rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8926

# **Abstract**

This paper investigates the link between inequality and demand for redistribution by looking at how individuals form their perceptions of inequality. Most of the literature analyzing demand for redistribution has focused on _objective_ inequality, rather than _subjective_ perceptions of inequality. However, a model that links demand for redistribution to subjective inequality is needed, given that recent empirical research has shown a growing gap between subjective and objective inequality. Using data from the International Social Survey Programme survey, the paper focuses on explaining individuals’ formation of inequality perceptions using objective variables. The paper then studies the relationship between these perceptions and individuals’ demand for redistribution. The analysis finds that objective macro variables are associated with individuals’ perceptions of inequality, and that individual circumstances, some of which relate to self-interest, like age, educational attainment, and income, also play an important role. Perceptions of equality, in turn, are significatively correlated to demand for redistribution and seem to substitute for any effect of objective variables. This result suggests that contextual macro variables only affect individuals’ demand for redistribution through their perceptions of equality and do not have a direct effect.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, ada.ferrer@iae.csic.es, a.giolbas@gmail.com, and itorre@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Escobal and Laszlo (2005) compare self-reports of the time in minutes it takes agricultural producers in Peru to get to the nearest populated center with the true travel time. The latter is measured by having surveyors walk with a random sample of respondents, and time their journeys, following the same route and pace as the respondent, and using GPS to measure latitude, longitude, altitude and distance. GIS is then used to compute travel time accounting for terrain for those in the survey who weren’t accompanied by the surveyor. They find respondents consistently under-report the time taken to reach the center. For example, among coffee producers in the Selva, mean self-reported time was 6.7 minutes, compared to a mean true time of 13.0 minutes. The correlation between self-reported time and true travel time is only 0.28 for the coffee producers, 0.29 for the potato farmers, and -0.08 for the rice farmers in their sample. Furthermore, Escobal and Laszlo do find that measurement errors are correlated with socioeconomic variables. Not surprisingly, individuals who own a watch give more accurate reports of travel times. They also find a negative correlation between the measurement error and education, so that more educated people have less measurement error.

These results therefore strongly suggest that self-reported distances will be misleading, with measurement errors correlated with outcomes of interest. GPS coordinates can then be used to give more accurate measurement. The simplest approach is to calculate the straight-line distance between points. This has the advantage of computational simplicity, and does not require the user to have additional geo-coded information on transport networks or topography. Alternatively, users can combine GPS point coordinates with information on the location of transportation routes, and perhaps information on road quality and topography, to measure exact travel distances and predict travel times.

The correlation between GPS straight-line travel distances and exact travel distances is likely to be much higher than the correlation between self-reported distance and GPS-distances. For example, in McKenzie, Gibson and Stillman (2006) we computed the distance in meters from each household in our sample in Tonga to the New Zealand immigration office in Nuku’alofa. The Pearson (Spearman) correlation between the straight-line and exact travel distance based on road networks is 0.82 (0.78). We do find the absolute percentage measurement error to be correlated with whether or not an individual migrates, and with their income from work in Tonga. The measurement error is greater for more remote individuals (on the other side of a lagoon), and this remoteness in turn is correlated with economic behavior. Nevertheless, the

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Table 10: The content of PTAs and trade in parts and components by income groups, PPML

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||<br>Imports|<br>ofparts and com|<br> ponents|
|No. of WTO+ provisions|0.00557|0.0160|0.0125*|‐0.00984|
||(0.00553)|(0.00989)|(0.00661)|(0.00683)|
|No. of WTO‐X provisions|0.00102|‐0.00956*|0.0151|0.0220***|
||(0.00328)|(0.00538)|(0.00959)|(0.00663)|
|No. of WTO+ provisions * South‐South||‐0.00359||0.0223**|
|||(0.0112)||(0.00990)|
|No. of WTO‐X provisions * South‐South||0.0246**||‐0.00691|
|||(0.0110)||(0.0113)|
|No. of WTO+ provisions *North‐South||‐0.0259**|‐0.0223**||
|||(0.0117)|(0.00990)||
|No. of WTO‐X provisions * North‐South||0.0315***|0.00691||
|||(0.00896)|(0.0113)||
|No. of WTO+ provisions *North‐North|||0.00359|0.0259**|
||||(0.0112)|(0.0117)|
|No. of WTO‐X provisions * North‐North|||‐0.0246**|‐0.0315***|
||||(0.0110)|(0.00896)|
|BIT|0.127***|0.130***|0.130***|0.130***|
||(0.0316)|(0.0319)|(0.0319)|(0.0319)|
|PTA (in force or inactive)|‐0.0619|‐0.0744|‐0.0744|‐0.0744|
||(0.0490)|(0.0465)|(0.0465)|(0.0465)|
|Observations|336,976|336,976|336,976|336,976|
|R‐squared|0.990|0.991|0.991|0.991|
|Country‐pair FE|Yes|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|Yes|

Clustered standard errors at the country‐pair level in parenthesis. *** p<0.01, ** p<0.05, * p<0.1 29
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 3 Productivity form and productivity can shed light on whether urban policy has a role to play in supporting city productivity and through that, economic growth.

In this paper we examine the linkages between urban form and city productivity using different urban form metrics for a comprehensive sample of Latin American cities with more than 50,000 inhabitants in the year 2010. We use a consistent time series of Defense Meteorological Satellite Programs - Operational Linescan System (DMSP-OLS) nighttime lights (NTL) imagery to identify city extents, characterize urban structure and create a proxy measure of a city’s economic performance. In terms of methodological approach, our first base model includes the estimation of urban productivity using time-lagged measures of urban form and other control variables. In a second model we follow Harari (2016) and explore the use of synthetic instruments that use the potential shape of city as a starting point, and calculate the city form indicators based on such potential form. The contribution of this paper is twofold. First, the analysis of linkages between urban form and city productivity has important policy implications in regards to the potential benefits or consequences of land use, transport and other city planning policies that can influence city form. Second, it contributes to the advancement in the measurement of urban form, by using open data available at a global scale that allow for benchmarking of cities across the globe.

The rest of the paper is organized as follows. Section 2 provides a literature review. Section 3 presents the empirical models. Section 4 describes the source data and the construction of the proxy measure of a city’s economic performance, the measures of urban form, and the construction of the synthetic instrument. Section 5 presents the empirical results. Finally, section 6 presents our conclusions.

# **2 Literature review**

Density, size and sprawl have been commonly referenced in economic literature as some of the key aspects of urban productivity. The most recurrent conclusion is that less dense cities face high commuting rates (Wheeler, 2001), have higher marginal costs of transporting intermediate goods (Ciccone and Hall, 1996), and lower knowledge spillovers (Lynch, 1981; Jaffe et al 1993 and Glaeser 1998). All of which can affect a city’s productivity levels.
# 1. **Introduction**

There is little available knowledge on what determines money wages in sub-Saharan Africa, or indeed on whether there exists a stable relationship among wages, productivity, GDP, inflation, policy changes and unemployment or informal sector activity. In this paper, we study how wages are affected by short- and long-run changes in the aforementioned variables in the case of Zimbabwe. The importance of knowing the answer to this question may be illustrated by considering economic reforms. Structural adjustment programs may cause reductions in aggregate demand due to decreased government spending and increased real interest rates. If wages are downwards-rigid, and, therefore, unable to adjust to changes in aggregate demand then the unemployment rate or informal sector activity increase. On the other hand, if wages are flexible, then the decrease in aggregate demand may be mitigated by falling wages and, therefore, causing a more limited increase in unemployment or informal sector activity. In the latter case the labor market is characterized by a macro-wage curve.

The main questions addressed are the following: (1) are wages affected by increased productivity, economic activity, prices, policy changes and unemployment; (2) does there exist a macro wage-curve; and, (3) did the economic structural adjustment program (ESAP) cause a structural change in the labor markets. The main findings are that wages are flexible-both upwards and downwards. Hence, the labor market is able to adjust to economic shocks. The main cause of falling real wages in Zimbabwe is reduced economic activity.

This paper is the first in a series of papers studying Zimbabwean labor markets. Another paper analyzes wage and productivity determination in Zimbabwe but in contrast to this paper, the study is based on findings from micro level data (see Verner, 1998). The findings, using data on employees matched with data on firms, can be summarized as: (1) female employees are paid less than males and the negative wage premium do not match a corresponding negative productivity premium; (2) experience is reflected equally in wages and productivity differentials over the life-cycle; (3) the wage premium increases
6 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE tains annual export flows (in current values) disaggregated by firm, destination and Harmonized System (HS) 6-digit product. Oil exports are excluded from the customs data due to lack of accurate firm-level data for many of the oil-exporting countries. For most countries total non-oil exports in the EDD are close to total non-oil exports reported in COMTRADE/WITS. More than 100 statistics from the EDD are publicly available at the origin-year, origin-product-year, origindestination-year, or origin-product-destination-year levels. These include average exports per firm as well as the number of exporting firms.

For the descriptive analysis in this section as well as for the regression and simulation work in the sections that follow we focus on a _core sample_ that consists of 50 countries (49 from the EDD and China) for which we have the firm-level data.<sup>6</sup> However, to use the most comprehensive sample of countries available we rely for the motivating plots below on an _extended sample_ that includes the 59 origin countries from the EDD plus China. Both samples cover a subset of years from 2003 and 2013 — see Table 1 and Table I1 in the Online Appendix.

We focus on EDD statistics based on products belonging only to the manufacturing sector. Specifically, relying on a concordance across the ISIC rev. 3 classification and the HS 6-digit classification, we consider only exports of HS 6-digit products that correspond to ISIC manufacturing sub-sectors 15-37. Using these data we calculate variants of average exports per firm, number of exporting firms, and total exports at the origin-destination-year level or at the originproduct-destination-year level. The product disaggregations that we use are HS 2-digit for the extended sample and HS 2-digit, HS 4-digit, or HS 6-digit for the core sample.

# **Importance of the intensive margin**

Let _Xij_ , _Nij_ and _xij ≡ Xij/Nij_ denote total exports, total number of exporting firms, and average exports per firm from country _i_ to country _j_ , respectively.<sup>7</sup> In Figure 1 we plot the intensive margin (ln _xij_ ) and extensive margin (ln _Nij_ ) vs. total exports (ln _Xij_ ) for the extended sample of countries. We restrict the sample to the origin-destination pairs with more than 100 exporting firms (i.e., _ij_ pairs for which _Nij >_ 100) to reduce noise associated with country pairs with few exporting firms.<sup>8</sup> All variables plotted are demeaned of origin-year and destination-year fixed effects. Each dot corresponds to (ln _xij_ , ln _Xij_ ) (Panel A) or (ln _Nij_ , ln _Xij_ ) (Panel B). The lines can be ignored for now.

> 6China is not included in the EDD due to confidentiality concerns.

> 7There is also variation in our data over time, so in principle we should add a time subscript as well, as in _Xijt_ . For simplicity, we suppress the time subscript.

> 8The core sample includes 1,305 unique country pairs with _Nij >_ 100 while the extended sample includes 2,087 unique country pairs with _Nij >_ 100. The total number of unique country-pairs is 8,401 in the core and 10,663 in the extended sample.
adds to official income but there have been no hard measures of the extent of informal sector activity in Montenegro. Some estimates have suggested 20 percent of employment in informal activities.<sup>2</sup> Calculations based on interviews conducted in Montenegro by the World Bank suggest that the informal sector increases income beyond official data by some 30 percent. As background for such estimates, official ILO statistics show extraordinarily low labor-force participation rates of 54.8% for men and 45.2% for women (for the four quarters to June 2013). Unemployment rates were 28.7 % for men and 22% for women. Significant private sector income appears to be derived from the informal sector. We note the evidence that informal sector activity requires high trust.

At the time of our study, democracy in Montenegro had been ineffective in giving rise to turnover in government. While Montenegro is a functioning democracy with parliament representing all strata of the population, and vibrant press and non-government sector, little had changed with regard to political dominance and control since the end of single-party socialism. Absence of effective political competition in formally newly democratic countries has been shown to result in political entrenchment and privileged use of political office. With development based on official statistics impeded but high trust shown to be present among private individuals, further research is required to establish how issues of governance and political entrenchment have affected development.<sup>3</sup> 2 See Institute of Strategic Studies and Prognoses (2010). For observations of the informal sector in Montenegro, see also the observations by Barić and Williams (2012).

> 3 On markets and politicized economic decisions, see the studies in Hillman (1991), and for an overview of political behavior as an impediment to development, see Hillman (2007). Acemoglu and Robinson (2012) present a far-reaching array of historical case studies demonstrating the inconsistency between self-interested rule and economic development. Gelb et al (1998) present a focus on political rent creation and rent seeking in transition economies. In Hillman (2004) “Nietzschean development failures” are described as occurring when the strong in a society are unimpeded by legal and ethical constraints in appropriative behavior toward the weak. Besley and Persson (2011) provide a perspective that focuses on requisites for successful development.

5
21 represented pre-export credit extended at a rate below the Treasury bill rate.

Despite policy rhetoric, the share of commercial bank credit allotted to the tradable sector relative to credit for the nontradable sector declined from about 1978 (Table 12). Accompanying this was a gradual decline in the combined share of medium and long-term credit in total bank lending.

The operation of DFIs was lackluster. An inspection of their lending records shows that they were not successful in allocating resources to priority sectors, particularly export-oriented industries. Their lending continued to be limited mainly to meeting the credit needs of a handful of established business groups with diversified interests. 2 1 This suggests that a large part of the credit subsidy was diverted into nonpriority sectors.

The failure of Sri Lanka's directed credit policy in the context of trade liberalization is a subject which needs further study. 2 2 However, the evidence considered so far supports tbe view that a directed credit policy is unlikely to be successful when the overall climate of incentives is moving in a different direction.

# 4.5 Financial Liberalization and the Informal Credit Market: Some Observations

So far, we have examined the implications of financial reform with respect to the formal money market (OMlM). In this section we briefly consider whether our basic conclusions neea to be qualified in light of the neo-structuralist crowding-out argument against financial liberalization (Section 2.3). A formal test of the crowding-out hypothesis is precluded by the absence of required time-series data. However, there is a 21. In a comparison of lists of client companies of NDB and DFC for 1984 we were able to detect 30 instances of the same client appearing in both lists. Most of these are well established import-substituting manufacturing firms with a long history of operation. Moreover, the DFC's sublist of large-scale clients for 1984 is not much different from a similar list for the mid-1970s. On the basis of a li';t of exporters (for 1984) prepared hy the Exprrt Development BoaFld, we werP able to identify only about 1r)-15 per-'ent **cof** the client companies of DFC and NDB as exporters.

22. This issue is of particular interest eiven the recent concern about ths need, in the context of the typical Jeve loping economy where there is nlo well developed equitv market. foat hi-'.ing a prr:nec halan-e 1-twepn the confli-ting o1bje( ti;'-s of maintAinilnw makL-ket el.aring intertL-t rates that would ensure effi i-t-t a1.1a **IT IIn If** rPsui es and p,i,-i Tinc a "growth-promoting" credit 1 **v** -h-. I **K** ' (ohaia. 1984: NIEKinnon. 1988; Collier and Mayer, 1989).
findings, and thus what we find is likely to be relevant in other countries as well, our paper is in the end about Ghana. While Ghana might be an adequate reflection of lowincome countries in much of Sub-Saharan Africa, it is unlikely to be reflective of the whole developing world. The best we can do in the context of this paper is simply to acknowledge this limitation, but going forward we can and will repeat this type of experiment in other countries. We have already done so in Timor Leste, where very few respondents use any financial services, and so the differences across treatments were not significant, which suggests that the concerns raised in our analysis are of second order importance in the most financially under-developed countries. A similar experiment is also underway for Jamaica.

We would remind readers that we live in a world of rough approximation when it comes to measuring the outreach of the financial systems of developing countries. The reliability of estimates from accounts-based approaches and approaches that meld accounts-based and survey-based information via regressions is difficult to assess. Our hope is that our results provide some practical guidance on how to generate comparable financial usage data across countries via surveys, as surveys would appear to represent the best vehicle for generating accurate data.

37
– Table A.3 Attrition

||(1)|(2)|(3)|T-t|est p-valu|es|Norma|lized dife|rences|
|---|---|---|---|---|---|---|---|---|---|
||Control<br>group|Treatment<br>group 1|Treatment<br>group 2|(1)-(2)|(1)-(3)|(2)-(3)|(1)-(2)|(1)-(3)|(2)-(3)|
|Attrition|0.112<br>(0.316)|0.060<br>(0.238)|0.068<br>(0.252)|0.001|0.006|0.629|0.185|0.155|-0.031|
|Attrition reason||||||||||
|Migrated|0.063<br>(0.243)|0.038<br>(0.192)|0.028<br>(0.165)|0.046|0.004|0.364|0.113|0.169|0.057|
|Death|0.010|0.002|0.016|0.088|0.319|0.020|0.104|-0.052|-0.148|
||(0.100)|(0.045)|(0.125)|||||||
|Refusals|0.021<br>(0.143)|0.012<br>(0.109)|0.008<br>(0.089)|0.220|0.063|0.517|0.070|0.109|0.041|
|Other|0.018<br>(0.133)|0.008<br>(0.089)|0.016<br>(0.125)|0.131|0.773|0.252|0.088|0.016|-0.073|
|Observations|1,000|498|502|1,498|1,502|1,000|1,498|1,502|1,000|

Notes: Columns 1-3 report the attrition rate for each of the experimental group. Columns 4-6 report the p-values for T-tests of differential attrition rates across experimental groups. Columns 7–9 report the standardized differences in attrition rates between experimental groups (that is the difference in means in units of standard deviation).

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