rafmacalaba/data-use-mentions-extended huggingface dataset:
Tables 3c and 3d report results in which remoteness is proxied by the distance to a major port. We find results that are broadly similar to Tables 3a and 3b, with one noteworthy exception. Remoteness is not related to changes in the service sector and overall GDP during the 2018/19 recession. This difference suggests that the changes during the 2018/19 recession were related to factors involving domestic demand, and it was the remoteness from the largest cities rather than the major ports that was associated with increased (service sector) public spending (Appendix Tables A1-A3).

Fourth, tables 3a-3d show that the large boom in construction activity during 2010-2011 was increasing in remoteness (for both measures). During 2010/2011, real construction sector per capita GVA grew by 2.9 percent (not significant) in the largest city, while it grew by more than 18 percent in provinces at the 90th percentile (i.e., more than 800 km away from the closest large city). Similarly, construction activity grew by 4.8 percent in provinces with a major port, to 15.6 percent in provinces that were at the 90th percentile of remoteness. These results use 2007 and 2012 as base years, and control for differential trends at the province level. A similar pattern emerges by examining simple averages (Table 1). During the 2010/2011 rebound, construction activity increased at an annual rate of 24 percent in the least developed provinces. This rate of increase is unique (in terms of its magnitude) across periods, levels of development, or sectors.

This section has reported results based on an analysis of province-level data. What emerges from this analysis is that Türkiye’s recessions and recoveries have been accompanied by very different changes at the provincial spatial scale. Further, Türkiye’s economy is rapidly urbanizing. This suggests that an analysis of economic activity within and across urban agglomerations may complement the analysis of traditional administrative units (i.e., provinces) conducted above. Such
average of economic conditions in US states where migrants from that municipality reside by weighing economic conditions in each US state according to the share of migrants from that municipality in El Salvador that reside in each US state. Hence, for every municipality _j_ and year _t_ the instruments are constructed as follows: where: _i_ represents every US state, _RealGDPpcit_ and _Povertyit_ are the real GDP per capita and poverty rates in US state _i_ and year _t_ , respectively. _wji_ is the share of migrants from municipality j in El Salvador (out of the total migrants from that municipality) residing in state i in the US. The basic intuition for why we use these variables as instruments is that we think that economic conditions in US states will affect the ability of migrants to send remittances back to their communities in El Salvador, but these variables are unlikely to directly influence the use of financial services by households back in El Salvador.

# **IV. Results**

Tables 3, 4, and 5 present the estimation results (marginal effects) of the probit regressions for the likelihood of having a deposit account, receiving a loan, and applying for a loan, respectively. Each table shows two sets of estimations: including the remittances dummy variable (columns (1)-(4)) and, separately, the per capita real amount of remittances (column (5)(8)). For each measure of remittances, we conduct different estimations, depending on whether we include fixed effects and if so at what level of aggregation. The first column of each set does not employ any fixed effects, the second controls for department fixed effects, the third includes municipality fixed effects, and the fourth allows for household fixed effects.

the last five years are similar to those at the time of our household surveys. In the case of Mexico, Hildebrandt and McKenzie (2005) show that the historic migration rates are a strong predictor of current migration rates.

10
Investment Climate Assessment reports have been often produced which in addition to an in-depth analysis of the survey data also undertake a deeper analysis of investment climate constraints and potential solutions.

- Identification Frameworks

Already implicitly present in tools such as Doing Business and World Economic Forum approaches, is the idea of providing a comprehensive and organized framework through sets of policy variables (the “pillars”) that aim to reflect all the policies that impact respectively private sector activity and competitiveness. However, these frameworks come short of providing a true analytical structure to relate the various policies to the desired outcome and identify which matter more particularly given a specific country context.

The _Growth Diagnostics_ proposed by Haussmann and Rodrik<sup>12</sup> focuses on formulating growth strategies (to maximize social welfare) by identifying key reform priorities through an analysis of where market imperfections and distortions, and therefore failure of public interventions result in a difference between private and social rates of return. Since wholesale reform is not realistic, the framework uses a methodology to identify binding constraints focusing on the supply side of investment (i.e., inadequate

> 8 The areas are: basic requirements (i.e., institutions, infrastructure, macroeconomic environment and health and primary education); efficiency enhancers (i.e., higher education and training, goods market efficiency, labor market efficiency, financial market development, technological readiness and market size); and innovation and sophistication factors (i.e. business sophistication and innovation).

> 9 <u>http://www.enterprisesurveys.org/Methodology/Enterprise-Surveys-versus-Doing-Business</u>

> 10 The surveys are administered by a central unit in the World Bank though there is region specific collaboration with other institutions (e.g., with EBRD in Eastern Europe and Central Asia where the surveys are known as Business Environment and Enterprise Performance Surveys).

> 11 The main limitation for sector analysis is that the surveys focus on manufacturing oriented formal firms.

> 12 Hausmann, Rodrik and Velasco
before December 2014, the employee received $0.20 of extra net benefits each month, which increased to $0.54 from January 2015. Panel (c) shows that aggregating costs over six months, the total cost to the employer is $3.69 (solely due to previously unpaid taxes and contributions). Panel (d) shows that aggregating benefits over twenty-four months, the employee gains $4.82 before December 2014 and $12.85 from January 2015, solely due to higher benefits as net wages do not change.

The gains of the employee from a (reported) earnings increase during the reference window always exceeded the costs to the employer, but they became 1.9-2.7 times higher after 2015. If the earnings increase was only a change in reporting behavior without a change in net earnings, then its costs to the employer were lower (only higher taxes, but not higher wages), but the benefits to the employee were also lower by the same magnitude (higher benefits but not higher wages), while the total cost to the government was the same in the two scenarios (government costs depend on taxes and benefits, but not directly on wages). The cases displayed in Table 1 are edge cases: they assume either no tax evasion or that the marginal dollar of extra gross earnings comes fully from an adjustment to envelope wages. Intermediate cases are also possible, with some adjustment to both net earnings and the envelope vs. official share in compensation.

# **3 Data and Sample**

We use administrative data from Hungary on employment, earnings, benefits, education, and health for a 50% random sample of the population, defined in January 2003 and followed until December 2017. We observe employment status and type (private sector employee, public sector employee, or self-employed) for each month. For private sector employees, we observe firm, occupation, and industry. For double-bookkeeping firms, we observe the annual average firm 5
Additionally, figure 5 below shows that being further away from the marketplace is associated with lower maize prices. It charts maize prices by distance to markets and shows that the further the household is from the market, the lower the farm-gate price. This suggests that a significant share of the difference between farm-gate prices in a given period and area for a homogenous good such as maize (henceforth referred to as price dispersion) can be accounted for by the distance of the household to the local marketplace. Additionally, figure 6 presents the coefficient of variation (a measure of price dispersion being the ratio of the standard deviation to the mean) by distance to market quartiles and by season. It shows that price dispersion increases with distance from the marketplace. So households that are further away from the market experience larger price fluctuations than those near to the market in both the harvest and lean seasons. It also shows that price dispersion is lower during the harvest season irrespective of distance to market.

_Figure 5 Figure 6_ <!-- Start of picture text --> Coefficient of Variation by<br>400<br>Distance to Market and Season<br>350 Lean Season<br>Harvest Season<br>300 25%<br>250 20%<br>200 15%<br>150 10%<br>5%<br>100<br>0 50 100 0%<br>Distance to market (in km.) 1st 2nd 3rd 4th<br>jan-feb mar-apr may-jun Quartile Quartile Quartile Quartile<br>jul-aug sep-oct nov-dec Distance to Market<br>Price of maize grain (in Tsh.)<br>Coefficient of Variation<br><!-- End of picture text --> Source: Author‟s computation using Tanzania Household Budget Survey 2007 To further explore the role of search costs and distances to markets and test our first hypothesis, we test a model of the determinants of maize prices using OLS regression. We commence with a simple
that has been found to disproportionately benefit workers from developing economies (Agrawal, Lacetera, and Lyons 2016). Whether developing economies can benefit from online freelancing platforms at the beginning of the digitalization process largely depends on their human capital endowment. As of 2021, around half of the world’s online freelancers were based in India, Bangladesh, and Pakistan because of their workforces’ strong English language and technology skills (Baldwin 2019). This pool of online freelancers may expand geographically, however, if English language dominance diminishes with the diffusion of AI-enabled machine translation (Brynjolfsson, Hui, and Liu 2019).

There is also evidence of the efficiency gains from online labor market matching in developing economies. Experimental evidence from India shows that a combination of advertising and applicant verification increased portal-based hiring by 68 percent (Fernando et al. 2023). Similarly, evidence from Ethiopia shows that lowering job application costs, by using the internet and precluding the need to apply in person, has enabled employers to attract higher-ability applicants (Abebe, Caria, and OrtizOspina 2021).

# **_2.2. Does digitalization increase the efficiency of business processes?_**

Evidence from advanced economies shows that the use of digital technologies during the first wave of digitalization reduced the cost of computing across internal business functions. Based on data from 20 European countries and 22 industries between 2010 and 2015, Gal et al. (2019) find that greater adoption of enterprise resource planning (ERP) software, customer relationship management (CRM) software, and cloud computing in an industry is associated with higher multifactor productivity growth in the average firm. In the United Kingdom, DeStefano, Kneller, 8
However, when the same regression is performed in the health sector, different associations emerge. First, a significant correlation of accountability is found with respect to health provision and outcomes, whereby a 1-point increase in the accountability index, is correlated with an increase in the health provision index by 0.406 points and the health outcomes index by 0.167 points for the average country in the sample. In these specifications, the effect of accountability on health service provision is larger and more significant than on outcomes, since outcomes may take more time to materialize. Second, when controlling for the public management condition, accountability remains significant for health provision, suggesting that it is a driving factor in this relationship. Conversely, for health outcomes, the opposite is true: accountability loses its significance and public management shows a positive and significant effect (0.259), suggesting that public management might be a more relevant driving factor than accountability for health outcomes.

||**(1)**<br>**Health Provision**|**(2)**<br>**Health Outcomes**|**(3)**<br>**Health Provision**|**(4)**<br>**Health Outcomes**|
|---|---|---|---|---|
|**Accountability**|0.406<sup>***</sup>|0.167<sup>**</sup>|0.423<sup>**</sup>|0.0599|
||(0.148)|(0.0668)|(0.174)|(0.0851)|
|**Public Mgmt.**|||-0.0415<br>(0.220)|0.259<sup>*</sup>
We also asked about 6 hypothetical forms of political violence, including whether they discuss protesting with friends or making trouble or conflict with the authorities (See Appendix D). We did not measure perceived social category directly.

An index of all three composites shows that all treatments decreased the self-reported acceptability of violent behaviors in the short run. Cash had little impact on self-reported attitudes in the long run, but therapy plus cash led to a .18 standard deviation decline. We cannot reject that the effects of therapy only and therapy plus cash are equal. The overall effect is driven by attitudes toward criminality and political violence.

As with self control, we divide the 29 questions into two indexes by high and low emphasis in the curriculum. Table 7 reports the ITT estimates after a year. The effect of cash and therapy is at least as low for the low emphasis components.

# **6.6 Other outcomes of interest**

NEPI’s therapy targeted other behaviors, especially ones they thought could contribute to antisocial behavior, including substance abuse or harmful peers. These possible mechanisms and also important outcomes. Table 8 reports impacts on all these other measures.

**Prosocial behavior** We measured various prosocial behaviors in the long run surveys (including group memberships, group and community leadership, contributions to local public goods, and trust in others). In contrast to the steep reductions in antisocial behaviors, we see no evidence that therapy or cash led to more positive social engagement.

**Mental health** We asked about two disorders we deemed relevant. We measured five symptoms of post-traumatic stress using existing Liberian instruments.<sup>46</sup> We also measured neuroticism, the tendency to experience emotional instability or anxiety, assessed with 8 questions from the NEO-5 factor personality inventory (Costa and McCrae, 1997). After one year, the therapy plus cash group reported 0.17 standard deviations lower post traumatic stress and 0.15 standard deviations lower neuroticism (though both are not significant
# **Figures and Tables**

**Figure 1. Large Devaluations: Average Normalized REER before and after Selected Episodes**

<!-- Start of picture text -->
1.8<br>N 1.6<br>o<br>r 1.4<br>m<br>a 1.2<br>l<br>i 1<br>z<br>e 0.8<br>d<br>0.6<br>R<br>0.4<br>E<br>E<br>0.2<br>R<br>0<br>-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8<br>Quarters before and after Dramatic Devaluations<br>Whole Sample Constant Sample<br><!-- End of picture text -->

Source: Author’s calculations based on data from the IMF. See text for details.

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2|97.1|97.2|97.3|97.5|97.6|97.8|98.0|
|Rural Poverty Incidence|78.3|94.4|94.7|94.9|95.2|95.5|95.8|96.1|
|Urban Poverty Incidence|30.5|107.6|107.2|106.8|106.4|106.0|105.6|105.3|
|Overall Poverty Gap|29.7|95.3|95.4|95.6|95.8|96.1|96.3|96.6|
|Rural Poverty Gap|38.7|92.5|92.8|93.1|93.5|93.9|94.3|94.7|
|Urban Poverty Gap|16.3|104.9|104.7|104.4|104.1|103.9|103.6|103.4|

Source: Author’s calculations This section replicates and extends the policy experiments reported in Devarajan, Lewis and Robinson (1990)<sup>7</sup> . Our extension concerns the disaggregation of the household account, the redistributive assumptions associated with public expenditure, and the computation of the poverty implications. Three indirect tax instruments are available to the government: tariffs on final and intermediate goods and an indirect tax on domestic sales. The base data in the SAM represent a solution of the model with no government intervention i.e. all taxes set equal to zero.

The results in table 3.2 involve seven experiments and are presented in the form of ratios (in percentage) of the value taken by a variable in a given scenario to the baseline value of the same variable. The reference experiment sets the tax on domestic sales at 15.4 percent and both tariffs
be performed using the majority of computers available to users. On the other hand, the Monte Carlo simulation requires the use of the census data. Census data, depending on the country, can range from anywhere between roughly 30,000 observations to almost half a billion. Depending on a user’s computer setup, a couple million observations can really slow down the computer to a crawl. Assuming variables in double type precision, a 5 million observation dataset with 30 variables will require roughly 1.1 GB of memory. Therefore, in order to speed up the second stage operation and to be able to operate with larger datasets, a couple of modifications are implemented.

The first modification concerns importing the census data and formatting it in a more memory friendly way. Along with the main command we supply a sub-command that allows the user to import the census data for the second stage, `sae data import` . The data is imported one vector at a time and saved into a Mata format file which is used for the processing of each regressor at a time for each simulation to obtain the predicted welfare. Proceeding in this manner, the maximum number of observations that can be used in the simulation stage is increased. As shown in the examples section, this setup requires preparing the target dataset beforehand. Due to this setup, the Monte Carlo simulations are executed one vector at a time. For smaller datasets it is likely that performing all simulations in one go results in quicker execution times,<sup>27</sup> once we move on to larger census data this method provides faster execution times and allows for more efficient memory management.

The second modification, is a plugin for processing the simulations and producing the required indicators. This is only used/necessary for processing indicators. The processing is also done one simulation at a time, just like the Monte Carlo simulations. The plugin speeds up the process considerably, especially when requesting Gini coefficients.<sup>28</sup>

# *
of child ́ marriage among married women aged 20 24, with a prevalence of 54%, behind Niger (76%), the Central African Republic (68%), Chad (67%), and Bangladesh (59%).<sup>8</sup> Second, in Mali, polygyny is legal and 34% of individuals live in polygynous households, which is the second highest share globally, behind only Burkina Faso (36%). More precisely, according to the 2009 population Census, 20.2% of married people in Mali were in polygynous unions (Diamoutene, 2015). For married women, this proportion rises to 25.7%, while, for married men, it was 14.4%. Tertilt (2005) and Rossi (2019) link polygyny

> 6FAO, 2017. Dual purpose sorghum and cowpea intercropping in Mali. Available online at http://www.fao.org/agroecology/database/detail/en/c/1027957/.

> 7See CIA World Factbook, available online at https://www.cia.gov/the-world-factbook/.

> 8UNICEF global databases 2020, based on Multiple Indicator Cluster Surveys (MICS), Demographic and Health Surveys (DHS), and other national surveys.

11
has become so important in securing livelihoods that young people refer to it as ‘Vitamin Waw’” (Mann 2014, 21). For example, more than 80 percent of Mann’s survey respondents who were between the ages of 20 and 24 learned about their current jobs through ‘personal contacts’ (as opposed to ‘direct application’, ‘newspaper/internet’, or ‘combination’). For respondents aged 30 to 34, the figure was 50 percent; for respondents aged 40 to 44, it was close to 60 percent; and for respondents aged 50 and older, it was less than 30 percent (Mann 2014). The KIIs and FGDs conducted for this report confirmed the abovementioned studies’ findings. Indeed, one of the focus group participants, a woman from Madani who was not working, made a comment identical to that of one of the participants in Mann’s study: “Young people call it [ _wasta_ ] vitamin ‘W’.” The usefulness of _wasta_ appears to be region specific. For those who were raised in the capital, Khartoum, and its environs, _wasta_ is about useful ties to those who are in power in the government and/or the private sector, for example, the oil industry. Mann’s research offers several examples of Khartoum youth who became involved in clubs that were organized and funded by prominent politicians or business leaders. Through their affiliations with these clubs—rather than through school or university—these youth were able to make connections that led to jobs. This is such a well-established practice that a fifth of the respondents in the Arab Barometer survey (2018) do not consider it corruption when government officials provide _wasta_ for relatives. Mann’s study shows that for those who are from outside Khartoum,

> 16 The study by Buttorff and Welborne primarily used data from the Arab Barometer’s 2006–2007 survey. 17 Mann’s research draws on, “...18 months of ethnographic fieldwork conducted on university
of consumption are detected using a set of cleaning rules to correct quantities and prices (see Appendix Cleaning rules for food consumption data). The non‐food data set only contains values without quantities and units. First, the same cleaning rules for currencies are applied (Table A.6), followed by a set of specialized cleaning rules (see Appendix Cleaning rules for nonfood consumption data). Likewise, for durables, the same cleaning rules for currencies are applied (Table A.6), and then a set of durables‐specific cleaning rules (see Appendix Cleaning rules for durable assets).

## 4.2. Consumption aggregate using the Rapid Consumption Methodology

The nominal household consumption aggregate is the sum of four components, namely expenditures on food items, expenditures on non‐food items, the value of the consumption flow from durable goods, and housing (Deaton and Zaidi, 2002). Without a housing market functioning well enough to derive credible estimates for the cost of housing, the SHFS consumption aggregate is based on the first three components: food consumption, nonfood consumption, and consumption of durable assets.

### _Food and nonfood consumption in the Rapid Consumption Methodology_

The SHFS used the Rapid Consumption Methodology to estimate the consumption aggregate. Pape and Mistiaen (2018) provide a detailed and general exposition of the Rapid Consumption Methodology including an ex‐post assessment of the methodology. The methodology is based on dividing food and nonfood consumption items in one core and several optional modules. With each household assigned the core module and one optional module, this methodology reduces the time spent on enumerating the consumption modules. Deriving the consumption aggregate with this methodology is a two‐step process. First, core and optional modules are constructed. Core items are selected based on their importance for consumption. The remaining items are partitioned into optional modules. Optional modules are assigned 17