rafmacalaba/data-use-mentions-extended huggingface dataset:
for the baseline survey. Given that all firms were only considering female applicants, the study sample comprises only women.

Study participants were then randomized into treatment and control, with two-thirds of applicants in the treatment group and one-third in the control group using a public lottery method. Once randomized, the treatment applicants were assigned a specific firm to interview with. This stage was complicated by the fact that each of the participating IP firms has slightly different hiring criteria.<sup>9</sup> These differences create multiple distinct applicant groups according to the intersecting hiring criteria. Those treatment applicants who were eligible for multiple firms were asked for a second random draw for firm assignment.

Once firm assignment was determined, the enumerators formally invited the treatment individuals to a firm interview and informed them that they will be given transportation to the firm at the Bole Lemi IP for the interview.<sup>10</sup> As such, both treatment and control individuals only learned about their treatment status after completing the baseline survey but before they left the screening center. On the day of the interview, an enumerator confirmed that only treatment individuals were provided with transport to the firm. After the interview process was completed, all candidates were transported back to the screening center.

The firms conducted interviews with each of the treatment candidates according to their own hiring procedures and decided whether or not to offer the applicant a job. With the additional in-person eligibility verification done at the screening centers, it was anticipated that the firms would select the vast majority of the treatment candidates who participated in an interview. After the interview and before an offer was made, candidates were required to complete any additional hiring procedures—such as a medical exams and/or aptitude tests—at this time. Once they received a job offer, treatment candidates had the option to take up the position and begin work or decline the offer. Nearly two-thirds of the applicants assigned to the treatment group attended a firm interview, and, of those who attended,
It might be conjectured that the signs of positive welfare impacts using SDD2 reflect some broader concept of ―welfare‖ than captured by consumption. Or one might argue that welfare recall uses different implicit weights, possibly reflecting missing or imperfect markets. Looking at the SDD2 results in Table 2, there is some sign of an impact on ―health‖ that may account for the implied gain in overall standard of living. Amongst the consumption goods, clothing shows the strongest positive impact using SDD2. However, there is less sign of impacts on any of the many other dimensions of welfare for which the recall questions were used. Nor is it clear why these effects would only emerge when one uses the SDD2 method.

As an additional test for differences between project and comparison villages in ―nonincome‖ factors in subjective welfare I exploited the fact that the recall module included questions on perceived current living conditions (for the same items in Table 2). I examined the relationship between the answers for overall standard of living and consumption per person in the 2004/05 survey data, to see if there are any signs that the relationship is different between SWP and non-SWP villages, as might arise from impacts of SWP on ―non-income‖ dimensions of welfare captured in the subjective assessments. The test entailed regressing each subjective measure of the level of welfare on log consumption per capita in 2004/05, a dummy variable for SWP villages and the interaction effect between these two variables. There were no significant differences between SWP and non-SWP villages for all except one of the categories in Table 2. The one exception was for roads (―are you satisfied with village road conditions?‖); households with higher consumption in the SWP villages tended to rate road quality higher, but there was no such gradient in non-SWP villages. One might take this to suggest that the SWP enhanced perceived road quality for better-off households, although one cannot dismiss the possibility that one will get at least one significant result in 30 tests purely by chance.

These observations are
The risk premium for each maturity is estimated by using ex-post excess returns on emerging market bonds over comparable default-free (German and U.S.) bonds. To calculate the risk premium, we need to obtain fi rst bond yields, spreads, and prices. We begin by estimating time series of German and U.S. yields curves and emerging market spread curves. The maturity-τ spread, st,τ , is de fi ned as the difference between the yield, yt,τ , on an emerging market zero-coupon bond of maturity τ and the yield, yt,τ<sup>∗,onadefault-freezero-couponbondofmaturityτ,st,τ= yt,τ−y</sup> t,τ<sup>∗.</sup> We use this information to obtain bond returns at different maturities and over time for every country. This enables us to make cross-country, cross-maturity, and over-time comparisons. Note that it would be impossible to carry out the analysis using the raw data because each country has a different set of bonds at each point in time, with varying maturity and coupon structure. Appendix B describes the methodology used to estimate yields and spread curves.

Figure 2 displays the estimated spreads over time for each country. The fi gure shows spreads at two maturities to illustrate how short-term (3-year) and long-term (12-year) spreads move over time.<sup>23</sup> The fi gure shows some interesting facts. First, spread curves are, on average, upward sloping. Second, spreads increase during periods of fi nancial crises. For example, during the crises in Argentina, Russia, and Uruguay, spreads jump to more than 25 percent or 2,500 basis points. Third, short-term spreads are more volatile than long-term spreads. In fact, during periods of very high spreads there is an inversion of the spread curve, with short-term spreads becoming higher than long-term ones.

Using the estimated spread curves and U.S. yield curves, we compute the price Pt,
; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by barren land or more than 90 percent covered by forest are excluded from the sample. _∗ p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 21
of the MDGs. Unfortunately, such data are not available in many countries for 1990, although estimates for recent years tend to be more complete. If no country data are available for 1990, we used the closest available information in the late 1980s or early 1990s as substitutes for the starting point, and then calculated the rate of progress required from that point to meet the MDG. This approach may be inaccurate if the data for the available starting point is significantly different from the level of MDG performance in 1990 or the sample period does not capture the latest progress. The latter is a particularly important issue now, since data generally are yet not available for 2009, the year of the recent global economic crisis. In addition, for countries without at least two data points, progress cannot be measured even if data are available for a recent year. Even so, the approach allows us to include more countries than if we relied only on data from 1990 and 2008.<sup>8</sup> We restrict our attention to six MDGs and nine development targets with explicit and quantifiable 2015 goals (United Nations 2008). Selected development targets are:

- MDG 1.a: halve between 1990 and 2015, the proportion of people whose income is less than $1.25 a day (Poverty headcount ratio at $1.25 a day, PPP, percent of population).

> 7 The average here is the mean of the off-target group, not the entire sample of countries.

> 8 See annex table A1 for country-specific results.

6
market conditions such as local unemployment rates at the time the worker joined the ...rm, but not by subsequent labor-market conditions. The reason is that the worker and ...rm will bargain for their optimal implicit contract at the beginning of their relationship, at which point the current unemployment rate will have an e¤ect on workers’bargaining power and hence on the wage agreed to; but if implicit contracts are strong and provide good insurance to the worker, for the remainder of the job, wages will simply follow the agreed-upon wage regardless of subsequent labor-market conditions. She ...nds that (i) a workers’current wage is a¤ected by initial conditions at the time of beginning of the job, independently of current labor-market conditions; and (ii) in industries hit by a rise in import penetration, the current wage is much more dependent on the current labor-market conditions compared with other industries. Together, these ...ndings suggest that (i) implicit contracts are important, and (ii) import competition indeed weakens them, as predicted by her model.

# 3.5 Labor Market Frictions.

Krishna and Senses (2009) o¤er an empirical study of the impact of openness to trade on domestic income. Whereas previous studies examine the impact on wage growth or wage premia, Kirshna and Senses examine the impact on wage volatility. Using longitudinal earnings data on workers from three panels (spanning 1993-2003) of the Survey of Income and Program Participation, they estimate the relationship between labor income risk (de...ned as the variance of unpredictable changes in earnings) and import penetration, a measure of industry exposure to international trade. They ...nd that a 10 percent increase in import penetration will increase the standard deviation in persistent (as opposed to transitory) income shocks by 20 to 25 percent for all workers. Their results are both statistically and economically signi...cant.

Krishna and Senses (2009) also estimate subsets of their
We collect information on firm demography from censuses and surveys for more than 25 African countries from all regions and income levels between 2010 and 2020. We extract information on firm size based on the number of employees, which forms the distinction between micro, small, medium, and large firms. Further, where available, we add information on firms’ formality status - whether a firm is registered or at least licensed in the country or province, as well as sector.

We also identify where national censuses and surveys do not cover key sectors (e.g. agriculture), or explicitly exclude informal or household-based firms. We discard outdated censuses and surveys collected before 2010 as well as censuses and surveys that lack information about how data was collected. We restrict ourselves to censuses and surveys up to the year 2020, as evidence shows that the COVID-19 pandemic caused much volatility in firm entry and exit, and potentially large fluctuations in the number of firms (Decker and Haltiwanger, 2022). Evidence from rapid surveys suggests that up to 90 percent of firms in selected Sub-Saharan African countries closed temporarily (Aga and Maemir, 2021). Many firms had to lay off workers in the short term only, with consequences for a firm’s employee count. While recent surveys and censuses may give a more up-to-date assessment of the number and characteristics of firms, they may also introduce comparability issues with censuses and surveys collected pre-pandemic.

Collected data varies across countries in the definitions of size strata, and coverage of large and informal firms and sectors. As mentioned above, this compromises the harmonization of the datasets. We find meaningful differences, in particular, in how countries define a microenterprise. For instance, only entrepreneurs with no employees are considered micro-enterprises in the Gambia, but in three countries, establishments with up to ten employees are identified as micro-enterprises. Small firms are defined as anywhere from 1-4 employees to 10-100 employees (Morocco). Few countries follow the standardized WBES definitions of firm size: 5 to 19 (small),
economic interdependence between them, we construct a series of indicators to measure the economic environment that a province faces.

Using panel provincial data from 1978 to 1999, this paper studies the spillover effects in the case of China, and came out with three findings: (1) at national and regional levels, the rapid growth of a province encourages that of the neighbors – the regional development is in a virtuous circle;<sup>4</sup> (2) spillover effects differ in size and in significance among different province groups – some coastal provinces serve as growth locomotives; and (3) the rapid take-off of some coastal provinces in the last two decades did maximize aggregate growth of the entire economy, while encouraging growth in the inland hubs that have strong forward/backward linkages with other provinces is the most effective way of reducing the regional development gap.

The paper is organized as follows: section 1 describes the growth trajectories and the center-peripheral regional growth pattern between coastal and inland provinces; section 2 focuses on the construction of a series of indicators of “neighbor performance”; section 3 examines the differentiated spillover effects on local growth issued from and received by different provinces; section 4 simulates the impact of a hypothetical performance enhancement of various provinces on regional growth, examines the effectiveness of the regional growth pattern, and identifies the target locations of development aid. Finally, the paper concludes.

# **Section 1: Center-peripheral unbalanced regional growth pattern**

The success of the Chinese economic reforms, which were launched in the late 1970s, is well documented in the literature.<sup>5</sup> Through incrementally reforming the centrally planned system to improve incentives and increase the scope of the market in resource allocation, and through building new institutions to support a market system before old institutions were destroyed, China’s transition has achieved a remarkable success (Qian, 2000; 2003). The GDP per capita of the Chinese provinces increased more than 5 times in average from 1978 to 1999 (
0.987 for _r_ =0.25), there are some large changes. For _r_ =0.5, Zimbabwe’s index rises by over 300%, from the lowest value (by far) of 0.140 based on ������ to 0.454; it also rises relatively, to be the 12<sup>th</sup> lowest—reflecting the fact that the additivity property of the Chakravarty index puts a higher premium on Zimbabwe’s schooling attainment. Using _r_ =0.25, the upward revision to Zimbabwe’s index is even more dramatic, with ����.��� =0.583. The largest decrease is that for New Zealand, for which the index falls by 0.094 in switching to ����.�� , and the ranking falls from third place to 18<sup>th</sup> . The differences are small at high HDIs using _r_ =0.25 (Figure 12).

# **6. Conclusions**

The Human Development Index was introduced in 1990 as an alternative to using national income per capita as the metric of development success. Until 2010 the index was an equally-weighted mean of scaled attainments in three dimensions: life expectancy, education and income. The simplicity of the HDI gave it a transparency that was clearly appealing to many users, although the HDI was never quite as simple as one might think at first glance, given the transformations embedded in its components. Over 20 years, the Human Development Reports (and numerous offshoot reports at national level) have applauded those countries that do well in the HDI, and offered advice to others on how they might do better in the HDI stakes.

A new version of the index was introduced in the 2010 edition of the HDR. The main change was to switch from the original additive aggregation function (the arithmetic mean of the three components) to a multiplicative function (their geometric mean)
aspects of cooperative behavior. The protocols are common across societies. For example, Marlowe et al. (2008) use data from such a large-scale, multi-country experimental study to show that larger and more complex societies engage in greater levels of third-party punishment of uncooperative or unfair behavior in order to enforce social norms. Heinrich et al. (2010) use data from the same experiment to show that market integration and fair behavior in the games are correlated, indicating the importance of institutions for individual behavior and social norms. In an earlier study, Heinrich et al. (2006) also find that altruistic behavior and costly punishment are correlated, again reflecting differences in social norms across societies. In a different study using more developed countries, Hermann et al. (2008) find that a weak rule of law and norms of civic engagement yield greater levels of anti-social punishment.

When we refer to culture, we employ a composite term which may include both formal and informal institutions. However, at the very least, it includes the informal rules of behavior; formal rules may, or may not be included. For example, to say that a society has a patriarchal culture is to say that _at least_ the informal rules have a male as the primary authority figure. Formal rules may reinforce males as authority figures (such as laws prohibiting women to work in Saudi Arabia), or may not weigh in on the matter at all. In some cases, formal rules may be in the opposite direction (for example, quotas in India for female representation in parliament) and may even exist to overturn informal rules of behavior. In addition, the extent to which norms and/or institutions govern behavior is yet another aspect of culture. Experimental methods allow us to unpack the influence of various aspects of culture on corruption.

# **Culture and Corruption: Theory and Empirics**

Culture interacts with corruption through formal institutions and social norms, both of which can differ across countries. For a government that seeks to inhibit corruption
induced by a given price change, as well as for changes in the price of nontradables. To summarize, the price elasticity of money-metric utility varies across households according to: (1) consumption shares, _νj_ , _including_ that of services, _νS_ ; (2) income shares due to land ( _λ_ Π) and manual labor ( _λW_ ); and (3) sectoral labor shares, the _βj_ (through _ψ_ and _δ_ ), for the district, or for the labor market more generally.

At a theoretical level, the welfare implications of a food price change look quite different when labor markets are effectively segmented.<sup>30</sup> In particular, the counterpart to equation (13) has where _λA_ is the share of income derived from agriculture (both from land and manual labor) and _λNA_ is the share of income derived from nonagricultural manual labor. Here, obviously, the sector in which the household supplies its manual labor matters. Inasmuch as _φ < ψ <_ 1 _/_ ( _αL_ + _αK_ ), landless households working in agriculture stand to gain more from higher food prices than those working outside of agriculture.

In what follows, I consider the distributional consequences of a _uniform_ percentage increase in all agricultural commodity prices _relative_ to the price of manufactures, the numeraire. According to equation (12), the elasticity of money-metric utility or household welfare with respect to this price is simply _ε_ = Ω _− νA_ , where _νA_ is the expenditure share of agricultural goods (essentially the food share); in the counterfactual segmented labor markets scenario, _ε_<sup>_′_</sup> = Ω<sup>_′_</sup> _− νA._

# **4.2 Baseline results**

Drawing on a sample of around 61,000 rural households in 472 districts of the 18 major states from the nationally representative consumer expenditure survey (CES) collected as part of

> 30Artuc et al. (2010) make
, creating new jobs. For instance, in the taxicab market, the “Uber technology” has been linked to increased number of rides and the creation of over 30,000 jobs just from one startup (Vergara-Cobos, 2020). Our survey of the literature suggests that future research should focus on developing new online business models and work routines in response to the COVID-19 pandemic. Among other works, for example, Leonardi (2020) claims that the new work-model that COVID-19 enabled (in other words, the “work-from-home” model) has changed the work routine of companies; consequently, such a work model will continue to drive institutional innovation for decades to come.

_Table 4: Main empirical findings from studies on ICT adoption and labor market outcomes_

|**Labor market**|**Main conclusions**|**Sample**|**Time coverage**|
|---|---|---|---|
|Iacovone and Pereira-<br>López (2018)|ICT adoption in the manufacturing sector has increased<br>the demand for more high-skilled workers.|Mexico|2008-2013|
|Czernich (2014)|The availability of broadband Internet does not reduce<br>unemployment.|Germany|2006|
|Hjort and Poulsen<br>(2019)|The probability that an individual is employed increases<br>between 6.9 and 13.2 percent when fast Internet<br>becomes accessible.|12 African<br>countries|2006-2014|
|Klonner and Nolen<br>(2008)|Employment increases by 15 percentage points when a<br>region receives mobile coverage and that mobile<br>coverage is linked to poverty reduction.|South<br>Africa|1995-2001|

17
within one‟s communities seem to increase the likelihood of political participation, whereas general trust in others seems to have the opposite effect on turnout. This is similar to the results obtained for participation at the school level. The measures of parental involvement with their children‟s education do not seem to affect the decision to vote; this suggests that participating in parliamentary elections represents a (maybe too) long route to holding teachers and school directors accountable.

# **5.2 District-level analysis**

In this section we investigate whether both parental participation in school accountability and participation in the 2009 parliamentary elections are higher in districts characterized by a higher fraction of individuals willing or able to cooperate with each other in collective action situations.

# _The short route to accountability_

We aggregate parents‟ decisions to invest in the group account in the public good game and parents‟ decision to participate in the election of parent class representatives at the district level, and investigate their correlation while controlling for district characteristics generated by the 2008 LSMS Albanian household survey.<sup>34</sup> The sample size is therefore reduced to 36 observations (i.e. the number of Albanian districts).

Table 8 reports estimates from OLS regressions where the dependent variable is the proportion of parents who participated in the school accountability system in a district, and the explanatory variable of interest is the proportion of parents who behaved cooperatively in the game. We control for the average income and average years of schooling in the district (column 2), district-level measures of social capital (column 3), and other district characteristics (column 4) discussed in previous section, i.e. religious heterogeneity, a northern region dummy, four geographical area dummies, whether or not the district is prevalently Muslim, and the standard deviation of income within the district. In all regressions we employ county fixed effects.

> 34 The 2008 LSMS is the third household survey conducted by the Albanian National Statistics Office with the technical assistance of the World Bank.