rafmacalaba/data-use-mentions-extended huggingface dataset:
earnings. There was a temporary decline in 2005, but then these measures have increased back to about the 2003 level.

**Figure 10.  Sample Means of Savings-related Variables over Time**

<!-- Start of picture text --> Sample means over time Sample means over time<br>0.5 90<br>0.4 80<br>70<br>0.3<br>60<br>0.2<br>50<br>0.1<br>40<br>0<br>2003 2005 2007<br>2002 2003 2004 2005 2006 2007<br>Operating Income to Sales Retained Earnings (Investment)<br>Any Investment Retained Earnings (Working Capital)<br><!-- End of picture text --> Source: ICA data.

# **6.2 Listed Firms Data**

Figure 11 presents behavior of cash stocks to assets and financial savings over time for firms in our sample. Since 2001 cash stocks have gradually increased over the sample period from about average of 10% of total assets to average of 15% in 2007. There was a slight decline in 2008, possibly as a result of the beginning of the crisis period. The largest increase was between the years of 2005 and 2007, which corresponds to years of rapid growth in Egypt.

Financial savings are positive, which parallels increasing cash stocks. However, the magnitude of financial savings is relatively small – the average is about 5% of total assets, across all years. Financial savings are slightly increasing until 2006, with a small decline in 2007 and a larger decline in 2008.

18
result in a decrease in profits following the training. However, if skills and capital are complements then profits will increase.

# **5. What was the impact of the training and grants?**

We want to estimate the impact of being assigned to training only, or to training plus a cash grant, on business outcome for firm _i_ in follow-up time period _t_ . We estimate the following Ancova model using the follow-up surveys: where is the baseline value of the outcome of interest, and is a dummy for being in randomization strata _s_ . The Ancova specification includes the lagged outcome variable and so is possible only for the current enterprises (since potential owners did not have baseline business outcomes). Ancova offers more power than either difference-in-differences or analysis using only the follow-up data, especially when looking at outcomes like microenterprise profits and sales which are not highly autocorrelated (McKenzie, 2011). We estimate equation (1) round-byround to determine how the treatment effects vary over time.

In addition, we can estimate the average treatment effect over all follow-up rounds. In doing so, we gain additional power by pooling together several rounds of follow-up surveys. We estimate the following regression: Where _q_ is the number of follow-up surveys, and the are survey round dummies. Standard errors are then clustered at the firm level.

Estimation of equations (1) and (2) gives the intention-to-treat (ITT) effect, which is the effect of being assigned to receive training only, or being assigned to receive training and cash. Under the assumption that the offer of training does not affect the outcomes of interest for those 16
commuting and telephone services over total household expenditure ( _Social_exp_ ) to measure the efforts for social network expansion. Although the data on cooperation level when facing fraud schemes is not available, we use the survey interviewer’s assessment about households’ compliance to proxy the general cooperation level ( _Cooperation_ ), which is a dummy variable that equals to 1 if the household respondent was assessed as very compliant during the interview.

Both the OLS and the 2SLS estimation results are reported in columns (4) to (6) in Table 9. The results are consistent with our conjecture. Credit-constrained households are more likely to use informal credit. Perhaps to facilitate the necessary social connections needed in informal borrowing, credit constrained households spend more on commuting and telephone services, and are more likely to behave cooperatively. The finding here that credit-constrained households tend to behave more cooperatively is consistent with a calculation that we made that a significant proportion (70%) of fraud victims attribute their victimization to trust and compliance.<sup>19</sup>

# **9. Conclusion**

Using a novel and comprehensive data set on fraud victimization from CHFS, we investigate the extent to which urban households’ financial conditions affect fraud victimization, and then explore potential mechanisms. The baseline regression results indicate that household credit constraints are associated with 2.3 percentage points higher probability of becoming a fraud victim and 20.4 percent higher economic losses among those being approached. To deal with endogeneity issues, we employ the IV approach, and use as instrumental variables the exposure to an unexpected nation-wide private property reform and the local bank density. To address potential sample selection bias, we further use the Heckman selection model where e-commerce coverage at the community level is used as the source of exogenous variations. The findings suggest that household credit constraints lead to both a higher probability of fraud victimization and higher subsequent economic losses upon being approached. Further analyses on potential mechanisms show that the personal discount rate (impatience) and the need for social network expansion are
Table 2 - Median Hourly Wages by Gender and Sector of Employment

|Hourly Wages|19<br>|98<br>|20<br>|06<br>|
|---|---|---|---|---|
||Male|Female|Male|Female|
|Government|1.25621|1.355311|2.458791|2.716758|
|Public|1.602564|1.572115|2.884617|2.509615|
|Private|1.153846|0.75|1.923077|1.098901|
|Others|1.141167|0.769231|2.183087|1.970567|

_Source_ : Authors’ calculations using ELMS 1998 and ELMPS 2006.

Because we are interested in exploring the hypothesis of complementarily of education and technology, we report on whether the individual owns a computer. Table 3 shows that personal computer (PC) ownership increases with educational attainment. Only 12 percent of workers report owning a computer, but the ownership rates are very different across education levels: less than 2 percent of workers with primary education owned a computer, 6 percent of those with preparatory level education, 10 percent with general secondary education, 17 percent with post-secondary education, 29 percent with university-4 education, and 53 percent with university-5 level education. The average rate of computer ownership, 12.4 percent, is similar to Colombia’s (13 percent), but significantly lower than in Chile (57 percent), Brazil (51 percent), and Mexico (32 percent) (World Bank, 2011).

Table 3 - PC Ownership

|Doesyour familyown a PC?(2006)|Yes|No|
|---|---|---|
|Illiterate|3.62%|96.38%|
|Read & Write|1.99%|98.01%|
|Primary|1.46
cohesion may be determined by the same social processes. In our case, the inflow occurred within a few months and led to an unprecedented increase in the number of asylum seekers. By exploiting pre-determined rules in the allocation of asylum seekers, we are able to estimate the causal effect of the local presence of asylum seekers on several dimensions of social cohesion.

# **4 Data and Research Design**

## **4.1 Data Sources, Key Variables and Descriptive Statistics**

To quantify the effect of the inflow of refugees and asylum seekers on various aspects of social cohesion, we combine regional data on asylum seeker inflows with survey data as well as data on anti-immigrant violence scraped from newspaper articles.

**Data on Asylum Seeker Inflows** The data on the presence of refugees and asylum seekers in Germany comes from the German Federal Statistical Office (Destatis). We observe the number of asylum seeker residing in a county in each year between 2007 and 2019, as well as the number of asylum seekers by gender, citizenship and category of protection status. In our analysis, we include all counties for which we have data on asylum seeker inflows for the entire period 2007-2019. This leaves us with 373 out of 401 counties, representing 91% of the German population in 2014.

13
often not enough to ensure that data are used by governments or individuals when the data are difficult to understand or there is a lack of skills to understand how to use the data. One example where data only attained its value when it was understood properly relates to public procurement. Too often, public projects are not implemented adequately due to poor procurement, such as inflated costs, corruption, or ghost contracts. Since 12 percent of global GDP is spent on public procurement, this matters tremendously for development outcomes (Bosio and Djankov 2020). In Uganda, in an attempt to improve procurement outcomes, local government entities made administrative procurement data from the bidding process down to the execution of contracts available to certain Civil Society Organizations (CSOs). Such data are not easily comprehendible to a non-trained audience, so the CSOs trained community members to understand the information in the contracts and conduct site checks to verify it. The findings revealed mismanagement of resources by contractors and government officials and high dependence on noncompetitive contracts. Aside from the direct benefits of assuring that contracts complied with national procurement standards, the national public procurement agency upgraded its procurement portal in line with international open contracting data standards, making Uganda the first African country to do so (Africa Freedom of Information Centre 2018 and Global Partnership for Social Accountability 2020).

18
al. 2020). The Nigerian data landscape presents two key advantages for survey-to-survey imputation techniques. First, while the GHS and NLSS differ in their data collection schedules, a wide range of nonmonetary variables were collected in exactly the same way in both surveys. Second, it is possible to check the comparability of the two surveys using data from the 2018/19 wave of the GHS, the timing of which partly overlaps with the data collection for the 2018/19 NLSS. The results of this analysis produce a poverty rate of about 44 percent for 2010/11, very close to the backcasted estimates of 42.8 (2010) and 41.8 (2011) percent. The imputed estimates for 2012/13 and 2015/16 suggest that poverty declined slightly until the 2016 recession, after which poverty stagnated or even increased: this is also entirely in line with the backcasted results. Changing the variables included in the consumption model does not substantially change this story, confirming the robustness of the results.

The finding that poverty decreased slightly in the first part of the 2010s, but subsequently stagnated is in stark contrast with the 17-percentage point drop in poverty suggested by simply comparing the estimates from the 2009/10 HNLSS and 2018/19 NLSS. The evidence presented in this paper should therefore encourage caution when using incomparable survey-based measures of household consumption for poverty measurement purposes. In the case of Nigeria, drawing a trend between the 2009/10 HNLSS and 2018/19 NLSS would impact not only the policy discussion on poverty reduction at the national level, but also any analysis of poverty trends in West Africa, Sub-Saharan Africa as a whole, and – as poverty becomes more concentrated in Sub-Saharan Africa and in conflict-affected situations – potentially the entire world (World Bank, 2020).

The paper is organized as follows. Section 2 provides general information on the country context and on Nigeria’s data landscape. Section 3 explains the data used in the analysis. Section 4 outlines the two methodologies used to estimate poverty
# **1 Introduction**

It is well established in the economic literature that infrastructure investment is a major driver of economic development, particularly through its impact on structural transformation (Berg et al., 2017). Infrastructure investments can have sizeable multiplier effects on local economies by boosting productivity through better factor allocation (Vagliasindi and Gorgulu, 2021), facilitating trade through improved access to markets (Jedwab and Storeygard, 2021), or removing barriers to entry that prevent the development of local economic activities (Perez-Sebastian and Steinbuks, 2017). As various types of infrastructure may differently contribute to growth, it is important to disentangle their effects and how these effects may vary when provided in isolation or in combination. In particular, are different infrastructure types complements or substitutes in driving local economic effects? The answer to this question bears much policy relevance as policymakers need to optimize costly infrastructure investments and their distribution over localities. It is also important for policy makers to understand how these effects may be nuanced and vary according to location and sector.

In this paper, we study the joint impact of access to transportation (highways)<sup>1</sup> and electricity infrastructure on local gross value added (GVA) using panel data over four decades and more than 3,000 Brazilian municipalities. The study fills an important knowledge gap by focusing on infrastructure complementarities. It estimates the causal effect of infrastructure complementarities using the difference-in-differences model with potentially heterogeneous treatments (De Chaisemartin and d’Haultfoeuille, 2020), and an identification strategy that relies on the plausible exogeneity of the timing and common trends across municipalities in the absence of treatment. It then establishes a causal mechanism of infrastructure investment impacts, distinguishing between productivity and relative input use channels.

We find that neither the provision of highways nor electricity alone impacts GVA per capita (our main variable of interest) or, separately, GVA or population. At the same time, we find a strong complementarity associated with the joint provision of highways and electricity, which increases GVA
1<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C13. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Civil participation indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C13.1. FOSD: Counting Vector

<!-- Start of picture text --> C13.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.1<br>1<br>.8 .05<br>.6<br>0<br>CCDF<br>.4 Difference<br>-.05<br>.2<br>0 -.1<br><!-- End of picture text --> C13.3. SOSD: Social Sustainability Index C13.4. Rank correlations 62
ILO LFS pilot study also found that in some contexts words like “business” were interpreted mostly as formal enterprises, and so respondents (often women) would be less likely to report their work as a business if they were self-employed in informal settings and/or had unpaid supporting roles in the family farm or business.

Women in contributing family roles are also considered employed, since it contributes to household profit, but are also often less likely to be identified (or self-identify) in this form of employment. The ILO LFS pilot study found that women in contributing family work often tend to identify themselves first as engaged only in domestic work, because of social and cultural norms that diminish the economic value of contributing family work, and since this work is also often conducted with domestic activity. As a result, follow-up or “recovery” questions were needed to hone in on this as well as other economic activity (also see Benes and Walsh, 2018a; Data2X, 2017). Women respondents in the cognitive testing stage, for example, often said they helped with stocking, storing, and other activities on the farm, but they had to be prompted specifically to report these activities. Figure 3 also shows, based on recent analysis of the ILO pilot data (Koolwal, 2018), that recovery questions on contributing family work re-classifies substantial shares of men and women — with the greatest effects for rural women — back into employment.

Figure 3. ILO LFS pilot countries: share of men and women re-classified as employed, through recovery questions on contributing family work <!-- Start of picture text --> Rural Urban<br><!-- End of picture text --> <u>Notes: Similar patterns observed for recovery questions on paid work in agriculture, and to a lesser extent on small/secondary jobs.</u> Source: Koolwal (2018), based on ILO LFS pilot study data accessed at ILO.

Careful attention to wording choice, adapted to local context
|57|
||2004|57433|111332|657195|24|
||2005|59459|114565|676830|23|

Notes: Number of observations = 61 countries. Source: Authors’ calculation from the World Bank’s World Development Indicators (WDI).

# **4.- Empirical Results**

There is a wide difference and variation of expenditure on Research and Development – R&D (as a percentage of GDP) across countries (see Appendix tables 6 and 7). There are also differences on New Business Entry Rate – NBER (New Businesses Registered - NBR as % of total business) across countries when grouped in terms of High-Medium and Medium-Low-Tech (see Table 3).

Some examples are Sweden<sup>4</sup> (high expenditure on R&D and low NBER), Turkey<sup>5</sup> (high NBER and low expenditure on R&D), India<sup>6</sup> (low expenditure on

> 4 The industrial sector plays an important role in Sweden’s economy. Recently the Agricultural sector has also contributed a lot to the country's Gross Domestic Product. The major industries in the country are iron, steel, wood pulp, paper products, and motor vehicles. The important agricultural products in the country are wheat, barley, sugar and milk.

> 5 In the case of Turkey the strong and rapidly growing private sector is a landmark of its economic success.

> 6 Agriculture is a major component of India’s economy. The industrial sector  includes manufacturing industries, textiles and handicrafts, etc. However, the service sector is greatly expanding and has started to assume an increasingly important role (e.g., India has become a hub

11
|40|0|0|40|80|
|7. Welding|0|0|40|0|0|40|
|**Total**|320|200|200|120|160|1,000|

_Source:_ Original table for this publication.

A survey firm, SRG Bangladesh, hired to collect data for this study, interviewed all 1,114 applicants faceto-face from May 7 to May 30, 2018, for the baseline data collection before grouping them into the treatment and control groups. They submitted the cleaned baseline dataset to the study team in June 2018. In January 2019, seven months after the RPL assessment, the survey firm conducted an end-line survey through faceto-face interviews. They successfully interviewed 1,097 applicants (98 percent response rate) and submitted the cleaned end-line dataset to the study team in March 2019.

Only after the end-line data collection, the project started to invite the control group participants for RPL certification. The understanding was that the project would prioritize control group applicants when the RPL Level 1 assessment resumed.

> 4 Fifty-one of the 114 applicants (45 percent) had participated in the assessments. Since some ranked lower in the waiting list have been assessed, bypassing the higher ranked applicants in some instances, we are not confident that the wait-listed applicants who were assessed are similar to those not assessed during baseline. We are worried that training providers used other criteria to select the trainees from the waiting list. As a result, we drop wait-listed observations in the analysis.

6
0.00|-0.07|0.02|-0.17|-0.13|-0.04|
||_0.80_|_0.77_|_1.00_|_0.29_|_0.77_|_0.08_|_0.07_|_0.53_|
|Cash only|-0.29|-0.02|-0.05|-0.20|-0.03|-0.23|-0.18|-0.05|
||**_0.04_**|_0.80_|_0.37_|**_0.00_**|_0.42_|**_0.03_**|**_0.03_**|_0.32_|
|Therapy + cash|0.02|0.03|-0.05|-0.03|0.06|-0.19|-0.16|-0.03|
||_0.91_|_0.57_|_0.37_|_0.66_|_0.25_|**_0.01_**|**_0.02_**|_0.48_|
|Observations|239|238|238|238|239|239|238|239|

_Notes:_ Columns 1 to 8 report the simple mean differences in the survey and validation measures for the full sample and for each treatment arm, along with p-values for as t-test of whether the mean is different from zero. We bold p-values _≤_ 0.05.

First, the results imply that the adjusted treatment effect of therapy and cash on sensitive behaviors overall is no lower than what we estimate with self-reported survey data, and may even be larger (Column 1). This holds true for each of the individual sensitive behaviors, save marijuana use. Despite the large standard errors introduced by the small validation sample, the adjusted treatment effect on
** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition.</u> For Bachelor-students, we do not find a systematic treatment effect of student loans around DS=0.08 and DS=0.15, but the impact of credits on student performance is positive at DS=0.30. A coefficient of 0.234 20
# **5. SIMULATION OF ALTERNATIVE SOCIAL POLICIES**

5.1 The serious targeting deficiencies observed in existing social policies motivate the search for alternative targeting mechanisms that might be more effective in channeling resources to the poor. The basic presumption is that any alternative mechanism should be based on an accurate targeting device and be limited to subsistence consumption levels. On this basis, a variety of alternative social policy mechanisms can be conceived, each of which is designed to remain within the current budget envelope for infrastructure subsidies. Both use of service subsidies and connections subsidies (where applicable) are considered in the simulation exercise.

## **TARGETING MECHANISMS**

5.2 In order to improve the targeting of subsidies, it is necessary to develop an accurate poverty proxy that can be used to identify low-income households. In Argentina, a number of potential targeting mechanisms have been publicly debated, including the use of consumption levels as a targeting device, as well as the allocation of utility subsidies to households that are already beneficiaries of other social programs (such as the Heads of Household program<sup>12</sup> ). Furthermore, provincial experience illustrates the possibility of developing multi-dimensional poverty proxies based on aggregating a number of different indicators of structural poverty. Each of these are now considered in turn.

## **Consumption**

5.3 In order to evaluate the hypothesis that physical consumption might be a good poverty indicator, a simulation was conducted using data on household electricity consumption in the Metropolitan Area, since this was the only service and geographical area for which there were sufficient reliable data points on household consumption. The simulation explores the possibility of allocating subsidies only to those households that consume (successively) less than 50, 100, 150, 200, 250 kilowatt-hours per month (Figure 13).

> 12 The Heads of Household program is a major welfare program introduced to attenuate the impacts of the severe 2002 economic crisis. The program makes a monthly payment of US$67.7 to