rafmacalaba/datause-encoder-data huggingface dataset:
# **3. The Study Sample and Tests**

## **3.1. Pakistan**

The Learning and Educational Achievement in Punjab Schools (LEAPS) project in Pakistan carried out the tests analyzed here, with an eye toward piloting new educational interventions. Previous work by the project team documented a sea change in the delivery of education in the country (Andrabi and others 2004, 2005, forthcoming). Despite concerns about high and rising enrollments in religious schools, or madrassas, the team estimated that less than 1 percent of children are enrolled in madrassas and found no evidence of an increase in religious enrollments during the 1990s. Instead, there had been an explosion in _private schools_ during the 1990s, which shows no signs of slowing down.<sup>6</sup> The LEAPS project was conceived as a multiyear study to pilot potential interventions and document the efficacy of various policies in an environment where villages had both private and public schools. The results presented here draw from the baseline survey of this project.

The team purposively chose three districts in Punjab on the basis of an accepted stratification of the province into North, Central, and South. Within these three districts—Attock in the North, Faisalabad in the Center and Rahim Yar Khan in the South—126 villages (42 in each) were chosen randomly from a list frame of all villages with at least one private school. In subsequent visits, 14 villages were eliminated and for the remaining 112 villages, the team mapped _all_ existing schools, defining catchments as a 15-minute circle from the boundary of the village in Attock and Faisalabad (where the villages are concentrated) and a 30-minute circle in Rahim Yar Khan (where they are more spread out).<sup>7</sup> The mapping exercise yielded 828 schools, for an average of 8 public and private schools in every village.<sup>8</sup> All children enrolled in third grade in these public and private schools were tested in Urdu,

> 6 Of all new private schools in 2000 50 percent were set up in villages, were relatively cheap (with a fee to median GNI ratio of 2.5 percent compared to 9 percent for the United States), and their largest growth sector was the rural poor. By 2010, the team projected that in Punjab province (the largest in the country with 50 percent of the population) one-half of all villages would have at least one private school.

> 7 In the villages that were dropped, either the population in these villages was too large (they were cities) or the private schools had shut down.

> 8 This simple mapping exercise dramatically alters the vision of a village with a single school. The large number of schools is not because of the sampling strategy; a mapping exercise in the rural parts of the Sindh province (one of the poorest in the country) shows that there is an average of _five_ government schools in every village. Anecdotal evidence from India suggests similar numbers, although there is currently no analysis that allows us to estimate the average number of schools (government and private) in a village.

- 9 -
closer approximation, since they are primary roads connecting the major cities of each country with each other, and with the major cities of other countries. Not all World Bank projects have focused on primary roads, but it seems reasonable to assume that most of them have, in light of generally-poor road conditions and the scarcity of resources for improvement. Accordingly, we use GIS to overlay the road network with an African rainfall map and compute rainfall intensity (average mm. of rainfall on days that it rains<sup>34</sup> ) for the area surrounding each link in the network. Then we compute country mean rainfall as the weighted mean of link-specific means, where the weights are proportional to link distances.

We hypothesize that incremental upgrading and maintenance costs are lower for countries where current road conditions are relatively good. We proxy this factor using countries’ percent of roads that are paved from the World Bank’s World Development Indicators. The available data are spotty and exhibit significant variability from year to year, so we have chosen a conservative approach: From the WDI estimates of percent paved for the period 1997-2002 (the latest year available), we use the minimum value as our estimate. We use percent paved rather than log percent paved because the former is already scale-invariant.

African road projects are labor-intensive, so local wages should be a significant determinant of upgrading and maintenance costs. To proxy relative wages, we use mean GDP per capita (in $US) at purchasing power parity for the period 2000-2003, from the World Bank’s World Development Indicators. To control for possible effects of governance quality (including corruption) on cost, we employ an index of transparency, accountability and corruption from the World Bank’s 2004 database for Country Policy

> 34 This analysis is based on data from the Climatic Research Unit, University of East Anglia, UK.

29
# **1. Introduction**

In recent years, the importance of the political economy (PE)<sup>2</sup> analysis in influencing policy reforms has been widely recognized. PE considerations determine whether reforms are implemented and to what extent they are implemented. While it is recognized that institutions that govern a country influence the country’s economic growth and development, (Rodrik, Subramanian, and Trebbi, 2002), it is argued that the development of good institutions is often not encouraged because they might not maximize returns for the politically powerful elite (North, 1981). Hence, weak and inadequate institutions are allowed to flourish resulting in limited support for sustained economic growth, disappointing reductions in poverty, and continued high levels of corruption and poor governance.

In its modern form, political economy studies refer to the study of the relations between political and economic processes which involve several factors such as incentives, relationships, and the distribution of power between various interest groups in society, all of whom have an impact on development outcomes. The fundamental idea is that to understand economic (development) performance, there is a need to understand first the politics that shapes it. Ultimately, political decisions determine whether growth occurs or Millennium Development Goals are achieved (DFID, 2010). Therefore, what PE analysis attempts to understand are the reasons why certain essential policies or programs are not being implemented, hence not resulting in desired development outcomes. The reluctance to implement such policies and programs is generally not based on economic considerations but is influenced largely by political considerations. Such political considerations could include the desire of policymakers to stay in power; to enrich themselves financially; to prevent political opponents from gaining power; and to dispense favors to supporters by preventing the introduction of new technologies, or improvements in the property rights of workers or competitors<sup>3</sup> . PE studies attempt to understand how political constraints (which arise from the need to make collective choice while dealing with conflicting and heterogeneous interests, and opportunistic behaviors) may explain the choice of economic policies (which are different from optimal policies) and how they affect the economic outcomes.

> 2 The use of the term political economy goes back to Adam Smith’s work _Wealth of Nations_ in 1776, and it is also found in the David Ricardo’s and Karl Marx’ works. What nowadays is understood as economics was called at that time “political economy” and referred to the “conditions of production organization in nation-states”. 3 See Acemoglu, Johnson and Robinson (2005).

2
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA

**Table 5.2 Impacts of road connectivity on crop production: Mozambique**

|Equations|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent variable|Ln (total crop<br>production, high-<br>input)|Ln (total crop<br>production, low-<br>input/subsistence)|Ln (total crop<br>production, irrigated)|Share of high-input<br>crop production in<br>total crop production|
|Estimation method|IV-Tobit|IV-Tobit|IV-Tobit|IV-Tobit|
|Ln (trvl tim t nrt 50K it)|–2.269***|–2.728***|–2.147***|0.009|
|ae e o eaes cy|(0.191)|(0.181)|(0.253)|(0.008)|
|Ln (otential rodction hih-int)|0.323***||||
|p pu, gpu|(0.049)||||
|Ln (potential production low-input)||–0.240***|||
|,||(0.055)|||
|Ln (potential production irrigated)|||0.440***||
|,|||(0.037)||
|Share of high-input potential production in||||0.035***|
|total potential production of all crops||||(0.013)|
|Ln ltin|0.202***|–0.085**|0.242***|0.017***|
|(popuao)|(0.044)|(0.042)|(0.055)|(0.002)|
|L ihb lti t|–0.525***|–0.442***|0.058|–0.007*|
|n (negor popuaon, aggregae)|(0.084)|(0.079)|(0.115)|(0.003)|
|Constant|yes|yes|yes|yes|
|Wald test of exogeneity|152.85|200.43|18.10|0.66|
|(p-value)|0.000|0.000|0.000|0.418|
|Left-censored observations|839|440|1,955|399|
|Uncensored observations|5,449|5,864|3,867|5,057|
|Total observations|6,288|6,304|5,822|5,456|

_Source:_ Own calculations. _Note:_ Total crop production (value) is a 20-crop aggregate using the medians of African country-level crop prices. Standard errors in parentheses.

* significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

The benefits of improved road networks, however, extend past simply increasing agricultural production. These investments also spur rural nonfarm activity and perhaps, more importantly, reduce rural remoteness, increasing access of households to health, education, and other opportunities. In this case, the investment in the main corridor roads significantly reduces travel time between cities on the corridors but does not greatly affect access to markets by northern Mozambican households (table 5.5 and figure 5.1).<sup>24</sup> Rural remoteness in northern Mozambique (defined as greater than 5 hours travel time to a city of at least 50,000 people) is dramatically reduced, though, in the second scenario (upgrading all existing roads to gravel, good-condition surfaces). This investment would reduce the number of people in northern Mozambique living in remote areas from the current 3.5 million to 0.8 million.

> 24 For details of the calculations, see Dorosh and Schmidt (2008).

27
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_Table 2: Summary of FTT-FLEX equations_

||**FTT FLEX-Equations**|
|---|---|
|**Technology-specific equation**||
|Core FTT equations|•<br>Investor preferences<br>•<br>Diffusion rate<br>•<br>Technologyshares dynamic|
|Learning rate|•<br>Investment learning rate (global exogenous technology<br>cumulation)<br>•<br>Operation &Maintenance (O&M) learning rate (global exogenous<br>technologycumulation)|
|LCOEs (mean and standard deviation)|•<br>Investment costs subject to global learning rate<br>•<br>O&M costs subject to domestic learning rate<br>•<br>Fuel costs grow with international fuel price projections.<br>•<br>Policy options: investment subsidies, fuel tax and carbon tax<br>•<br>Total LCOE is the sum of investment, O&M, fuel costs and policy<br>costs|
|Max and Min|•<br>Penalties equations when share breaches exogenous maximum or<br>minimum limit.<br>•<br>Total augmented cost is total cost LCOE plus penalties (this is the<br>cost thatget used in thepreference equation)|
|Production|•<br>Production bytechnology|
|**Aggregated equations**||
|Electricity price|•<br>Weighted average LCOEs (without penalties)<br>•<br>Weighted average LCOEs(withpenalties)|
|Emissions|•<br>Aggregate CO2emissions|
|Investment|•<br>Total capital stock(delta = new investment less depreciation)|
|Fiscal|•<br>Carbon tax revenues<br>•<br>Energy tax revenues<br>•<br>Total subsidyspending|

The full detail of the FTT-FLEX equations is given in Annex I.

FTT-FLEX model is implemented in two versions: one coded in EViews and the other in Python using ModelFlow, a Python-based business logic programming language (Hansen, 2023). Both versions are available to users upon request.

# **3.5 FTT-FLEX application in other sectors**

The FTT-FLEX model has been deliberately designed to be adaptable to a variety of sectors. This study focusses on its application in the power sector given its status as a major polluter and the well-defined nature of its technologies. The FTT-FLEX framework can also be applied in other sectors that have clearly defined competing technologies and comprehensive technology information (including lead time, lifetime, costs, energy type per production unit, and existing shares), for instance:

- Passenger or freight transport: diesel, petrol, hybrid, hydrogen, and electric vehicles, with total demand assessed in terms of passenger kilometers.

- Heating: gas boilers, oil boilers, electric heaters, solar options, and heat pumps, with total demand measured in heating units.

10
Figure 2.1: Structure of Assets <!-- Start of picture text --> BRL 930 BN BRL 769 BN BRL 46 BN BRL 2,559 BN<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Retail Corporate Government Banking System<br>Cash, Reserve Requirements and BNDES On-Lendings Repos, Interbank Placements, Securities and Derivatives<br>Net Loans and Leases Other Assets<br>% of Assets<br><!-- End of picture text --> _Source: Study calculations based on Bacen data._ Figure 2.2: Structure of Liabilities <!-- Start of picture text --> BRL 930 BN BRL 769 BN BRL 46 BN BRL 2,559 BN<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Retail Corporate Government Banking System<br>Deposits Reverse Repos, Acceptances, Interbank and Borrowings<br>Other Liabilities Net Worth / Regulatory Capital<br>% of Liabilities<br><!-- End of picture text --> _Source: Study calculations based on Bacen data._ 11
**Table 4. Impact of schooling attainment on spouse/partner characteristics** **<u>Sample consists of individuals whose current union is their first union (88%)</u>**

||<br>Age gap<br>(own - p<br>age)|<br> <br>artner|<br>Partner's<br>schoolin|<br> <br>g|<br>Partner's<br>assets at u|<br>nion|<br>Height o|<br>fpartner|BMIof|partner|
|---|---|---|---|---|---|---|---|---|---|---|
|Outcomes|Males|Females|Males|Females|Males|Females|Males|Females|Males|Females|
|_(1) Schoolin_|_g only (OL_|_S)_|||||||||
|Schooling|-0.071|0.113|0.507|0.612|499.518|1395.827|0.173|0.280|-0.062|0.016|
|t|-1.03|1.25|**10.47**|**8.68**|1.52|1.49|**1.99**|**2.38**|-0.91|0.21|
|F|1.07|1.56|109.60|75.39|2.31|2.21|3.96|5.67|0.83|0.05|
|p-value|0.30|0.21|0.00|0.00|0.13|0.14|0.05|0.02|0.36|0.83|
|n|401|479|396|350|399|327|349|317|345|313|
|_(2) Schoolin_|_g (IV)_||||||||||
|Schooling|-0.392|0.134|0.585|0.833|70.288|2105.613|-0.133|-0.833|-0.250|-0.448|
|z|**-1.97**|0.43|**4.54**|**4.77**|0.14|0.74|-0.58|**-2.33**|-1.13|**-2.11**|
|Over-id|16.34|12.45|14.68|25.75|13.20|20.69|20.34|13.11|26.9|20.01|
|p-value|0.57|0.82|0.68|0.11|0.78|0.30|0.31|0.79|0.08|0.33|
|F|3.85|0.18|20.49|22.63|0.02|0.54|0.33|5.38|1.27|4.44|
|p-value|0.05|0.67|0.00|0.00|0.89|0.46|0.56|0.02|0.26|0.04|
|n|401|479|396|350|399|327|349|317|345|313|
|_(3) Schoolin_|_g and heig_|_ht (IV)_|||||||||
|Schooling|-0.313|0.169|0.630|0.730|912.440|3028.543|-0.085|-0.345|0.091|-0.455|
|z|-1.27|0.55|**4.40**|**4.27**|1.16|1.04|-0.34|-1.17|0.33|**-2.02**|
|Height|-0.432|0.169|0.045|-0.069|407.410|-616.239|0.293|0.269|0.412|-0.133|
|z|**-1.95**|0.72|0.35|-0.41|0.59|-0.34|1.36|0.87|1.64|-0.64|
|Over-id|14.39|12.68|20.66|38.57|14.17|24.06|23.52|25.37|21.61|9.45|
|p-value|0.64|0.76|0.24|0.00|0.66|0.12|0.13|0.09|0.20|0.93|
|F|2.30|0.41|9.63|9.71|1.21|0.67|0.97|1.47|1.34|2.03|
|p-value|0.10|0.67|0.00|0.00|0.30|0.51|0.38|0.23|0.26|0.13|
|n|302|374|298|269|301|251|260|243|258|239|
|_(4) Schoolin_|_g and heig_|_ht (IV) and f_|_amily backg_|_round_|||||||
|Schooling|-0.264|0.069|0.643|0.740|922.406|2831.334|-0.065|-0.388|-0.064|-0.529|
|z|-0.97|0.22|**4.43**|**4.22**|0.73|0.89|-0.25|-1.21|-0.24|**-2.20**|
|Height|-0.551|0.021|0.030|-0.009|40.341|-1890.197|0.378|0.167|0.212|-0.190|
|<br>z|**-2.49**|0.09|0.22|-0.05|0.05|-1.16|**_1.74_**|0.52|0.95|-0.91|
|Over-id|13.20|16.48|20.77|35.18|13.64|18.56|22.18|26.29|25.77|7.74|
|p-value|0.72|0.49|0.24|0.01|0.69|0.35|0.18|0.07|0.08|0.97|
|F|1.79|0.83|9.88|6.14|0.82|3.86|0.77|0.77|0.96|1.49|
|p-value|0.10|0.55|0.00|0.00|0.56|0.00|0.59|0.59|0.45|0.18|
|n|302|374|298|269|301|251|260|243|258|239|

t- and z-statistics in bold are significant at 0.05 level except those that also are in italics that are significant at 0.10 level.
**Non-Pharmaceutical Interventions (NPIs)** :  We use mandatory closures of nonessential businesses as both most relevant to the issue of economic mobility and figuring most prominently in the policy debate.  State-level data for the United States are collected from Raifman et al (2020) and NPIs enter as indicator variables taking a value of 1 if a given NPI is implemented and 0 otherwise.  Globally, we employ information on national NPIs available from the Blavatnik School of Government at Oxford University. For select countries for which we employ subnational mobility data to explore the impact of local case incidence, we use national data on the nationally implemented NPIs as controls. The exception is Brazil for which NPIs are established by states, and we collect data at that level.

# **III. Results: United States**

Figure 1 plots the level of mobility against the log of the number of cases per capita by U.S. state for the United States. It further divides the sample by whether the states are covered by restrictions on nonessential businesses (red) or not (blue). Two drivers appear as potentially important. First, the data are consistent with restrictions leading to lower levels of mobility. However, more strikingly, there is a clear downward sloping relationship between reported cases and mobility independent of such restrictions.

**Figure 1: Mobility, COVID Cases and Official Restrictions, United States**

<!-- Start of picture text --> US<br>-5 0 5 10<br>Log(case per 1 million people)<br>No shutdown Shutdown<br>20<br>0<br>-20<br>-40<br>-60<br>Workplace mobility (% deviation from baseline)<br><!-- End of picture text --> Notes: Workplace mobility is Google measure of work-related mobility index. See text for sources.

Table 1 more formally tests this relationship by estimating Table 1 more formally tests this relationship by estimating M M M M i 0+ 1C C i −1 2 A A C 3 N i −1 i (1) ii−1 ii ii = ββ ββ CC + ββ CCAACCMMCC C CC + ββ NN + μμ + vv + εε Where mobility is the Google measure, Cases is the log incidence, Aggregate Cases is the national analogue, NPI are non-pharmaceutical intervention(s), and ui are subnational (state) fixed effects that also effectively put cases in per capita terms, and vt, time fixed effects. There are clear issues of bi-directional causality here.

5
4 provides evidence against the perfect capital paradigm.<sup>2</sup> Frequently the econometric strategy consists of adding financial variables, in particular cash flow and debt, to a standard investment model, usually based on a neoclassical technology with adjustment costs, yielding a relationship between investment and the market value of the firm relative to the replacement cost of capital (Tobin's Q). Under the assumption of perfect capital markets, financial variables should not matter, given Q. Its significance is taken as a sign of imperfections in the capital markets.

Another set of contributions starts from a model with adjustment costs, like the first group of papers, and directly estimates the Euler equation for investment, without making use of Q. The idea here is that the standard Euler equation will be mispecified for those firms that either have reached the (exogenous) maximum amount of debt allowed, or are at the point of paying no dividends. A correctly specified Euler equation would contain multipliers associated with the constraints, and their omission would lead to mispecification.3 The Euler equation approach is particularly appealing for developing countries (where stock markets are not well developed) because it does not require information on stock market values. In this section we extend the Euler equation in two directions. First we assume that agency problems can be thought of as giving rise to an increasing cost of borrowing above the safe rate, as the degree of leverage increases (see also Galeotti et al. (1989)). Second we introduce ceilings on the maximum degree of leverage that various firms are allowed, and discuss whether this form of imperfection can be distinguished from the rising cost of funds schedule. We then ask empirically whether these two forms of imperfection affect firms differently, and whether they become less severe after financial liberalization.

The lack of appropriate firms' panel data for LDC's has hindered the econometric investigation of the relationship between investment and financial conditions. With a few exceptions, the work on investment has been conducted u.sing aggregate data that are inappropriate for determining the role of financial factors in capital accumulation decisions and how their relevance varies across different groups of firms and over time, particularly with regard to the effects of financial liberalization.<sup>4</sup> 2 Examples are Fazzari, Hubbard and Petersen (1988), Gertler and Hubbard (1988), Hoshi et al. (1988), Blundell, Bond, Devereux, and Schiantarelli (1992), and Devereux and Schiantarelli (1990).

> 3 See Gilchrist (1989), Whited (1992), Hubbard and Kashyap (1992), Galeotti, Schiantarelli and Jaramillo (1990), Gertler, Hubbard, and Kashyap (1990).

> 4 Tybout (1983) uses three digits manufacturing industry data for Colombia. He partitions his