rafmacalaba/datause-encoder-data huggingface dataset:
15 Useful results can be obtained initially with relatively aggregated Census level inputs, but the same modeling engine is able to accept the most disaggregated customer location data that can be provided. Other inputs representing the costs of best international practice in specific areas can also be derived from current values used in the HCPM since these are driven by technology rather than local conditions. At a later time, these inputs can be customized to fit local conditions as required.

But, what is the usefulness for regulation of cost proxy models like the HCPM? Several questions arise in this context: What is the least information a regulator should have at hand if he is to trust the results given by the model? What is there to be gained if better data is available? What can the regulator do with certain qualities of information? Precision will normally pay, so it is likely to be convenient to make some investment in data collection and elaboration.

We mentioned that the HCPM had been designed with the expectation that it would ultimately be used with a source of geocoded data (i.e. geocoded customer location data or Census block level data). But does lack of geocoded locations imply that the model cannot be applied? The answer to the question is no (with some qualifications). If a map of the study area were available, a grid could be placed on top of it, and blocks could be constructed, where each would be given a location in the grid. In this most primitive case _(least information case),_ we assume that only the total number of residential and business lines for each wire center, locations of the wire centers, and a fairly detailed country or regional map are available. We assume that a user is capable of manually "gridding" the map, creating a matrix in which areas that appear from the map to be populated are given the value '1' and areas which appear not to be populated are given the value '0'. Each populated mesh in the grid is assumed to have the same number of lines and the same area; these data are converted via a straightforward computer program into input files usable by HCPM. The program assigns each populated mesh block to the nearest wire center as measured by earth surface distance between the coordinates of the mesh block and the switch.

Alternatively, another minimum information case might be one in which the wire center locations are known and customer locations are known at a more aggregate level;
14 Finally, for deindustrialization, we use as a proxy the decline in the GDP share of MFG over time. Indeed, if MFG productivity decreases relative to the world, MFG employment should decrease. The GDP share combines information on productivity and employment.

**3. Resources, (De-)Industrialization, and Urbanization**

**Data.** We study 116 relatively large countries, with a developing status as of 1960, and for which data on urbanization, resource exports, and the GDP shares of manufacturing (MFG) and services (SERV) are available every 5 years between 1960 and 2020.

We obtain urban shares (%) from the United Nations (2018) and the share of fuel and mining exports in total exports for the period 1960-2010 from GJV16 who rely on data from World Bank (2021) and the _Mineral Industry Surveys_ of USGS (2020). We extend their data up to 2020, modifying them when needed. The USGS data are important due to the fact that the World Bank data are incomplete or incorrect in many cases.<sup>17</sup> Measurement error, if classical, would make us under-estimate the contribution of these resources to urbanization. The export shares of all agricultural products come from FAO (2020). Using these shares and knowing from World Bank (2021) the export-to-GDP ratio of each country in each year, we calculate NRX/GDP (%) as in Sachs and Warner (1995, 1997). Next, we obtain the time series of the GDP share of MFG+SERV by relying on World Bank (2021), Central Intelligence Agency (2021) and United Nations (1960-1980, 2020c) and the GDP share of FIRE c. 2020 from United Nations (2020a).<sup>18</sup>

**Country-level results.** For countries _c_ , we estimate the following cross-sectional model:

URB _c,_ 20 = _α_ + _β_ NRXGDP _c,_ 60 _−_ 20 + _γ_ MFGSERV _c,_ 20 + _δ_ DEINDU _c,_ 80 _−_ 20 + _Xcπ_<sup>_′_</sup> + _μc_ (8) In this equation, URB is the urban share (%) in 2020, NRXGDP – the mean export-to-GDP ratio of natural resources (%) in 1960-2020, MFGSERV – the GDP share of MFG and SERV (%) in 2020 (capturing structural change due to manufacturing or tradable services), and DEINDU – the absolute decline in the GDP share of manufacturing (%) between 1980 and 2020 transformed so that a positive value indicates _more_ deindustrialization (= 0 if no

> too many countries. Furthermore, NRX/GDP has the advantage of attributing to NRX the “value” of any input used in producing the resources, as such inputs also directly contribute to urbanization. 17According to the WDI of World Bank (2021), fuel and mining exports accounted for 8% of Botswana’s exports in 2015. According to USGS: “Botswana’s exports were $6.33 billion, of which diamonds accounted for 82.9%; copper and nickel, 5.9%; and soda ash, 1.4%.” Likewise, according to the WDI, fuel and mining exports accounted for 6% of Burkina-Faso’s exports in 2013. According to USGS: “The International Monetary Fund estimated that gold production [...] accounted for about 71% of the country’s total exports.” 18See Web Data Appx. Section D for details on the data sources.
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<!-- Start of picture text --> Middle class +)<br>Poor and the vulnerable<br>- Industry agriculture<br>8 i) .<br>} Q construction<br>8<br>i]<br>=<br>S Services-<br>fe}<br>=<br>Cy)<br>u<br>3 6<br>B<br>o size of the buble<br>3<br>g # of employed<br>rT]<br>o<br>=<br>-6 -4 -2 0 2 4 6 8 10 12<br>Employment growth, 2000-2010<br><!-- End of picture text --> <!-- Start of picture text --> 2003 2007 2009 Ww 109<br>7cw 0 ¥cc go<br>SE s<br>oS -0.2 3 60<br>Go =<br>oS a 40<br># = 70.4 3<br>> 2 ny<br>E= 0.6 o 20<br>Esoo Ry<br>TB YW -08<br>< “3 male female male female male female<br>§s 7 2003 2007 2009<br>-1.2 Hiotalemployed unemployed S retired<br>mstudent @ OLF<br>munconditional conditional<br><!-- End of picture text --> <!-- Start of picture text --> Ww 109<br>¥cc go<br>s<br>3 60<br>=<br>a 40<br>3<br>ny<br>o 20<br>Ry<br>male female male female male female<br>2003 2007 2009<br>Hiotalemployed unemployed S retired<br>mstudent @ OLF<br><!-- End of picture text -->
**Table 2: Determinants of Life Satisfaction in MENA: Ordinary Least Squares Estimates**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|
||DEV<br>MENA|DEV<br>MENA|DEV<br>MENA|DEV<br>MENA|DEV<br>MENA|DEV<br>MENA|DEV<br>MENA|Arab<br>Spring|
|Dissatisfied with freedom to choose life: Yes||-0.351***|||||-0.033|-0.019|
|||(0.030)|||||(0.031)|(0.053)|
|Dissatisfied with standard of living: Yes|||-1.333***||||<br>-1.238***|<br>-1.213***|
||||(0.029)||||(0.030)|(0.053)|
|Income (1,000’s)|||<br>0.023***|0.029***|||<br>0.023***|<br>0.025***|
||||(0.002)|(0.002)|||(0.002)|(0.003)|
|Dissatisfied with efforts to increase high quality||||-0.361***|||-0.154***|-0.139***|
|<br>jobs: Yes||||(0.031)|||(0.032)|(0.053)|
|Dissatisfied with the educational system or the||||-0.340***|||-0.166***|-0.158***|
|schools: Yes||||(0.030)|||(0.029)|(0.051)|
|_(Reference group: Full-time Employed)_|||||||||
|Self-employed||||0.077|||0.041|-0.024|
|||||(0.064)|||(0.061)|(0.100)|
|Unemployed||||-0.534***|||-0.335***|-0.475***|
|||||(0.082)|||(0.079)|(0.145)|
|Out of workforce||||<br>0.003|||<br>-0.019|<br>-0.028|
|||||(0.049)|||(0.047)|(0.076)|
|Underemployed||||<br>-0.267***|||<br>-0.114|<br>-0.242*|
|||||(0.082)|||(0.080)|(0.133)|
|_(Reference group: Other)_|||||||||
|<br>Working for the government||||0.245***|||0.190***|0.309***|
|||||(0.055)|||(0.052)|(0.084)|
|Undetermined||||-0.011|||-0.019|-0.280***|
|||||(0.051)|||(0.049)|(0.095)|
|Corruption widespread within government: Yes|||||-0.277***||<br>-0.077**|<br>-0.056|
||||||(0.036)||(0.035)|(0.054)|
|People cannot get ahead by working hard: Yes||||||-0.496***|<br>-0.223***|<br>-0.210***|
|||||||(0.041)|(0.039)|(0.080)|
|_(Reference group: Muslim)_|||||||||
|Not Muslim/Other religion|0.269***|0.237***|0.202***|0.168**|0.275***|0.239***|0.171**|0.176|
||(0.075)|(0.075)|(0.069)|(0.074)|(0.075)|(0.074)|(0.068)|(0.152)|

16
# 1. Introduction

This paper develops a Structural Ricardian model to measure climate change impacts on agriculture that explicitly captures adaptation decisions by farmers (Seo and Mendelsohn 2007). Many recent studies of climate change impacts on agriculture have failed to fully include adaptations (Rosenzweig and Parry 1994, Schlenker et al. 2005, Deschenes and Greenstone 2007). By failing to properly capture adaptations, these studies overstate the actual damages that will occur with climate change. However, one of the most important insights of microeconomics is that economic agents will adapt to changing conditions. The Structural Ricardian model explicitly models the adaptive behaviors of farmers in measuring the impacts of climate change on agriculture.

The traditional Ricardian model (Mendelsohn, Nordhaus and Shaw 1994) captures adaptation in its measurement of impacts, but the adaptations are a black box, never explicitly measured or identified. In contrast, the Structural Ricardian model explicitly identifies adaptation measures and quantifies their influence on impacts. Several versions of the Structural Ricardian model have been explored that examine irrigation, crop species choice, and livestock species choice (Kurukulasuriya and Mendelsohn 2007; 2008; Seo and Mendelsohn 2007). The approach was also applied to South American farm types (Mendelsohn and Seo 2007). This paper extends these early efforts to look at the choice of farm type in Africa. We examine five possible farm types: crop-only rainfed, crop-only irrigated, mixed (both crop and livestock) rainfed, mixed irrigated, and livestock-only farms. We rely on a multinomial logit regression to estimate the link between farm type choice and climate and other exogenous variables. We then estimate the conditional income from each farm type controlling for selection bias (following Heckman 1979 and Dubin and McFadden 1984). We carefully choose seasonal water flow and price variables to identify the choice equation (Ekeland, Heckman, and Nesheim 2002).

We apply the Structural Ricardian Model to study African agriculture. This is an important application because many millions of Africans depend on local farms for income and African agriculture is expected to be very sensitive to climate change We estimate the model using economic surveys collected through a GEF/World Bank project 2
WPS4249 We only observe the sign of the variable _Mi*_ , which represents the selection rule for not receiving remittances i.e. we observe only whether the household receives remittances or not, which in turns is equivalent to a negative or positive value for _Mi*_ . Identification of this model requires an exclusion restriction: a set of variables _Zi_ that are related to the migration and remittances choice but which do not directly affect earnings for households without -migrants. The term<sup>λ</sup> _i_<sup>is the selection inverse Mill’s ratio, defined as:</sup> with where<sup>μ</sup> _i_<sup>is the error component in the earning equations and</sup> θ = cov( ν _i_ , μ _i_ /) var( μ _i_ ) .

Controlling for<sup>λ</sup> _i_<sup>allows the remaining unexplained componentε</sup> _i_<sup>to have the usual</sup> desirable i.i.d. properties. If<sup>λ</sup> _i_<sup>is a significant predictor of earnings, it suggests that the selection</sup> into the non-migration status is indeed correlated with factors that affect the household earnings – and that OLS estimates of equation (12) would be inconsistent.

The exclusion restrictions that we employ for the no-remittances selection equation are an index of household assets, the percentage of households that receive remittances in the respective county of residence (a proxy for the presence of migrant networks), and their interaction. When county level indicators cannot be calculated (Guatemala and Haiti), the variable that represents the percentage of households with remittances is measured at the department/province level. In this case, only the interaction of that variable with the household assets index is included. Finally, when information on household assets is missing (Bolivia, Ecuador and Honduras), the network variable is interacted with the number of adult males, which ensures variability at the household level.<sup>25</sup>

> 25 Our motivation for using the interaction between migrant networks with household assets or the number of adult males (when information on household assets is missing) is to allow migrant networks to have a different effect on households with different predispositions to migrate in the first place. In this respect, households with a larger number of adult males have a higher likelihood of sending migrants abroad, while the same is true for those with more assets, whom are more likely to be able to afford the costs of migration.

15
expenditure in their model is determined by a wide range of regressors, including the existing stock of infrastructure, population density, the urbanization rate, the urban-rural balance, the labor force participation rate, per capita GDP, the internal and external balances, size of the foreign sector, terms of trade shifts, debt obligations, the level of institutional development, level and mix of foreign funding, and the degree of antipoverty commitment on the part of government.

In our approach, we broadly follow the Fay (2001) and Faye and Yepes (2003) specification, taking into account recent work of Estache (2005) and the World Bank (2005a) that includes estimates of investment needs in the entire Sub-Saharan African region. The methodological innovation is the use of a panel of 52 low- and middleincome countries employing dynamic heterogeneous panel estimation methods in order to arrive at robust estimates of “demand” for infrastructure in electricity and telephony as a first step towards forecasting investment needs in South Africa.

# **The Econometric Approach to Estimating Demand for Infrastructure**

# **_The general approach_**

We adopted a three-stage, general econometric approach in estimating the demand for electricity and telephony, and for projecting the dollar investment needs in electricity and telephony for South Africa through 2010.

In the _first stage_ of the research, we perform a panel data analysis on 52 countries for the period 1980-2002 from the World Bank World Development Indicators database to estimate two separate demand equations, for electricity and total telephone lines (fixed and cellular). The estimator employed in this context distinguishes between long-run equilibrium and short-run dynamics, and allows for panel group (country) heterogeneity in the dynamics of adjustment to long run equilibrium. In doing so, we avoid the danger of bias and inconsistency in estimation under the assumption of group homogeneity. The result is a set of more accurate parameter estimates, underlying the remainder of the forecasting exercise.

Then, in the _second stage_ , we use the estimated demand equations to project the electricity and telephone demand until 2010 using consensus forecast of per capita growth for South Africa and the shares of manufacturing, agriculture, and services. This provides projections of the physical stocks and flows of electricity and telephone lines.

In the _third and final stage_ , we use appropriate unit prices and the projected physical demand for electricity and telephone lines to arrive at the average dollar value of investment needs per year under the current growth scenario (average annual growth of 3.6% per year) and the alternative, government-targeted accelerated growth scenario of 6% per year. The resulting estimates provide average annual investment requirements in electricity and telecom up to 2010 for two alternative growth scenarios.

8
Before presenting poverty estimates for the years when the HEIS are not available, we provide two further validation tests: within-year and across-year validation; if the imputationbased poverty estimates are not statistically different from the true rates for both of these tests, this would provide supporting evidence for the imputation model. For the within-year validation test, we impute from the HEIS into the standardized LFS in the same year and show estimation results in Table 7, Panel B. Estimation results are quite encouraging overall. Estimates for each quarter in both 2008 and 2010 are roughly within the 95 percent confidence interval of the true rates, and estimates for 2008 even fall within one standard error of the latter. Furthermore, results are rather stable with estimates being almost identical for each quarter in the same year. For comparison, we also provide poverty estimates that are based on the original and nonstandardized variables in the LFS in Panel A, which clearly show a large downward bias. For the across-year validation, we impute from the HEIS in 2008 into the standardized variables in the LFS in 2010, and can verify that estimates (not shown) are even better and within one standard error of the true rate.

Table 8 then provides poverty estimates for the other years where only the non-consumption LFS data are available. Since the LFS data are nationally representative on a quarterly basis, to help increase the consistency with standardizing the variables, we benchmark the LFS variables on a quarter-to-quarter basis for each quarter in 2009 and 2011-2013 respectively against the HEIS-standardized variables in the corresponding quarter in 2008 and 2010. For example, for quarter 1 in 2011, the means and standard deviations ( μ 2 _t_ ,<sup>σ</sup> 2 _t_ ) in (10) are from quarter 1 in 2010 (but ( μ 1 _t_ ,<sup>σ</sup> 1 _t_ ) are from the 2010 HEIS for all years from 2010 onwards).

Estimation results in Table 8 indicate a decreasing trend for poverty rates over time. Since data for each quarter are representative of the population, we can then average the estimates for 32
lower emissions of a number of pollutants, especially in low-income countries. The findings regarding democracy and civil society suggest that the institutional preconditions of environmental governance are complex, and that only in particular contexts – such as the existence of a well-organized, informed civil society – do participative political institutions deliver a higher quality of environmental governance.

# **The Relationship between Social Institutions and the Environment**

If the empirical links from institutions to environmental governance remain ambiguous, the theoretical links are better established. In this section of the paper we present arguments as to why higher quality social institutions may be beneficial for environmental outcomes.

# _Gender Participation_

A growing literature supports the view that societies with greater gender inclusion may achieve better environmental outcomes. Especially in rural areas of developing countries, rural women depend on communal resources for subsistence needs, due to the lack of access to private land, employment, and other productive assets (Agrawal, 1994). Prasal et al (1987) attribute this close relationship to necessity. Based on their four village study in rural Nepal, they report that women have a more responsible attitude towards forests than men because it plays an important role in their daily lives. As female children and women are responsible for collecting firewood and fodder, additional hardship they and their children would face as a result of depleted forests would motivate them to become more responsible than their male counterparts. Thus, the division of labor within the household and women’s responsibility towards procuring resources such as water, fuel and wood, make women both more dependent on common property resources and at the same time more vulnerable to the negative externalities of natural resource degradation (Manion, 2002). Forest protection movements such as “Chipko”, in what is now known as the Uttarakhand Hills in India, in which women play a major role, confirm women do understand vulnerabilities and can mobilize and demonstrate in favor of environmental protection, if needed (Karan, 1994).

Molinas (1998) in an empirical study drawing on data from 104 peasant cooperatives in Paraguay, finds that the degree of cooperation within these cooperatives increases with the level of female membership. Turning to groups concerned specifically with the environment, Westermann et al. (2005) compare the performance of 46 natural resource management (NRM) groups across 20 countries in Latin America, Africa and Asia. They find that women’s groups tend to behave more collaboratively and have greater capacity to sustain collective action than groups made up entirely of men, or mixed groups containing both men and women.

Turning to evidence from the experimental economics literature, Andersen et al (2008) play a public goods game in three different regions in India, and find that fewer people 16
- 16 -

policy question is how much it costs to procure a package of necessary equipment, civil works and facilities to supply 100 kWh to people. It must be composed of generators, transmission and distribution lines, dispatch systems and other facilities. Another example is this: Some procurement packages aim to purchase both generation turbines and transmission lines, and others not. In some cases, both water treatment plant construction and treated water distribution pipe installation were contracted out together, but in other cases they are procured separately. Then, how much does a “standard” contract package for the electricity or water system? By not disaggregating data, we can answer this question.

_Zi_ is a set of bidder-specific attributes. To control bidder heterogeneity, bidder nationalities are included in _Zi_ . Firms from different countries naturally have different cost advantages. Local firms registered in a project host country may have better informational and physical access to local markets than foreign companies. Local firms may also have better knowledge of local administration and public administration. On the other hand, foreign firms may be more familiar with advanced technologies and have an accumulation of similar development projects.

Our data cover 211 procurement auctions for infrastructure development projects in 29 developing countries from 1997 to 2007. In total, 862 winning and losing bids are observed. The road, electricity and water sectors attracted 394, 193 and 329 bids, respectively. Table 2 summarizes our data coverage in terms of country and sector.

It is by no means comprehensive. First of all, our sample covers only 5 percent of total ODA in infrastructure. The total amount of contracts in our sample is about 6 billion U.S. dollars, which are distributed over the last decade or so. Total official assistance for infrastructure development amounts to US$ 12 billion a year.

Second, the country coverage is narrow and partial. China’s projects are dominant. Road projects concentrate on three East Asian countries: China, the Philippines and Vietnam. The country coverage for water and sewage projects may be relatively broad, including Iran,