rafmacalaba/datause-encoder-data huggingface dataset:
We estimate economic activity in the geographical extent of each hexagon using nighttime lights, as they are measured by the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) series of satellites. We use the _DMSP OLS: Nighttime Lights Time Series Version 4_ product of the DMSPOLS. Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. We analyze the _stable_lights_ band, which contains the lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires, have been discarded. The background noise was identified and replaced with values of zero.

One of the limitations of DMSP-OLS is the lack of on-board calibration between satellites. The preflight calibration for the individual instruments is of little value, because the gain commands made to the instrument are not recorded in the data stream (Elvidge et al. 2014). We adopt a methodology developed by Elvidge et al. (2009) to intercalibrate the values of the satellites and allow a temporal analysis. This procedure relies on areas (known as reference areas) where little or no change in lighting has occurred over time. In their analysis, the authors find that the area of Sicily, Italy, has the most favorable characteristics to serve as a reference area: it consists of an even spread of data across the full dynamic range (0-63) and a clearly defined diagonal cluster of points. A second-order regression model is performed for each satellite, using F121999 satellite as the reference (this satellite had the highest digital values) to adjust all other satellite years to its data range. Because in some years multiple satellites have collected data (see table A2.1), for each year we select one satellite that we use for the analysis. The satellites we use are: F101992, F101993, F121995, F121996, F121997, F121998, F121999, F152000, F162008, F162009, F182010, F182011, and F182012. An intercalibration is applied based on the coefficients presented in table A2.2 (adopted from Elvidge et al. (2014)). The form of the calculation is: y� c� � c�x�c�x<sup>�</sup> (A2.1)

# **Table A2.1 Intercalibration Coefficients**

|**Satellite**|**Year**|**_c0_**|**_c1_**|**_c2_**|
|---|---|---|---|---|
|F10|1992|-2.057|1.5903|-0.009|
|F10|1993|-1.0582|1.5983|-0.0093|
|F12|1994|-0.689|1.177|-0.0025|
|F12|1995|-0.0515|1.2293|-0.0038|
|F12|1996|-0.0959|1.2727|-0.004|
|F12|1997|-0.3321|1.1782|-0.0026|
|F12|1998|-0.0608|1.0648|-0.0013|
|F12|1999|0|1|0|
|F15|2000|0.1254|1.0452|-0.001|
|F15|2001|-0.7024|1.1081|-0.0012|
|F15|2002|0.0491|0.9568|0.001|
|F15|2003|0.2217|1.5122|-0.008|
|F16|2004|0.2853|1.1955|-0.0034|
|F16|2005|-0.0001|1.4159|-0.0063|
|F16|2006|0.1065|1.1371|-0.0016|
|F16|2007|0.6394|0.9114|0.0014|
|F16|2008|0.5564|0.9931|0|

26
The actual measured temperatures are first converted into temperature anomalies, by subtracting the average “normal” temperature for each station-month, as calculated for the reference period 1960-90. All the temperature anomaly series are plotted in Figure A1 in the Appendix.

Once we have the series of temperature anomalies, it is straightforward to test whether there is a significant trend. This is done by regressing the anomaly on a trend-variable which has been scaled so that the coefficient can be directly interpreted as temperature change per decade in degrees Celsius. We use a confidence level of 95 percent to decide whether the trend is statistically significant, which means that the P-value of the trend coefficient should be less than 0.05 for the trend to be significant.

Table 6 shows the estimated trends in temperatures for each of the 18 stations in Bolivia. Of these, four stations show significant warming since the middle of the previous century and nine show no significant change, and 5 show significant cooling.

**Table 6: Estimated temperature trend (oC/decade) for 18 weather stations in** **<u>Bolivia, 1948-2008</u>**

|**Station**|**Trend**|**t-value**|**P-value**|**# of obs.**|
|---|---|---|---|---|
|Camiri|-0.07|-1.79|0.075|535|
|Charaña|-0.25|-6.91|0.000|412|
|Cobija|0.19|6.95|0.000|562|
|Cochabamba|0.15|4.85|0.000|557|
|Concepción|-0.01|-0.34|0.736|515|
|La Paz / El Alto|-0.27|-10.54|0.000|639|
|Oruro|-0.19|-5.94|0.000|547|
|Puerto Suarez|0.10|2.38|0.018|354|
|Riberalta|0.03|1.26|0.207|557|
|Roboré|-0.06|-1.45|0.148|467|
|Rurrenabaque|-0.02|-0.80|0.427|557|
|San Ignacio de Velasco|0.22|5.90|0.000|458|
|San Joaquin|-0.01|-0.44|0.658|448|
|Santa Cruz / El Trompillo|0.04|1.16|0.247|557|
|Sucre|-0.28|-5.65|0.000|321|
|Tarija|-0.11|-1.71|0.089|318|
|Trinidad|-0.05|-1.75|0.081|561|
|Yacuiba|-0.14|-3.34|0.001|527|

_Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

None of the stations in Bolivia get even close to having observations for all the 721 months in the 1948-2008 period, but some stations have reported more consistently than others. If we limit ourselves to the 15 stations that have at least 400 observations, we find that three show significant warming, four show significant cooling, and eight show no significant trend. A similar distribution is found if we limit ourselves to the 11 stations that have at least 500 observations.

9
**Table 1 Studies on capital formation in China**

|**Author**|**Year of**<br>**Publication**|**Methodology**|
|---|---|---|
|Liu and Yeh|1963|Based on capital stock data for 1955 published by the State<br>Statistical Bureau in 1957, back-cast to 1952 using investment data.|
|Chow|1993|Based on data for 1952 of the original value of the fixed capital stock<br>of state-owned units published in the Statistical Yearbook.|
|Wu|2016|Partially based on the 1951 census of national assets|
|Baillu, Kruger_et al_|2016|Based on growth rate of investment and depreciation rate|
|Maddison|2007|Based on his own estimates of investment in the pre-revolutionary<br>period.|
|Perkins and Rawski|2008|Capital-output ratio set at an assumed level|
|Perkins|1998|Capital stock set a multiple of net material product in 1952|
|Wu Yarwi|2016|Assumes capital stock of zero in 1900, uses an estimated series for<br>investment between 1900 and 1952 to generate a capital stock in<br>1952.|
|Jun_et al_|2004|Based on growth rate of investment and assumed depreciation rate|
|Feenstra_et al_|2016|Based on cross-country analysis for Penn World Tables|

**Table 2 Estimates of the capital stock in 1952**

|Author|Capital Stock<br>current prices<br>1952|Capital-<br>output<br>ratio 1952|Comments|
|---|---|---|---|
|**_Studies that exclude housing_**||||
|Liu and Yeh|55.9|0.7|Excludes government|
|Chow|76.6|0.9|Excludes government|
|Wu|160.0|1.9|Includes government|
|Maddison|254.0|1.2|Based on his own GDP|
|**_Studies including housing and government_**||||
|Perkins and Rawski|82.4 or 164.8|1.0 or 2.0||
|Perkins|202.1|2.5||
|Wu Yarwi|133.8|1.6||
|Zhang_et al_|74.8|0.9||
|Feenstra_et al_|175.6|2.6|<sup>Capital stock is broken</sup><br>down into four asset types|

5
Table 2: Summary statistics of household characteristics, _kharif_ 2001, 2009 ( _n_ = 236)

|||2001||||2009|||
|---|---|---|---|---|---|---|---|---|
|Characteristic|mean|s.d.|min.|max.|mean|s.d.|min.|max.|
|Age of hh. head (years)|44.8|12.2|24|80|49.1|12.2|20|87|
|Educ. of hh. head (years)|3.10|3.23|0|14|3.19|3.34|0|15|
|Household size|5.04|1.95|2|14|6.09|2.15|2|14|
|Landholding (acres)|2.31|2.94|0|30|2.82|3.20|0|30|
|No. of milk animals|0.60|1.25|0|7|0.86|1.87|0|14|
|No. of draught animals|1.42|1.09|0|6|1.73|1.56|0|7|
|No. of sheep and goats|1.15|2.49|0|24|3.42|4.46|0|36|
|Nearest rural road <sup>_a_ </sup>(km.)|||||0.61|1.31|0|5.7|
|Nearest district road (km.)|||||5.58|3.56|0|12.0|
|Nearest highway (km.)|||||10.59|7.95|1.5|35.0|

> _aKutcha_ or all-weather. Source: van Dillen (2008) and re-survey.

to be independent. As Deaton (1997: 14) puts it so cogently, ‘In the (absurd) limit, if everyone in the same cluster were replicas or clones of one another, the effective size of the sample survey would not be the number of households, but the number of clusters.’ Some of the data also relate to various periods, and here, too, care is needed.

With this warning in mind, and as a preliminary to the sections that follow, let _xijt_ denote the value of some characteristic or action of household _i_ in village _j_ in period _t_ . Suppose _xijt_ is given by the linear relation where: _μ_ is the population mean; _djt_ = 1 if village _j_ has an all-weather road in period _t_ , and _djt_ = 0 otherwise; _ui_ is a term specific to household _i_ representing unobserved heterogeneity; _vj_ and _wt_ are analogously defined for village _j_ and period _t_ , respectively, whereby _wt_ represents time-varying common effects such as the weather and macroeconomic conditions; and _eijt_ is an idiosyncratic disturbance term. Observe that this specification involves the assumption that the adjustment to the provision of the road 6
- Federated States of Micronesia: 107,000 population, $US2,340 GNI per capita, 36.9% brain drain rate.

- New Zealand: 4.3 million population, US$27,940 GNI per capita, 15.8% brain drain rate.

- Papua New Guinea: 6.4 million population, $US1,010 GNI per capita, 19.8% brain drain rate

-

- Tonga: 110,000 population, $US2,560 GNI per capita, 65.1% brain drain rate.

These countries offer an interesting range of population sizes, levels of development, and also opportunities for migration. Micronesia and New Zealand both have free mobility to an important migrant destination - Micronesia to the United States and New Zealand to Australia – whereas in the other countries individuals can only migrate through satisfying the requisites of particular immigration categories.

# **2.3 The Sample Frame and Survey**

In each country we assembled a sample frame of the top academic achievers in the country, for individuals graduating high school between 1976 and 2004, using a mixture of government and school records. Appendix 1 discusses the specifics of sample frame construction for each country. We then attempted to track down these individuals and survey them in their present country of residence. The tracking effort was extensive, and involved visits to the high schools and home communities, online search, the involvement of school alumni networks where they existed, phone book searches by surname, and asking located students for help in identifying others. Individuals were then administered a survey with detailed questions on their migration and educational histories, their current occupation, and the channels through which they interact with their home countries when abroad. These surveys were carried out online, in-person in the five source countries, and, in some cases, by phone. The survey efforts began with the Tongan sample in late 2007, and finished with the Ghanaian sample in late 2009.

Table 1 summarizes the results of this surveying effort. Our total sample frame consisted of 4,131 individuals from the five countries, of which we were able to interview 1,240 (30%). The survey interviewed individuals who are now living in 45 different countries. The survey success rate varied across source countries, ranging from 15 percent in Ghana to 73 percent in Tonga. This reflects both differences in our ability to track individuals from different countries, as well as differential survey response rates, with fears about identity theft making some high achievers reluctant to participate in an online survey. Even in cases where we could not survey 7
philippines, Smith and Choung (1986) fou,nd that parental occupational **and** educational **level** shaped children's school attainment, with the **some** level of magnitude, **since** the **early** 20th century. Similarly, using household income **as** a rough proxy for student social class, Chernichovsky and Meesook (1985) found significant educational attainment effects in Indonosia. In Nepal, Jamison and Lockheed (1987) found that parental and grandparental social class (landholding, ca-te, schooling) strongly determined child school participation. Adult literacy **was** *lso measured, and found to **be** strongly associated with **years** of schooling, a finding recently repeated in Brazil (Psacharopoulos and Arriagada, 1987). This study underscores tho importance of separating school attainment (enrollment and persistence) from level of academic achievement while in school **as** two distinct outcomes.

Students' family background also affects lkarning while in school. For example, Schiefelbein and Simmons (1981) found that social class significantly helped predict achievement in 28 of 37 Third World studies reviewed. Moreover, family background may be quite salient for familiar subjects, such **as** reading, and less so for subjects relatively less familiar to many Third World communities, such as science. On this point, Schiefelbein and Simmons noted that student family characteristics accounted for a higher proportion of variance in reading achievement than in science achievement in multivariate stu .ies from India, Peru, and Malaysia. And Heyneman and Loxley's findings, while widely celebrated, applied only to achievement in science.

Third, family background appears more strongly to deternine student achievement within urban schools, whereas school factors have

- 4 -
- Frischtak, C. R. and J. Mourão (2017a): “O Estoque de Capital de Infraestrutura no Brasil: uma abordagem setorial,” _Desafios da Nação_ , 133.

- ——— (2017b): “Uma Estimativa do Estoque de Capital de Infraestrutura no Brasil,” _Desafios da Nação_ , 99.

- Hub, G. I. (2019): _PPP Risk Allocation Tool 2019 Edition - Transport_ , Global Infrastructure Hub Limited.

- International Monetary Fund (2022): “Global Financial Stability Report—Navigating the HighInflation Environment,” Tech. rep., IMF - Washington, DC.

- Irwin, T. (2007): _Government guarantees: Allocating and valuing risk in privately financed infrastructure projects_ , World Bank Publications.

- Kamps, C. (2006): “New estimates of government net capital stocks for 22 OECD countries, 1960–2001,” _IMF staff papers_ , 53, 120–150.

- Kessides, I., B. mondiale, and Weltbank (2004): _Reforming Infrastructure: Privatization, Regulation, and Competition_ , A World Bank policy research report, Oxford University Press.

- Matsukawa, T. and O. Habeck (2007): _Review of risk mitigation instruments for infrastructure financing and recent trends and developments_ , vol. 4, World Bank Publications.

- Matsukawa, T., R. Sheppard, and J. D. Wright (2003): _Foreign exchange risk mitigation for power and water projects in developing countries_ , World Bank Group, Energy and Mining Sector Board.

- Menezes, F. M. and P. K. Monteiro (2004): _An introduction to auction theory_ , Oxford University Press.

- Persaud, A. (2023): “Unblocking the green transformation in developing countries with a partial foreign exchange guarantee.” Tech. rep., Climate Policy Initiative.

- Phillips, P. C. and S. Jin (2014): “Testing the martingale hypothesis,” _Journal of Business & Economic Statistics_ , 32, 537–554.

- Songwe, V., N. Stern, and A. Bhattacharya (2022): “Finance for climate action: Scaling up investment for climate and development,” _London: Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science. Retrieved 9th October_ .

- Vasconcelos, V. S., F. Q. Silva, R. L. Rovai, M. d. M. C. De Liberal, and C. D. A. Rached (2019): “Identification of the main risk factors in infrastructure projects of transporting people on rail by public-private partnerships,” _International Journal of Entrepreneurship_ , 23, 1–17.

- Verdouw, W., D. Uzsoki, and C. D. Ordoñez (2015): “Currency risk in project finance,” Discussion paper. Available at https://www.iisd.org/system/files/publications/ currency-risk-project-finance-discussion-paper.pdf.

50
|<br>**Attrition Equations**<br>**Probit Estimates: Probability of Being in the Panel**<br>**(Marginal Effects, Standard Errors in Par**<br>**APPENDIX TABLE 1**|(1)<br>(2)<br><br><br>**Estimating Sample**<br>**entheses)**<br>In Panel Sample|
|---|---|
|Initial Village Inequality (Mean Log Deviation)|-0.516<br>-0.920|
||(1.698)<br>(1.726)|
|**Household Variables (in initial period, 1987-88):**<br>Log Initial Income|**_18.614_**<br>**_-0.195_**|
||(5.633)<br>(0.063)|
|Log Initial Income - Squared|**_-2.873_**|
||(0.908)|
|Log Initial Income - Cubed|**_0.144_**<br>(0.049)|
|Share of Income from Agriculture in 1987-88|0.180<br>0.197|
||(0.217)<br>(0.217)|
|Share of Income from Wages in 1987-88|0.082<br>0.093|
||(0.192)<br>(0.196)|
|Share of Income from Family Businesses in 1987-88|0.010<br>-0.012|
||(0.185)<br>(0.184)|
|Household Education in 1987-88|-0.004<br>-0.003|
||(0.010)<br>(0.010)|
|Log Household Size|0.087<br>0.097|
||(0.074)<br>(0.073)|
|Dependency Ratio|0.183<br>0.190|
||(0.143)<br>(0.142)|
|Log Cultivated Land|0.009<br>0.010|
||(0.023)<br>(0.024)|
|Head Age <= 30|-0.089<br>-0.094|
||(0.078)<br>(0.078)|
|Head Age between 31 and 40|0.030<br>0.029|
||(0.052)<br>(0.052)|
|Head Age between 51 and 60|**_-0.184_**<br>**_-0.180_**<br><br>|
||(0.065)<br>(0.064)|
|Head Age 61 and over|**_-0.275_**<br>**_-0.257_**<br><br>|
||(0.098)<br>(0.097)|
|**Village Variables (in initial period, 1987-88):**||
|Mean Log Per Capita Income|-0.095<br>-0.074|
||(0.256)<br>(0.259)|
|Average Education|0.049<br>0.054|
||(0.065)<br>(0.065)|
|Cultivated Land Per Capita|-0.115<br>-0.093|
||(0.153)<br>(0.155)|
|Village Dependency Ratio|0.193<br>0.369|
||(1.598)<br>(1.583)|
|Village Share of Income from Agriculture|-0.652<br>-0.563|
||(1.492)<br>(1.458)|
|Village Share of Income from Wages|-0.650<br>-0.504|
||(1.522)<br>(1.500)|
|Village Share of Income from Family Businesses|-0.677<br>-0.462|
||(1.700)<br>(1.642)|
|Village Tax Revenue Per Capita|**_0.954_**<br>**_0.847_**|
||(0.427)<br>(0.432)|
|Village Government Expenditure Per Capita|**_-0.846_**<br>-0.769|
||(0.405)<br>(0.411)|
|Sample Size(1987-88)<br>_Notes:_<br>2) All specifications include village location and province dummies<br>3) Statistically significant coefficients highlighted (at the 5% level)<br>4) Specification (1), with the cubic in household income, is used to gene<br>the attrition corrections throughout the paper.<br>1) Dependent variable is the probability of being in the primary estimatin<br>1987 through 2002, with complete variable set)|4847<br>4847<br>rate inverse probability weights for<br>g sample (observed in all years from|
Table 4: Roads and Battles (ACLED)

||(1)<br>events|(2)<br>events|(3)<br>events|(4)<br>fatalities|(5)<br>fatalities|(6)<br>fatalities|
|---|---|---|---|---|---|---|
|project ongoing|-0.032|||-0.024|||
||(0.020)|||(0.028)|||
|project completed||-0.081***|||-0.071||
|||(0.029)|||(0.043)||
|road project stock|||-0.034*|||-0.031|
||||(0.020)|||(0.029)|
|Observations|18722|18722|18722|18722|18722|18722|
|R2|.527|.527|.527|.409|.41|.41|

Note: * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001. Standard errors are clustered at the cell level. Cell and year-fixed effects are always included. Events and fatalities are in logs (+1).

Overall, the association between roads and violence reduction is strongest for violence that targets civilians and there is some evidence on reductions of battles in the ACLED data. Road projects are not associated with a reduction in state and non-state violence.

In additional results in Appendix table A4 we show that ruggedness, measured by strong slopes in a cell, do not seem to interact strongly with roads. However, for one-sided violence we find that the effect of road project is dramatically increased in rugged terrain.

As we do not have forecasts for ACLED fatalities or sub-categories of the UCDP data, we do not apply the PreMDiD method to other violence types as the control group cannot be properly matched.

# **5.2 The Role of Mining Revenues**

Access to mining revenues is a driver of violence, and improved roads facilitate access to mining resources. We first control for the presence of mines as a robustness check for our main estimates of the impact of roads and then interact the presence of mines with road projects to understand how the impacts of road projects vary depending on the presence of mines.

First, we add changes in mining revenues in the first specification (Table 1). We multiply the number of mines in a cell times by the commodity price of these mines to get to a proxy of revenue and add it as controls. We show the results for artisanal gold mines and the two most common types of large mines: copper and cobalt. The main estimates for the impacts of road completion and the stock of roads remain similar (Table A5). The results of the PreMDiD are also robust to additional mining controls.

Second, we interact mining revenues with the road stock variable in the first specification to understand the interactions of roads and mines on violence. We show results when 26
The second set of experiments follow the same logic of reallocation and changes in the composition of government expenditures. Likewise, one of these experiments assumes a 5 percent uniform reduction in net lending and therefore reduced interest payments on government liabilities in the same order of magnitude. These savings are either redeployed to fund public expenditures or indirectly support private investments through lost tax revenues. This experiment considers the two alternatives: the option of raising public expenditure from reduced debt burden is contrasted with the alternative of boosting private investments in the same order of magnitude and corresponding cut in the stock of government debt (Figure 3 below).

**Figure 3 - Cameroon: Different Scenarios on Budget Allocation**

<!-- Start of picture text --> Cameroon: Increase in private and public capital<br>accumulation on the reduction of net lending<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Actual per capita GDP<br>Estimated per capita GDP ‐ allocation to private investment<br>Estimated per capita GDP ‐ allocation to public investment<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> Under these two alternative scenarios, the hypothesized predicted output is still significantly higher than the actual, further supporting the expected benefits of fiscal expansion during economic downturns, interestingly in a region where pro-cyclical fiscal adjustments have been at the heart of macroeconomic reforms [World Bank (2005), Rodrik (2006)].<sup>27</sup> The potential benefits under this hypothesized alternative are further illustrated by the sustained decline in Cameroon’s actual output during most of the adjustment era, especially between 1985 and 1996 [World Bank (2003)].<sup>28</sup> As Figure

> 27 In most countries, the protracted economic downturn is reflected in the recurrence of large and sustained balance of payment deficits and negative terms of trade shocks. In some cases, and particularly in natural resources dependent countries, these deficits were both the manifestation and cause of the crisis.

> 28 According to the World Bank, Cameroon went through a long period of economic depression from the mid1980s through the mid-1990s. During that window of major economic contraction, it experienced serious economic difficulties, with per capita income falling by over 50 percent and fiscal deficits rising rapidly. In response, the government undertook large cut in nominal wages, drastic cuts in social and infrastructure spending. It also accumulated large external and domestic arrears [World Bank (2003)].

18