rafmacalaba/datause-encoder-data huggingface dataset:
explainable by the variables of equation (1). This is about the average rate of market integration for these 19 commodities.

A first step in understanding why market integration has not occurred as rapidly as one would have hoped is to examine further the characteristics of cities whose prices have remained most persistently low or high relative to all-Russia average prices.

Prices were surveyed in 28 weeks of 1992 for 19 commodities. If a city had prices whose residual (after equation 1) excess over the all-Russia price for a commodity is in the lower 10 percent of all the cities for more than 100 of the (28 x 19 =) 532 observations (19 weeks), then we call this city persistently low-priced. Persistently high-priced cities are defined analogously.

Table 8 summarizes the results of applying these definitions. Spe .ial circumstances apply in both the high- and low-priced cities. For example, low-priced cities may be company towns dominated by a large industrial enterprise that provides cheap food, or the city may be an administrative center of an autonomous republic that maintains food pricing policy different from federal policy. The high-priced cities tend to be in the Far East, plus Moscow and St. Petersburg.

# Regional Market Integration

Because the prices that are furthest from what one would expect are in cities in extreme locations and special industrial situations, the all-Russia regressions and residuals may well mask more complete economic integration within areas that are large, but not so vast as the whole of Russia.

We focus on four regions, omitting the most northern and eastern parts of the country. The regions, and cities surveyed in each, are shown in Table 9. The regions have 14 to 18 cities sampled, and cover areas of roughly 400 by 400 miles (Central), 400 by 600 miles (North Caucasus), 300 by 900 miles (Volga), and 400 by 750 miles (Urals); that is, they are roughly the size of 2 or 3 U.S. Midwestern states, or in the case of the Volga region, the lower Mississippi basin from St. Louis to New Orleans.

23
# **I. Introduction**

Recent empirical research, especially in the context of developing countries, argues that democracy mitigates political favoritism (Hodler and Raschky, 2014; Burgess et al., 2015). Burgess et al. (2015), for example, document the prevalence of political bias in the geographic distribution of roads in post-colonial Kenya and conclude that “favoritism disappears during periods of democracy.” These findings, however, contrast with a large literature linking distributive politics to electoral competition, a defining feature of representative democracy.<sup>1</sup> In a democracy, politicians can often resort to opportunistic budget allocation by rewarding specific electorates or special interest groups in exchange for votes and rents (Grossman and Helpman, 1996).

In this we combine road and election data to the paper, geo-referenced investigate extent of distributive politics in the spatial allocation of federal highways in Mexico. Specifically, we examine whether municipalities that vote for legislative candidates from the president’s party subsequently receive a disproportionate share of federally-funded highways. We focus on the post-1990 period, an era that many commentators portray as one of genuine political competition in Mexico (Font et al., 2003). During the same period, the country also embarked on an aggressive public investment strategy marked with the implementation of the National Solidarity Program (Pronasol) in the 1990s and the launch in 2007 of the first National Infrastructure Program (NIP).<sup>2</sup> This unique institutional and policy context over more than two decades gives us the opportunity to shed light on the interplay between politics and public investment.

To identify political bias in the geographic distribution of federal highways (‘ _Highway_

> 1 See Golden and Min (2013) for a comprehensive review of literature on distributive politics in different institutional environments.

> 2 The National Solidarity Program (Pronasol) is a poverty reduction program launched in 1988, which devoted US$15 billion to highway construction and improvement projects. The National Infrastructure Program (NIP) is a half-decade long and multi-sectoral infrastructure development project aiming at boosting the competitiveness of infrastructure and generate wider economic and social benefits. The first phase of the program was estimated to cost between US$156 and US$312 billion over the period 2007-2012. The second NIP was launched in 2014 and is expected to cost about US$600 billion dollars, 17% of which will be devoted to transportation projects.

1
**_55_**

## **VIII. USE of PERIOD AVERAGE**<sup>**EXCHANGE**</sup> **RATES in the BANK**

**_75._ In** principle, the Bank adopts the unweighted<sup>annualaverageof the official exchange</sup> rate (line **rf** in **IFS) for conversions between**<sup>**nationalcurrency**and</sup><sup>**U.S.**dollarvalues,</sup> except in those instances where<sup>it was seriously consideredthat this rate was "inappropriate"</sup> and that consideration of an<sup>alternativeconversionfactoriswarrantedonlywhenthe</sup> difference between the official exchange<sup>rateandthatapplyingtotheaverageofforeign</sup> transactions is egregiously<sup>large.</sup>

# Shorter Periods to Longer<sup>PeriodRates</sup>

**76.** Market rates, however, vary from day<sup>today, but the various types of dataseries that</sup> need to be converted to a common currency<sup>(e.g.,</sup><sup>**GNP,**foreigntrade, balanceof payments,</sup> etc.) are compiled over a period **--**<sup>usuallyquartersoryears.Thuslongerperioddataare</sup> the sums of the compilation period. It<sup>followsthattheuseofprevailingmarketrate</sup> conversion requires the use<sup>of averages.</sup>

**77.** The primary requirement for converting<sup>longerperioddataisthattheconversion</sup> should be made from shorter period data and that<sup>longerperioddatabe calculatedas the</sup> sums of the converted short period data. For example,<sup>themonthly(quarterly)foreign</sup> trade statistics, if available, in national currencies should be converted with the respective
-16-

in poverty is the same, so that the three dLstrLbutions have the same head- count ratlo (H q/n). Yet, it is intuitive that the poverty situations descrlbed by these dlstributions are different. Contrast distributions fl(y) and f<sup>2</sup> (y) in panel (a): clearly, fl(y) is better in the sense that the poor are, all of them, less poor than in f<sup>2</sup> (y). Yet, the head-count ratio would not indicate this. Horeover, if the income of any poor household (or a111) increased, but still remained below z, the head-count ratio would remain invariant. Evidently, however, poverty would be less. Panel (b) illustrates a different phenomenon. The poverty level of all the people below z is not the same. Those close to q are almost non-poor, while those close to the origin are the poorest of all. Yet, the head-count ratio provides no information about this. Statements like "x number of Mexicans are poor" or "y X of Mexicans are living in poverty" are therefore only partly useful. The problem with the head-count ratio is that it says nothing about the severity (or depth) of poverty nor about the distributlon of poverty. Put differently, the head-count ratio satisfies neither the monotonicity axiom (severity of poverty) nor the transfer axiom (distributicn of poverty).

Foster, Greer and Thorbecke (1984), FGT, have developed a class of poverty indices that incorporate these concerns. Define the poverty gap for the ith individual, gi, as:

- - (1) gi max [(z YL), 01

The FGT poverty index, denoted by P(a,z), is: q a (2) P(a,z) - 1/n E (gi/z) for a 2 0. i-l where q is the number of individuals for which gi > 0, i.e., the number of people below the poverty line. The parameter a is interpreted as a measure of societies' aversion to poverty; as it increases greater weight is attached to the poverty gap of the poorest individuals. FGT show that for a > 0 P(.) 21 satisfies the monotonicity axiom, while for a > 1 it satisfies both axioms .

> 21In addition, for a > 2 P(.) satisfies a further axlom known as the transfer sensitivity axiom: If a transfer t > 0 of income takes place from a poor individual with income y to a poor individual with income (YL + d), d > 0, then the magnitude of the increase in poverty must be smaller for larger Yi. In other words, this axiom gives more weight<sup>to transfers</sup> at the lower end of the distribution than at the higher end.
Figure 9. Bank Account Ownership – Household Level

# Equity Adjust <u>Coverage, (Household level, LITS)</u>

<!-- Start of picture text --> 50 45.5 47.1<br>44.2<br>45 41.1<br>38.1<br>40 36.1<br>35<br>29.4<br>30<br>25 22.8 20.3 22.4 22.8<br>20 16.3<br>15 10.7 11.9<br>10 6.2<br>5<br>0<br>Russian Federation Bulgaria Turkey Albania Montenegro<br>Coverage Dissimilarity EACR<br><!-- End of picture text --> <u>Shapley Decomposition (Household level, LITS)</u> <!-- Start of picture text --> 100%<br>90% 7.8<br>14.9<br>80% 43.2<br>70% 54.6<br>60% 25.0<br>50% 70.1 47.1<br>40%<br>10.0 18.1<br>30%<br>20% 34.2<br>10%<br>0%<br>Russian Federation Bulgaria Turkey Albania Montenegro<br>Age Education Gender Income Urban<br><!-- End of picture text --> _Notes:._ LITS is household level. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries.The variable control list for LITS and FINDEX varies. _Sources_ : LITS Differences in income and level of education are the main sources of dissimilarity in having a bank account. The importance of education is a potential reason for the drop in coverage at the household level. This drop could have occurred because households with high levels of income and education have multiple individuals with bank accounts while households with lower income and education have none. Compared to benchmark countries, gender and income seem to account for a high level of dissimilarity in Turkey while the urban and rural populations have more equality in terms of bank account ownership.

An analysis of the individuals that do not own an account shows that not having enough money (50 percent of respondents) is the main reason for not having an account in Turkey (Figure 10). A lack of trust in financial institutions, and the high costs associated with banking are the two largest subsequent reasons at 27 percent apiece. This trend is similar to the rest of ECA while in other regions the cost of owning an account seems to be the distinctive second biggest reason of not having one. One interesting aspect of survey answers in Turkey is that a significantly lower portion of individuals listed lack of money when compared to the rest of ECA and the BRICS, and a slightly lower level than even the developed world. Considering the income levels of these different country groups, individuals in Turkey seem to have a different perspective regarding the budget necessary to use a bank account which may explain the higher usage of accounts in the country.

11
<!-- Start of picture text --> Unconditional support Conditional support<br>15 15<br>KH 3<br>s Fa<br>ec A c=} 1<br>=£<br>=5<br>%&<br>==<br>= 5 Ss 05<br>= 3<br>be 5<br>= oO<br>Oporto ese eons scenescen eeb ence ences Openscede eee eee eee ee cee nen bee eenee<br>-05 ~05<br><!-- End of picture text --> @ Bottom 40% @ Middle 20% ® Top 40%
**Figure 7: Graduation by Cumulative Classes Completed in Year 5.**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Tier 1 Tier 2 Tier 3 Tier 4<br>Graduation rate On−time graduation rate<br>Percent<br><!-- End of picture text --> _Source_ : SPADIES. Students belong to the 2006 entry cohort (first semester).

_Notes_ : The figure refers to students who start year 5 and classifies them into tiers based on their cumulative classes completed by the end of the year. For each tier, the blue bar shows the percent of students who graduate, and the green bar shows the percent of students who graduate on time, relative to all graduates from that tier.

**Figure 8: Average Number of Classes Attempted in Year 1, by Ability.**

<!-- Start of picture text --> 20<br>19.5<br>19<br>18.5<br>18<br>17.5<br>17<br>1 2 3 4 5 6 7 8 9 10<br>Ability decile<br>Avg. number of classes taken<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES for students from the 2006 entry cohort (first semester). _Notes_ : Figure shows the average number of classes attempted by students in year 1 by ability decile.

# **5 Model**

We model a representative cohort of high school graduates who differ in ability, family income, and idiosyncratic preferences. They choose whether to enroll in college or enter the labor force as high school graduates. College graduation requires the completion of a set number of classes. The combination of ability, effort, and performance and dropout risks determines class completion and college outcomes. Students can mitigate risk by being on track in class completion. After college, students enter the labor market and remain there until retirement; wages depend on educational attainment (high school graduate, college graduate, or college dropout) and years of work experience.

## **5.1 Student endowments and preferences**

High school graduates differ in ability, _θ ∈{θ_ 1 _, θ_ 2 _, . . . , θnθ}_ , and parental resources received if they enroll in college, _y ∈{y_ 1 _, y_ 2 _, . . . , yny }_ . The combination of ability and parental resources defines the student’s type _j_ = 1 _,_ 2 _, . . . , J_ , with _J_ = _nθ × ny_ . For simplicity we refer to _y_ as “income.” Students receive _y_ only if enrolled in college; if not, they earn a labor market wage as explained below.

A period, _t_ , is equal to a year. For college students, instant utility depends on consumption, _c_ , and study effort, _e_ . Effort cost is heterogeneous across students as it depends on ability. The instant utility at _t_ is _U_ ( _ct, et, θ_ ) = _u_ ( _ct_ ) _− g_ ( _et, θ_ ). Function _U_ satisfies _u_<sup>_′_</sup> _>_ 0 _, u_<sup>_′′_</sup> _<_ 0 _, g_ 1 _, g_ 11 _>_ 0, _g_ 2 _<_ 0 _,_ and _g_ 12 _<_ 0 _._ Thus, effort cost is increasing and convex in effort, and total 13
<!-- Start of picture text --> Food Share in Total Expenditure<br>.05 .15 .25 .35 .45 .55 .65 .75 .85 .95<br>Quintile<br>95% Confidence Interval OLS<br>RIF-OLS<br>.2<br>.1<br>0<br>-.1<br>-.2<br>-.3<br><!-- End of picture text --> 116
of firms in developing countries. The economics of conflict literature extends the focus of North (1990) on national institutions and strong property rights regimes (Svensson, 1998) to highlight the effects of a breakdown in institutionally-enforced order. Classic examples have highlighted the macro-economic consequences of violent conflict on GDP in Spain (Abadie and Gardeazabal, 2003), on investment in Israel (Fielding, 2004), and on housing prices in Ireland (Besley and Mueller, 2012). In the spirit of Guidolin and La Ferrara (2007), who study the effect of Angola’s conflict on diamond companies, we focus on the micro-level of firm behavior in conflict settings, and introduce a novel measurement approach that enables a focus beyond publicly-traded firms.<sup>7</sup> We also build on recent work by Besley and Mueller (2017) who use World Bank enterprise survey data on the costs of protection that firms incur in insecure environments. Consistent with their suggestion that large firms in such contexts will be more susceptible to predation, we find that the negative effects of violence are concentrated among larger firms, though it is unclear if this effect originates from large firms being more affected by violence or more capable of responding.

Regarding industrial organization, Klapper and Richmond (2011) note that research on “the survival of formal firms has important implications for development strategies. In order for the private sector to act as an ‘engine of growth’ and advance the development process it is necessary for firms to survive and grow.” While generally not written in the context of conflict settings, the literature on industrial organization, including in developing countries, can help generate hypotheses regarding the structure of firms in such environments.<sup>8</sup> As Cefis and Marsili (2005) observe, “the growth and survival of firms will depend on their ability to successfully adapt their strategies to changing environments.” The behavior of smaller firms is of potential importance to the competitiveness and growth of a nation’s economy as well. As Beck et al. (2005) point out, bilateral and multilateral aid agencies have sought to promote the Small and Medium-Sized Enterprise (SME) sector through various programs

> 7As a complement to Ciarli et al. (2015), who find higher rates of self-employment in conflict affected areas of Afghanistan using household survey data, we examine the behavior of private firms.

> 8For a recent review see Li and Rama (2015).

5
category with the combined mean of the other two categories. Several significant differences appear between villages that were early and late recipients of IDs, and we observe a general pattern consistent with the likelihood that early recipients of IDs were less remote, had smaller households, were less concentrated in agriculture and had higher consumption levels. In the fourth line for each item of Table 2, we report p-values of t-tests for the equality of means across categories after partialing out province fixed effects and geographic dummies for hilly or mountainous locations. After controlling for these variables, we observe fewer differences across villages in 1988 that are systematically related to timing of ID availability. Still, the existing differences suggest that we must control for these and other unobserved differences across villages by including village fixed effects in all our estimated models, and identifying the effect of migrant networks off of nonlinearities in the years since ID cards were distributed.

Even after controlling for village location with village fixed effects, one might be concerned that the timing of ID card receipt was endogenous. Specifically, the recognition that rural residents were migrating may have led county officials to issue IDs in response to a sharp rise in migration. If true, issuing IDs would have little to do with new migration, but might be correlated with existing migrant flows. The lowess plot of change in village migrant share versus years-since-IDs were issued indicates that out-migration accelerates immediately after or as IDs are issued and then slows by 10 years after issue (Figure 4). The pattern also suggests non-linearity in the relationship between the changes in the size of the village migrant outflow and the years since ID cards were issued.<sup>23</sup> Although Figure 4 appears to demonstrate a pattern consistent with ID cards facilitating increased migration, a common time trend could be driving the observed relationship between receipt of IDs and change in out-migration. To address this possibility, we separate the sample into villages receiving IDs in 1988 or earlier and those receiving IDs after 1988, and plot the relationship between change in migration and ID receipt across these two groups of villages (Figure 5). While the estimated rate of increase in migration with ID distribution is not as steep for villages that were later recipients, this difference is not statistically significant, leading us to conclude that the apparent impact of ID distribution is not simply the result of a common trend.

In order to motivate allowing the effects of ID distribution to vary with riskiness of the local economy, we next use the lowess estimator to plot changes in the number of migrants in each village against years-since-IDs were issued by terciles of rainfall variance (Figure 6). For villages in the first

> 23 One might be concerned that the pattern shown in Figure 4 is driven exclusively by the 41 villages receiving IDs in 1988, and so we plotted this relationship excluding villages receiving IDs in 1988 and observed no difference in the bivariate relationship.

15
6 economic crises or a decline in commodity prices (which tend to have memory). The period covered by the panel may be too short to fully encompass the period of the shock (e.g. precipitous commodity price decline) and the shock may affect all households in the sample leaving the researcher in effect without a control group. Estimates of the welfare effect of an economy wide shock based on welfare before and after the shock will be biased, if there are secular trends.

Furthermore, the availability of repeated observations on a household’s consumption and income does not eliminate the need for explicit information on shocks to estimate the welfare effects of shocks. While changes in consumption are sometimes regressed on changes in income (Harrower and Hoddinott, 2005), attenuation bias, due to oft observed measurement error in the latter, would lead us to underestimate the effect of an income shock. At the same time, imputation errors in valuing consumption from own food production in constructing the consumption and income variables may lead to a spurious positive correlation between total household consumption and income, biasing the income coefficient upwards (Deaton, 1997). Direct information on shocks usually provides the necessary instruments to address this problem. It also enables inference on the effect of shocks on income and consumption.

In the absence of panel data, but given cross sectional data on household consumption (Cijt+1), explicit information on shocks experienced during t+1 (Sijt+1) and comprehensive recall data on households’ assets and their coping capacity (Xijt) the differential effect of different shocks across households can be explored through estimation of equation (6), in effect using a retrospective panel approach and assuming E(Xijtvij) = E(Sijt+1vij)=0.