rafmacalaba/datause-encoder-data huggingface dataset:
In Table 6, we present results for the telecommunications variables. Columns 1 through 6 use the sum of fixed and mobile phone lines. This variable is positive and significant both when introduced in level (columns 1 and 3) and in growth rate (columns 2 and 4). As for the growth rate effect, an additional point in the average growth rate of the number of per capita phone lines results in 0.23 additional average per capita growth over the period. Again, when interactions with the MENA dummy are introduced, we see that the effect is actually lower for this subgroup of countries. For example in column 3, the overall effect is 0.048, but only 0.022 for MENA countries (marginal effect = 0.048 – 0.026*MENA). Finally, when instrumented the growth rate estimate rises to 0.36 in column 7. Again, exogeneity is rejected by the Wu-Hausman test for the variable in level but not for the growth rate. As for electricity, while MENA countries have made progress in reforming their telecommunications sector, this process has not been deep enough to induce more efficient and productive use of ICT technology in the economy. In MENA, Internet penetration remains relatively low, and e-services remain overall under-developed.

In columns 7 to 9, we consider mobile phones only.<sup>11</sup> The positive and significant effect on growth remains, and again is lower in the MENA subsample (0.054 versus 0.117 in the overall sample). However, when instrumented, mobile phones are no longer significant, but exogeneity is not rejected by the Wu-Hausman test for this variable.

**Table 6: Telecommunications**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|2SLS|2SLS|OLS|OLS|2SLS|
||pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|Pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|pcgdpgrowth|
|Constant|-0.009|-0.276|-0.009|-0.291|-0.005|-0.428|-0.014|-0.013|-0.011|
||(0.011)|(0.095)***|(0.012)|(0.096)***|(0.014)|(0.137)***|(0.012)|(0.012)|(0.011)|
|pcgdp75|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000||||
||(0.000)***|(0.000)***|(0.000)***|(0.000)*|(0.000)|(0.000)||||
|pcgdp80|||||||-0.000|-0.000|-0.000|
||||||||(0.000)***|(0.000)***|(0.000)**|
|school_enrol_sec|0.000|0.001|0.000|0.001|0.001|0.001|0.000|0.000|0.000|
||(0.000)**|(0.000)***|(0.000)**|(0.000)***|(0.000)*|(0.000)***|(0.000)*|(0.000)|(0.000)|
|school_enrol_pri|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)*|(0.000)|(0.000)**|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|inv_gdp|0.110|0.109|0.112|0.118|0.109|0.090|0.135|0.136|0.132|
||(0.029)***|(0.031)***|(0.029)***|(0.029)***|(0.036)***|(0.030)***|(0.023)***|(0.023)***|(0.025)***|
|pctel|0.037||0.048||-0.052|||||
||(0.017)**||(0.014)***||(0.094)|||||
|pctel_gr||0.231||0.244||0.362||||

> 11 We do not consider growth rates for mobiles phones, as all the series start from _0_ and therefore are characterized by very high rates in the first years. However, the variable considering jointly fixed and mobile phones should capture the growth effect in the number of lines linked to the introduction of mobile phones.

18
issues with the help of econometric analysis based on 239 Mexican merger cases occurring between 1997 and 2001.

Several studies, based on country-specific information, have endeavored to identify the determinants of the antitrust enforcement authorities’ decisions. In the United States for instance a number of papers, including those by Posner (1979), Katsmann (1980) and Coate _et al._ (1990, 1992), are concerned with the case selection process. With the use of a probit model, Coate _et al._ (1990, 1992) found evidence that political variables, such as pressure from the Congress, have an influence on merger decisions. The same study shows that the authority did not consider efficiency gains while evaluating a would-be merger. The authors also found that lawyers at the Federal Trade Commission (FTC) had more influence than the authority’s economists. More recently, Coate and Kleit (2005) modeled the merger review process in which the FTC interacts with the acquiring firm to determine the outcome of antitrust regulation. They analyzed what sort of factors influence in the firm’s decision to litigate, fold or settle. Their main finding was that the efficiency variable played a significant role in the firm’s decision-making process. Potential efficiencies are positively linked with the probability of a firm engaging into a litigation process if the FTC challenges the merger. Moreover, the authors found that firms deterred from fighting the FTC by the potential of negative impacts on their reputations. In the United Kingdom, Weir (1992, 1993) uses a probit model to evaluate the relationship between the resolutions reached by the Monopolies and Mergers Commission (MMC) and the criterion set out in the statutory “public interest” test, such as prices and quality, cost reduction, new entry and foreign trade among the others. Weir shows that very few of the issues which are part of the “public interest” appear to influence the Commission’s decision. For example, the author found that mergers are more likely to be allowed if they do not affect either competition or prices. However, potential benefits, such as greater employment or increased exports, do not consistently help the would-be merged firm. Davies _et al._ (1999) applied the same binary approach to 73 monopoly cases handled by the MMC finding that the Commission’s decision is greatly explained by market shares of the participating firms. Khemani and Shapiro (1993) found that the Canadian antitrust authority has applied the merger guidelines in a consistent manner. Bergman _et. al._ (2005) used a logit model to 3
Overall, both the McCrary and balance tests suggest that it is unlikely that the close election outcomes we analyze were subject to an unobservable influence, and may thus constitute a credible setting to quasi-experimentally evaluate the political determinant of road investment in Mexico.

# **V. Main Results**

## **A. Highway Politics in Mexico**

Table 2: Impact of Ruling Party Status on Federal Highways, RD Estimates

||LLR|with bandw|idth <sup>ˆ</sup>_h_|Polyn|omial of or|der_p_|
|---|---|---|---|---|---|---|
||ˆ_h_1= .3|ˆ_h_2= .2|ˆ_h_3= .1|<br>_p_= 2|<br>_p_= 3|<br>_p_= 4|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. No covariat|es||||||
|President|1.066**|1.062*|2.436***|1.039**|1.371***|1.396**|
||(0.453)|(0.565)|(0.880)|(0.426)|(0.525)|(0.631)|
|Observations|4,988|3,400|1,752|9,220|9,220|9,220|
|R-squared|0.374|0.359|0.392|0.346|0.346|0.346|
|B. Covariates i|ncluded||||||
|President|1.018**|1.118**|2.303***|0.943**|1.470***|1.529**|
||(0.447)|(0.548)|(0.838)|(0.425)|(0.524)|(0.631)|
|Observations|4,877|3,332|1,715|8,905|8,905|8,905|
|R-squared|0.391|0.374|0.406|0.365|0.365|0.365|
|Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ The table shows the regression discontinuity estimates of the presidential party status in legislative elections on multi-lane road (federal highway) outcomes. The estimation methods include: local linear regression in columns 1-3 with two MSE-optimal bandwidth choices ( _h_<sup>ˆ</sup> 1 and _h_<sup>ˆ</sup> 2) and one CER-optimal bandwidth choice ( _h_<sup>ˆ</sup> 3), calculated according to Calonico et al. (2017); spline polynomial approximation in columns 4-6 with second, third, and fourth polynomial, respectively. Estimations in panel A has no covariates while estimations in panel B include the following covariates: state capital dummy, US border dummy, log area size, log population size, and proxies for higher education and employment. Robust standard errors clustered at the municipality level are reported in parentheses. All specifications include state, time, and state-interacted-with-time fixed effects.

** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

Table 2 presents the extent of presidential pork in federal highway construction from 15
Table 1: Sub-Saharan Africa: Nine Highest-Income Countries

|Countries|GDP per capita (USD) -2019|
|---|---|
|Gabon|7,767.00|
|South Africa|6,001.40|
|Namibia|4,957.50|
|Angola|2,790.70|
|Nigeria|2,229.90|
|Ghana|2,202.10|
|Kenya|1,816.50|
|Zambia|1,305.10|
|Lesotho|1,118.10|

and 25.9 percentage points (DHS), respectively. It follows that the effect of greater investments in road networks tends to be larger for more-developed economies. However, the differential effect is smaller in the DHS sample, which, likely offers a more accurate picture because of the greater number of countries included in it (27 countries) than in the LSMS sample (7 countries).

In areas with high access to road networks, being 10 km closer to an electric grid increases the probability of working by 25.0 percentage points in more-developed countries, compared to 5.5 percentage points for less-developed countries in the LSMS sample. In the DHS sample, the corresponding estimates are 11.0 versus 13.7 percentage points, respectively. Again, the effect tends to be larger in more-developed economies.

In locations that are far enough from road and electricity networks, increasing proximity to both has a positive effect, but this effect is stronger in more-developed countries. These findings imply that the complementary role of roads and electricity is stronger in more-developed African economies.

Results from the OLS regressions (reported in Appendix Table 13) confirm these findings. Corresponding coefficients have similar signs, although the OLS estimates are smaller. Overall, the findings show that investment in infrastructure promotes job creation, and that the impacts are larger in more economically advanced African countries. This differential effect tends to validate the idea that the nature of jobs varies across countries with different levels of development, and that the expansion of roads and electricity provides fewer benefits to the sectors that predominate in less-developed economies, and greater benefits to sectors that predominate in more-developed countries.

# **6.2 Place of residence**

We examine how the employment impacts of expanding road and electricity access differ for urban and rural areas. Results are reported in columns 1 and 2 of Table 5 in the Appendix. There are net employment gains in response to road expansion in both urban and rural areas in the DHS sample. The marginal employment effect of roads is larger in rural areas. Estimates from the DHS sample shows that increasing proximity to the road network by 10 km marginally increases employment by 8.0 percentage points in urban areas, compared to 23.8 percentage points in rural areas along the grid. Similarly, in 26
27

# **8. References**

- American Automobile Manufacturers Association. 1996. _World Motor Vehicle Data._ Washington, D.C.

- Bates, J.J., H.F. Gunn, and M. Roberts. 1978. "A Model of Household Car Ownership." _Traffic Engineering and Control_ 19 (11/12): 486-491, 562-566.

- Beesley, Michael E., and John F. Kain. 1964. "Urban Form, Car Ownership and Public Policy: An Appraisal of Traffic in Towns." _Urban Studies 1_ (2).

- Bennathan, Esra, Julia Fraser, and Louis Thompson. 1992. "What Determines Demand for Freight Transport?" Policy Research Working Paper 998. World Bank, Washington, D.C.

- Button, Kenneth, A.D. Pearnan, and A.S. Fowkes. 1982. _Car Ownership Modeling and Forecasting._ London: Gower Publishing Company Ltd..

- Button, Kenneth, Ndoh Ngoe, and John Hine. 1993. "Modeling Vehicle Ownership and Use in Low-Income Countries." _Journal of Transport Economic and Policy_ 27 (January): 51-67.

- Canning, David. 1998. "A Database of World Infrastructure Stocks 1950-1995." Policy Research Working Paper 1929. World Bank, Washington, D.C.

- Chin, Anthony, and Peter Smith. 1997. "Automobile Ownership and Government Policy: the Economics of Singapore's Vehicle Quota Scheme." _Transportation Research A_ 31 (2): 129-140.

- Deaton, Angus. 1987. _The Demandfor Personal Travel in Developing Countries._ Washington, D.C.: World Bank.

- _The Economist._ 1996. "Taming the Beast-A Survey on Living with the Car." 22 June 1996, 1-18.

- Glaister, Stephen. 1981. _Fundamentals of Transport Economics. New_ York: St. Martin's Press.

- Hau, Timothy, D. 1992. "Congestion Charging Mechanisms for Roads: An Evaluation of Current Practice." Policy Research Working Paper 1071, World Bank, Washington, D.C.

- Hensher, David A., Frank W. Milthorpe, and Nariida C. Smith. 1990. "The Demand for Vehicle Use in the Urban Household Sector." _Journal of Transport Economics and Policy_ 17 (3): 119-137.

- Hirschman, Albert 0. _1958. The Strategy of Economic Development._ New Haven: Yale University Press.

- Hughes, Gordon. 1987. "The Incidence of Fuel Taxes: A Comparative Study of Three Countries." in D. Newbery and N. Stern, eds., _The Theory of Taxation for Developing Countries,_ 533-559. New York: Oxford University Press.
We also estimate the Pareto exponent _α_ , which was not explicitly targeted by the model, for both degree distributions. We obtain an _α_ of 0 _._ 36 from the model and 0 _._ 37 from the data. In addition, since we have previously shown that the formal firm network is spatially concentrated, we also assess how well the model predicts the share of buyer relationships in the economy that originate from firms in Nairobi, Mombasa, to firms in other counties. We find that the model performs well on this dimension too (Table 5). In the administrative data, 69% (11%) of all outlinks in the economy are captured by Nairobi (Mombasa) based firms and the model predicts a share of 70% (11%).

# **5 Predicting a Revised Network**

With the estimated model at hand, we are now able to tackle the question of how including informal firms might affect the spatial patterns of domestic trade. Our proposed thought experiment is the following: suppose we were to observe informal firms. What would happen to the outdegree distribution of various types _θ_ ? To obtain a ‘revised network’ that accounts for informal firms, we rely on updated information on the spatial and sectoral dispersion of economic activity in Kenya – now including the informal sector. In model terms, our exercise shifts the probabilities _p_ ( _θ_ ) with which we observe nodes of certain sector-region-size types _θ_ to be born. Knowing _r_<sup>_∗_</sup> and our updated _p_ ( _θ_ )s, we can then once again predict the type-by-type matrix of firm-to-firm links _π_ , keeping everything else constant. We will also make additional assumptions about the linking probabilities of informal firms.

## **5.1 Predicting the Sector-County Profile of Non-VAT Firms**

To incorporate informal firms into the network, we first update the firm-type probabilities _p_ ( _θ_ ) for each sector-county-size cell, this time accounting for the entire firm size distribution. To update _p_ ( _θ_ ), we ideally would want to observe the number of firms _Ncs_ in each sector _s_ , county _c_ , and size cell – irrespective of their formality status. However, none of the KNBS records available to us feature a breakdown of the firm count along both the sector _s_ and the county _c_ dimension, let alone size dimension. Therefore, instead of the firm count, we rely on granular sector and region level information on formal and informal employment in the 2019 census labor force module to compute our alternative entry probabilities _p_ ( _θa_ ): 23
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- Sigificant at the S % stAtistical test<sup>leveL</sup>

- Significant at the 10 % statistical test<sup>leve.</sup>

Our sample in this industry consists of<sup>10 and it contains no state-owned rmor firs employing other African middle or upper</sup> manaRers. Many firns also display missing<sup>values for ownership and in the remaiing i-rrms the ownership variablos are h-ghly</sup> corrolied with the other esplanatory<sup>varnables (especially the effective rate of protection). mhe</sup> ownership variables are hence excluded in the above regressions.
following: Whereby _ct_ is consumption in period t, _ct_ +1 is consumption in the future, _r_ is the interest rate, _st_ stands for savings in period _t_ ( _ct_ +1 = (1 + _r_ ) _st_ ), $CT is the cash transfer, $ _m_ (1 _− ψ_ ) is the foregone income from child labor, whereby (m) is the child wage rate and ( _ψ_ ) is the time allocated to child labor; _β_ is the utility discount rate factor, _α_ denotes parental altruism towards the children and _ω_ + is the price (value) of _Zgt_ perceived by the parents.<sup>32</sup> Let us define the cost _φ_ of accessing the governmental services _Zgt_ as a constant which reflects the cost of transport to access the services and the opportunity cost of the time spent to use the services.<sup>33</sup> We assume that the conditionality constraint binds exactly. Hence, _Zgt_ = _Zct_ and _ψ_ = 0. Once in the program, parents accept to use _Zgt_ under the cost of _φ_ . To solve for _Zit_<sup>_∗_as a function of</sup><sup>_Zgt_from</sup> the optimization problem (4), we need to specify a functional form for the production function of human capital. Let us assume<sup>34</sup> that _E_ [ _V_ ( _hct_ +1)] = _u_ (( _θZgt_<sup>_ρ_+ (1</sup><sup>_−θ_)</sup><sup>_Z_</sup> _it_<sup>_ρ_)(1</sup><sup>_/ρ_)),whereby</sup> 0 _≥ θ ≥_ 1. The household’s maximization problem can be written as: Given that _Zgt_ is provided free of charge, its price for the households becomes the cost _φ_ of

> 32 _Zgt_ is provided free of charge, hence, there is no explicit price for the household. We can assume that the price of _Zgt_ perceived by the parents is equivalent to the cost of _Zgt_ to the government. If _ω_ + _> φ_ , the service _Zgt_ will enter in the budget constraint with a positive value. If _ω_ + _< φ_ , parents will only use _Zgt_ if confronted with a monetary incentive such as in a _CCT_ program or if another enforcement mechanism (e.g. a law that makes school attendance compulsory) is put in place.

> 33In addition to the loss of income from child labor, the opportunity cost of time may include non-remunerated work in the household or in agriculture and parents’ effort to support children in using the government services (e.g. tutoring, bringing children to school).

> 34While also other factors ( _H, hct_ ) contribute to children’s human capital formation, _Zit_ are the only determinants which parents can control directly – their own human capital H as well as children’s initial conditions _hct_ are predetermined to period t and can be treated as scalars.

13
widespread global health problem (see, among others, Nokes et al., 1992, and Pollit, 1990).

By providing cement floors to households with dirt floors, _Piso Firme_ interrupts the transmission of parasitic infestations, particularly for young children. For this reason, we expect to find lower incidence of diarrhea, malnutrition and micronutrient deficiency (particularly anemia) and thereby improved cognitive development, among children in beneficiary households. Our study of this intervention is similar in spirit to the study by Kremer and Miguel (2004), which evaluated the impact of chemotherapeutic de-worming of school-aged children using a randomized experiment in Kenya and found that such deworming reduced school absenteeism by one quarter. Our study complements Kremer and Miguel’s results as we demonstrate that cement floors can reduce the incidence of those parasites (amoebas) that remain even after de-worming.

# **3. Identification Strategy**

In order to identify the impacts of replacing dirt floors with cement floors, we needed to assess the counterfactual –i.e., to examine what would have happened to the households in the treatment group had they not received treatment. We exploit a geographical discontinuity in the implementation of the program to construct a comparison group that estimates this counterfactual.<sup>2</sup> This study focuses on the State of Coahuila, in Northern Mexico, which began implementing _Piso Firme_ in the year 2000 and provided cement floors to more than 35,000 households by 2005. While _Piso Firme_ began as a state program in Coahuila, it was later adopted by the Federal Government and gradually scaled up to other states over time. One state that did not scale up _Piso Firme_ by 2005 was the neighboring State of Durango, which straddles the south-western corner of Coahuila and shares a major urban area. Our identification strategy takes advantage of this geographical discontinuity in the implementation of the program to identify a valid comparison group and conduct the counterfactual analyses.

> 2 As a retrospective study where benefits had already been assigned to the universe of eligible population, random assignment of the treatment was not feasible, and so an alternative source of exogenous variation was required to construct valid a counterfactual.

7
Recent analyses of the incidence of public spending indicate that the extent to which the social deviates from private benefits will vary by level of education. In fact, when one takes redistribution into account, the social benefit to primary education would be boosted relative to that of other levels of education. This point is illustrated in Table 3 which shows that the poorest quintile in several countries do not benefit from public subsidies to tertiary education.

<u>Table 3: Proportion of Public Subsidy Received by the Poorest Quintile by Education Level</u>

||Primary|Secondary|Tertiary|
|---|---|---|---|
|Colombia, 1992|39|21|5|
|Kenya, 1992/93|22|7|2|
|Ghana, 1991/92|22|15|6|
|Mexico, 1996|30|19<sup>a</sup>|1|
|Morocco, 1998/9|17|4|2|

a For junior secondary education; for senior secondary education, the proportion is 5% Source: Filmer 2003 _Correcting for Market Failures_ . Another reason that wage differentials fail to capture the true returns to education is because of imperfect labor markets. The screening literature, for example, argues that the estimated earnings gains may overestimate the value of education because these gains are not necessarily due to the added productivity of the young student but because of the labor market values the sorting that schooling does between the highly and lowly skilled. If so, then, correcting for such information failures may be a better investment than the large capital and recurrent costs needed to expand a school system. This would argue for a social benefit curve that is lower than the private one depicted in Figure 2. However, we know of no studies in developing countries that can provide evidence of this.

18
**Table 4 : Major refugee‐hosting countries in SSA, 2013**

|**Total refugees**||**Refugees per 1 USD GDP**<br>**capita)**|**(PPP per**|**Rank in**<br>**the**<br>**world**<br>**(per 1**<br>**USD**<br>**(PPP)**|
|---|---|---|---|---|
|Kenya|534,938|Ethiopia|336.36|2|
|Chad|434,479|Kenya|295.11|3|
|Ethiopia|433,936|Chad|199.01|4|
|South Sudan|229,587|South Sudan|176.43|5|
|Uganda|220,555|Dem. Rep. of the Congo|153.38|6|
|Sudan|159,857|Uganda|151.80|7|
|Cameroon|114,753|Burundi|79.71|11|
|Dem. Rep. of the Congo|113,362|Liberia|78.85|12|
|United Rep. of Tanzania|102,099|Niger|74.51|13|
|Rwanda|73,349|Sudan|67.06|15|
|South Africa|65,881|United Rep. of Tanzania|63.33|16|
|Niger|57,661|Rwanda|52.67|18|
|Liberia|53,253|Cameroon|47.91|19|
|Congo|51,037|Central African Rep.|26.44|28|
|Burundi|45,490|Togo|18.99|29|
|Burkina Faso|29,234|Burkina Faso|18.61|30|
|Angola|23,783|Zambia|13.45|34|
|Zambia|23,594|Mali|11.78|37|
|Togo|20,613|Congo, Rep. of|11.35|38|

Source: Authors’ presentation based on UNHCR Global Trends 2013 (UNHCR 2014).

18