rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2. Estimated** **<u>output elasticities with respect to transport accessibility</u>**

||Burundi,|Rwanda &|Ken|a|Tanzania||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Uga|nda|||Tabora||Mbeya||Arusha||Coastal||
||_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|_T_0|_T_1|
|Rice|-0.010|-0.117 ***|0.019|-0.091 *|-0.013|-0.100 ***|0.000|-0.244 ***|0.005|-0.125 ***|-0.025 *|-0.037|
||(0.017)|(0.024)|(0.032)|(0.053)|(0.010)|(0.021)|(0.020)|(0.035)|(0.021)|(0.044)|(0.014)|(0.028)|
|Maize|0.011|0.028 *|0.020|-0.142 ***|-0.019 **|-0.048 ***|-0.021 **|-0.082 ***|-0.008|-0.203 ***|-0.043 ***|-0.068 **|
||(0.011)|(0.017)|(0.013)|(0.023)|(0.008)|(0.016)|(0.010)|(0.019)|(0.012)|(0.027)|(0.012)|(0.028)|
|Cassava|-0.005|-0.039 **|0.022|-0.169 ***|-0.002|-0.207 ***|-0.134 ***|<br>0.047|-0.001|-0.109 ***|-0.024|-0.252 ***|
||(0.013)|(0.019)|(0.017)|(0.028)|(0.016)|(0.037)|(0.018)|(0.029)|(0.013)|(0.030)|(0.020)|(0.038)|
|Bananas|-0.020 **|-0.013|-0.049 **|0.060 *|-0.011|0.005|-0.040|-0.042|0.008|0.041|0.010|-0.109 ***|
||(0.009)|(0.013)|(0.019)|(0.032)|(0.025)|(0.041)|(0.037)|(0.071)|(0.015)|(0.028)|(0.022)|(0.034)|
||Burundi,|Rwanda &|||Tanza|nia|||||||
||Uga|nda|Keny|a|(all regi|ons)|||||||
||_T_0|_T_2|_T_0|_T_2|_T_0|_T_2|||||||
|Coffee|-0.003|-1.644 ***|-0.045 **|-0.804 ***|-0.055 **|-0.437 ***|||||||
||(0.007)|(0.097)|(0.018)|(0.094)|(0.023)|(0.076)|||||||
|Tea|-0.013 **|-0.254 ***|0.029|-1.270 *|-0.005|-0.218 ***|||||||
||(0.007)|(0.030)|(0.024)|(0.767)|(0.032)|(0.060)|||||||
|Cotton|0.004|-0.647 ***|-0.020|-4.623 ***|-0.009|-0.853 ***|||||||
||(0.009)|(0.027)|(0.021)|(0.340)|(0.012)|(0.077)|||||||
|Tobacco|-0.014 **|-0.831 ***|0.090 ***|-3.312 ***|0.004|-1.157 ***|||||||
||(0.007)|(0.262)|(0.030)|(0.634)|(0.008)|(0.039)|||||||

Source: Based on the SPTobit estimation results (shown in Annex Tables).
contemporaneous momentum trading (buying current winners and selling current losers) and lagged momentum trading (buying past winners and selling past losers). Contemporaneous momentum trading is stronger during crises, and stronger for fund investors than for fund managers. Lagged momentum trading, on the other hand, is stronger during non-crisis periods, and stronger for managers. We also find that funds engage in contagion trading, **by** which we mean that they systematically sell assets from one country when asset prices fall in another. This contagion trading is due primarily to underlying investors, not managers.

The paper is organized as follows. The next section outlines our approach to measuring momentum trading and contagion trading. Section III describes our data. Section IV presents our momentum and contagion results. Section V addresses whether return autocorrelation within Latin America can rationalize our section-IV results. Section VI concludes. The appendix provides some related regression-based analysis.

# **H.** Strategies: **Momentum Trading and Contagion Trading**

This section presents our approach to testing whether funds employ momentum and contagion trading strategies. Momentum trading-also called positive feedback trading-is the systematic purchase of stocks that have performed well, and sale of stocks that have performed poorly ("winners" and "losers"). Contagion trading is the selling of assets from one country when asset prices are falling in another. Contagion trading is thus **a** crosscountry phenomenon, in contrast to momentum trading, which is a within-country phenomenon. (This type of cross-country analysis is not possible using recent singlecountry data sets, such as those of Kim and Wei **1999** and Choe, Kho, and Stulz **1999.)**

> First, we review the existing finance literature on momentum trading. Second, we present our approach to testing for momentum trading, an approach that draws from this

> earlier literature. Then we turn to contagion trading, presenting first a brief review of the

> "contagion" literature, followed **by** our approach to testing for contagion trading. The approach we adopt in testing for contagion trading is in the same spirit as our test for momentum trading.

**3**
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21
the household survey and tax data earnings distributions look closer than they are, and thus understates the extent of under-capturing of high earners.<sup>5</sup> Piketty et al. (2022) note that tax evasion and avoidance can reduce measured top income shares compared to the true number and that these can both vary across time and countries, making within and between country comparisons difficult. Jouste et al. (2023) show that an increase in the top marginal tax rate in Tanzania, from 30% to 40%, which affected the top 1% of earners, may have resulted in declines in reported incomes. In addition, that income in tax data does not include incomes important at the top of the distribution, like retained earnings in firms (Piketty et al., 2022), is also likely to be important in middle-income countries, although probably not for low-income countries with very large informal sectors.

A final challenge is the survey and tax data may not identify the same units, and tax and other forms of administrative data may not give a welfare measure that economists care about (Ravallion, 2022). Many countries have joint tax filing for married couples, and this can make it more difficult to use the tax data to complement surveys. Tax data also usually tell us little about households and thus cannot easily be used to create a measure of household per capita income. Ravallion (2022) points out that using tax data to improve surveys can often require researchers to define and use taxable income from the surveys to match what is available in the tax data, but that taxable income is not an appropriate measure of welfare, ignores transfers and is thus likely to overestimate income inequality.

# Measuring the Upper Tail of the Wealth Distribution

Thus far we have focused on the challenges when measuring the top of the income distribution. But much of the preceding discussion applies directly to wealth. In this section we focus on aspects of measuring the top of the wealth distribution where there is not a simple correspondence between the literatures on measuring the top of the income and wealth distributions.

All of the challenges that we have discussed above in measuring the top of the income distribution using household surveys also apply to measuring the wealth distribution, although some are even more of a concern. The ex-ante solutions we discussed above can also be used to solve wealth challenges, as can the ex-post solutions using survey data. But other solutions that researchers have used have relied on novel types of data. We discuss these issues in this section.

> 5 This is not an issue if a survey asks about earnings over the last year to all individuals, including those not currently employed, as the US CPS does. In countries where most employed people are employed throughout the year, this may also not be a substantial concern.

22
# **1. Introduction**

High income inequality can affect individual welfare in multiple ways, including by influencing tax rates, investment in human and physical capital and economic growth (Alesina and Rodrik, 1994; Persson and Tabellini, 1991) or through its effects on collective action and the provision of public goods (Bardhan, Ghatak and Karaivanov, 2007), or crime (Ehrlich, 1973). It can also directly affect reported happiness: Alesina, Di Tella and MacCulloch (2003) find, for example, a large negative effect of inequality on happiness in Europe, but not in the US (where social mobility is perceived to be higher).

It is therefore to be expected that individuals may have preferences over the distribution of income in society. Such preferences (based on perceptions of inequality – and beliefs on what governments should do about it) may translate into actual policies depending on the political model that applies in any given country, whether the median voter theorem holds, or whether economic elites or interest groups drive policy outcomes.<sup>2</sup> To the extent that individual redistributive preferences have an influence on redistributive policies, differences in who supports income redistribution, for whom and why can lead to different equilibrium levels of inequality and redistribution in society.

This paper examines redistributive preferences across transition countries of Europe and Central Asia, and contrasts transition countries with a number of countries in Western Europe. It assesses the degree of inequality aversion and support for various redistributive policies across transition countries, and examines the characteristics of those supporting a reduction in inequality and greater assistance to the poor. In addition, the paper tests various explanations for redistributive preferences (including past mobility, aspirations for future mobility, beliefs about fairness, the impact of the recent financial crisis, and the degree of risk aversion of the respondents) in Europe and Central Asia, using a single survey instrument (the Life in Transition Survey). It also analyzes preferences for redistribution to specific population groups, which also sheds light on the motives of individual support for redistribution.

The paper is structured as follows. Section 2 reviews various explanations for redistributive preferences in economic theory and literature, and presents the empirical approach for testing these hypotheses in this paper. Section 3 presents the data and descriptive statistics at the country level. Section 4 discusses the results of the empirical analysis of individual support for reducing inequality and preferences for redistribution to specific groups. Section 5 concludes.

# **2. Explaining preferences for redistribution: Theory and empirical approach**

> 2 For example, in the U.S., Gilens and Page (2014) reject the hypothesis that ordinary citizens have unique, substantial power over policy decisions and finds that they have little or no independent influence on policy. Instead, to the extent their preferences are reflected in policy, it is because they are positively correlated with those of economic elites.

2
# The Extent of the Market and Stages of Agricultural Specialization

M. Shahe Emran<sup>1</sup> Department of Economics and ESIA George Washington University Forhad Shilpi The World Bank JEL Codes: O12, R11, R12 Key Words: Structural Change, Agriculture, Crop Specialization, The Extent of the Market, Market versus Home Production, Commercialization

> 1 Emails for correspondence: shahe.emran@gmail.com (M. Shahe Emran), fshilpi@worldbank.org (Forhad Shilpi). We would like to thank Bryan Boulier, Tony Yezer and Stephen Smith for helpful discussions. The standard disclaimers apply.
highest administrative classication, the Patwar-Circle (PC), which typically covers four villages, to approximate the radius rule. In terms of actual land area, this is a reasonable approximation; dividing the size of the province by the number of PCs shows that one school in every PC would satisfy the radius requirements of the rule. Formally: Since GSSs could have been built in any year between 1981 and 1998, we assign a value of one to Rulei if it was the largest village in its PC based either on its 1981 or 1998 population. In addition, for the 4.5 percent of villages in our sample that are alone in their PC, we assign a value of 0 to the instrument. Our results are robust to the using either 1981 or 1998 population exclusively or assigning the value 1 to Rulei for single-village PCs.

The eligibility rule is non-linear and non-monotonic in population. It drops to 0 for larger villages when there is an even larger neighboring village within the PC. In using this rule as an instrument, we are thus able to explicitly control for continuous functions of village population (these covariates have a large direct impact on the existence of a private school). We also include a full set of PC xed eects in our specication, thus exploiting rank variation only within a small set of proximate villages. Our nal specication is of the form: where the Xit controls also include indicators of village wealth and area. We estimate Equation (5) using Rulei as an instrument for GSSit.

patwar-circle that has a GSS. This is problematic since we are worried about the endogenous placement of GSS in the rst place.

14
Economic Parameters of Deforestation 57

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# **Table 4: A transaction level analysis, LPM**

||(1)|(2)|
|---|---|---|
|(t-r)|0.629***|0.556***|
||(0.029)|(0.026)|
|log(value)|-0.011***|-0.002***|
||(0.000)|(0.000)|
|(t-r)*log(value)|0.042***|0.010***|
||(0.004)|(0.003)|
|prc||-0.059***|
|||(0.000)|
|oth_regime||-0.251***|
|||(0.001)|
|collpriv||-0.214***|
|||(0.000)|
|for||-0.623***|
|||(0.000)|
|oth_owner||-0.616***|
|||(0.001)|
|HS4 fixed effects|Yes|Yes|
|Observations|13,716,691|13,716,691|
|R-squared|0.051|0.305|

Notes: A linear probability model is adopted. Dependent variable is a dummy indicating whether the final exporter is a producing firm (0) or trading firm (1), using the conservative classification. (t-r) refers to the unrefunded export VAT rate in percent, ranging from 0 to 0.17 (not in percentage as in other tables, adjusted to avoid very small coefficient estimates). log(value) is the logarithm of the transaction value in million current dollars. prc equals one if the exporting firm involved in the transaction belongs to processing trade, and zero otherwise. oth_regime equals one if the exporter involved in the transaction belongs to any of the trade regimes other than normal trade and processing trade, and zero otherwise. collpriv equals one if the exporter involved in the transaction is either a collective or a private firm, and zero otherwise. for equals one if the exporter involved in the transaction is a foreign wholly owned company, or an equity joint venture, or a contractual joint venture, and zero otherwise. oth_owner equals one if the exporter involved in the transaction belongs to any of the ownership types other than SOEs, collective & private, and foreign firms, and zero otherwise. *** p<0.01, ** p<0.05, * p<0.1.

36
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Brown et al. (2011) for a somewhat larger sample of BEEPS firms.<sup>30</sup> This is also confirmed by the responses to the 2008-9 survey for firms that had a loan in 2005. Only 37.9% of small firms that had loans in 2005 also had a loan in 2008-9. For large firms, 76.2% of firms that had a loan in 2005 also had one in 2008-9, suggesting greater persistence in the lending relationships for large firms.

There was also persistence among small firms that reported having no need for a loan. Sixty-five percent of those that reported no need in 2005 also reported no need in 2008-9. Large firms showed less persistence on that measure. Only half that reported no need in 2005 also reported no need in 2008-9; 41.7% of them reported that they had a loan in 2008-9 while 8.3% reported that they applied for a loan but were rejected. A relatively high share of the large firms that had loans in 2005 had their loan applications rejected in 2008-9 (14.3%). In comparison, the share of small firms that had their loan applications rejected in 2008-9 was only about 7%, a figure that was similar regardless of the firms‘ credit status in 2005. This pattern is also consistent with the notion that small firms were unlikely to apply for loans if their owners felt the probability of rejection was high. It is also telling that a large share (22-37%) of small firms was discouraged from applying for loans in 2008-9 regardless of their credit status in 2005. By contrast, no large firm reported being discouraged in 2008-9.

In all, the patterns suggest strongly that small firms had substantially less access to credit than large firms, and were therefore less willing to apply for loans, both in 2005 and 2008-9. By contrast, large firms, many of whom reported having no need of a loan in 2005, turned to banks as a response to the crisis in 2008-9. A surprisingly high share of those applications was rejected, but no large firms reported being discouraged from applying for loans. Against this backdrop, it is not surprising that the change in reported financial constraints was greater from 2005 to 20089 for the large firms that were actively involved in acquiring credit to manage the crisis than the small firms that tended to remain outside the formal banking system.

> 30  Recall that our identification strategy is based on following reported financial constraints for the same firms over time, which restricts our sample to only firms that responded to all three rounds of the BEEPS. Brown et al. (2011) do not restrict their sample in this way.

30
out in Peru and what constrains it is critical for policy aimed at promoting regional productivity convergence.<sup>5</sup> It would also be useful to understand the relative roles of technology diffusion (or more broadly, TFP catchup) and capital accumulation in the labor productivity convergence that we find in manufacturing and mining. Limitations in the regional data set prevent us from analyzing these mechanisms in more detail. A companion paper to this study uses firm‐level data to show that within industries, Peruvian firms are converging to the domestic frontier in labor productivity, even after adjusting for changes in capital per worker (Iacovone et al, 2015). This suggests that TFP convergence accounts for some part of the regional convergence in labor productivity.

The rest of this paper is organized as follows. Section 2 describes the data and presents summary statistics for departments, including the levels and rates of change in poverty, labor productivity and the geographic concentration of different sectors. Section 3 specifies the methodology used for measuring convergence. Section 4 presents the results, and Section 5 discusses the link to the issue of structural transformation. Section 6 concludes with a discussion of key takeaways for policy discussion and future research.

# **2. Data and Descriptive Statistics**

## **2.1 The Data**

Our primary data source on regional output during 2002‐2012 is the Regional Information System for Decision Making (SIRTOD) maintained by INEI, Peru’s Statistics Agency. For this period, the database provides department‐level data at yearly frequency.<sup>6</sup> We use SIRTOD data on Value Added and GDP by industry and department (expressed in year 1994 constant new soles), and population by department.

Because of gaps in the regional and industry level employment estimates in SIRTOD, we complete this data using the employment information – also at regional and industry levels – found in the National Household Survey (ENAHO). ENAHO is also our source for data on poverty rates by department. The poverty rate is the percentage of population living below a specified threshold of income.

The definitions of the key variables in our study are as follows:

1. Value Added per worker = Value Added _i_ , _j_ Total employment _i_ , _j_ 2. GDP per capita = Value Added _i_ Total population _i_ 3. Poverty level = Population below income threshold _i_ Total population _i_

Here, _i_  Department, _j_  Industry

> 5 We discuss this in more detail in Section 5.

> 6 Departments of Lima and Callao are grouped into one region (Lima‐Callao), following the classification used by INEI in the initial years of the sample (early 2000s).

4