over public versus private, as well as choice over the type of private school. In this context, marketlevel interventions that can improve the performance of the schooling sector as a whole can yield rich dividends. What we have been able to show here is that the dissemination of credible and comparable information on learning quality is an intervention that can improve performance in the private sector and simultaneously strengthen the public sector. Government policies that do not account for such multiplicity of providers may miss key opportunities for reform. Fixing market failures in the private sector should remain a priority—and in doing so, can yield broad improvements across the public and private sectors, both on efficiency and equity.

# **References**

**<mark>Akresh, R., De Walque, D., & Kazianga, H.</mark>** <mark>(2013). Cash transfers and child schooling: evidence from a randomized evaluation of the role of conditionality. Working Paper, World Bank: Washington, DC.</mark>

**Allcott, H., & Mullainathan, S.** (2012). External validity and partner selection bias (No. w18373). National Bureau of Economic Research _._

**Alderman, H., Orazem, P. F., & Paterno, E. M.** (2001). School quality, school cost, and the public/private school choices of low-income households in Pakistan. _Journal of Human Resources_ 36 (2): 304-326.

**Andrabi, T., Das, J., Khwaja, A., Vishwanath, T., & Zajonc, T** . (2002). Test Feasibility Survey, PAKISTAN: Education Sector. Harvard Kennedy School Working Paper.

**Andrabi, T., Das, J., Khwaja, A., Vishwanath, T., & Zajonc, T.** (2007). The Learning and Educational Achievement in Punjab Schools (LEAPS) Report. _World Bank, Washington, DC_ .

**<mark>Andrabi, T., Bau, N., Das, J., & Khwaja, A. I.</mark>** <mark>(2010). Are Bad Public Schools Public “Bads”? Test Scores and Civic Values in Public and Private Schools. Working Paper.</mark>

**<mark>Andrabi, T., Das, J., Khwaja, A. I., & Zajonc, T.</mark>** <mark>(2011). Do value-added estimates add value? accounting for learning dynamics.</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>,</mark> _<mark>3</mark>_ <mark>(3), 29-54.</mark>

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# TABLE OF CONTENTS

||Page No.|
|---|---|
|SUMM|ARY ..... . . . . . . . . . . . . . . . . . . . . . . . .<br>.<br>i|
|I.|INTRODUCTION .... . . . . . . . . . . . . . . . .<br>..<br>1|
|II.|UNEMPLOYMENT AND THE PROBECAT PROGRAM: BASIC FEATURES<br>.<br>.<br>.<br>3|
||Background: Basic Characteristics of Unemployment<br>in Mexico.<br>3<br>Program Features.<br>5<br>Data Sources.<br>5|
|III.|THE EVALUATION METHODOLOGY.<br>7|
|IV.|TRAINING EFFECTS ON EMPLOYMENT . . . . . . . . . . . . . . .<br>9|
||Time to First Job.<br>9<br>Employment Probabilities Over Time . . . . . . . . . .<br>12|
|V.|MONTHLY EARNINGS, HOURS OF WORK AND HOURLY WAGE . . . . . . .<br>15|
||Data and Summary Statistics . . . . . . . . . . . . .<br>15<br>The Regression Results<br>..<br>16<br>**Overall**Pro gram Effects<br>16<br>Training Effects by Schooling Group<br>.18|
|VI.|CONCLUSIONS AND IMPLICATIONS.<br>20|
||Summary of Findings<br>..<br>20<br>Cost Effectiveness of PROBF.CAT . . . . . . . . . . . .<br>21<br>Policy Implications.................<br>23|
2008. In October 2008, December 2008, and February 2009, tracking protocols required the team to re-contact all respondents ahead of the spring 2009 follow-up survey. In the process of tracking migrants through firms, the team found that 9 percent of workplaces had closed in the 14 months following the November–December 2007 census; by matching workplaces to migrants they had employed, the team estimated that 13 percent of migrants in the 15 cities surveyed were affected by post-crisis shutdowns. If this lay-off rate were applied to an estimated 142 million long-term migrant workers in 2008, perhaps some 18.5 million migrant workers could have lost their jobs as a result of enterprise closings during the crisis period.

Not all cities were affected equally. Kong, Meng, and Zhang (2009) found that 34 percent of migrants in the export-oriented city of Dongguan (in Guangdong province) would have lost employment through enterprise closings. Across industry sectors, the study found that manufacturing (which is export oriented) was hit hard, but so too were construction and services. With large-scale layoffs in export-oriented sectors, however, it would not be surprising to find that sectors specializing in nontraded goods (construction, real estate, and services, for example) would be affected as well.

However, the Kong, Meng, and Zhang (2009) study has limitations that should lead to a healthy skepticism toward the results. First, the authors are unable to account for layoffs from firms that did not close. Second, the study makes a strong counterfactual assumption that no firms would close in the absence of the financial crisis (as a result of adjustments associated with the 2008 Labor Contract Law, for example, or the credit tightening associated with macroeconomic policies initiated in 2007). In practice, even a booming economy will have some failures, and some firms may be lost from the sample if they move locations to expand or move as a result of urban redevelopment initiatives. Furthermore, the authors do not appear to account for new firm creation or the ability of migrant workers to shift immediately from closed workplaces to other jobs. All of these problems may lead to an overestimation of the contribution to layoffs from firm closings as a result of the crisis.

7
larger factor for non-enrollment in primary schools, rather than the presence (or not) of a segregated school (Holmes 2003, Lloyd, Mete, and Sathar 2005a, Alderman, Orazem and Paterno 2001). The advantages and experience with co-education and whether female teachers help attract girls to school is a critical issue for the development of private schools and for education in the country.

There are also certain questions left unanswered in our paper. To the extent that private schools save on costs by hiring local, female and moderately qualified teachers, are they compromising on quality? Figure 6 is a preliminary response to this concern. Here we plot teacher absenteeism for government and private schools separately for male and female teachers.<sup>23</sup> Private school teachers are considerably less absent than government school teachers in a given month. Government school female teachers are absent more than twice as many days (4 vs. 1.8) in a month than their private school counterparts. So, if what really matters for primary schooling is that the teacher puts in _effort_ , this may more than compensate for lower qualifications—a teacher with a PhD teaching in a primary school will have no impact if she never shows up for work. The extent to which qualification matters in teaching rather than effort remains an important research topic.

A second basic question is why people send their children to private schools. Is it because there is a perceived or actual quality difference between public and private schools? Can poor illiterate parents judge the difference between a good and a bad school? How do parents make decisions when offered more schooling choices? Answering these questions is essential because any educational policy that expands the educational space and does not take into account this dynamic of school choice is subject to essentially a version of the “Lucas” critique where the parental reaction to any policy could lead to policy ineffectiveness.

The third question left unanswered is the equity implications of private schooling. There is a growing concern in Pakistan that private education leads to the emergence of two classes—the English-medium trained elite and the vernacular Urdu-medium taught masses. This point has been raised numerous times in the popular press, by academics and by populist politicians.<sup>24</sup> We don’t answer this question fully, but LEAPS and the PIHS data can shed some light on this. The rise of private schooling in the rural areas is likely to bring rural and urban areas closer in terms of quality education, and at the same time increase disparities within rural areas—that is, children

> 23 See Banerjee and Duflo (2005) on importance of absent teachers.

> 24 See Najam (1998), and The News, March 24, 2004 report on Imran Khan’s views on education. <u>http://www.jang.com.pk/thenews/mar2004-daily/23-03-2004/metro/k11.htm</u>

19
Finally, we should note that pollution-related deaths remain at high levels even in the strict regulation scenario. Figure 27 displays the prospects. Faced with industrial output increases in the range of 150-300% during the next two decades, our focal cities face very heavy loss of life even with greatly-increased regulation of pollution. The cases of Beijing and Chongqing are particularly grim, with total deaths above 70,000 in the pure reform scenarios and dropping into the range 50,00060,000 with the 10% annual levy increase. Chongqing loses over 50,000 people in all the scenarios; Shenyang and Shanghai over 10,000.

<!-- Start of picture text --> LivesSaved by 502 Regulalon, 197.2020<br>258O<br>.Levy a500 5h<br>1L -J<br>5.000<br>0<br>i I ,<br>Figure 26: Lifesaving inTwo Scenarios<br><!-- End of picture text --> <!-- Start of picture text --> In view of these projected losses, it is<br>Total Deaths from Industrial Air Pollution,aprrittoosthqutonCn<br>1997-2020 appropdate to pose the question: Can<br>further abatement possibly be too costly to<br>70000 . justify the loss of so many lives? This<br>60,0W bim seems hard to believe on the face of it,<br>SO,OW 1or since many Chinese cities are moving<br>40,0D O.LvYO%j toward decent public health standards in<br>20,000 l.other domains. In the next section, we<br>turn to the assessment of benefits and<br>o L + L $ t; | l, , costs: How much air pollution in China's<br>D~ > t S Nurban centers would it be socially<br>mO F c Dworthwhile to abate?<br><!-- End of picture text -->

Figure **27:** Cumulative Deaths from Air Pollution, 1997 **-** 2020

# **6. Is Stricter Regulation Worthwhile?**

Certainly, lifesaving through air pollution reduction is a laudable objective. However, China remains a poor country with many basic needs unmet for hundreds of millions of citizens. Public investment in health facilities and education can also yield major health benefits, and direct investment in productive capital can improve health by increasing incomes. An appropriate welfare analysis of air pollution abatement must therefore consider its costs, as well as its benefits.

Our econometric results show that the cost of abating China's industrial pollution rises steadily at the margin with degree of abatement. At some level of abatement, further pollution reduction may no longer be warranted because the same resources could be used for other investments with greater lifesaving potential. Using two cases to illustrate our 31
Figure 2 visually illustrates the use of boat-light detection data. This figure presents a side-by-side comparison between two raw satellite snapshots from VIIRS’ VBD images. In each of the two panels, we process the original raster-formatted data published by the NOAA for the two months of May 2016 (right; i.e. the month immediately after the _Formosa_ breakout) and May 2015, resampling the light maps into 10-mile-square grids covering the entire marine space within the maritime EEZ of Vietnam.<sup>16</sup> The brighter pixels in these figures represent fishing grounds with higher boat density. One could visually notice the impact of _Formosa_ on fishing intensity by contrasting the two snapshots. First, while nearshore fishing boats were densely detected along the coast of all central coastal provinces in May 2015, this region experienced a marked decrease in boat density the first month _Formosa_ took place (May 2016), especially in the near-shore region from Ha Tinh to Hue – the directly affected area. _Formosa_ seemed to also affect major offshore fishing grounds (further out from the 20nm fishing ban zone) of Quang Binh, Quang Tri and Hue, where the brightest cluster of densely-fished area became significantly dimmer. Interestingly, the coastal area north of Ha Tinh seems to experience a significant _increase_ in brightness after the incident, suggesting a level of fishing concentration in this area after _Formosa_ took place. As we will discuss empirically in the next section, this observation on the transition of fishing grounds from within to outside the contaminated zone (to the northern sites) offers a direct explanation to the large geographic distribution of the disaster’s impact.

Figure 4 provides further evidence supporting the parallel pre-trend assumption in nearshore fishing intensity (i.e. boat counts) between the treatment (left) and control zones (right). In this figure, we plot the aggregate monthly boat counts for all the months between 2014 and 2017, separately for the two groups.<sup>17</sup> Even under the apparent existence of fishing seasonality, it is clearly visible that 2016 was an anomalous period with low monthly boat counts in the affected region (left panel). The number of boats captured in the peak month of July 2016 was just above 4,000, compared to that of greater than 6,000 in other years. While this sharp downward deviation is noticeable in the treatment zone, it is not the case for the control region; the 2016 near-shore average monthly boat counts in the southern provinces seemed to closely follow its yearly pattern.

> 16 Note that each of these pixel stores a composite monthly-aggregate boat count value made feasible by the VIIRS’s automatic boat detection identification capability.

> 17 Notice that our main regression exercises only focus on estimating the immediate impact of _Formosa_ , hence restrict to a 2013-2016 sample. This also provides consistency with the LFS sample. For robustness purposes, we subsequently discuss estimates using an extended sample of monthly observations between April 2012 and May 2018 in the Appendix.

10
In the dynamics of local prices in each province, two features attract our attentions. First, due to the regional differences in the dynamics of food price in the post-crisis regime, the regional dispersions of prices have widened since 2007. Second, the prices of maize and cassava increased discontinuously with steps after 2007. In sum, the price level, the persistence, and the volatility seem to differ due to the different level of agricultural market integration.

# **5.  Empirical Results**

# **5.1 Capital investment  decisions**

In Table 4, we aim to empirically clarify the effects of price shock (including its non-linearity) and household characteristics on farm investments. The table shows benchmark specifications using the FD model of Eq. (7), which we also use with some modifications in other tables coming later. We use the sample of 762 agricultural households who made positive amounts of agricultural investments between 2007 and 2009. Note that, as we found the price effect was clearly concave in the preliminary analysis, all specifications include the square term of the price change.

In column (1), we assume identical investment responses to price shocks across all farmers. The negative estimate of the square term of ∆P indicates that the impact of food price shocks on households’ investment decision was non-linear (concave), though the linear effect is insignificant. Both the initial wealth and the landholding size had significantly positive effects on agricultural investments. A large family size helps to expand agricultural activities by increasing investments, as they are endowed with larger family labor force.

18
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Figure 12. Hidden costs**

<!-- Start of picture text --> Percentage of revenue<br>a. Evolution of hidden costs in Niger’s water sector b. Hidden costs of select water utilities in West Africa<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Collection inefficiencies Losses Underpricing Collection Inefficiencies Underpricing Losses<br>Percentage of revenues Percentage of revenues<br><!-- End of picture text --> _Source:_ Derived from Baneerjee and others (2008b) and Briceño-Garmendia, Smits, and Foster (2009).

# **Challenges**

The frequency of open defecation in Niger is probably the worst in Sub-Saharan Africa and has increased over time. As of 2006, 82 percent of the population practiced open defecation in Niger, more than twice as high as the average for LICs (table 10). Furthermore, between 1998 and 2006 around 2.3 percent of the national population annually reverted to open defecation. The situation was even worse in rural areas, where around 2.7 percent of the population went back to open defecation. The slow progress in improving latrines has not been enough to counteract the country’s population growth at 3.4 percent per year (figure 13b).

Progress in improving rural water supply is insufficient. While, as of 2006, 90 percent of the water in rural areas was supplied through wells and boreholes, only 22 percent of this was supplied by protected wells or boreholes. Given that around 30 percent of rural water facilities are not operating properly, mainly due to lack of rehabilitation and maintenance, effective access to safe water from wells and boreholes has been further reduced.

The financial equilibrium of the water supply sector is at stake if tariff adjustments do not take effect shortly and government arrears continue to grow. In January 2006 the government froze water tariffs as part of its poverty alleviation policy, and since then tariffs have remained at 34 cents/m<sup>3</sup> for residential customers and 80 cents/m<sup>3</sup> for non-residential customers (table 10). Even though SPEN has managed to cover its operational costs, the tariffs are not enough to cover its capital costs; as of 2007 revenues were covering only 67 percent of total costs (table 10). Until 2006 public customers were current on their payments, but since then arrears have grown, reaching more than $2 million as of 2009.

25