rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

The vast majority of the world measures poverty based on cross-sectional data that captures an estimate of income or consumption at a single point in time.<sup>1</sup> Typically, in rich countries, survey instruments ask a set of questions about income over the last 12 months.<sup>2</sup> In low-income countries, household surveys frequently inquire about how much the household has consumed in the last week, month, or quarter.<sup>3</sup> In almost all cases though, households are asked these questions about income and consumption during a single visit by the enumerator. Given that the household measure of wellbeing (whether measured by income or consumption) is assessed from a single visit, the time of the year when that visit occurs can be an important factor in the measurement of poverty.

Seasonality is observed in many dimensions and countries as a general phenomenon. In richer countries, consumption and employment spikes during the summer and holidays (Barsky and Miron, 1988);<sup>4</sup> and in poorer countries, consumption is typically much greater after harvest.<sup>5</sup> As one extreme example, D’Souza and Jolliffe (2012) note that independent sub-samples of households implicitly stratified on season reflect consumption patterns that are a third lower in the lean season as opposed to the post-harvest season. Khandker (2012) discusses the devastating 1 For example, the global poverty estimates reported in World Bank (2014) are based almost exclusively on cross-sectional household surveys that collect information on consumption or income during one visit. 2 For example, in the United States, the data that are used to produce the official estimates of poverty come from Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC). This supplement to the CPS is administered in March and asks about income from last year.

3 The recall period typically varies by the type of item being discussed, with the recall for food frequently being 7 or 14 days, and the period for nonfood items frequently being one or three.

4 This seasonality is often difficult to uncover since most macroeconomic data are released to the public after it has been adjusted to remove seasonality.

5 See Carletto, Jolliffe and Banerjee (2014) for a discussion of the importance of small-scale agriculture to the wellbeing of the vast majority of the poor in sub-Saharan Africa.

2
Sanchez, Gabriella and Luigi Achilli (2020) “Stranded: The impacts of COVID-19 on irregular migration and migrant smuggling”, _Migration Policy Centre Policy Brief 2020/20_ . European University Institute.

Wojcieszak, Magdalena and R Kelly Garrett (2018) “Social Identity, Selective Exposure, and Affective Polarization: How Priming National Identity Shapes Attitudes Toward Immigrants Via News Selection”, _Human Communication Research_ 44(3): 247-73.

Yayboke, Erol (2020) “Five ways COVID-19 is changing global migration”, _Center for Strategic and International Studies_ , March 25. <u>https://www.csis.org/analysis/five-ways-covid-19-changing-</u> - <u>global migration</u> [accessed 13 January, 2021] 21
There is no _apriori_ clear relationship between different war outcomes and peace processes of different lengths: wars that end in peace treaties typically last longer than wars that end in victories, but treaties may clear the way for a faster transition to peace. UN peace operations differ in their length and mandate. There is also no _apriori_ clear relationship between UN peace missions of different durations and the probability of peacebuilding success: both long and short UN missions could potentially be successful, depending on underlying conditions. A long UN mission may indicate that the peacebuilding ecology is more difficult (hence the need for the UN to stay longer) and the UN obviously has more opportunities to fail the longer it stays. But the opposite can also be true: the longer the peace is kept, the more likely it is that a stable peace will be built partly because of the contribution of the UN mission. In all cases, we seek to evaluate the effect of the “therapy” on the eventual health of the “patient” country, two years after the therapy is complete. Treaties and victories are “therapies” with relatively obvious dates. A UN peace “therapy” ends when the military forces are withdrawn.

Thus, we code participatory peacebuilding success as a binary dependent variable, coded 1 if all the components of participatory peace are satisfied two years after the end of the war (including the departure of the peacekeepers). If war resumes before then, if sovereignty is divided, if there is significant residual violence, or if the polity score is below our threshold, we code a 0 (peace failure).<sup>4</sup> We code failures while the UN is still there – just no successes. This sets a high bar for success and we lose several cases of successful peace _keeping_ – i.e. cases where there is participatory peace but the UN is still in place, hence the peace process is still unfinished.<sup>5</sup> Our dataset includes peace processes after 145 civil wars from 1945 until the end of 1999. Each post-civil war transition is an observation for our statistical analysis. We exclude wars that were ongoing at the end of our analysis period (December 31, 1999) and/or cases where there was no significant peace process prior to that point. If a peace 4 We code a peacebuilding failure if the country’s polity score is under 3 on a 20-point scale. This captures extreme autocracies and the most repressive of regimes. Such regimes are not considered successes by our peacebuilding definition, which excludes the “peaces of the grave” (the enemy is all dead or in prison).

5 Results using the DS2000 definition, which measures outcomes at the 2-year or 5-year mark regardless of whether or not the UN is still present are somewhat stronger.

4
# **V. Are MICs Really Different?  Some Old Approaches Applied to a “New” Concept**

The above section provided some definitions and places some parameter values on MICs’ growth rates to achieve success in some reasonable (but admittedly loosely defined) timeframe.<sup>13</sup> The MIC trap concept inevitably invokes a literature from the 1990s and early 2000s on the patterns of economic growth (for example, Pritchett 1997 and 2000) as well as the patterns of transition across the inter-country distribution of income (Quah, 1993, 1996 and 1997, and Kremer et al., 2001).

One approach, inspired by the literature on the inter-country distribution of income, is to look at transitions across income categories (in either relative or absolute terms). Are MIC-toHIC transitions more infrequent than other transitions?<sup>14</sup> Or is the inter-country distribution of income per capita similarly stable for all income categories? Another simple empirical approach to exploring the relevance of the “trap” term is to just look at MICs’ growth patterns. In simple terms, do MICs seem to perform differently from other income classes of countries?

# a. Transition Matrices and the MIC Trap:  Are MICs Less Upwardly Mobile?

We follow the transition matrix approach previously laid out in Quah (1993) and Kremer et al. (2001) to describe the dynamics of income per capita distribution across countries. We classify countries into groups by relative income per capita taking the United States as a benchmark.<sup>15</sup> Rather than estimating one-year transition probabilities as in Quah or five-year

> 13 Felipe et al (2012) define a trap threshold based on the median number of years it takes countries to cross lower MIC and upper MIC absolute thresholds.

> 14 The authors would like to thank Aart Kraay for suggesting this approach.

> 15 Our results remain the same even if we take the top 5 richest countries rather than just the United States. This is apparent if we simply look at the growth differential between the initial high income countries and the United States.

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Ethiopian civil war not be correlated with year of birth and pre-reform levels of schooling in the same way as the FPE reform. Potential bias generated by contemporaneous changes in educational investments, the impact of the civil war, and the 2000 law banning marriage for those under 18 are explored in more detail in Section 6.4. Additionally, it would be problematic if women and families relocated at the time of the reform’s implementation in such a way that higher-ability students sorted into areas with higher predicted intensity of the reform. However, this type of sorting is unlikely to occur in the studied setting. First, data from the 2016 Living Standards Measurement Study show that 86 percent of respondents in the relevant cohort range live in their region of birth. Furthermore, the intensity measure is explicitly designed to predict a greater impact of the reform in areas with lower initial levels of schooling. A violation of this assumption would entail the unlikely scenario that higher-ability students’ families were moving to areas that were worse off at the time of reform implementation, even though they could have received the same reduction in fees in their original education zone.

# **6 Results**

## **6.1 Effect of FPE on Years of Schooling and Fertility**

To begin the analysis, the OLS relationship documents the general correlation seen in the data. This is done by estimating equation (9) using the reported years of schooling from the data, not the predicted level from the first stage. A negative relationship between fertility and schooling has been well documented in the literature (Ainsworth et al., 1996; Lam and Duryea, 1999; Schultz, 1994, 1997). The OLS estimates are shown in column 1 of table 2. The estimates in panel A use data from both the census and the DHS, those in panel B use census data only, and those in panel C use only DHS data. Unsurprisingly, the OLS model estimates a strong negative relationship between schooling and fertility. However, these estimates are unlikely to describe a causal relationship between schooling and fertility if unobserved characteristics that impact women’s schooling also affect the fertility decision. To address this concern, an exogenous increase in schooling generated by the FPE reform in Ethiopia is identified, and an IV technique is used to investigate the impact of this increase in education on women’s fertility.

To examine whether exposure to FPE in Ethiopia generated an identifiable increase in years of schooling, the first-stage equation, equation (8), is estimated using each combination of data. The estimates in column 2 of table 2 show that for each additional year of free schooling made available, years of schooling increased by over one-tenth of a year. This relationship is statistically significant at the 95 percent confidence level for all three samples, and at the 99 percent confidence level when the census data are included. The first10
28 and "incomes" will be used interchangeably.

22 For more on the various components of total expenditure, see Datt, Joliffe and Sharma (note (20) above).

23 On this point, see Howarth Bouis and Lawrence Haddad, "Are Estimates of Calorie-Income Elasticities Too High: A Recalibration of the Plausible Range," Journal of Development Economics 39 (December): 333-364.

24 The five dummy variables for geographic regions include those for: (1) metropolitan (Cairo, Alexandria, Suez); (2) urban Lower Egypt; (3) rural Lower Egypt; (4) urban Upper Egypt; and (5) rural Upper Egypt. 25 For more on this procedure, see Howarth Bouis and Akhter Ahmed, "The Egyptian Food Subsidy System: Impacts on the Poor and an Evaluation of Alternatives for Policy Reforms" (final draft report, International Food Policy Research Institute, Washington, DC, March 31, 1998).

26 In most cases, the distributional impact of Egyptian food subsidies on different quintile groups is the same using either "fitted" or non-fitted expenditure values. However, using "fitted" expenditure values provides more reasonable estimates of the levels of calorie availability for the different expenditure quintile groups.

27 In 1995/96 imports provided 83 percent of the wheat grain for subsidized baladi bread and subsidized baladi flour, and over 90 percent of the oil for subsidized edible oil. See Sonia Ali and Ali Abdel Rahman, "Subsidized Baladi Bread in Egypt" (draft report prepared for Food Security Research Project in Egypt, Cairo, January 1996, p. 5).

28 According to these calculations, in 1996/97 the "free market" nonsubsidized price for each of the four subsidized foods was as follows: 0.121 LE per loaf for baladi bread, 1.24 LE per kilogram for baladi wheat flour, 3.0 LE per kilogram for edible oil and 1.00 LE per kilogram for sugar. Data on the way these "free market" prices were calculated are available from the author.

29 Tables 6 and 9 are expressed in terms of calorie availability (calorie supply) rather than actual calorie intake. Even so, the figures for calorie availability (supply) in these tables may seem high when compared with international standards. However, two points should be noted. First, when measured by household budget surveys, calorie availability in Egypt is usually quite high. Second, because of problems relating
T T is a dummy variable that equals one for domestic listed firms and zero for foreign listed iiT ii DDfirms. We consider three variants of the control group: all foreign listed firms, foreign listed excluding those with A shares (dual listed), and foreign listed excluding dual listed and those listed in Hong Kong SAR, China. We analyze the 2000-20 period to study equity issuance patterns around the establishment of the QFII and RQFII programs, the Stock Connect implementation, and the MSCI incorporation, which targeted domestic listed firms. Finding statistically significant changes in the equity issuance activity of domestic listed firms relative to foreign listed firms would suggest an impact of the internationalization events.

We conduct a second difference-in-differences analysis to remove potentially confounding effects from contemporary reforms or financial shocks affecting domestic capital markets, such as the rise in shadow banking around 2010-12 (Acharya et al., 2020b; Chen et al., 2020). We focus on domestic listed companies, distinguishing between connected and unconnected ones. Here, T T is a dummy variable that equals one for connected firms and zero for unconnected firms. iiT ii DDWe focus on the 2007-20 period to study equity issuance patterns around the Stock Connect and the MSCI incorporation, which targeted connected firms.<sup>20</sup> Still, the selection of firms is not random, and the connected treatment group could be fundamentally different from the unconnected control group. Indeed, on average connected and unconnected firms differed in some important financial and real variables in 2010-12 (Table 2, Panel B). The most significant differences between connected and unconnected firms are in sizerelated variables, such as total assets, market capitalization, and total debt.<sup>21</sup> We attempt to address

> 20 We also conduct separate event studies for the Shanghai-Hong Kong Stock Connect in 2014 and the ShenzhenHong Kong Stock Connect in 2016, where we restrict the sample to firms listed in each market, and the dummy variable captures only the connected firms in each event (Section 4.2). This exercise not only allows us to compare the impact of different internationalization events, but also provides additional evidence that our estimates are likely to capture the impact of these episodes instead of other concurrent shocks or policy changes in the domestic financial markets.

> 21 The addition of stocks to domestic equity indexes (and to the Stock Connect program) depends on the firms’ market capitalization. Therefore, size is expected to be a major difference between connected and unconnected firms.

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# **II. Overview of the Model and Key Data**

## **Production and Geographic Structure**

There are 30 sectors in the model that are listed in table 1. There are three types of sectors: perfectly competitive goods and services, imperfectly competitive goods sectors and imperfectly competitive business services sectors.

The geographic decomposition of our model of Russia is shown in table 2. In the first instance, we obtain data from the publication the _Regions of Russia_ by Rosstat on 88 regions of Russia. The 88 regions have several names in Russian; the most common legal jurisdiction is referred to in Russian as an “oblast.” Oblasts are analogous to states in the United States or provinces in Canada. But there are also jurisdictions known as territories, federal cities, autonomous districts and an autonomous region. Since we want to use the term “region” for another purpose in the model, we use the Russian term “oblast” for all of these 88 geographic areas,<sup>4</sup> with the understanding that they are not all oblasts in the Russian sense of the term.

We group several contiguous oblasts into what we call Regional Markets (RMs). The mapping of oblasts into regional markets is also shown at the top of table 2. In this paper, we shall analyze effects at the level of the Regional Market. Value-added, exports and imports by sector for our ten regions of Russia are presented in tables 3-11.

We assume that firms and sectors operate at the Regional Market level, primary factors of production are not able to move between Regional Markets (unless otherwise notes). We assume a nested CES structure of demand. Since this implies that the structure of demand is both a homothetic and weakly separable, consumers and firms in a representative Regional Market r employ multiple stage budgeting for all goods.

## **Price determination for competitive goods and services sectors**

Firms in each Regional Market have three choices for sales: sell in their own Regional Market; sell to other parts of Russia; or export to the rest of the world. This is depicted in figure 1. Firms maximize revenue for any given output level based on their transformation possibilities between the three types of goods which is defined by a constant elasticity of transformation production function. For all firms within the same Regional Market, the product they export to other parts of Russia (including other oblasts within their own regional market) is homogeneous. It follows from our assumptions of

> 4 Several of the territories are part of oblasts, so it was necessary to adjust the data to avoid double counting of the territories.

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XXXU INThODOCN

# _Box 6 Countries and Economies Classifled_

Afrime ONO :lcrm} South-Central and Indian Ocean Southarn Africa Department (A06) Department (AF3) Angola **occidntatl** d Central **Africs** Burundi **Botsawa** Dqpartment (AFI) Comoroe Lesotho enin Dlibouti MaLawi C _.o~ ZMadagascar Nozmbuiqu. **Central** African Rep. Rwanda Swaziland Congo, People's Rep. of the SeycheLtea Tanzania **Cot0 d'lvoire Zaire** Zambia EqJetorat Guinea Zimboabu G ben Western Africa Department CAF4) _Cuiln_ GhanO Nonborrowers Togo Guineasirsau Western Africa Department CAF4) GhanO Guineasirsau Liberia Nigeria Sao Tome **and** Principe Sierra Leone Sub-Saharan Africa Namibia Reunion South Africa Other UWet Africa

**Eastern** Africa Department (AF2) Nigeria Ethiopia Sao Tome **and** Principe **Keny** Sierra Leone Naurltiuu S **tlIe** Sahelian Deagrtment CAIS) Sudan Burkina Faso Uganda Cape Verde Chad Gambia, The **MaLi** Mauritania ligpr Seneral

**Asia Rsfan (and Ocania)**

Country Department I Banglade Bhutan Nepal Sri Lanka Country Department 1I Kaepuchoa Korea, **lep.** of Lao Peopltua Dum. Rep. Malaysia 4yarmar Philippines **Thailand** Viet **NM** Country Department III China (excluding Taiwan)

## Nonborrowers

Country Department IV India East and Southeast Asia Srwiei Nong **tong** Japan Country Department V Srwiei Fiji Nong Indonesis Japan Ki-ibati **Korea,** Maldives Macao Papua **New** Guinea Solomon Islands Tonga Vanuatu Western Samoa Oceania

**Korea,** _Om._ People s Rep. of Macao Mongotia Singapore Taiwan, China

## Australia

Fed.States of Micronesia French Polynesia Guam New Caledonia New Zealand Other Micronesia Other Polynesis
The system (conditional on long run portfolio choice _θ_ ) can be solved by any standard solution method for linear rational expectations models. I use the package of Schmitt-Grohe and Uribe (2004).

Next, I will substitute the solution (condition on _θ_ ) into the second-order approximations of the two portfolio choice Euler equations (C13) for the two countries: Subtracting one equation from the other, I obtain: Note that since the local expert cost _τ_ is of second order, it does not appear in the system of first-order approximation , but does appear here. Equation (21) can be interpreted as follows: the covariance between the excess return and the difference in marginal utilities equals the local expert cost (note that up to second order, the covariance is time-invariant). If the cost _τ_ is zero, the covariance is zero because domestic and foreign agents will have the same level of marginal utility regardless of the interest rate difference. In other words, both domestic and foreign investors are completely insured against country-specific risks ( _i.e._ the market is effectively complete). A positive _τ_ makes foreign investment less attractive, thereby creating home-biased portfolios and thus market incompleteness. As a result, the difference in marginal utilities is negatively correlated with the realized excess return because a country whose equity yields a higher return can afford to consume more, and has lower marginal utility.

I solve for _θ_ by substituting the conditional result of the system into (21).

Denote the normalized current account, NFA position and valuation effects as _cat ≡_ Γ _<u>CAt−t</u>_ 1<sup>,</sup><sup>_nfat≡_</sup><sup>_<u>NFA</u>_</sup> Γ _t−_ 1<sup>_<u>t</u>_,and</sup><sup>_vet≡_</sup> Γ<sup>_<u>V E</u>_</sup> _t−_<sup>_<u>t</u>_</sup> 1<sup>.Thefirst-orderapproximationsofalltheeconomic</sup> 13