rafmacalaba/datause-encoder-data huggingface dataset:
# A. Background

## SOEs’ role in Belarusian economy

**In the 1990s, most members of the former Soviet Union implemented deep political and economic transformations and sharply reduced the share of state ownership in economic activity. Belarus followed a different tack: it preserved functioning political and administrative institutions and opted for enterprise consolidation forcing better performing enterprises to take over loss makers rather than to divest, shed labor and close down chronically unprofitable ventures** (World Bank, 2010_a).  This strategy had a clear short-term payoff in that it avoided the collapse of governance that prevailed at the time in several other countries of the region, and resulted in deep social hardship and losses of output; whether it just postponed temporarily an unavoidable collapse is a matter of contention. What is not arguable is that the path chosen slowed reallocation of resources away from inefficient sectors and has been an obstacle to the introduction of organizational and technical innovations to adapt the SOEs to compete in the world market.

**With this strategy in place, the importance of SOEs has not diminished following the opening of the economy to the private sector** (Table 1.1, Figure 1.1).  This is especially the case in capital intensive sectors such as ferrous metals, production of chemicals and petrochemicals and construction materials sectors where SOEs account for more than 90 percent of output. On average, SOEs account for 55 percent of Belarus’s output and two-thirds of overall employment (Table 1.1).

Figure 1.1: Share of SOEs in output by economic sectors, % total <!-- Start of picture text --> Machine building<br>Agriculture 2009<br>Construction<br>2004<br>Trade and catering<br>Transport and communication<br>Fuel<br>Ferrous metals<br>Chemicals and petrochemicals<br>Wood and paper<br>Construction materials<br>Light industry<br>Food industry<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br><!-- End of picture text --> Source: Belstat, Authors estimates.

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rates or deferred payments are not counted as expenditures everywhere—they tend to be counted in high-income countries but rarely in LMICs. In low-income countries, tax expenditure reports don’t always cover all special tax regimes, sometimes explicitly stating so.<sup>10</sup> The reporting weakness in official data is reinforced by the observation that poorer countries display lower tax gaps than richer countries, which could reflect incomplete measurement rather than true differences. Compared to the data from those reports, our CIT expenditure estimates are homogeneous and comparable.

# **3.2 Country-Level ETR-Firm-Size Curves**

Which firms benefit from the sizable aggregate tax expenditures documented above? We rank firms based on their size within their country and assign each firm to a percentile of revenue from 1 to 100. To study the behavior of the largest firms, we further separate the top 1 percentile of firms into five bins, each representing 0.2% of firms. We then measure the average ETR across firms in each quantile. We show average ETRs by quantiles for two samples of firms. The first sample considers all firms, including zero-profit and loss-making firms to which we assign a zero ETR. The second sample is restricted to profitable firms (keeping the quantiles of revenue constant).

Figure 2 shows the pattern of ETRs in each of the 13 countries, for all firms. We rank countries by their top statutory tax rate (STR), ranging from 9% for Montenegro to 30% in half of the countries. We show the average ETR in each firm-size quantile and a polynomial fit capturing the ETR-firm-size relationship. The grey shaded area corresponds to the top 1% of firm size, which is graphically expanded to zoom in on the largest firms’ ETRs. We observe two main patterns. First, in every country the ETR rises between the first and ninth decile of firm size. The difference in ETRs between the first and ninth deciles is large in some countries—e.g. 19 percentage points in Costa Rica—and smaller in others—e.g. 1.2 percentage points in Senegal. Second, the relationship between firm size and ETRs flattens or reverses within the top decile. In most countries, the largest firms (top 1%) pay a lower ETR than other top decile firms. Together, these patterns produce a humped-shaped relation in most countries, such that firms at the ninth decile of the size distribution face the highest ETR. This pattern is most visible in countries with a higher statutory tax rate.

What factors can explain the relationship between ETRs and firm size? We next analyze the

> 10For example, Table 1 of Rwanda’s 2020-2021 tax expenditure report prepared by the Finance Ministry qualifies its income tax expenditure estimate, stating that it “ _excludes some tax expenditures not currently measurable_ ”.

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# **Results**

Table 3 presents the results of the two models. In the first model, linking farm profits to farm exogenous variables, the irrigation water quota was omitted. The second model in Table 3 includes irrigation water in a linear form. A third model was also estimated that included irrigation water in a quadratic form but it was not significant and so it is not shown. All models were estimated using heteroskedasticity- robust standard errors.

Comparing the Israeli results to MNS reveals that the value of R<sup>2</sup> is low. There are several reasons for this: 1) farm profits for one year tend to fluctuate more than farmland value, 2) Israel is a small country which means low variance in climate conditions, and 3) this data set has individual farms as observations whereas MNS relied on county averages for observations.

Examining the significant coefficients of the control variables in Table 3 reveals that they have the expected sign. Soil type 'sand2' has a positive significant effect on farm profit level. Profits increase with the age of the farmer. Age reflects experience and thus managerial skills of farmers. It should be noted that we tried to include the age variable in a squared form but it was not significant.

Soil type ‘Sand2’ and level of salinity do not a have a significant effect on profit level of farmers. The variable ‘hectare’ (farm size) which is considered in the regression in order to account for economies to scale and the farm system is also not significant. The reason that a variable reflecting farm type does not appear separately in the regression analysis is the high correlation it has with farm size. The collective farm system, Kibbutz, is much larger than private farm systems. Adding a dummy variable for the Kibbutz farm system to the analysis not only does not increase the R<sup>2</sup> but it also increases the variance of the coefficient of ‘Hectare’ due to the multicolinearity.

The estimated second order climate coefficients in Table 3 imply that the farm profit function is u-shaped (convex) in temperature and hill-shaped (concave) in precipitation. The coefficient of the water quota variable is positive and significant. This means that an increase in yearly water quota to the farmer leads to an increase in the annual profits per hectare. The assumption that the water quota is exogenous to the farmer was tested by running a regression of the water quota on the climate variables, i.e., annual temperature, annual temperature squared, annual precipitation and annual precipitation squared. The R<sup>2</sup> was found to be 0.08 and all the coefficients were not significant. These results confirm our assumption that the water quota does not depend strongly on climate conditions and thus can be considered exogenous.

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to excessive risk-taking. Indeed, purely rational and profit-maximizing loan officers should indiscriminately approve all applications under scheme C, and exert no screening effort. Second, high-powered incentives will increase effort by increasing the rewards for a profitable lending decision and increasing the penalty for originating a loan that ultimately becomes delinquent. Thus, the amount of effort exerted under various treatment can be ranked B > A > C. Third, high-powered incentives will induce more conservative lending behavior by increasing the cost to the loan officer of making a bad lending decision. Fourth, if a loan officer’s discount rate is greater than zero, the amount of effort induced by deferred compensation will be less than the amount of effort induced by A and B. Finally, if credit officers are intrinsically motivated, or believe that their performance on this task may affect their reputation, they may invest in screening loan applications even when such scrutiny will not yield additional remuneration.

# **4 Experimental Context and Design**

## **4.1 Experimental Task**

The experiment is designed to closely match the lending process for low-documentation small business loans, a class of loans for which the accurate assessment of credit risk depends crucially on the judgment of the bank’s loan officers. Loan officers recruited from leading Indian commercial banks visit our labs outside of work hours to evaluate credit applications. The credit application files were obtained from a leading commercial lender in India, and are described below. Participants evaluate these applications, complete a risk evaluation form, and make a recommendation about whether the loan should be approved.

While the lending decisions are hypothetical, in the sense that loans underlying the experiment have been previously made and their outcome is observed, participants receive 11
the infrastructure, ranging from highways to ports to cities to broadband, required to enable its continued growth and modernization. Beyond India, several recent studies find mixed evidence regarding economic effects for non-targeted locations due to transportation infrastructure in China or other developing economies.<sup>4</sup> These studies complement the larger literature on the United States and those undertaken in historical settings.<sup>5</sup> Related literatures consider non-transportation infrastructure investments in developing economies (e.g., Duflo and Pande, 2007; Dinkelman, 2011) and the returns to public capital investment (e.g., Aschauer, 1989; Munell, 1990; Otto and Voss, 1994). Several studies evaluate the performance of Indian manufacturing, especially after the liberalization reforms (e.g., Ahluwalia, 2000; Besley and Burgess, 2004; Kochhar et al., 2006). Some authors argue that Indian manufacturing has been constrained by inadequate infrastructure and that industries that are dependent upon infrastructure have not been able to reap the maximum benefits of the liberalization's reforms (e.g., Mitra et al., 1998; Gupta et al., 2008; Gupta and Kumar, 2010).

# 2 India's Highways and the GQ Project<sup>6</sup>

Road transportation accounts for 65% of freight movement and 80% of passenger traffic in India. National highways constitute about 1.7% of this road network, carrying more than 40% of the total traffic volume.<sup>7</sup> To meet its transportation needs, India launched its National Highways Development Project (NHDP) in 2001. This project, the largest highway project ever undertaken by India, aimed at improving the GQ network, the North-South and East-West (NS-EW) Corridors, Port Connectivity, and other projects in several phases. The total length of national highways planned to be upgraded (i.e., 4 For example, Brown et al. (2008), Ulimwengu et al. (2009), Baum-Snow et al. (2012), Banerjee et al. (2012), Roberts et al. (2012), Baum-Snow and Turner (2013), Faber (2014), Xu and Nakajima (2017), Qin (2017), and Aggarwal (2018).

5 For example, Fernald (1998), Chandra and Thompson (2000), Lahr et al. (2005), Baum-Snow (2007), Michaels (2008), Holl and Viladecans-Marsal (2011), Hsu and Zhang (2014), Duranton and Turner (2012), Fretz and Gorgas (2013), Holl (2013), Duranton et al. (2014), Donaldson and Hornbeck (2016), and Donaldson (2018).

6 The first part of this section is taken from Ghani et al. (2016).

7 Source: National Highway Authority of India website: http://www.nhai.org/. The Committee on Infrastructure continues to project that the growth in demand for road transport in India will be 1.5-2 times faster than that for other modes. Available at: http://www.infrastructure.gov.in. By comparison, highways constitute 5% of the road network in Brazil, Japan, and the United States and 13% in the Republic of Korea and the United Kingdom (World Road Statistics, 2009).

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# **Figure VI-2: Spectral Density Function of Filtered Series**

## **A- Oil exporters**

<!-- Start of picture text --> Bahrain: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Kuwait: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>UAE: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Iran: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Oman: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Algeria: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> 30
# **c) Women**

<!-- Start of picture text --> 100%<br>5.9 5.0 4.9<br>7.4 9.2<br>9.8 12.4 80%<br>17.4<br>15.1 17.8<br>60%<br>15.0<br>18.4<br>40%<br>58.3<br>48.3<br>39.9 20%<br>0%<br>2009 2014 2019<br>Agriculture and forestry Mining Food, beverage and tobacco<br>Textile and apparel Wood Construction<br>Sales Transport Public administration<br>Others<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data the CSES household survey.

# **3. Trade Policies and Trade Flows**

Cambodia's dedication to trade liberalization is apparent through the implementation of a series of trade-related policies. Besides the measures noted, in 1996, the U.S. granted Cambodia most favored nation (MFN) status, followed by the Generalized System of Preferences (GSP) in 1997, and the European Union's Everything-but-Arms (EBA) agreement in 2001. It is also a signatory of the 2006 Trade and Investment Framework Agreement (TIFA) with the U.S. These agreements facilitated intra-regional and international exchange of garment manufacturing and the entry of new investors from Southeast Asia into Cambodia.

Other measures further promoted private sector development, including the 1994 law on investment, which allowed 100% foreign-owned investment and provided guarantees against nationalization and price regulation. To facilitate foreign investment, the country created the Council for the Development of Cambodia (CDC), which functioned as a one-stop for investors. In recent years, Cambodia has also introduced significant cross-border trade facilitation improvements, resulting in faster export and import procedures. Compared to other Southeast Asian countries, Cambodia is known for having the least trade restrictions in services.

Trade plays a critical role in Cambodia’s economy. Since Cambodia’s economic and political transition of the 1990s, it has fostered an export-led economy. Since 2000, gross domestic product (GDP) and GDP per capita have increased significantly. In fact, several estimates have projected that Cambodia will transition out of Least Developed Country (LDC) status as early as 2027. Table 3.1 summarizes Cambodia’s trade agreements (RTAs) as reported to the World Trade Organization (WTO).

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# **Dep. variable: Log consumption per person**

**Dep. variable: Log income per person**

||**Model (1)**<br>**OLS**<br>Param.<br>t-stat|**Model (1)**<br>**IV**<br>Param.<br>t-stat|**Mode**<br>**OL**<br>Param.|**l (2)**<br>**S**<br>t-stat|**Mode**<br>**restri**<br>Param.|**l (2)**<br>**cted**<br>t-stat|**Model (1)**<br>**OLS**<br>Param.<br>t-stat|**Model (1)**<br>**IV**<br>Param.<br>t-stat|**Mode**<br>**restri**<br>Param.|**l (2)**<br>**cted**<br>t-stat|
|---|---|---|---|---|---|---|---|---|---|---|
|S(LE)*No of fcmale adults (at least 15 years)|||-0.0150|<br>-1.60|-0.0139|<br>-1.67|||||
|S(LE)*Diversity of employment|||0.0195|<br>1.50|<br>0.0259|<br>2.28|||0.0280|2.41|
|S(LE)*Member<br>of a cooperative<br>or NGO|||-0.0309|<br>-0.91|||||0.0818|2.56|
|S(LE)*Owns land|||-0.0454|<br>-1.56|-0.0473|<br>-1.75|||-0.0798|-2.43|
|S(LE)*Social capital index|||0.0009|<br>0.01|||||-0.0890|-2.18|
|S(LE)*Infrastructure<br>capital index|||0.0497|<br>0.83|||||||
|S(LE)*Commercial<br>capital index|||-0.1001|<br>-1.49|-0.0669|<br>-1.73|||||
|Nuimberofobservations|38710|<br>38710||38710||38710|38585|38585||38585|
|Number of estimated<br>parameters|233|233||263||249|233|233||263|
|R - square|0.5890|<br>0.5869||0.5904||0.5902|0.5890|<br>0.5869||0.5904|
|F test<br>Ptob > F|||||F(14,391<br>0.8|7) = 0.65<br>3|||||

_Note:_ The t-statistics allow for design effects due to the stratification and clustering of the APIS sample. The sample has 168 strata and 3378 primary sampling units. Each of the regressions also allow for strata fixed effects and 32 provincial dummy variables to control for missing _barangay_ data. The estimated income models exclude 125 observations with negative reported incomes.

_Source:_ Calculated from 1998 APIS data.

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# **4. The Effect of Migration on Fertility**

One possible channel through which migration may affect observed child health outcomes is through fertility. Migration may alter the fertility decision through a number of avenues, such as changes in household income and the opportunity cost of time and changes in knowledge about contraceptive practices. Since births that occur at very short intervals experience higher risks of infant death (see e.g. Bongaarts, 1987), the decision of whether and when to have a child may impact on child health outcomes, irrespective of any other effects of migration on health. We therefore first use the full sample of all women aged 15-54 in our sample to assess whether any differences in child health between migrant and non-migrant families are in part due to differences in fertility.

Our measures of child health are only for households that have had a child since 1 January 1994. The first four columns of Table 4 examine whether women aged 15-54 in migrant households are any more or less likely to have had a child in this period. Columns 1 and 2 use a probit specification, while Columns 3 and 4 use regression. The probit, OLS and instrumental variables results all show a small and insignificant effect of being in a migrant household on the likelihood that a woman gave birth after the start of January 1994. Columns 5 and 6 then investigate the impact of migration on the number of children ever born to women aged 15-54 as of the survey date. The OLS results show a significant positive effect of being in a migrant household. However, after instrumenting, we find this effect to be insignificant. We therefore conclude that household migration status does not affect either the likelihood a women had a child since 1 January 1994, or total fertility. For the remainder of our analysis we therefore consider health outcomes of children conditional on the children being born.

# **5. Does Migration Affect Child Health?**

# **5.1. The Impact of Migration on Infant Mortality**

Table 5 examines the impact of migration on infant mortality, an extreme measure of child health outcomes. Columns 1-5 consider the effect of having at least one household member who is a migrant, while Columns 6-10 use the proportion of adults in the household with U.S. migration experience. When migration is treated as exogenous we find a small, negative, and insignificant effect of migration on infant mortality. However, once we instrument for migration status, we

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