rafmacalaba/datause-encoder-data huggingface dataset:
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|---|---|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Support to Uruguayan Public Schools Project|Francisco Haimovich Paz|
|Indonesia - Third National Program for CommunityEmp|owerment i Sentot Surya Satria|
|Indonesia - Third National Program for CommunityEmp|owerment i Sentot Surya Satria|
|Indonesia - Third National Program for CommunityEmp|owerment i Sentot Surya Satria|
|Indonesia: PNPM Peduli|Natasha Hayward|
|Indonesia: PNPM Peduli|Natasha Hayward|
|Indonesia: PNPM Peduli|Natasha Hayward|
|Indonesia: PNPM Peduli|Natasha Hayward|
|Indonesia: PNPM Peduli|Natasha Hayward|
|Indonesia: PNPM Peduli|Natasha Hayward|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|KP/FATA Governance Reforms|Sher Shah Khan|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Pakistan: RevitalizingHealth Services in KP|Tayyeb Masud|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
|Third Rural Sector Support Project|Aimee Marie Ange Mpambara|
intervention packages through two impact channels: health and labor productivity. The analysis is performed by means of an urban climate model that simulates expected future heat stress conditions with and without interventions combined with quantitative methods utilized in the public health and labor productivity impacts literature. We conducted simulations in three 'deep dive' cities: Surat, Chennai, and Lucknow. These cities were chosen as intervention areas due to their diverse climatic regions and regional importance. The findings are further generalized to the wider setting of large cities in India. The work is intended to provide an order-of-magnitude understanding of heat impacts and adaptation financing needs, and to support prioritization of measures within Heat Action Plans.

_Table 1: City heat action: Intervention packages_

|**Category of intervention**|**‘Medium ambition’ package**|**‘High ambition’ package**|**Impacts quantified**|
|---|---|---|---|
|**Urban greening**|Increase tree cover by 10%<br>from by 2050|Increase tree cover by<br>30% by 2050|Averted mortality<br>Averted<br>labor<br>productivity loss|
|**Heatwave early warnings**|Introduce heatwave early<br>warnings<br>reducing<br>heat<br>related excess mortality by<br>20%|Introduce<br>heatwave<br>early warnings reducing<br>heat<br>related<br>excess<br>mortality by 40%|Averted mortality|
|**Working hours shift**|Shift working hours towards<br>early<br>morning<br>and<br>late<br>afternoon<br>for<br>25%<br>of<br>outdoor workers|Shift<br>working<br>hours<br>towards early morning<br>and late afternoon for<br>50% of outdoor workers|Averted<br>labor<br>productivity loss<br>|
|**Cool**<br>**roofs**<br>**program**<br>**(homes/workplaces/public**<br>**buildings)**|Introduce cool roofs on<br>representing 12.5% of the<br>total roof area of the city|Introduce cool roofs on<br>representing 25% of the<br>total roof area of the city|Avoided cooling costs|

# **2. Methods**

# **2.1 Heat hazard for three Indian cities under climate scenarios up to 2050**

Heat hazard was modeled for Chennai, Surat and Lucknow using UrbClim, a computer-based framework for modeling climate variables and high spatial resolution (De Ridder et al. 2015). A detailed representation of each city’s surface including land cover, built-up and vegetated area, terrain, and human-generated heat were constructed using remote sensing datasets (see Supplementary Figures - Table 1); this information was combined with data on the historical and projected future climate from the ERA-5 and CMIP-6 datasets respectively. For future periods, urban growth was incorporated into the modeling through use of projected population extents corresponding to future Shared Socioeconomic Pathway (SSP) scenarios calculated in a separate study (Wang et al. 2022). Model runs were performed for a present-day reference period and two 4
24. Ranis, G. and F. Stuart (1973), “Rural Non-Agricultural Activities in Development: Theory and Application.” Journal of Development Economics, vol. 33, o.7 pp. 1045-1060.

25. Renkow, M., 2006, “Cities, towns and the rural nonfarm economy," in Haggblade, S.,

- P. Hazell and T. Reardon (eds), Transforming the Rural Nonfarm Economy, Baltimore: Johns Hopkins University Press.

26. Venables, A. J. and N. Limao, 1999, "Geographical Disadvantage: A von Thünen Model of International Specialization", mimeograph.

27. von Thünen, J. H. 1966, Isolated State: an English Edition of Der Isolierte Staat, Oxford: Pergamon Press.

28. World Bank, 2004, Bangladesh: Promoting The Non-Farm Sector in Bangladesh, Report No. 29719-BD.

29. Van de Walle, D. (2002), “Choosing rural road investments to help reduce poverty," World Development, 30, 4:575-589.

30. WARPO (undated), GIS Database for Bangladesh, Water Resources Planning Organiza-

- tion, Ministry of Water Resources, Dhaka.

25
# **_1.  Introduction_**

This paper explains how to set up and run a general equilibrium model for policy analysis using EViews<sup>1</sup> as a computing platform. A public policy is a collective choice made either by the whole society for itself (using a voting mechanism) or for society by its elected representatives (Quade 1982:3). The quality of such a choice hinges critically on the nature of the underlying process. In a broader sense, the process involves four phases: (i) understanding the relevant issues; (ii) searching for feasible courses of action; (iii) adopting and implementing the most desirable ones; and (iv) monitoring and evaluating the consequences to determine whether intended outcomes are being attained in a desirable manner.

_The search for feasible actions and the identification of the most desirable among the feasible are necessarily constrained by the analytical input._ The reliability of such input is in turn determined by the reliability of the underlying view of the world or organizing framework, and the information set to which this framework applies. There are at least two approaches in policy-making. The normative approach involves essentially: (i) the specification of the workings of society and instruments of policy intervention; (ii) the selection of an evaluation criterion; and (iii) the computation of values of the instruments that will either maximize the criterion or lead to some improvement of performance as measured by the chosen criterion. The standard way to proceed under this approach is to maximize a social welfare function subject to the economy’s resource and technology constraints as if the government could set activities and make all decisions for all economic agents. The outcome of such a process is supposed to be implemented by a set of competitive and complete markets subject to the prevailing ownership of resources (Dixit 1996: 4-5).

The positive approach, as expressed in the public choice or the _contractarian_ framework is based on the distinction between the policy regime (or _constitution_ ) that governs the whole policy-making process and individual _policy acts_ (Dixit 1996:13). This approach, which emphasizes the political economy of policy-making, generally

> 1 EViews stands for Econometric Views, a Windows version of a software designed by Quantitative Micro Software (QMS) primarily for time-series analysis and for the conduct of general econometric analysis. This paper is based on version 4.1 (November 4, 2003).  The language is based on the fundamental concept of object, a collection of related information and operations.

1
coverage from 1991 to 2003 to identify which schools receive the AGE and/or any of the other Compensatory Program interventions. Similarly, we use administrative data on other educational interventions to control for their presence in the school. Data on school level grade repetition, failure and drop out as well as other characteristics comes from the Mexican School Census ( _Censo Escolar_ ). We use the 1990 and 2000 Population Census and the 1995 _Conteo_ to construct socioeconomic locality indicators that will help us identify the evaluation sub-sample. The unit of analysis is the school.

# **_4.1. Estimation and Identification_**

In principle, we would like to compare school performance when schools have an AGE to the counterfactual – i.e. quality for the same schools without an AGE at the same time. Since the counterfactual is never observed and we do not have a controlled randomized trial, we are forced to turn to quasi-experimental methods that mimic the counterfactual under reasonable conditions.

We propose to use the phased rollout of the AGE to identify treatment and comparison groups, with the treatment group being schools getting AGE early and the comparison group being those who got AGE later. A major concern is that the late adopters could be different from the early adopters, and that these differences may be correlated with school performance. For example, the schools that received AGE early could be located in poorer rural areas while the ones that received it later could be in wealthier areas. In this case, the correlation between AGE and performance could be confounded with the wealth effect. Alternatively, it could be that schools with the strongest potential for improvement – schools with more engaged parents and motivated 10
# **3 Results**

What did the community sanitation intervention achieve? As outlined in section 2.4, we concentrate in this section on Ahmednagar, the only district that, in fact, received the experiment. We document evidence for an effect in four stages. First, the experiment balanced observed baseline properties. Second, the experiment improved sanitation coverage, but not completely. Third, in an independent confirmation of this effect on sanitation, villages assigned to the treatment group were more likely to subsequently win a government prize for being open defecation free. Finally, we show a statistically robust effect on children’s height.

## **3.1 Balance of observed baseline properties**

Did the random assignment of villages to treatment and control groups achieve balance on observed baseline characteristics? That is, do the data give reason to be comfortable claiming that the groups are truly comparable, as one would hope if randomization were accomplished? Table 2 shows that the answer is yes, both for the district Ahmednagar where the program was implemented, and for the other two districts where it was not. Across a range of variables, in no case is there a statistically significant difference between the treatment and control groups in variables observed in February 2004, before the program. The greatest _t_ -statistic in absolute value is 1.52 for having a separate kitchen, in Nanded and Nandurbar districts. Households in the treatment and control groups are similar in the first and second principal components of a vector of assets asked about in the baseline survey. The summary statistics in the table reflect the poverty and poor health in the studied districts. As an illustration of their poverty, we note that only about three-fourths of households owned a clock or watch.

14
risks associated with the goods in regard to health, safety and environment (from now on, HSE). To control these risks, the border inspection agency searches shipments of goods from time to time. We assume that each shipment is associated with a unique good and a unique importer. There is an upper bound on the maximum number of searches the agency can perform, which is given by _S_ .<sup>31</sup> When a search is performed, we assume that a noncompliant shipment is identified with probability 1. There is a continuum of shipments and each shipment can be categorized with respect to its observed characteristics, such as country of origin, transportation method, etc. For expositional purposes, assume that we have two mutually exclusive characteristics, say _A_ and _B_ .<sup>32,33</sup> Some shipments are in category _A_ , some are in category _B_ . As a result, we have a total of 2 groups, say 1 and 2 with measures _N_ 1 and _N_ 2, respectively. Throughout, index _l ∈{_ 1 _,_ 2 _}_ will denote the group.

There is a threshold level of compliance that must be satisfied. If a shipment is searched and found not to satisfy this compliance threshold, it cannot enter the country and the importer incurs a loss of $ _L >_ 0.<sup>34</sup> We assume that importers can secure the satisfaction of this threshold by incurring some cost that depends on their shipments’ particulars. Let _c_ ( _y_ ) represent the cost required for a shipment with particulars _y_ . We assume that _c_ ( _._ ) is invertible and _c_<sup>_′_</sup> _>_ 0 and _y_ is a realization of a random variable _Yl_ with the cdf of _Fl_ and pdf of _fl_ for _l ∈{_ 1 _,_ 2 _}_ . Thus, we allow the distribution of shipments’ particulars to differ across groups. Moreover, both _Yl_ ’s are non-negative, and their supports include zero. A shipment’s particulars, hence compliance cost, is unobservable to the border inspection agency. On the other hand, given the shipment’s characteristics, the inspecting agency can observe the group to which the shipment belongs.<sup>35</sup>

> 31One possible way to justify this assumption is to consider a case where a budget for search is limited. Alternatively, it can be due to a time constraint.

> 32There are different ways to interpret these characteristics. For example, _A_ may represent shipments from developing countries whereas _B_ may represent shipments from developed countries. Alternatively, _A_ may represent shipments of new importers while _B_ may represent shipments of seasoned importers.

> 33It is straightforward to generalize the model to any number of mutually exclusive characteristics.

> 34This loss can represent either the loss incurred by not entering the country or a fine charged for failing to comply. It can potentially vary from one shipment to another. We simply assume it to be constant since our results continue to hold as long as deterrence is not perfect.

> 35This setup can also be used to model a customs agency’s problem of minimizing the expected number of importers that misreport customs declarations. To see this, consider the following. There are tariffs imposed

19
16 In this case the housing bonds could also be indexed, maintaining the pari passu evolution of outstanding loans and bonds.

In case (i), Table 3 illustrates a possible example of irnerest rate increases. Resulting new estimates for cash outflows are depicted in Figure 5. The lower half shows the corresponding budgetary savings, both in billions of forints and in % of GDP. In this scenario, in 1992/93 for example, savings in interest subsidies would be equivalent to some 0.4 % of GDP.

Table 3 Progressive Rates Adjustment Scenario

|Interest Rate of Initial Contract|1990|i991|1992|1993|**1994**|
|---|---|---|---|---|---|
|**0**|2|4|6|8|11|
|l|3|**5**|7|9|**11**|
|**2**|**4**|**6**|**8**|**10**|**11**|
|3|5|7|**9**|10|**11**|
|3.5|5|7|9|10|11|
|**4**|5|7|9|10|**11**|
|6|7|8|9|10|**II**|
|8|9|10|**1I**|**1I**|**11**|
|10|**10**|**II**|**II**|11|**11**|
|11|11_|**1I**|**I**|**11**|**11**|
|12|12|12|12|**12**|**12**|
|**13**|**13**|13|13|13|**13**|
|**14**|14|**14**|**14**|-<br>**14**|**14**|
|**s15**<br>**.5**||**15**|**15**|i|**15**|
determinants of return migration of Stark and Taylor (1991) and Stark (1992). It is expected, ceteris paribus, that migrants who have better (worse) occupations are less (more) likely to reveal return intentions.

Other variables of interest capture possible ‘brain-gain’ pathways. Firstly information on whether the migrant has undertaken any training is captured. Table 1 illustrates that 11-12 percent of our sample undertook at least language or vocational training while in the host country, additional to education undertaken in Romania. By controlling for such training we can test whether there is any potential brain-gain mechanism where perceived or expected returns to this human capital investment are greater in the host country or Romania. Another potential form of brain gain that we can capture is the risk-taking attitudes of migrants. We create a dummy variable that equals 1 if the migrant has acquired skills or experiences that have influenced investment interest. Socioeconomic characteristics such as age, marital status, children and education level are controlled as well as a group of industry dummies within which migrants work. Rather than individual country dummies, we place countries into six categories, Southern Europe, Northern Europe, non-Romance Southern Europe, Eastern Europe, Non-European English Speaking and other countries.

There are some weaknesses with the data. Firstly we do not have any information on actual return migration, only the intention to return. Longitudinal studies have shown that intention to initially migrate is a good predictor of actual migration (Van Dalen and Henkens, 2008; Boheim and Taylor, 2002), a finding consistent with the theoretical work of De Jong (1999, 2000) and Burda et al. (1998). While initial migration and return migration are driven by different factors, it is hard to think that intention and actual return migration would not also be correlated for our sample of emigrants.

Secondly the data we use in the analysis are not drawn from a random sample. Of the migrants interviewed, the poorly paid, unemployed, those that live far away, those who left more recently and illegal migrants will likely be underrepresented. Thirdly we do not control for selection into the initial decision to migrate since this is not contained in the data set. If migrants are less likely to be high-skilled or highly educated, then they are negatively selected with respect to these two human capital characteristics. Borjas and Bratsberg (1994) argue that return migrants from this group will tend to be the best of the worst. If migrants were positively selected with respect to human capital characteristics, then return migrants would tend to have either the highest or 10
countries (although this expansion was significant only for countries in the bottom tercile), this policy stance took place amid a narrowing fiscal space for both groups.

# **4.2.  Balance sheet composition and external debt position**

Table 2 reports the medians of the indicators of balance sheet composition and external debt position for the region, sub-groups within the region and other benchmark regions of the world for the periods 2010-13 and 2015-16. The analysis in this subsection focuses on solely two indicators of the balance sheet composition of governments: concessional external debt as a percentage of general government gross debt, and share of short-term debt as a percentage of total debt.<sup>9</sup> The discussion of external debt indicators focuses on total external debt as a percentage of GDP, and short-term debt as a percentage of reserves.

# _Sub-Saharan Africa vis-à-vis Benchmark regions_

The balance sheet composition of SSA and non-SSA developing countries remained (statistically) invariant in 2015-16 when compared with 2010-13. During the period 2015-16, about 40 percent of the general government gross debt was concessional while short-term external debt accounts for only 5 percent of total external debt. For non-SSA developing countries, 17 percent of the public debt burden is concessional and short-term external debt represents about 13 percent of total external debt (see Table 2).

In terms of external solvency, SSA and non-SSA developing countries also have very low shortterm external debt to international reserve ratios. This ratio has remained unchanged in 2015-16 compared with 2010-13 for both groups of countries. Short-term external debt accounts for 13 and 34 percent of international reserves for SSA and non-SSA developing countries, respectively. Finally, external debt stocks (as percentage of GDP) increased for both groups of countries: it significantly increased for SSA countries from 27 percent in 2010-13 to 32 percent of GDP in 2015-16. For non-SSA developing countries, external debt rose from 45 percent of GDP in 201013 to 52 percent of GDP in 2015-16 —although this increase seems to be statistically not significant.

# _Sub-Saharan Africa by resource abundance_

When looking at SSA countries by their extent of resource abundance, we observe that the composition of balance sheets remains invariant in 2015-16 relative to 2010-13. Concessional external debt accounted for 46 percent of gross public debt among non-resource rich countries in 2015-16 while it represented 41 and 18 percent for non-oil and oil resource rich countries. Short-

> 9 The fiscal space database developed by Kose et al. (2017) contains additional indicators of balance sheet composition, such as the share of general government debt in foreign currency, share of debt securities held by nonresidents, and share of central government debt held by nonresidents. Due to the lack of data for Sub-Saharan African countries, the averages for these indicators for 2010–13 and 2014–16 were not calculated.

14