rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Destination characteristics and export prices in cross section, 1997**

|||dep. var.: frm-pro|duct log export price|
|---|---|---|---|
||(1)|(2)|(3)<br>(4)|
|richer than Portugal|0.09***|0.11***||
||(0.03)|(0.03)||
|log GDP/cap.|||0.03**<br>0.03***|
||||(0.01)<br>(0.01)|
|log GDP|0.01|0.00|0.01<br>-0.00|
||(0.00)|(0.00)|(0.01)<br>(0.01)|
|European Union|0.06**|0.03|0.07***<br>0.04|
||(0.03)|(0.02)|(0.03)<br>(0.03)|
|landlocked|-0.02|0.03|-0.05<br>0.01|
||(0.04)|(0.05)|(0.04)<br>(0.06)|
|log distance|0.05***|0.04***|0.06***<br>0.04***|
||(0.01)|(0.01)|(0.01)<br>(0.01)|
|product efects|Y|N|Y<br>N|
|frm-product efects|N|Y|N<br>Y|
|R2|0.73|0.94|0.73<br>0.94|
|N|35438|35438|35438<br>35438|

Notes: Sample is all firm-product-destination observations for firms in estimation sample. Petroleum exports excluded. Robust standard errors, clustered by destination, in parentheses. *10% level, **5% level, ***1% level.
for, integration of renewable technologies may not increase the long-term costs of electricity supply from a grid.

Mai et al. (2021) examines the impacts of meeting renewable energy portfolio standards introduced or planned by various states in the USA, using National Renewable Energy Laboratory’s electricity capacity expansion model. They find an expansion of non-fossil electricity generation that accounts for 45% of the total generation would increase electricity system costs by 0.4% to 0.8% depending upon the discount rate. If the share of non-fossil fuels in the total generation reaches 78%, the system costs will increase by 2.7% to 3.6%. Using an energy system model, LEAP, Liang et al. (2019) find in China that increasing the share of renewable electricity capacity by 23.6 percentage point in 2050 (from 27.1% in BAU scenario to 50.7% in RE expansion scenario) would increase the electricity system costs by 7%. Using the LEAP model, Kumar (2016) finds that expanding renewables in Indonesia to share 40% of the total electricity generation by 2050 (or increasing renewable capacity by 1.3 times from the business-as-usual case) would increase the electricity system costs by 40% from the BAU case. The same study finds that increasing renewable capacity to account for 39% of the total capacity by 2050 (increasing renewable capacity by 3.4 times from the BAU case) would increase the electricity system costs by 82% (from the BAU case) in Thailand.

Schulte & Fletcher (2021) argue that the cost of attaining RE penetration in a grid above a threshold or inflection point (the point at which the penetration of RE begins to exceed hourly load) gets very expensive for the retail utility customers. At this point, REs must be turned down or curtailed. This situation results in diminishing returns for REs. This could be a technical barrier to achieve a 100% clean energy goal for an electricity grid.

In certain circumstances, the expansion of renewable electricity could reduce the average costs of an electricity supply system or grid and electricity prices. Such cases arise when the electricity market is well-interconnected and is fully deregulated; electricity prices are based on short-run prices, which reflect variable costs of the network at a given time, such as day-ahead price. In the EU electricity market, which represents a well-interconnected system of electricity grids, increased penetration of renewables could reduce electricity prices at certain points in time (i.e., certain hours in a day). Würzburg et al. (2013) list several studies that report a reduction of short-term costs of electricity in the EU and the corresponding prices due to the increased penetration of renewables. However, some caution needs to be taken as to whether RE sources 28
# **2 Data**

We use a novel longitudinal data set on large-scale mineral mines in Africa, from InterraRMG. We link the resource data to survey data for women and their partners from the Demographic and Health Surveys (DHS), using spatial information. Point coordinates (GPS) for the surveyed DHS clusters, a cluster being one or several geographically close villages or a neighborhood in an urban area, allow us to match all individuals to one or several mineral mines.

From a mine center point, given by its GPS coordinates, we calculate distance spans within which we place every person. These are concentric circles with radii of 5, 10, 15, 20, 25 km and so on, up to 200 kilometers and beyond 200 km.

We construct an indicator variable that answers the questions: Is there at least one active mine within x kilometers from the household? If not, is there at least one future mine (coded as inactive), or one past mine (coded as suspended) within x kilometers? If still no, the person will be coded as living in a non-mining area. If she lives within a given distance from more than one mine, she will belong to the treated group if at least one mine was producing in the year she was sampled. We assume that individuals seek employment around any mine situated within x kilometers from the home location and that benefits from an active mine dominate those from an inactive mine. A future mine is assumed to have little effect on the local economy, even if there may be economic activity associated with the pre-production stages. Thus, a person close to an active mine as well as an inactive mine will be assigned active=1 and inactive=0, since the categories are mutually exclusive. When we look at mine opening effects, all women close to suspended mines are excluded from the analysis.

Beyond the cut-off distance of x kilometers, transportation costs are assumed to be higher than benefits accruing from employment opportunities. Behind this assumption lie two assumptions, i.e., the costs in terms of transportation and information increase with distance, and the footprint of a mine decreases with distance. The chosen baseline cut-off distance is 20 kilometers, but the assumptions motivate us to try different distance cut-off points.

A woman lives on average 246 kilometers away from a mine (variable _distance_ ) and 363 kilometers away from an active mine ( _distance to active_ ) as given by Table 1. 8,195 women (1.6% of the sample) live within 20 kilometers of at least one active mine, 2,334 (0.5%) live within 20 kilometers of at least one inactive mine (but no active and no suspended mines), and 6,812 (1.3%) live close to a suspended mine.

## **2.1 Resource data**

The Raw Materials Data (RMD) data comes from InterraRMG (see InterraRMD 2012). The dataset contains information on past and current industrial mines or future industrial mines with potential for industrial-scale development, geocoded with point coordinates and yearly information on production levels. The panel dataset consists of 874 industrial mines across Africa. For these mines, we have production levels in 1975 and then for each consecutive year 4
Table 4: Correlates of deviations

|Dependent Variables:|Commitm|ent (p.p)|Verifcati|on (p.p)|Paymen|t (p.p)|
|---|---|---|---|---|---|---|
|Model:|(1)|(2)|(3)|(4)|(5)|(6)|
|_Variables_|||||||
|Log(GDP)|0.536<sup>_∗∗∗_</sup>|-0.136|1.28<sup>_∗∗∗_</sup>|0.007|0.569<sup>_∗∗∗_</sup>|-0.464<sup>_∗_</sup>|
||(0.055)|(0.074)|(0.143)|(0.062)|(0.178)|(0.221)|
|Log Population|-0.650<sup>_∗∗∗_</sup>|0.200<sup>_∗_</sup>|-1.37<sup>_∗∗∗_</sup>|0.126|-0.737<sup>_∗∗∗_</sup>|0.728<sup>_∗_</sup>|
||(0.075)|(0.089)|(0.136)|(0.093)|(0.183)|(0.338)|
|Year Fixed Efects|✓|✓|✓|✓|✓|✓|
|State Fixed Efects|✗|✓|✗|✓|✗|✓|
|_Fit statistics_|||||||
|Observations|26,792|26,792|26,168|26,168|26,129|26,129|
|Dependent variable mean|1.0|1.0|2.5|2.5|4.2|4.2|
|RMSE|2.7|2.6|4.0|3.6|5.2|4.3|
|R<sup>2</sup>|0.04|0.12|0.07|0.24|0.05|0.36|
|Adjusted R<sup>2</sup>|0.04|0.12|0.07|0.23|0.04|0.36|

**Notes** : This table presents results from OLS regressions where the deviation outcomes are defined as Dmt = (Tmt<sup>BE−T</sup> mt<sup>SICONFI</sup> )/Tmt<sup>SICONFI</sup> for each stage (commitment, verification, payment), as described in Section 3. “% Procurement” measures the percentage of all expenditures directed to procurement. Each observation is a municipality-year. The data cover 6 states (CE, MG, PB, PR, RS, SP) and their corresponding years described in Table 1. Robust standard errors in parentheses. Significance levels: ***: 0.01, **: 0.05, *: 0.1.

34
The FSA summarizes its supervisory role with the following picture.

**Table 18.  The FSA’s Supervisory Role.**

||**System functionality**|**Consumer protection**|
|---|---|---|
|**_Supervision of system stability_**|Focus: Supervision of companies’<br>management, financial strength<br>and risk management, etc. to<br>ensure operational stability and<br>adequate risk management.|Focus: Provision of products with<br>transparent product contents and<br>descriptions that ensure that<br>commitments to customers can be<br>understood and met.|
|**_Supervision of the market_**|Focus: Supervision of how<br>financial actors perform<br>independently and together with<br>the aim of ensuring market<br>efficiency and public confidence in<br>themarket.|Focus: Correct and relevant<br>information and fair treatment of<br>customers.|
|Source. FSA 2004.|||

The FSA intervenes in the operations of a company if two criteria are met. First, there must be an impending situation or risk that the market can not handle satisfactorily on its own. Second, the benefits of an intervention must be considered to outweigh the market efficiency loss implicit in an intervention.

In the supervision of insurance companies, the first question asked is whether the company’s survival is in danger and whether there is a risk that payments must be cancelled. If the answer is affirmative then the FSA will interact with the company to achieve a solution to the problem. If there is a systemic risk, which is not an immediate solvency problem, the FSA will initiate procedures for restructuring of the company – if this has not already happened through the initiative of the company itself. This is likely to result in a change of ownership through merger or takeover. An immediate solvency problem will require the intervention of Ministry of Finance and recapitalization through injection of government funds. In principle, this rule should even hold for the PPM, although this is not explicitly stated in the law, as has already been mentioned.

# _5.6.2 Determination of Solvency_

An insurance company is considered solvent if 1) capital base ≥ 4% of technical reserves for policies where the company bears the financial risk + 1 % of technical reserves for other policies + an amount related to mortality risk exposure; and if 2) capital base essentially = Value of market assets – Technical insurance reserves, with deduction of intangible assets and addition of subordinated loans.

The technical reserves are the reserves needed to cover the current _guaranteed_ liabilities to pensioners.<sup>29</sup> 29 Note that in Tables 16 and 17 above, the liability component in the ratio of assets to liabilities includes both the guaranteed and the committed bonuses, not just the guarantee component as in the solvency calculation. For 50
Table A3: Effect of Self-interviewing (SI) on Placebo Questions, Alternative Definition of Passing

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Farm work<br>(past year)|Market visit<br>(past week)|Maize/Rice<br>(next week)|Meat<br>(next week)|
|**Panel A. Malawi**|||||
|SI _×_ Screen Pass (_β_)|-0.01|0.07**|0.11***|0.10***|
||(0.01)|(0.03)|(0.03)|(0.04)|
|SI _×_ Non-Pass (_γ_)|-0.03|0.10**|-0.01|0.06|
||(0.02)|(0.04)|(0.04)|(0.04)|
|Screen Pass|0.02|0.06*|0.02|0.05|
||(0.02)|(0.03)|(0.04)|(0.04)|
|FTFI _×_ Non-Pass mean|0.93|0.43|0.53|0.26|
|_p_-value (_β_ =_γ_)|0.262|0.660|0.024|0.496|
|Observations|1,718|1,713|1,345|1,228|
|_Efect of SI for the average _|_respondent_||||
|Pooled SI efects|-0.02|0.08|0.06|0.08|
|_p_-value|0.144|0.001|0.021|0.002|
|**Panel B. Liberia**|||||
|SI _×_ Screen Pass (_β_)|-0.00|0.07**|-0.02*|0.02|
||(0.03)|(0.03)|(0.01)|(0.03)|
|SI _×_ Non-Pass (_γ_)|0.05|0.10**|-0.06**|-0.03|
||(0.04)|(0.04)|(0.02)|(0.05)|
|Screen Pass|0.04|0.05|0.02*|0.02|
||(0.03)|(0.04)|(0.01)|(0.04)|
|FTFI _×_ Non-Pass mean|0.78|0.64|0.96|0.68|
|_p_-value (_β_ =_γ_)|0.247|0.581|0.118|0.443|
|Observations|1,259|1,260|1,226|1,101|
|_Efect of SI for the average _|_respondent_||||
|Pooled SI efects|0.02|0.09|-0.03|-0.00|
|_p_-value|0.490|0.001|0.002|0.969|

Note: Alternatively “Screen Pass” is defined by not only selecting “yes” to all questions in Table 2 but also choosing “no” to the question “Did you travel outside of the country in the past week?” By this alternative definition, 59% in Malawi and 62% in Liberia are in the “Screen Pass” group. Regressions are at the respondent-question level. Regressions include individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

26
||**The effects of**<br>Fixed-|**Table 7**<br>**growth on**<br>effects model<br>**Panel A**|**Excess Savings**<br>by firm||**Panel B**||
|---|---|---|---|---|---|---|
|**Independent Variables**|**Dependent**|<br>**Variable: Exc**|**ess Savings**|**Dependent**|**Variable: Gr**<br>**Formation**|**oss Capital**|
|Trend|0.00152***|0.000917**|0.00227***|-0.00185***|-0.00156***|-0.000943***|
||(0.000221)|(0.000395)|(0.000383)|(0.000173)|(0.000315)|(0.000274)|
|Dummy Recession|0.00870***|0.00814***|0.0113***|-0.0107***|-0.0134***|-0.0117***|
||(0.00152)|(0.00155)|(0.00245)|(0.00116)|(0.00121)|(0.00182)|
|logAT|0.0249***|0.0277***|0.0355***|0.00362**|0.00394**|-0.00304|
||(0.00242)|(0.00235)|(0.00456)|(0.00175)|(0.00179)|(0.00270)|
|L.leverage|-0.0509***|-0.0563***|-0.0436***|0.0478***|0.0552***|0.0543***|
||(0.00980)|(0.0101)|(0.0139)|(0.00668)|(0.00731)|(0.00991)|
|L.clta|-0.0349***|-0.0288**|-0.0501***|0.117***|0.125***|0.163***|
||(0.0115)|(0.0118)|(0.0165)|(0.00901)|(0.00961)|(0.0140)|
|L.liquidity_demand|_-0.334***_|_-0.331***_|_-0.382***_|_0.291***_|_0.303***_|_0.331***_|
||(0.0127)|(0.0129)|(0.0205)|(0.00833)|(0.00861)|(0.0121)|
|L.assets_nonop|-0.224***|-0.225***|-0.331***|0.141***|0.150***|0.198***|
||(0.0137)|(0.0141)|(0.0186)|(0.00822)|(0.00877)|(0.0121)|
|total_debt_variation|-0.471***|-0.475***|-0.446***|0.358***|0.393***|0.361***|
||(0.0109)|(0.0109)|(0.0159)|(0.00901)|(0.00952)|(0.0129)|
|current_debt_variation|-0.128***|-0.122***|-0.157***|0.207***|0.224***|0.264***|
||(0.0120)|(0.0122)|(0.0174)|(0.0111)|(0.0119)|(0.0165)|
|sales_growth|-0.0376***<br>|||0.103***<br>|||
||(0.00913)|||(0.00825)|||
|sales_growth_t|0.00454***|||-0.00284***|||
||_(0.000860)_|||_(0.000735)_|||
|tobin_q||-0.00568**|||0.00644***||
|||(0.00247)|||(0.00229)||
|tobin_q_t||0.000737**|||-0.000285||
|||(0.000305)|||(0.000223)||
|rd|||-0.750***|||-0.00264|
||||(0.183)|||(0.0706)|
|rd_t|||0.0257**|||-0.0138**|
||||(0.0105)|||(0.00544)|
|Constant|-0.269***|-0.306***|-0.433***|-0.129***|-0.146***|-0.0624|
||(0.0354)|(0.0348)|(0.0732)|(0.0260)|(0.0275)|(0.0439)|
|Number of observations|60,589|59,676|30,729|61,422|60,419|30,992|
|R-squared|0.294|0.293|0.365|0.499|0.476|0.486|
|Number of firms|8,393|8,283|4,422|8,467|8,352|4,439|

Each column of Table 7, reports the regression results corresponding to one measure of growth. In Panel A the dependent variable is Excess Savings, in Panel B the dependent variable is Gross Capital Formation. Each column reports the coefficient estimates for the trend, our control variables, the measure of growth and the interaction term between this measure and the trend. Our control variables are a dummy variable indicating an aggregate contraction of private investment in the country where the firm is based during year t (Dummy Recession), total and current debt variation between the periods t and t-1, and the lagged values of the ratio of Total Liabilities/Total Assets (leverage), the Current Liabilities/Total Assets ratio (clta), the Cash & Short Term Investment/Total Assets ratio (liquidity_demand), and the Non-Operating Assets/Total Assets ratio (assets_nonop). The measures of growth used are the first
exception of consumer electronics (radio/television), where employment was estimated to be some 70 percent lower than it would have been in the absence of import competition. Freeman and Katz (1991), Gaston and Trefler (1997) and Revenga (1992) are other early studies that conclude that trade does have effects on labor market outcomes—as measured by inter-sectoral changes in employment—but that domestic factors (demand for skilled labor, skill-biased technical change) were much more important drivers of job losses in the developed countries studied (mostly the US and Canada). In general, little impact of trade (policy changes) on wages was observed.

More recent work has suggested more mixed conclusions regarding the impact of trade (and trade reforms) on sectoral employment in developed countries. Kletzer (2000) found a relationship between trade and job displacement in sectors identified as import sensitive, but not for other sectors. Conversely, Dewatripont, Sapir and Sekkat (1999) finds essentially no effect of (developing country) trade on European labor markets. The evidence from plant-panel data for OECD economies is also not uniform. Some studies find increased trade exposure is associated with more labor churning and sometimes negative net effects on employment.

Much of the work on developed countries has focused on the impacts of exchange rate changes as opposed to trade reforms, the former being a more important source of changes in the terms of trade. Klein, Schuh and Triest (2003) use establishment panel data to analyze how the pattern of gross job flows in the US is affected by the path of the real exchange rate. They find that changes in the trend of the real exchange rate affect allocation but not net employment, whereas cyclical variation of the real exchange rate induces changes in net employment mainly via job destruction. In related work, Klein et al. (2002) study the joint impact of trade liberalization (NAFTA) and real exchange rate changes in the US. The way in which the reduction in tariffs impacted upon job flows is similar to the effect of a trend appreciation of the currency. Other studies in this genre focusing on the US include Gourinchas (1999a, b), Goldberg and Campa (1998), Goldberg and Tracy (2001) and Revenga (1992). Gourinchas examines the exchange rate response of gross job flows at the four-digit level over time and finds that appreciations are associated with substantial job churning, while periods of depreciation do not display such reallocation. Goldberg and Campa (1998) conclude that exchange rate 13
# **Figure 1. Prospects and risks from resource investment**

_Following a decade of major resource discoveries, the drop in oil prices raises concerns that long-planned investment to develop discoveries into production is delayed in low-income countries. This would set back growth._

# **A. Rig counts in Africa and North America  B. Resource discoveries eventually**

# **converted into production**

<!-- Start of picture text --> Number of rigs Number of rigs<br>3000 North America Africa 180<br>160<br>2500<br>140<br>2000 120<br>100<br>1500<br>80<br>1000 60<br>40<br>500<br>20<br>0 0<br>01-2013 05-2013 09-2013 01-2014 05-2014 09-2014 01-2015 05-2015 09-2015<br><!-- End of picture text --> <!-- Start of picture text --> Number of discoveries<br>Sub-Saharan Africa<br>25<br>Latin America & Caribbean<br>20<br>15<br>10<br>5<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->

# **C. Contribution of investment to real GDP growth, 2010-14**

# **D. Growth in low- and middle-income countries with resource discoveries**

<!-- Start of picture text --> Percent age point, percent<br>12 Investment<br>10 GDP growth<br>8<br>6<br>4<br>2<br>0<br>Mozambique Tanzania Niger Uganda Guinea<br><!-- End of picture text --> <!-- Start of picture text --> Percent Average 5-year growth pre-discovery<br>12<br>Average growth since production<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>Congo (DRC) Mongolia Lao P.D.R. Peru Eritrea<br><!-- End of picture text --> Sources: World Bank staff estimates, Baker Hughes, World Development Indicators, MinEx Consulting. A. The rig count is the number of oil rigs in operation.

C. Contribution of investment in percentage point, GDP growth in percent.

We add to a sparse existing literature by modeling the duration time from the discovery of these gold and copper resources to production. Specifically, we analyze whether, all else equal, the time duration or delay between discovery and production is affected by country-specific institutional and governance factors, by the macro-environment and by external global factors, notably whether commodity prices are high or low around the time of discovery. We use both non-parametric 3
13

**<u>Table 4:  Indicators for Analysis</u>**

|Postal index|Index of postal success calculated as (log) actual minus (log) predicted<br>letters/capita from regression of log letters/capita against log GDP/capita<br>(PPP). Letters/Capita from UPU, GDP/Capita from WDI.|
|---|---|
|GIT Postal trust|Score on GIT index of ‘trust on postal services’ (survey response to<br>question: ‘Do you trust your country’s postal system sufficiently to have a<br>friend mail a small package worth $100 to you (1=not at all 7= yes)’).<br>Source Kirkman et. al. (2002)|
|Illiteracy|Illiteracy rate, adult total (percentage of people ages 15 and above). Source:<br>World Bank, 2002.|
|Urban population|Urban population (percentage of total). Source: WDI.|
|Savings and giro|Number of post office savings and giro accounts per 1,000 people. Source:<br>constructed from data in UPU.|
|Money orders|Number of money orders, domestic service per 1000 people. Source:<br>constructed from data in UPU.|
|Letters/staff|Letters/staff. Source: constructed from data in UPU.|
|Receipts/Expenditure|Postal receipts/Postal operating expenditure. Source: constructed from data<br>in UPU.|
|KM/Office|Average area covered by a permanent post office (km2). Source: UPU.|
|% of Offices staffed private|Secondary offices staffed by nonadministration as a percentage of total<br>offices. Source: constructed from data in UPU.|
|Expenses/Letter|Postal operating expenses/letter (SDRs). Source: constructed from data in<br>UPU.|
|Receipts/Staff|Postal receipts/staff (SDRs). Source: constructed from data in UPU.|
|Offices/Population|Post offices/population (1,000). Source: constructed from data in UPU.|
|Staff/Office|Total postal staff/post offices. Source: constructed from data in UPU.|
|Population unserved|Percentage of the population without postal delivery, 2000. Source: UPU.|
|GDP/Capita|GDP/Capita,PPP. Source: WDI.|

**<u>Table 5:  Descriptive Statistics</u>**

||**Number of Obs**|**Average**|**Standard**<br>**Deviation**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|Postal index|110|0.00|1.26|-3.39|3.22|
|GIT Postal trust|51|4.36|1.50|1.80|6.70|
|Illiteracy|87|20.97|20.93|0.20|84.07|
|Urban population|110|54.30|22.68|9|100|
|Savings and giro|41|177|286|0.20|1574|
|Money orders|88|347|1096|0.02|7322|
|Letters/Staff|105|31,830|38532|196|174930|
|Receipts/Expenditure|103|1.07|0.50|0.11|4.73|
|KM/Office|106|1,491|4358|0.80|29182|
|% of Offices staffed private|106|29|30|0.00|95|
|Expenses/Letter|103|2.07|13.96|0.001|142|
|Receipts/Staff|101|13,288|21900|58.84|132579|
|Offices/Population|106|0.16|0.18|0.00|1.32|
|Staff/Office|101|7.73|7.60|0.54|57.44|
|Population Unserved|94|7.74|18.89|0.00|97.00|
|GDP/Capita|110|8,389|8720|523|50061|