rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1 Introduction

Trust is an essential ingredient of altruistic behavior. A growing body of literature emphasizes its importance in shaping individual decisions to support the provision of public goods. In a prominent example, a social experiment found that charitable provision was significantly lower if the solicitors of contributions belonged to a minority group that inspired a lower level of trust among potential donors (List and Price 2009). This behavior can be explained by a principal-agent problem: the principal (i.e. potential donor) can choose, under no obligation, whether to give money to benefit a group on whose behalf the agent (i.e. donation solicitor) is purportedly acting. The agent’s optimal strategy is to provide the principal with credible signals that the transferred resources will be used as the principal intends. Mechanisms that enhance the credibility of principal-agent transactions can be particularly important in securing support for public goods, including those that benefit only a subset of the population– such as redistributive policies.<sup>1</sup> Obtaining citizens’support for redistributive policies may be especially important in countries where resources are scarce and governments historically enjoy little public trust in their capacity to deliver goods and services fairly and effi ciently.<sup>2</sup> In fact, even in nondemocratic regimes, citizens have ways to retaliate against unpopular use of public funds or to try to control rent-seeking (Acemoglu, Hassan, and Tahoun 2014). But while there are high expectations about the positive effects of increased transparency on citizens’ trust in – and therefore their support for – redistributive policies, there is a dearth of rigorous evidence on this topic.

In this paper, we offer experimental evidence on the effect of trust-enhancing measures on public support for redistributive policies. We conducted a behavioral experiment on a nationally representative sample of the middle class in Jordan– the Jordan Gives experiment– to identify the effect of enhanced transparency on the support for, and the preferred design of, social safety nets.<sup>3<
*|33.7***|56.3***|46.1***|
||(8.44)|(7.99)|(9.71)|(10.0)|
|Observations|2340|1978|1978|1978|
|R-squared|0.060|0.040|0.050|0.13|
|Mean dependent|154.70|134.71|134.71|134.71|
|Sample|All|Has skills measures|Has skills measures|Has skills measures|
|District FE|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|
|a1 + b1=0||||10.05***|
|||||(2.49)|
|a2 + b2=0||||-1.92<br>(10.22)|
|a3 + b3=0||||2.27|
|||||(7.19)|

* p<.10 ** p<.05 *** p<.01

_Notes_ . This table reports estimates of the relationships between schooling, skills, migration, and earnings for men in Pakistan. All the columns report median regressions estimates. The dependent variable is top-coded at 100,000 PKR per month (961.5 USD). The cognitive skills index is the mean of the Urdu, Mathematics, and English scores computed using Item Response Theory with a two-parameter logistic (2PL) model. The socioemotional (SEM) skills index is computed using principal-component factor analysis. The Out of village variable is a dummy variable taking the value 1 if the respondent lives outside the village where we originally surveyed their household. The sample for column (1) is all men in the sample who are 88
to genuine economic phenomena – or reflecting the effects of measurement errors and transitory variations in income. If there is measurement error of the "classical" form (uncorrelated with the true value and over time), then the expected income increase is positive for someone with a below-average income and negative for someone with an above-average income. As a result, some of the observed progressivity in income growth may be spurious.<sup>11</sup> As Baulch and Hoddinott (2000) phrase it, “... some of the observed movements in and out of poverty will be a statistical artifact.” This is also known as “Galton fallacy”.

> 10  In their analysis there is no contradiction between the results of cross-sections and panel.  Egypt data analyzed in this paper demonstrates a more extreme case of diametrically opposite results from two types of analysis. 11 Van Kerm and Jenkins (2008) use an elegant metaphor to illustrate this point.  If one rolls a standard die, the expected number of spots at any roll is 3.5 (the sum of the possible scores divided by six). If the first roll in fact produces a 1, then the expected increase in the score when the die is rolled again is positive (+2.5). By contrast, if a

> 6 comes up first, the expected gain at the second roll is negative (–2.5). So, despite there being no association

5
We use different empirical specifications to define the treatment and control groups in the matched reform-establishment census data. First, we define a dummy variable which is equal to 1 in 2013 (2014) and the years after if an establishment has been impacted by at least 1 procompetition regulatory reform (treatment group) and zero otherwise (control group). We further exploit the different intensity of regulatory changes that impacted establishments by estimating the impact of the number of regulatory reforms per municipality. The availability of different reform indicator variables due to multiple reform years, levels of subnational aggregation, and types of regulatory changes allows us to conduct a series of empirical checks to test for the robustness of our findings.

# **5. Empirical Specification**

## **5.1 Deriving establishments’ TFP and markups**

We follow the integrated control function approach of De Loecker and Warzynski (2012) to estimate the unbiased measures for the output elasticities of inputs, allowing to compute (revenue) productivity and markups. The approach is critical for our purpose since it allows to control for unobserved establishment-level productivity shocks, permits technologies to differ across establishments within the same industry, and is valid under imperfect competition.

We restrict the analysis to production functions with a scalar Hicks-neutral productivity term and with common technology parameters (β�) across the set of producers ( _I_ ). We emphasize that the latter does not imply constant output elasticities of inputs across establishments within an industry, except for the special case of Cobb-Douglas. The main advantage of production technologies of this form is that we can rely on proxy methods such as Ackerberg (2015) to obtain consistent estimates of the technology parameters β�. We can thus define an establishment’s production function as follows: where β� and ω��� govern the transformation of inputs ( _X_ ) to units of output ( _Q_ ) for each establishment _i_ in industry _s_ at time _t_ . For our baseline empirical specifications, we use a value 14
67<br>92,426|
||_−_28_._4|_−_16_._6<br>_−_11_._6|27<br>53,174|
|**Urban**|_−_31_._5|_−_17_._3<br>_−_9_._3|69<br>135,127|
||_−_31_._3|_−_20_._0<br>_−_14_._2|27<br>73,999|
|**Low education**|_−_33_._6|_−_19_._4<br>_−_8_._1|59<br>92,320|
||_−_35_._2|_−_22_._5<br>_−_12_._6|23<br>55,169|
|**High education**|_−_29_._1|_−_17_._8<br>_−_7_._9|59<br>89,509|
||_−_28_._2|_−_20_._7<br>_−_12_._7|23<br>47,497|

_Source_ : World Bank estimates based on COVID-19 High-Frequency Phone Surveys.

in the initial phase of the pandemic, and subsequent gains in employment either proceed at roughly the same pace among women and men, or gender disparities actually widened over time (Figure A6.1).

Employment also fell proportionately more among those with lower levels of education in April-June 2020, following the onset of the pandemic. Relative to prepandemic employment levels, this gap appears to close in subsequent HFPS waves, in the sample of all countries and in the country panel. However this is driven by previously unemployed workers in the low education group starting jobs at a faster rate, rather than those that stopped working since the pandemic returning to jobs more quickly (Table A6.2 and Table A6.3).

To
In Figures 4 and 5, NSOs above the 45o line undersupply data, NSOs near the 45o line meet the demand for data, and NSOs below the 45o line oversupply data, given the characteristics of their countries. We discuss different capacity-building strategies for these three groups of countries in the next section.

Our econometric approach could be criticized from multiple perspectives. The coefficients of our regressions could be biased because of reverse causality. One could argue, for example, that poor statistical performance negatively affects a country’s GDP or prevents it from becoming part of global value chains, reducing economic complexity. Although these concerns have merit, we think that statistical performance in many developing countries is sufficiently weak that it reflects rather than causes the factors we examine.

Some unobserved factors could affect both the SPI and our independent variables. Given our data limitation, we see no way to address such a bias. Once the next round of SPI data becomes available, some of these concerns could be addressed.

We replicate our analysis on the earlier version of SPI developed by Cameron et al. (2021). Given the commonalities in design, both the SPI and the earlier version of the index are highly correlated (Figure A in the Appendix) and the analysis generates qualitatively similar results.<sup>9</sup> The similarity of the two sets of results provides a degree of confidence in the robustness of our main conclusions.

# **5. Discussion**

Our findings suggest three questions that could stimulate the discussion about building sustainable statistical capacity in developing countries.

# **_Question 1: Should technical assistance delivery mechanisms change, given the marked difference between the cluster of high-supply/high-demand countries and the rest of the world described in Figure 1?_**

Over the past two decades, high-performing NSSs have provided much of the technical expertise to improve developing countries’ statistics. The organization, technology, and data sources of high-performing NSSs are drastically different from those of the rest of the world, however.
‐FDI firms is only significant over the period 2010‐2013. While the finding may be related to global GVC trade slowdown, Appendix 1 shows that the 2014‐17 surveys contain many Latin American, Sub‐Saharan African and some Middle East and North African countries that are less integrated into GVCs and trade, which could have driven the results.

In order to rule out that the overall findings are driven by female‐intensive sectors only, we exclude wearing apparel (ISIC 18) and office, accounting and computing (ISIC 30) in the regressions. Figure 10 shows that the female labor share premium for trading firms becomes somewhat smaller for exporters and GVC participants, but not for importers and FDI firms. For the underlying regression results, see Appendix 5 and 6.

One potential omitted variable bias could stem from the lack of gender‐specific wage data in the Enterprise Surveys. We are able to address this issue by using gender‐specific wage data that are available 19
35 complete and there is a strong need to continue the adjustments. The authors calculate their own relative cost of living index comparing prices of the same consumption basket in a couple of Eastern European countries including Poland and Austria. The results are striking: the overall index for Poland in percent of the Austrian index equals 60.4%, the food index- 67.3% and the index for utilities - just 22.6% ! 37 While the calculations were made using data from March 1994, the size of undervaluation of utilities as compared to other CPI categories should not differ significantly as the relative prices of Polish utilities have hardly risen since 1994 (see Fig 6 and Fig. 7). Pujol and Griffiths also calculate the coefficient of variation that captures the extent to which individual ratios of commodity prices in both countries deviate from the overall index. The relatively high value of that coefficient for Poland suggests that undervaluation varies enormously for different goods. In particular, authors note that much of this variation comes from relatively high undervaluation of utilities. They conclude that while the low value of the overall cost-of-living index points to significant undervaluation of the Polish currency, the desired appreciation" will not eliminate the existing disparities within the index. Therefore, it is important to continue corrective adjustments of prices of utilities (which seem to be most undervalued) to bring the relative price structure closer to world levels. Upward adjustments in these prices are also important if the sectors are to be provided with full cost recovery as well as funds for necessary investments. Finally, bringing these prices up to the real levels would induce their proper economical use and discourage overconsumption.

The need for further adjustments entails the question of the optimal path of the process. Putting aside the implications of such adjustments in the sphere of political economy, what is the least distorting way of establishing higher relative prices ? Is the big-bang approach of large onetime increases more desirable than continuos gradual adjustments that take longer to achieve the same effect ? The results of the model estimated
*|1.155***|1.319***|1.156***|1.038***|1.023***|
||(0.211)|(0.211)|(0.423)|(0.407)|(0.332)|(0.333)|
|_R_<sup>2</sup>|0.809|0.814|0.821|0.826|0.830|0.830|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|730|730|730|730|730|730|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI 22
# Tables

**Table 1:** Female labor force participation and Gini index (fixed effects panel analysis, all countries).

|Dep. var.: Gini index (1=100)|||||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Female LFP (% of female population ages 15+)|0.302***<br>[0.116]|0.535***<br>[0.151]|0.731**<br>[0.343]|0.656**<br>[0.323]|
|Female LFP squared|-0.003**<br>[0.001]|-0.005***<br>[0.001]|-0.006*<br>[0.003]|-0.006**<br>[0.003]|
|Services value added (% of GDP)||0.176***<br>[0.049]|0.165***<br>[0.052]|0.128**<br>[0.050]|
|Exports of goods and services (% of GDP)||0.049*<br>[0.027]|0.013<br>[0.027]|0.025<br>[0.028]|
|Imports of goods and services (% of GDP)||-0.012<br>[0.021]|0.027<br>[0.032]|-0.024<br>[0.032]|
|Foreign direct investments (% of GDP)
br>countries|
|---|---|---|---|---|---|---|
|Indicator|Unit|2008|2000|2005|2008|2008|
|GSM coverage|% of population under signal|63|—*|22|46|96|
|Mobile phone|Subscribers/100 people|24|0.02*|2.4|12.8|95|
|International bandwidth|Bits/capita|24.8|0.04|2.4|7|209.3|
|Internet|Users/100 people|3.5|0.04|0.3|0.7|7.0|
|Landline|Subscribers/100 people|0.8|0.2|0.2|0.4|9.2|
|Price of mobile basket|$ per month|11.0|17.0|16.8|12.8|9.2|
|Price of fixed-line basket|$ per month|10.4|10.8|12.4|11.8|11.4|
|Price of fixed broadband|$ per month|287|—|131|52|57|
|Price of a call to [[the United<br>States?]]|$ per minute|0.7|0.6|0.7|0.5|0.6|
|Price of an inter-Africa call|$ per minute|0.9|—|0.4|0.7|1.1|

Source: Adapted from ARM, Zain, and Orange reports, and World Bank ICT At-a-Glance.

Note: Converted to $ using annual average exchange rates (2010 figures using 2009 exchange rate). Penetration figures derived from population data of the National Statistical Institute. GSM = global system for mobile communications
.850*|0.186|−0.138*|
||(0.085)|(0.986)|(0.114)|(0.069)|
|Number of friends|−0.005|−0.018|0.008|0.009|
||(0.008)|(0.081)|(0.010)|(0.011)|
|Control for baseline value of dep. var.|No|No|Yes|No|
|Mean of dep. var. control group|0.26|1.58|0.37|0.18|
|_P_-value: treatment * (% of friends)<br>=spillover * (% of friends)|0.0195|0.0662|0.0170|0.0335|
|Observations|4,246|3,843|3,868|3,334|

_Source_ : Authors’ analysis from student surveys. _Note_ : Dependent variables not standardized. All outcome variables are assessed six months after treatment. Standard errors clustered at the school level in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. Column 1 includes students attrited for written survey but later tracked over the phone. Table does not control for baseline value of the dependent variable, because outcome was not measured at baseline (except for column 3).<sup>§</sup> 3,358 students of the full sample agreed to be contacted for this part of the study. Specification controls for whether individual had a cellphone.

Overall, while the anlysis reveals that network effects are clearly identified among treated students, it is not surprising that the study had little power to detect network effects among spillover students given the very few friendship network links that occur across classrooms in the data.

# Cost-Effectiveness and Cost-Benefit Analysis
).</mark> We need accurate contract/lot level estimated contract values to calculate relative prices achieved by procuring authorities in the contracting process. However, the North Macedonia’s data portal only publishes estimated contract values at the tender level with no way to accurately break it down to the contract or lot level. Therefore, the analyzed sample had to be restricted to single-lot tenders where tender and lot values are necessarily the same. Furthermore, we also restrict relative prices to fall between 0.5 and 1.3 – as ratios outside this range might be due to data reporting errors. In Figure 15, panels A and B, we display the distribution of tenders in the dataset split over single-lot and multi-lot tenders.<sup>5</sup> Single-lot tenders represent 78.3% of all tenders (132,902 tenders) and 65.6% (335.5 billion MKD) of the total contract value available in the dataset.

> 5 We count the single-lot tenders based on the CFT notice url appearing only once in our lot level dataset.

24
term “world city” to describe the phenomenon. We take the theoretical underpinnings of this definition from the work of Doel and Hubbard (2002), who clarify key definitions and develop the conceptualization of globalization of cities. The two conceptualizations of world cities that we draw from include the following definitions:

> 3   See Annex D for the list of cities in the current version of the database.  We use various sources of data for this analysis, detailed in the Annex.  First, a comprehensive city level data set is taken from the UN Urban Observatory which has data on 232 cities in 114 countries for the year 1998.  We refer to this database as the UN database.  We also use data from the GaWC research which we call the Taylor database, which includes a roster of 265 cities constructed from data on the number of firms (advertising, accounting, banking, and law) and number of practitioners/offices in 265 cities for the year 2000.  We construct a special database which we refer to as the KLM (Kaufmann, Léautier, Mastruzzi) database which includes data in 412 cities from 134 countries for the year 2003, based in large part from a worldwide enterprise survey (EOS).  This database consists of 36 variables of governance as well as city and country economic data.

> 4 The expanded city level dataset includes over a hundred variables.  However, since part of this consolidated dataset is aggregated from different existing data sources comprising a different set of cities, the consolidated dataset contains many missing values. One of the objectives for the future is to build up this city level dataset and fill in these data gaps.

3
funds in all years is 1,359,750. The portfolio holdings have been matched to identify the country to which each stock belongs, tracking holdings over time. We use daily returns at the fund level between September 1989 and June 2006 for 36 fund families that have a variety of mutual funds for which useful comparisons can be made.<sup>3</sup> We work with a total of 722,885 daily observations, comprising returns for all funds from these families.

Three stylized facts about mutual fund international investments emerge from our analysis. First, global funds have grown much more than specialized funds. For example,

> 3 For the return data, we focus on the largest mutual fund families in terms of assets under management and/or number of individual mutual funds. We are constrained by the need to concentrate on families with both global and specialized funds to make useful comparisons.

3
cussed in (Deaton and Dr`eze 2009). (Duh and Spears 2017) show that infant mortality and other indicators of disease related to nutritional absorption (e.g. latrine ownership, diarrhea, and open defecation) are strongly associated with within-district changes in NSS caloric intake between 1988 and 2005 as well as in the cross-section. They find that this pattern holds even when controlling for household and individual variables that capture much of the variation in TEE explored in this paper, concluding that an improved disease environment can account for a substantial decline in caloric intake in India because households “lose” less calories to disease and therefore choose to consume less. Relative to their work, this article tests a different potential mechanism discussed in (Deaton and Dr`eze 2009), using the most detailed available data on physical activity and physical size to directly quantify TEE. Overall, the finding that TEE is fairly flat over the 1983-2012 period, combined with the evidence on modest gains in nutritional status, suggests that most of the decline in NSS caloric intake is a result of measurement error and lower caloric burden of disease rather than large decreases in population TEE.

The paper is organized as follows: Section 2 describes the data and methodology used for estimating total energy expenditure and discusses some important sources of variation across households, Section 3 presents results for changes in total energy expenditure over time and discusses the broader context of changes in caloric intake measured in the NSS, and Section 4 offers concluding comments and suggestions for future research.

# **2 Data and measurement of components of Total Energy Expenditure (TEE)**

This section briefly outlines the methodology and data used to measure TEE from household datasets. The Appendix contains a more detailed description including robustness to alternative assumptions. TEE measures the amount of energy used by the human body during a given period. When TEE equals caloric intake there is no weight gain or loss. A standard method to calculate TEE is the factorial method described in FAO (2001). (Indian