rafmacalaba/datause-encoder-data huggingface dataset:
historical political capital. The only exception is Douala, Cameroon, which is larger than Yaounde. The historical political capitals that are not current formal political capitals are Dar es Salaam, Tanzania, which was replaced by Dodoma, Abidjan, Cˆote d’Ivoire, replaced by Yamoussoukro, and Lagos, Nigeria, replaced by Abuja.

# **A.3 Roads**

The African Infrastructure Country Diagnostic database contains comparable roads data for all countries of the African mainland with no Mediterranean coastline, plus Madagascar, except for Djibouti, Equatorial Guinea, Guinea-Bissau, and Somalia.<sup>51</sup> Two datasets were produced for Sudan: one for the north and one for the south. The datasets generally have comparable metadata on road surface, quality, and hierarchy (primary/secondary/tertiary), as well as estimates of traffic. Still before they could be used for the current analysis, several changes had to be made. All steps below were carried out using ArcGIS 9.3 software, except for some tabular cleaning done in Excel and Stata. Nearly all steps in ArcGIS, and all steps in Stata, were automated in Python, Arc Macro Language (AML), or Stata scripts.

The roads data were cleaned tabularly, to ensure that the relevant fields were coded consistently, and projected to a sinusoidal projection, which is more conducive to distance calculations than their native plate carr ́ee (latitude and longitude).<sup>52</sup> Next, roads from all countries were combined into one large dataset, and a topology was built with the rule “no dangles”. This means that every dead end was flagged. In most cases, dead ends are legitimate features of the road network. In other cases, however, they are artifacts of a data generation process in which some segments that are connected in the real world are not connected in the dataset. This is critical in the network analysis to follow.

Problematic dangles were fixed in several ways. First, using the topology “Extend” tool, dangles were extended up to 100 meters if that would cause them to no longer be dangles. In theory, the topology “Trim” tool could be used for the opposite task to dangles less than 100 meters long. However, a bug in ArcGIS made this infeasible. But extra dangles only affect final results to the extent that they cause additional “spiders” to be created (see below).

The Extend operation does not close all gaps of less than 100 meters. To see this, imagine the forward slash and backslash characters typed with a space between them: _/ \_ . Extending either character individually, even by doubling its length, would not

> 51http://www.infrastructureafrica.org/

> 52The projection’s central meridian is 15 degrees east longitude.

48
complementarity effects. This said, our results for the provision of single infrastructure types indicate that the subsample does not seem to suffer from sample selection bias.

Table 6: DIDM estimates of infrastructure provision on TFP and capital-labor ratio: 1970-1980

|||All S<br>|ectors<br>|Ind<br>|ustry<br>|
|---|---|---|---|---|---|
|||N2E|N2H|N2E|N2H|
|K/L|coef.|-0.128|-0.103|-0.361|-0.292|
||s.e.|(0.137)|(0.180)|(0.255)|(0.492)|
|TFP|coef.|0.166<sup>_∗_</sup>|0.367<sup>_∗∗_</sup>|0.618<sup>_∗∗∗_</sup>|0.100|
||s.e.|(0.094)|(0.173)|(0.199)|(0.241)|
||Obs./Switchers|87 / 25|89 / 19|87 / 25|89 / 19|

_Notes: Bootstrapped standard errors (200 replications) in parentheses. ∗p<0.1, ∗∗p<0.05, ∗∗∗p<0.01. Dependent variables are in natural logs._ Table 6 shows the impact of electrification and highway provision on capital intensity and TFP, pooling all sectors together, and separately for the industry sector. We do not observe any statistically significant effects of the provision of electricity or highways on the capital/labor ratio at the aggregate level. On the contrary, the provision of either infrastructure type has strong positive effects on TFP. Electrification increases TFP by almost 18 percent, although this effect is only marginally significant.

When focusing on the industry sector, we see that electrification significantly increases TFP, which is 86 percent larger in electrified municipalities. This is consistent with the previous literature documenting positive total factor productivity effects in the industry sector following electrification (Kassem, 2021). These findings are also consistent with our estimated changes in industry GVA share following electrification (see Table 5), with TFP being the driver of this change (for a detailed explanation of this mechanism, see Perez-Sebastian, Steinbuks, et al., 2020).

Similar to electrification, we find a positive and significant effect (a 44 percent increase) of highway provision on aggregate TFP. The effect of highway provision on the industry sector TFP is positive but not statistically significant. This is consistent with the absence of significant effects of highway provision on the industry GVA share for the 1970-1980 period in Table 5.

18
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third-party agents have a higher measure of effectiveness (reflecting the k in Tables 2a and |cc 2b).<sup>67</sup> θθ<sup>�</sup>

To illustrate the usefulness of our methodology in generating new findings, consider Bolivia in these figures. Governance structures including more than just bilateralism are relatively more prevalent in Bolivia than in all others. This is surprising. Bolivia is the least developed of the six countries and scores lowest on standard measures of the quality of the legal system (e.g., the World Governance Indicator's rule of law index). Given the low levels of personal trust in Bolivia <mark>(Latin American Public Opinion Project, 2016/7),</mark> it is tempting to think that this might reflect comparative, rather than absolute, advantage in the legal realm. However, this cannot be a complete explanation, since LCA's estimation of a firm's governance class does not use data on a firm's country. Therefore, the greater effectiveness indicated for legal institutions in Bolivia than in, say, Uruguay, is inconsistent with the latter country's much higher ratings on indexes of legal institutions.<sup>68</sup> This is a puzzle, and one that is most clearly raised in an exercise like the current one.

# **V.2 Cross-Regional Variations in Governance Structures**

We next look at within-country, regional variation in governance structures. We apply the third step of the 3-step method to each country separately, using regional dummy variables as covariates. Table 4 reports the _p_ -values of within-country Wald tests of the null hypothesis that there is no variation across a country's regions in the patterns of governance structures. On the

> 67 Thus the ordering of classes is different from that in Tables 2a and 2b, which followed LCA's somewhat arbitrary ordering. 68 Bolivia has the highest estimated shadow-economy presence among the six countries studied in this paper (Medina and Schneider, 2019). Since the WBES use representative samples of formally registered firms, the population of firms from which the sample is drawn in Bolivia may have a higher share of large firms than the populations in other countries, among other potential differences across the populations of firms that may be relevant for firms' governance structures. These differences due to variation in the prevalence of shadow economy, and thus variation in the share of the economy that the WBES represents, cannot be a complete explanation either however, since Peru, where the prevalence of governance structures differs markedly from Bolivia, follows Bolivia closely in the estimated rate of the shadow economy.

39
# **3 Discussion**

Using twelve years of data from across the Midwestern and Eastern US, we show that exposure to low-density smoke plumes increases both corn and soybean yields relative to days without smoke plumes. Days with high-density smoke plumes, however, have a negative impact that is roughly double the positive impact estimated for low-density smoke days.

This difference in marginal effects is consistent with the expectation that low-density smoke plumes increase the fraction of light that is diffuse and slightly reduce the total available light, so that the gains in photosynthetic efficiency from the increase in diffuse light dominate. Highdensity plumes, on the other hand, absorb more light than they diffract, so the reduction in available light dominates the increase in diffuse light. We are unable to test this hypothesis directly (see SI Section SI1.4), but we find that high density plumes substantially increase AOD while low density plumes have only a mild impact (Figure SI2). Our effects do not appear to be driven by changes in ground-level pollutants induced by wildfire smoke plumes, but we cannot rule out that some of our measured effect may be due to these changes.

Our estimates suggest that, if current trends in the number of low-density and high-density smoke days continue, the present positive impacts on yield will be substantially reduced by 2050. Projections of the number of low- and high-density smoke days by mid-century suggest that, for soy, the average county will experience a positive impact on yields roughly a third of today’s impact. The impact on the average corn-growing county will be a substantial reduction in yield relative to that under today’s smoke conditions. If low-density smoke days grow 1.5–2 _×_ faster than high-density smoke days, however, the effects will be reversed, and smoke will have an even higher yield-boosting effect.

The reduction in yields due to future smoke exposure may impose significant costs on farmers. Using 2020 production and price data from the USDA, combined with our estimated yield losses, we can conduct a back-of-the-envelope calculation of lost revenue. Based on the total tonnage of corn and soybeans produced and the price of corn and soybeans in 2020, we estimate that the combined yield losses due to increased smoke exposure in 2050 would reduce farm revenue by roughly $1.5 billion annually in 2020 dollars. This is comparable to the reduction in farm revenue due to decreased sulfur fertilization after the implementation of the Acid Rain Program.<sup>28</sup> This calculation is a rough approximation of the costs and ignores how prices and production will evolve over time—although, as we discuss below, changes in the location of production may increase these damages. The point is not to provide a precise dollar estimate, but rather to highlight that, while the percent change in yields may be small, the lost revenue could be substantial.

Over our sample period, crop production has shifted towards counties that are more impacted 13
Table 2. CEE: Maturity, Sectoral, and Nationality Distribution of International Bank Lending

|||Mat|urity||Sectors||
|---|---|---|---|---|---|---|
|Position vis-A-vis|Total|Short-term|Long-term|Banks|Public sector|<br>Nonbank<br>private sector|
||_USS billions_||_Perc_|_entage of tot_|_al claims_<sup>_2_</sup>||
|Central and Eastern Europe|||||||
|End-1994|32.1|31.1|55.6|49.6|26.7|23.4|
|End-1995|37.1|39.0|50.5|50.1|24.9|24.8|
|End-1996|44.0|40.7|47.2|48.6|19.7|31.5|
|End-1997|49.1|40.2|47.9|46.6|13.7|39.7|
|Asia|||||||
|End-1996|367.0|62.3|29.7|43.3|9.0|47.6|
|End-1997|381.0|60.6|31.9|40.7|7.4|51.8|
|Latin America|||||||
|End-1996|242.4|53.7|37.9|24.2|27.9|47.7|
|End-1997|283.0|54.8|38.6|26.0|21.2|52.6|
|_Memorandum item_|US$ billions||||||
|_Total debt stock (1997), CEE_|_130.9_||||||
|_Total debt stock (1997), Asia_|_665.7_||||||
|_Total debt stock (1997), Latin America_|_677.9_||||||

1. Consolidated cross-border claims.

2. Percentage shares do not total to 100 because of unallocated claims. _Source:_ BIS, _Consolidated International Statistics,_ several issues.

In summary, private (rather than official) capital flows have become the dominant source of external funding for CEE countries, with those more advanced in the transition process being more successful in mobilizing it. The increase of private capital flows has been concentrated in a small number of countries: Hungary, the Czech Republic, and Poland. Since 1993 "reversible" capital inflows (shortterm capital and portfolio equity flows) have been increasing rapidly. By 1997 these types of flows accounted for about a quarter of total net private capital inflows. Debt-creating flows (loans and bonds) remain the key component of the CEE's external financing, rather than foreign direct investment or equity investment (as observed in other developing regions). Within those debt flows, international commercial banks are the primary suppliers, at increasingly shorter maturities, while domestic banking systems are the main avenue for absorbing that lending.

6
> <sup>17</sup>

yearly observations divided by the number of years the episode lasts in the sample (a maximum of 26 years, since the data run from 1975 – 2000).

# **Estimation strategy**

The ideal test of the hypothesis that the credibility of pre-electoral political promises influences policy would be: (1) _Policy choice i_ = _ß0_ + _ß1_ ( _credibility of political competitors i_ )+ **X** _i_ **Β** + _e i_ .

where _i_ indexes the democratic episode under observation. Two problems make this approach difficult and are the focus of this section. First, credibility is unobservable. Second, credibility is likely to be a function of some of the elements in **X** , giving rise to multicollinearity.

The previous discussion suggests a close relationship between the number of continuous years of competitive elections and credibility, suggesting the elections variable as a natural proxy for unobserved credibility. In particular, because credibility can emerge from reputation, and reputation is a possible product of repeated experience with politicians, one might expect a positive association between the continuous years of competitive elections and the ability of politicians to make credible pre-electoral promises. Given this, we can express credibility as a function of the elections variable, (2) _Credibilityi_ = _α 0_ + _α 1_ ( _continuous years of competitive elections_ ) + _ui_ ..

Substituting (2) into (1) yields, (3) _Policy choice i_ = _ß0_ + _ß1_ ( _α 0_ + _α 1_ ( _continuous years of competitive elections_ ) + _ui_ .)+ **X** _i_ **Β** + _e i_ .

# **_Attenuation bias and multicollinearity_**

It is always the case that the use of a proxy variable introduces attenuation bias in the estimate of the coefficient of the proxied variable. That is, because the years of continuous elections captures the effects of credibility imperfectly, with error _u_ , the estimate of _ß1_ is biased downwards. The attenuation bias naturally increases the less close is the connection
8 Malaysia Immigration policy in Malaysia consists of a system similar to that found in Singapore, namely a series of three permit types: (i) a visit pass for temporary employment; (ii) a visit pass for professional employment; and (iii) an employment pass. In applying for a permit, employers identify the prospective employee and specify the job to be performed and its duration. As such, permits are firm- and jobspecific and are non-transferable. Furthermore, foreign workers are subject to age and nationality restrictions (Table 2 contains a detailed summary of the system).

**Visit pass for temporary employment.** Unskilled and semi-skilled workers are eligible for visit passes of short duration, up to one year. Skilled workers are also eligible if they fall below the salary cap of RM1,200 per month (equivalent to US$302).<sup>4</sup> Workers must be between 18 and 45 years of age, and no resettlement of dependents in Malaysia is allowed. A security deposit is required to cover the cost of repatriation following permit expiry, and levies are imposed at differential levels depending on skill. Visit pass levies range from RM840 per year for unskilled manufacturing jobs, to RM1,200 per year for semi-skilled jobs, to RM1,800 for skilled workers in manufacturing. Viewed as a payroll tax, the permit fee for skilled workers is equivalent to at least 12.5 percent of the wage. Only citizens of the Philippines, Indonesia and Thailand are issued passes for domestic/household employment; for the manufacturing, construction, plantation and service sectors, the list of eligible nationalities is extended to include Bangladeshis and Pakistanis. There are minimum income requirements for households to eimploy domestic workers, in order to minimize fraud, and visit pass fees for domestic workers are tax deductible for employers.

**Visit pass for professional employment.** Workers on short-term contracts with professional qualifications can be retained with a visit pass for professional employment. Contract duration is up to 12 months, and does not permit resettlement of families in Malaysia. A security bond is required by employers hiring foreign workers under this type of pass.

> 4 Conversions to U.S. dollars are made using the average exchange rate over the first three quarters of 1998 equal to 3.97RM/US$ (International Monetary Fund, 1998).
To further illustrate this contrast between _Pantawid_ and more generous CCT programs, we examine the schooling and child labor effects of the Mexican _Prospera_ program when it was first implemented in the late 1990s (Parker, Rubalcava, and Teruel, 2008). This comparison is insightful for three reasons. First, the rural target populations of the two programs had comparable levels of school attendance and child labor: the 1996 Mexican National Survey of Household Income and Expenditure (ENIGH) shows that 84 percent of children aged 12 to 14 were in school while 15 percent were engaged in economic activities with boys, especially in rural areas, being almost twice as likely to work as girls. About 76 percent of children were in school only, 9 percent were idle, 8 percent combined school and work, and 7 percent were in work only. Second, _Pantawid_ was explicitly modeled after _Prospera_ in terms of both conditions and relative transfer size to household income (Barber and Gertler, 2008; Skoufias and Parker, 2001). The experimental phases of both programs were targeted at the poorest communities with household poverty defined by a PMT (World Bank, 2013). However, in contrast to _Pantawid_ , the education grant from _Prospera_ was explicitly “set to cover the opportunity costs for students, estimated on the basis of observed children’s incomes” (Fiszbein and Schady, 2009, p. 182) and the maximum-possible education grant covered about twothirds of the earnings of a full-time working child (Schultz, 2004). Third, the pilot phase of the-then _Progresa,_ and now _Prospera_ , program was accompanied by a similar cluster-randomized evaluation design (Parker, Rubalcava, and Teruel, 2008). In the late 1990s, 495 rural localities were randomly allocated to an early treatment group (313 localities) and a late treatment group (182 localities), with take up rates of approximately 97 percent (Ozer et al., 2011). We use the baseline household survey administered in 1997, follow-up household survey administered in 1999, as well as 1999 locality level information, to construct variables comparable to those used in the _Pantawid_ analysis above (variable construction is described in Appendices 2 and 4).

Replicating the _Pantawid_ estimation procedure, we estimate the effects of _Prospera_ on children's schooling and work based on regression specification (1). Table 9 presents our estimates of 22
the only-relatively income poor in richer countries are, on average, proportionally closer to the relative line than to the absolute line than the incomes of the only-relatively income poor in low-income countries.

The results summarized in Table 1 help us to understand why, when we compute global poverty (see Section 6.4), _HHS_ is about halfway between _HS_ and _HA_ . The reason is that income poverty is concentrated in highly populated developing countries such as India and Indonesia, where the endogenous weight is close to 0.5.

**Table 1:** Endogenous weight _ω_ by country income group

|Country income group|Median|SD|
|---|---|---|
|Low income|0.49|0.06|
|Lower-middle income|0.48|0.05|
|Upper-middle income|0.42|0.08|
|High income|0.31|0.06|

_Note:_ Country income groups as defined by the World Bank.

# **6.3 Country-Level Poverty**

We begin by comparing poverty at the country level between the societal headcount ratio ( _HS_ ), the absolute headcount ratio ( _HA_ ), and our proposed index ( _HHS_ ).

Our first example illustrates how the change of index that we propose can affect the poverty diagnoses of individual countries. Table 2 displays the level of poverty as well as the rankings by those three poverty measures for Botswana and Egypt in 2008. According to _HS_ , Egypt has more poverty than Botswana (the fraction of income poor individuals is 33% and 30%, respectively). However, this comparison masks the very different composition of their income poor populations. The vast majority of income poor individuals in Egypt are only-relatively income poor, whereas about half of the income poor individuals in Botswana are absolutely income poor. Specifically, the fractions of absolutely and only-relatively income poor individuals are, respectively, 5% and 28% in Egypt, and 16% and 14% in Botswana. Similarly to _HA_ , Botswana has more poverty than Egypt by _HHS_ . The reason for these opposite diagnoses between _HS_ and _HHS_ is that while _HS_ gives the same poverty score to all income poor individuals, _HHS_ down-weights individuals who are only-relatively income poor to around 0.5. As explained in Section 2, a weight smaller than one reflects the fact that not all only-relatively income poor individuals are welfare poor.

36