rafmacalaba/data-use-mentions-extended huggingface dataset:
on the association of relative and absolute income positions with individuals’ values, attitudes and behavior. Most empirical studies of middle class values in emerging countries classify people in the middle class based on self-perception of either status or position in the income distribution (PRC, 2008; Amoranto, Chun and Deolalikar, 2010; OECD, 2011), but self-reported status is a very poor predictor of someone’s income, education or occupation. And attempts to use income measures (Cárdenas, Kharas and Henao, 2011) are limited by the lack of accurate information about income in values surveys, which is either absent or classified in broad categories. Most studies also fail to compare income effects with relevant individual characteristics that could affect values (such as education or occupation), and that could be in part captured by income.

In this paper we complement existing empirical efforts along several dimensions. First, we attempt to address the generalized lack in values surveys of accurate information on income by constructing measures of permanent income based on asset ownership (see below, Section 3 for details). Second, we merge two strands of literature by looking at the association of values with both people’s relative position in the income distribution, and absolute income levels that are comparable across countries. We also explore the possibility of nonlinearities in the relation between income and values. Finally, we consider other predictors of values, such as origin, education and occupation, which in the sociological literature are also linked to middle class status (Goldthorpe 2000), and assess whether they relate more to people’s values than income itself.

The paper addresses the following questions: How do political and social values vary across income and class? To what extent does class, as opposed to education and social origins, have a net association with values? And does the Latin American middle class hold specific values that distinguish it from both the upper and lower classes, or is the relationship between social classes and values a monotonic one? It is important to state
# **Conclusions**

This paper has analyzed new registrations and company‐level tax records with a focus on three specific questions. First, of the large number of companies registered in recent years, how many are currently ‘formally’ active? Second, using the number of registered companies in a sector as proxy for interest, which sectors and subsectors have received the most attention from investors and entrepreneurs? Third, what has been the contribution of newly registered businesses to the overall economy in terms of taxes, tax‐bearing employment, and exports, and how does this vary across sectors and subsectors?

This paper finds approximately 40 percent of registered companies (up to 2012) to be active. While this might seem low, the growth in newly registered and active companies is impressive — with approximately 9,300 companies added between 2008 and 2012, and approximately 1,000 additional companies expected to become active in 2013.

Registrations in the services sector have dwarfed the other sectors, comprising 75 percent of total company registrations. Within services, half of these registrations have been recorded in the wholesale and retail trade. There are a number of possible explanations for the dominance of trade in registrations. First, the move to diversify away from agriculture — particularly as retail trade constitutes a low start‐up capital alterative. Second, increasing domestic purchasing power could justify growth in internal trade. Third, existing informal businesses may have decided (or have been strongly encouraged) to formalize their operations via registration.

Newly registered companies contributed 24 percent of the increase in tax declarations, with 19 percent coming from the services sector and 4 percent from industry. The contribution from the services sector can be explained by two factors: the large number of businesses in trade, and the entry into the market of major telecommunications operators. Industry is driven by the subsectors of mining, manufacturing, and construction. The marginal role of agriculture is likely due to its largely tax‐exempt status.

Newly registered companies have helped to widen the tax
br>D. Euro and yield differential with U.S.<br>Exchange rate Percentage points<br>1.4 0<br>1.3 -0.4<br>1.2 -0.8<br>1.1 -1.2<br>US$ per Euro<br>German-US yield spread (RHS)<br>1 -1.6<br>2012 2013 2014 2015 2016<br>F. Swiss Franc and foreign reserves<br>Exchange rate Billions of Swiss Francs<br>1.00 700<br>Euro per Franc<br>Official reserve assets (RHS)<br>600<br>0.95<br>500<br>0.90<br>400<br>0.85<br>300<br>0.80 200<br>2012 2013 2014 2015 2016<br>Euro Area Sweden Denmark Switzerland Japan Hungary<br><!-- End of picture text --> Sources: J.P. Morgan, Bloomberg, Haver Analytics, World Bank.

Notes: A.B.C. A decline in the exchange rate (negative value) indicates a depreciation. NIRP announcements are June 2014 for euro area, February 2015 for Sweden, December 2014 for Switzerland, September 2014 for Denmark, January 2016 for Japan, and March 2016 for Hungary. Last observation is June 2016.

D. German-US yield spread is the percent difference between 2-year government bond yields in Germany and the United States. Last observation is June 2016.

E. Japan-US yield spread is the percent difference between 2-year government bond yields in Japan and the United States. Last observation is June 2016.

F. Last observation is June 2016.

22
)<br>|MK 12 million, depreciated ove<br>300 KM round trip, 8 KM/liter,<br>|r 5 years<br>MK 290/liter|
|Time for withdrawal<br>Number of withdrawals/season|2 minutes<br>3||
|<br>OIBM processing time for deposit<br>|30 minutes<br><br>||
|Number of deposits (tobacco sales)/season<br>OIBM management cost<br>|4<br>MK 500,000/season<br>||
|Cost of OIBM smart card (to customer)<br>Customer transportation cost to bank|MK 1200<br>MK 300||
|Customer waiting time at bank<br>|15 minutes<br>||
|Customer opportunity cost of time<br>Probability of early withdrawal|10%<br>MK 110/day (5 hours)||

Assumptions Exchange rate OIBM staff salary Time for opening an account Cost of OIBM smart card (to OIBM) Time for conducting a group training Group size OIBM transportation time to training OIBM cost of transportation (vehicle) OIBM cost of transportation (fuel) Time for withdrawal Number of withdrawals/season OIBM processing time for deposit Number of deposits (tobacco sales)/season OIBM management cost Cost of OIBM smart card (to customer) Customer transportation cost to bank Customer waiting time at bank Customer opportunity cost of time Probability of early withdrawal
whether or not the child finished primary school on time (i.e. with zero grade-age delay). The indices are multiplied by 100, so the possible range is 0-100. The 39 countries included - all of them in either Africa or Latin America - is the full set available at the time of writing. As noted earlier, they come from Molinas Vega et al. (2011) for Latin America, and World Bank (2012a, b) for Africa. Following the authors, the table also reports the simple average of the school attendance and primary school completion indices, as the HOI for education, and the simple average of the other three indices as the HOI for housing conditions. The simple average of these two numbers in turn yields the overall HOI reported in the last column of the table.

The motivation behind the HOI, as initially proposed by Barros et al. (2009), was to measure the extent to which children in various developing countries have access to basic opportunities. Although the authors do not motivate it this way, one could view the index as an example of the ex-ante approach applied to a multidimensional advantage space, with each dimension corresponding to access to a particular service – such as water or schooling – and the valuation of the opportunity set of each type being given by the coverage of the service in that type. The particular inequality index applied to that smoothed distribution of probabilities is the dissimilarity index (see equation 4).

> 16 The averaging procedure is the same suggested by Barros et al. (2011) for the HOI summary index: first calculate a HOI for education obtained as the mean of the two education components and a HOI for housing conditions (the mean of the other three components). Then obtain a summary HOI as a simple average of the two.

14
only slightly higher than the overall poverty rate, the rural population is especially prone to falling back into poverty. Among the rural population, 41.2 percent is affected by vulnerability to poverty, while this only applies to 13.5 percent of the urban population. The vulnerability rate in rural areas is 1.3 times the poverty rate.

**Our paper reveals that the vulnerability rate in El Salvador decreased constantly over 2016-2019, in both rural and urban areas.** Our findings demonstrate that the vulnerability rate decreased from 39.4 percent in 2016 to 23.7 percent in 2019. This is in line with recent poverty developments in the country but different from developments in the operational vulnerability measure. Operational vulnerability,<sup>3</sup> which is purely monetary, increased in El Salvador during the period under consideration, from 34.6 percent in 2000

> 2 This is the poverty rate measured by the vulnerability model applied in this paper, and not the one observed from household data. Poverty is measured in 2011-PPP-US$, and relying on the 5.5 US$/day threshold.

> 3 The “operational” definition widely used in the LAC region defines vulnerable households as those with daily incomes between $5 and $13 a day (in 2011 PPP dollars).

2
to an increase in the term premium and a resulting decrease in the maturity structure of debt, generating a negative comovement.

In this section, we study whether the behavior of the quantity data are consistent with demand or supply type of shocks. We concentrate on two sets of variables, the amount issued at different maturities and the average bond maturity. We study the time-varying patterns of these variables. In particular, we analyze how their behavior is affected by crises, by different measures of spreads, and by the term premium.

To study the amount issued at different maturities, we run a separate regression of issuance at each maturity as a function of each conditioning variable. We use a Tobit model, estimated by maximum likelihood, pooling all observations. These estimations take into account the fact that observations are left censored at zero. The dependent variable is the amount issued at each maturity in any given week, normalized by the average weekly issues for each country. This normalization takes into consideration that the average amount issued varies across countries. We use four different bond maturities: up to 3-year maturity (short term), more than 3-year maturity and up to 6-year maturity (medium-short term), more than 6-year maturity and up to 9-year maturity (medium-long term), and more than 9-year maturity (long term). As explanatory variables we use, alternatively, the crisis dummy de fi ned above, the 3-year spread, the 9-year spread, the emerging market bond index (EMBI) spread for each country, and the predicted term premium for each country. For spreads, we use log-spreads, de fi ned as log(1+ st,τ ). The EMBI spreads, calculated by J.P. Morgan, are well-known measures of long-term spreads in emerging markets. They not only provide an alternative estimate of long-term spreads, but also extend the sample for Mexico to cover the Mexican crisis (though they exclude Uruguay). We use the predicted term premium because it most directly captures the cost of issuing long-term
failings are inability to foster meaningful forms of local participation<sup>27</sup> , and the tendency for village management committees or panchayati raj institutions to be 'captured' by factional politics (Blair 1996, Ahluwalia 1997).

The depletion of village commons has been brought about by various processes operating in parallel. Owing to the failure of land ceiling laws to bring about any significant redistribution of privately owned ceiling-surplus land in practice, many states have instead sought to redistribute some public land ('wastelands') to landless households. Such _dejure_ privatization of commons has not always led to _defacto_ control over land by the landless. In Orissa, for example, the very act of pressing a claim to such land is regarded as illegal in the first instance, so that the rights are practically impossible to 'regularize' (Mearns and Sinha 1998). Although significant benefits have accrued to the recipients where land has been redistributed, in spite of the small size of land parcels allocated, much of the land redistributed is of low quality and generates low and uncertain crop yields. Many beneficiaries do not have access to the complementary resources (labor, capital, draft animals) required to make more productive use of such land. Moreover, state land redistribution has often disproportionately benefited non-poor rather than poor households. In Jodha' s survey, for example, the disparity in land holdings between poor and non-poor widened as a result of the privatization of village commons in Gujarat, Karnataka, Madhya Pradesh, Maharashtra and Rajasthan (Jodha 1986).

Alongside such _dejure_ privatization there has also been a parallel process of _de facto_ privatization or encroachment on commons in many areas, involving the permanent or seasonal annexation of formerly common land for private crop production. Often it is the relatively higher value patches of land (e.g. moist depressions in drylands) that are encroached first, which leads to a reduction in the benefits to other users out of proportion to the share of common land lost. Those who encroach on commons tend
We will use the EBRD transition index to size the shock across countries and to follow its evolution along time.

The EBRD transition index aims to reflect each country’s moment in the transition towards a market economy. Hence it deals, among other things, with issues such as the emergence of a private sector, the size of the government and so on, which are some of the indicators of the business environment we have considered in section 2. Therefore we could have some multicollinearity problems in the regressions, that is, relationship between explanatory variables which could distort the estimation results.<sup>12</sup>

> 12 However, even so, the regression estimates will still be unbiased and consistent.

14
**Table 5:** Effects of Turnovers on Alignment with Citizens’ Preferences

|||**Bureaucr**|**at survey**||**Villag**|**e head survey**|
|---|---|---|---|---|---|---|
||**Offcials/c**|**itizens agree on:**|**Complain**|**ts received about:**|**Village he**|**ad takes action on:**|
|**Village Services:**|Priority|Worst-quality|Priority|Worst-quality|Priority|Worst-quality|
||(1)|(2)|(3)|(4)|(5)|(6)|
|New village head|0.105|0.236<sup>_∗∗_</sup>|0.162<sup>_∗∗∗_</sup>|0.174<sup>_∗∗_</sup>|0.127<sup>_∗_</sup>|0.213<sup>_∗∗_</sup>|
||(0.102)|(0.103)|(0.059)|(0.067)|(0.065)|(0.087)|
||[0.116]|[0.117]|[0.068]|[0.079]|[0.079]|[0.103]|
|Observations|1067|1067|1067|1067|443|443|
|Control mean|0.75|0.32|0.87|0.71|0.94|0.88|
|Robust p-value|0.204
2004. First, from 1985 to 1990, increases in aggregate (output-weighted) productivity were nearly exclusively due to increases in average (unweighted) productivity, while reallocation productivity actually fell. Between 1991 and 1994, reallocation productivity rose sharply while average productivity initially fell, then rose more slowly. Finally, from 1998 onwards, reallocation productivity stagnated, while average productivity improvements once again became more important.

The increases in aggregate productivity are linked to the trade and FDI liberalization that took place during the 1990’s. Our main specification indicates that the average declines in final goods and input tariffs were associated with aggregate productivity increases of 3.2% and 21.8%, respectively. Meanwhile, the FDI reforms implied an aggregate productivity increase of 2.1%. Using a constructed panel of firms from from 1985 to 2004, we confirm that the trade and FDI reforms were associated with increases in within-firm productivity, after controlling for unobservable, timeinvariant firm characteristics. We also use the panel to illustrate the importance of correctly identifying firm exit when using decomposition methods that rely on the panel nature of the data.

Our results lend support to the importance of market-share reallocations in increasing productivity. In the case of India, however, we show that such reallocations were only important at the beginning of the major trade liberalization period, and that over the 20-year period from 1985 to 2004, average productivity improvements played a larger role in determining aggregate productivity growth.

# **References**

Aghion, Philippe, Robin Burgess, Stephen Redding, and Fabrizio Zilibotti, “The Unequal Effects of Liberalization: Evidence from Dismantling the License Raj in India,” <u>The American Economic Review, September 2008, 98 (4), 1397–1412.</u> Amiti, Mary and Jozef Konings, “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia,” <u>The American Economic Review, December 2007, 97 (5),</u> 1611–1638.

28
project characteristics. We use the logarithm (in millions USD) of the total amount of World Bank lending committed to each project as a basic measure of size ( _LNSIZE_ ). Larger projects tend to be complex, with multiple components, tranches and counterparts who implement individual components, all of which adds complexity to project implementation. We also have information on the start and end dates of each project, which

> 6 A potential concern regarding the CPIA measure is that it also is scored by Bank staff, leading to a possible mechanical relationship between country-level CPIA scores and project-level outcome ratings.  This however is less of a concern given that we average all the country-level variables over the life of the project, while the outcome is measured at or after the end of the project.  In any case, as a robustness check we also used the Worldwide Governance Indicators Rule of Law measure (www.govindicators.org) and found very similar results (not reported for reasons of space).

10
In the initial step of our analysis, we confirm the negative association between governance quality and the share of short-term in total debt. In particular, the marginal effects of governance variables on short-term debt are all negative in our baseline regressions. For instance, a 10percentage point increase in our overall firm governance index, which ranges from zero to one, leads to a 10.7 percentage point decrease in the portion of short-term debt in total debt. The use of alternative governance metrics in regressions leads to similarly large economic effects. In running these regressions, we control for a large set of firm- and country-specific factors that may affect short-term debt.

Next, we investigate whether the relationship between debt maturity and governance depends on the institutional environment that determines the extent of investor protection in a given country. We argue that countries with better legal protection of shareholders would reduce the need for costly external monitoring using short-term debt. If strong shareholder rights reduce discretion of insiders and allow for more efficient internal monitoring, then we would expect governance and short-term debt to act as substitutes. To empirically test this conjecture, we interact each of our governance variables with a variable that is likely to capture the level of investor protection in sample countries and include them as explanatory variables for short-term debt. Our candidate proxy is the minority investor protection index that is constructed in the World Bank’s Doing Business Survey. In our estimations, these interaction terms attain statistically significant and negative coefficients, suggesting that governance and board independence are negatively related to short-term debt in countries with greater investor protection. Thus, good governance and short-term debt become stronger substitutes when the investor rights are protected by law.

To alleviate endogeneity concerns, we instrument investor protection with legal origin. La Porta et al. (1998) suggest that investor rights are largely a function of national legal origin, and that legal systems based on common law offer external investors better protection than those based on civil law. Hence, we use instrumental variable estimation and use legal origin as an instrument
avoid such endogeneity, we drop observations during and after the year of the first consolidation we observe in the time period for any firms that eventually consolidate. For workers with multiple jobs within a given year, we keep the job with the highest associated earnings.

> 12The crucial assumption for this interpretation of the AKM decomposition to hold is that the idiosyncratic log earnings component _εit_ is unrelated to the sequence of worker _i_ ’s employers _j_ ( _i, t_ ). For a discussion and test of the validity of this conditional exogenous mobility assumption, see Abowd, Kramarz and Margolis (1999) and Card, Heining and Kline (2013). Schneck (2021) presents an application using similar Dutch micro data as ours.

12
_Figure 18: Balance of responses on the positive and negative role of employment and wages in well‐being changes by place of residence_ <!-- Start of picture text --> 6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>2014 2015 2016 2017 2014 2015 2016 2017 2014 2015 2016 2017 2014 2015 2016 2017 2014 2015 2016 2017<br>average Minsk Large cities Small cities Rural area<br>employment wage dynamics<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

# 4.2. Reported coping strategies

Respondents who were faced with a reduction in their well‐being, were asked what coping strategies they used in order to adapt to the new circumstances. The most common strategy is reducing expenditures. During the crisis, 79 percent of those experiencing a reduction in well‐being (29 percent of the population overall) had to cut expenditures.<sup>5</sup> Strategies, related to the search of possibilities of increasing income were employed by only 8.6 percent of those who faced a well‐being reduction in 2016 (3.2 percent of total population). They were more common at the beginning of the crisis (employed by 4.2 percent of the population and 12.4 percent of those suffering a worsening of well‐being), as employment possibilities were not that scarce, and again became more common with the onset of the economic recovery in 2017. Other strategies, including reliance on help from relatives or the state, are generally not common, and households did not increase their reliance on these coping strategies during the crisis.

Households vulnerable to the risk of poverty appear to rely to a greater extent on coping strategies related to the search of opportunities for additional earnings than the average household (Figure 19). Pre‐crisis