rafmacalaba/data-use-mentions-extended huggingface dataset:
extreme during the same period (high leverage). Consequently, care needs to be taken to ensure the results reflect a general relationship, rather than a few high-influence periods. To improve our robustness to influential observations, we first winsorized GDP growth rates data at the 98% level for country-level data and at 90% for NUTS2-level data.<sup>26</sup> Second, we dropped countries and year observations one-by-one to identify influential observations (where the multiplier estimates change substantially with their exclusion). At the country level, this algorithm identifies Estonia in 2011 (EE11) as influential in OLS regressions, and Croatia in 2016 (HR16) for IV specifications (the first-stage changes). Consequently, we drop both for all country-level specifications. No individual observations are influential by the algorithm in NUTS2-level specifications. Descriptive statistics are presented in Online Appendix Table A2.1 and illustrate the extreme volatility of many of our variables.<sup>27</sup>

# **2.3 Measuring EU Fund Spending**

Unlike most types of government spending, EU structural funds mostly operate on a _reimbursement basis_ which can disconnect the recorded timing of spending from the time it actually occurred.<sup>28</sup> This is potentially problematic for estimating fiscal multipliers – which require spending and its effects to happen at the same time or within a fixed interval after that—and can lead to biased multiplier estimates.

EU structural and investment fund spending proceeds in a roughly three-step process. First, is the _planning stage_ at the start of the 7-year programing period, when the EU spells out the type and size of each program, governments make submissions, and those submissions are approved by the EU. This means that the plan on types and total amount of spending to each country and/or subnational NUTS2 region is usually known well in advance before any actual spending takes place.<sup>29</sup> Second, is the _implementation step_ during the 7-year program which involves (i)
Policy Research Working Paper 10220

# **Abstract**

In many low- and middle-income countries, the lack of access to essential infrastructure such as roads, electricity, and information and communications technology may hinder the provision of many critical services such as health care. For instance, scarce and deficient roads might limit the patients’ access to health facilities, restrict the supply of qualified staff, and constrict access to key inputs such as medicines and vaccines. Likewise, lack of reliable electricity and internet connection may limit the ability of health facilities to power essential equipment, have better access to information, potentially serve more patients, and manage their supply chain efficiently. This paper combines exhaustive health facility surveys with geospatial data to study the extent to which better access to infrastructure in Kenya might improve the functioning of health care facilities and the quality of their services. First, the paper documents the gap in access to infrastructure in the health care sector in Kenya and reviews the literature on this topic.

Then, using a novel data set, it finds that access to electricity and good quality roads is associated with more accurate provider diagnostics for both general illnesses and those primarily affecting children and pregnant women. Additionally, access to electricity is associated with (i) higher availability of vaccines for children—mostly by making it possible to have a working fridge, which is essential to store most vaccines; and (ii) higher availability of essential and priority medicines, by facilitating the use of information and communications technology for supply chain management. Finally, access to good quality roads, electricity and use of information and communications technology for supply management are positively related to the availability of antenatal care tests for pregnant women. Overall, the results suggest that increased investment in infrastructure and communications technologies may improve health service provision in Kenya.

This paper is a product of the Human Development Chief Economist Office and the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution
**Table 1. Percentage of wage and self-employed workers, sample countries**

||Wage/ salaried<br>workers|Self-employed<br>workers|Number<br>employed|
|---|---|---|---|
|Armenia|87.87|8.98|1,047|
|Bolivia|51.85|32.89|1,788|
|Colombia|53.20|35.62|1,735|
|Georgia|84.13|10.23|958|
|Ghana|35.67|50.80|2,181|
|Kenya|56.59|33.03|2,419|
|Ukraine|88.83|6.96|1,307|
|Vietnam|56.34|28.32|2,366|

_Note_ : “Employed” includes employers and unpaid workers. _Source_ : STEP Surveys (2014).

In keeping with standard practice, the top 1 percent hourly earners were removed from the analytic sample to avoid potential outliers, and extremely low wages were imputed to those reporting zero hourly earnings.<sup>10</sup> Finally, cases missing information on any of the key variables were dropped from the sample. The proportion missing constitutes less than 0.1 percent of the sample.<sup>11</sup> This is a small proportion of observations; we believe that excluding them would not bias the estimates due to nonrandom loss of sample. The effective sample size for the empirical analysis ranges from 849 to 1,953 observations across the countries in our sample.

# **_Description of the sample_**

The general landscape of the labor market in each country is presented in Tables 2 and 3. Overall, labor force participation across the sample of countries is high and ranges from 49.5 percent in Armenia to 84.3 percent in Ghana. However, the employment rate
engage in quality upgrading and technology adoption when they access wider varieties of imported inputs.

The development economics literature that analyzes empirically the impact of better telecommunications tends to focus on the supply side, and especially how technology upgrades improve the efficiency of suppliers in their operations as better information emerges to improve sales (e.g. about weather, input market conditions, local market prices, labor market trends), as better credit options materialize through fin-tech (e.g. wider banking service access, easier credit ratings), and also as knowledge spillovers disseminate faster – within firms, industry-wide and along value chain through both backward and forward linkages.

Notably, Hjort and Poulsen (2019) find that the availability of fast internet connection through submarine cables in Africa has resulted in large positive employment effect regardless of the skill profile of workers, and the positive employment effect is attributed to higher rate of firm entry, productivity gains and exporting that have come with the arrival of fast internet connection. In addition, Dutz et al. (2012) study how establishments’ innovation activities affect their employment growth using enterprise-level data from a large sample of countries, including 11 SubSaharan African countries. Their results show that innovating establishments registered significantly faster employment growth compared with their non-innovating counterparts, and this relationship is more pronounced when establishments use internet; it is shown that internet usage is a positive correlation of more innovation and higher productivity. Using Brazil’s staggered internet rollout, Dutz et al. (2017) also show that in the manufacturing sector, internet access induces positive employment and wage effects in both medium- and high-skill occupations.

In addition to our focus on manufacturing, another distinctive feature of the paper is the exploration of the demand channel whereby internet affects the behavior of producers through providing broader access to price and product information to customers rather than through technological impact on the production function or access to lower-cost inputs.

We investigate the relation between improved access to price information through the transition from 2G to 3G network (measured at the woreda
the dummy variables of whether the worker commutes to another municipality; and whether he/she works in Mexico City. The results imply that informal workers spend less time commuting. For instance, the probability of commuting to a different municipality, decreases on average, between 8.0 and 25.0 percentage points for informal workers. Similarly, informal workers are less likely to work in the CBD between 4.0 and 9.0 percentage points. In the fourth column, I show that differences in transportation modes are not driving these effects.

Overall, the results from Table B1 and Figure 2 suggest that informal workers spend less time commuting. One potential interpretation of this result is that informal jobs are easier to substitute across locations than formal ones.<sup>18</sup>

**_Finding 2:_** _Most formal jobs are located in the central areas of the city, while most informal workers reside in the outskirts._

The second finding is that most formal jobs are available in the center and west of the city, while informal workers reside in other, less connected areas. As a consequence, workers that cannot afford the high rents in the center of the city, live in outlying areas with poor access to formal jobs.

Figure 3 presents a heat map of informality rates in Mexico City and adjacent municipalities in the State of Mexico in terms of jobs and the residence of workers. Panel A in figure A10 in the Appendix shows that the west and the center of Mexico City have the highest level of economic activity.<sup>19</sup> Combining these two figures, we see that informality rates are lower in the west and the center of the city than in the east and the periphery of the city. This suggests that workers who live in remote locations usually have poorer access formal employment.

# **3.2.2 Difference-in-Differences Specification**

**_Finding 3:_** _Informality rates decline with transit improvements that improve market access of
relocate in response to shocks (Okun 1962.). The short-term measure is particularly useful for monetary policy since supply constraints or adverse demand shocks, even if they are not permanent, reduce the effective slack in the economy and therefore influence the policy interest rate at a given decision point.

_Long-term_ potential growth is a function of the available capital stock, labor input, and current technology (Solow 1962). As such, long-term potential growth captures movements in the slowmoving fundamental drivers of output, assuming allocation of all factors of production to their most productive uses regardless of temporary supply shocks. Long-term potential output sets the underlying trend of short-term potential output as well as actual output (see Box 1 above for various estimation methods).

Other studies have also documented a potential growth slowdown in Russia (Entov and Lugovoy 2013; IMF 2012; Kaitila 2016; Kuboniwa 2011; OECD 2018). This paper fills a gap in the literature by providing a cross-country perspective using a large data set of comparable estimates (World Bank 2018). The analysis is also innovative in its examination of the impact of multiple reforms in a single platform. While the appendix provides the technical details, Box 1 provides an overview of how potential growth is measured.

# **Box 1: How to measure potential growth?**

Estimates of short-term output may be computed using filtering techniques, including univariate and multivariate filters, while estimates of long-term potential output rest on structural models or long-term growth expectations.

**Filtering techniques.** Univariate filters involve estimates of trend output using only GDP series. Multivariate filters take into account the relationship between GDP and other variables (such as inflation or unemployment rates) to help distinguish short-run deviations of output from trends.

**Production function approach.** This approach represents potential output as a (CobbDouglas) production function of the amount of full-employment capital and labor, as well as technology and efficiency of factor allocation that drive total factor productivity (TFP).

**Expectations.** The
# **<mark>Table 1 - Summary Statistics</mark>**

Panel A exhibits macro level data of states and municipalities. Panel B presents statistics on the fiscal balance components of local governments. Panel C presents a series of statistics of bank lending to local governments. See Table A1 in the Appendix for variable definitions.

|governments. See Table A1 in t|he Appendi<br>Obs.|x for varia<br>Mean|ble definition<br>p10|s.<br>Median|p90|Std. Dev.|
|---|---|---|---|---|---|---|
|**Panel A. Economic Indicators**<br>|**of States/M**|**unicipali**|**ties**||||
|DebtStates,16Q1|30|0.86|0.20|0.64|2.25|0.70|
|Maturitys,16Q1|30|14.6|8.3|14.2|19.3|3.9|
|DebtBankMunim,Mar16|984|0.24|0.02|0.34|0.37|0.15|
|DebtMunicipalitym,16Q1|1,083|0.03|0.00|0.00|0.08|0.05|
|Employment|434|0.006|-0.021|0.006|0.033|0.022|
|- Primary|434|0.004|-0.13|0.003|0.141|0.109|
|- Secondary|434|0.011|-0.049|0.009|0.074|0.05|
|- Tertiary|434|0.006|-0.028|0
the context of specifying the damage function D(.) as outlined above, lies in mis‐specifying the function for values of the hazard (shock) that were unavailable for the analysis‐ in particular, extreme values that occur only very infrequently (e.g. a once‐in‐a‐generation severe drought, typhoon, tsunami etc).

For the initial model estimation Hill and Porter (2014) combined the years 2004‐5 and 2010‐11 in order to allow the broadest (artificially) cross‐sectional range of values for our shock. In some pre‐ analysis, we conducted a careful analysis of the range of data (see LEAP document). The issue in the context of our dataset is the limited number of observations available at some of the high levels of crop loss (e.g. greater than 50 percent crop loss).

Figure 2.1 shows the distribution of the crop loss data for 2011 and 2005 combined, and it can be seen that the distribution is left‐skewed. Where we identify data paucity that challenges the strength of fit of the modelled relationship at this extreme end of the crop loss spectrum, we need to highlight a caveat regarding the sparse data above 60% crop loss. Table 2.3 outlines in more detail the number of observations in each “bin” of data at 5% intervals. 2005 is somewhat worse year than 2011, with a higher mean crop loss, and 300 observations above 50%. However in 2011, just under 100 observations lie between 50 and 60%, and none at all are above 60%. The distribution can then be said to be somewhat different between years, and we therefore examine the difference between the pooled dataset that has a broader distribution, with the use of one round of data as the training data, and another round as the testing data, in order to test the stability of the drought‐consumption relationship over different drought distributions.

> 13 EV1) is discussed as
of Chicago Press.

- Harberger, A. 1978. "Perspectives on Capital and Technology in Less Developed

- Countries." In Contemporary Economic Analysis, edited by M.J. Artis and A.R. Nobay. London: Croom Helm.

- Harrison, A. 1996. "Openness and growth: A Time-Series, Cross-Country Analysis for Developing Countries." Journal of Development Economics 48: 419-447.

- IDB [Inter-American Development Bank]. 1996. Economic and Social Progress in Latin America, 1996. Washington, DC: IDB.

- Islam, N. 1995. "Growth Empirics: a Panel Data Approach." Quarterly Journal of Economics 110: 1127-70.

- Lefort, F. and Solimano, A. 1994. "Economic Growth after Market-Based Reform in Latin America: The Cases of Chile and Mexico." Working Paper. World Bank, Macroeconomics and Growth Division, Whasington, DC

- Loayza, N. 1994. "A Test of the International Convergence Hypothesis Using Panel Data." The World Bank Policy Research Working Papers Series No. 1333. Washington, DC: The World Bank.

- Marfan, M. and Bosworth, B. 1994. "Savings, Investment, and Economic Growth." In The Chilean Economy: Policy Lessons and Challenges, edited by Bosworth, B., Dornbusch, R., and Laban, R. Washington, DC: Brookings Institution.

- Nehru, V. and Dareshwar, A. 1993. "A New Database on Physical Capital Stock: Sources, Methodology and Results." Revista de Analisis Economico 8 (1): 37:59.

- Sachs, J. and Warner, A. 1995. "Economic Reform and the Process of Global Integration." Brookings Papers of Economic Activity 1: 1-118.
|-3.6608***|-0.3850*|-1.1553***|
|(male=1, female=0)|(0.7196)|(0.3688)|(0.7596)|(0.5531)|(0.2236)|(0.3021)|
|Ethnic minorities (yes=1,|-3.0134**|-11.8797***|15.4604***|-0.7899|1.0040***|-0.7815***|
|Kinh=0)|(1.4817)|(0.9528)|(1.7760)|(0.4945)|(0.2947)|(0.2687)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|28.6026***|13.1056***|24.7434***|14.5162***|8.9215***|10.1106***|
||(3.0573)|(1.2043)|(3.6762)|(1.6011)|(1.2172)|(0.9337)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.191|0.092|0.296|0.111|0.127|0.059|

Note: This table reports district fixed-effect RDD-DD regressions of share of income from different sources of households using householdlevel observations.

Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p
(3.6) _logCr_ = o +0 log j l+iog ) 3 lAiogc logE +X5 gy **+E** where Cr* = Environmental complaints per 10,000 population ADr, Total airborne dust (particulate) emissions

- As,= Total airbome SO2 (sulphur dioxide) emissions

- Wcr = Total waterborne COD (chemical oxygen demand) emissions

- Tr = Provincial area

- Er = Provincial literacy rate

Yr = Provincial real consumption per capita (the best available proxy for income) Er = A random error term To be a reliable environmental monitoring index, the incidence of complaints should meet the following conditions on signs and magnitudes of estimated parameters (the O3's are from (3.5)): l,j X 2, X3 >> 0;2 2=0; 13>1 + k 5 >1

# **_5._ Results**

# **5.1 Regression Results**

We have estimated equation (3.6) using a random effects model which captures both intertemporal and interprovincial effects (Table 5). We have run two sets of regressions, since our data on SO2 emissions are available for a shorter period (19911993) than the other emissions data. For the shorter period, we have included all three pollutants. Our results on variables other than SO2 density are very similar in both sets of regressions.

Dust (suspended particulate) density has a consistently significant, large impact on the incidence of complaints. Our random effects estimates for the entire period 198710
new private sector after the fall of the communism.

# **_Data_**

To estimate the coefficients corresponding to each of the institutions in model (1), we use an unbalanced panel of 28 ECA countries over 14 years: from 1988 to 2002. Unfortunately we have only one year of information for most of the institutions introduced in section 2. It is unfortunate because it would be interesting to explore how the reforms introduced in the business environment over the last decade have affected the capacity of the economy to respond to shocks. Given the data constraint, we have to assume that the institutional framework in which firms have operated

> 11 The first period is left out so it becomes the constant. Therefore the country dummies can be interpreted as the unemployment in the first period.

13
, we use information on the presence of visa-related provisions for a wider set of RTAs: while Orefice (2015) and Figueiredo et al. (2016) rely on a sample of 96 RTAs, the present paper benefits from recent World Bank data on the content of 279 RTAs. Third, we consider the specific type of visa provision in RTAs in affecting the bilateral settlement of immigrants (i.e. provisions directly reducing the administrative burden to get a visa, legally enforceable provision implying dispute settlement, etc.). Finally, we explicitly consider the effect of Job Posting provisions in RTAs on bilateral migration. To the best of our knowledge this is the first paper that looks at the migration effect of each specific type of visa provision. Finally, we highlight an important heterogeneity in the effect of visa-related provisions on bilateral migration stocks among countries with different income levels and attitudes towards immigration.

Our results have two relevant policy messages. First, the inclusion of visa provisions in RTAs may be used by countries as an indirect migration policy: when governments have their

> 1Bertoli and Fernández-Huertas Moraga (2015) exploit the cross-section variation in the presence of visa requirement on the bilateral stock of immigrants. With respect to Bertoli and Fernández-Huertas Moraga (2015) we also extend the sample of countries covered in the analysis. See section 2.1.

> 2To obtain data on a wider set of destinations countries, Figueiredo et al. (2016) use World Bank data on the _stock_ rather than _flows_ of migrants.

> 3Figueiredo et al. (2016) rely on cross-section identification (i.e. they do not control for country-pair fixed effects).

2
Quarterly Journal of Economics, 110, pp. 551–583.

Porto, G. (2005). “Informal Export Barriers and Poverty,” Journal of International Economics vol. 66, pp. 447–470.

Porto, G. (2006). “Using Survey Data to Assess the Distributional Effects of Trade Policy,” Journal of International Economics 70, pp. 140–160.

Ural Marchand, B. (2012). “Tariff Pass-Through and the Distributional Effects of Trade Liberalization,” Journal of Development Economics, 99(2), pp. 265–281.

34
in the mid-1920s focusing on mobilizing peasants as the key political support base (Ch’en, 1983). When famines forced peasants to sell their meager land, this would only make them more prone to revolution propaganda. Mao’s assessment of the impact of economic hardship on support of Communism echoed the views of Lenin, who viewed the 1891-1892 Volga famine in Russia as fuel for revolutionary fervor. Lenin, as quoted by Vodovozov in Ellman (2005), hailed the Russian famine as able to “force the peasant to reflect on the bases of the capitalist system, demolish faith in the tsar and tsarism, and consequently in due course make the victory of the revolution easier.” (Ellman, 2005, page 823). We thus expect: _The famines-and-communism hypothesis_ : The regions experiencing warlord-era famines would be more likely to witness the spread of communism due to the appeal of Communist ideology as well as its policy of land redistribution and reduction in land inequality.

# **3. Data and Measurements**

In this section we discuss our data and our key measurements.

# **3.1 Warlords and Border Ruggedness**

Information about warlords is from The Chronicles of Beiyang Government Officials, which contains details on the appointment and ranks of all government officials between 1912 and 1927. In the post-1916 warlord era, there were 78 warlords that controlled the 289 prefectures in 18 provinces. A warlord’s rule was fragile and often short-lived. They were frequently replaced by other warlords, or could lose control of some of their territories due to military conflicts, or even assassination. In our sample, the average ruling period of the warlord in a prefecture was only four years. However, the overall scale and casualty-count of the wars during this period were relatively small (Chi, 1976). Most of the time, soldiers in the battlefield had little fighting spirit or loyalty to their generals, and wars usually served as a negotiation tool for a calculating warlord. The low casualty rate could also be the