rafmacalaba/data-use-mentions-extended huggingface dataset:
autocratic governments, length of regime or regime type tenure, and democratic system (such as presidential versus parliamentarian).

For this exercise I find it useful to start with a rather coarse classification of government types and population groups, to make a preliminary investigation of what factors are likely to mainly drive their interests in each case. I also distinguish between fuel types and focus on gasoline and kerosene which may seem as rather “opposite” in terms of political backing and interest in terms of constituencies interested in low prices: gasoline is presumably, on the whole, preferred by households in the middle class, and kerosene by households with lower average incomes (in the lower class by my classification). Classes are defined in a stylized way.<sup>3</sup> By middle class I will

> 2 Car stock data have, been subject to serious empirical scrutiny in previous research. See e.g. Dargay (1991; 2001; 2002), Fridstrøm (1998);  Lescaroux and Rech (2008), Medlock and Soligo (2002) focusing on high-income countries; and Dargay, Gately and Sommer (2007) and Storchmann (2005) where also lower-income countries are included.

> 3 I do not discuss separately an upper class. In a democracy, this would be non-consequential given that it is very small as compared to the two other classes. In an autocracy it could be more consequential. My approach here is then simply (for such cases) to lump it with the middle class by assuming that households in both classes consume, and are concerned with the price of, gasoline.

2
Without reference to Beck and Webb (2003), Erbas and Sayers (2006) focus their research on the link between non-life insurance development and institutional quality. Based on experimental findings by other authors, they argue that institutional quality is a good indicator of uninsurable uncertainty, which is detrimental to insurance demand. This is because a country’s institutional framework determines the reliability of norms, which in turn determine the subjective evaluation of economic risks, for example the risk that a legitimate insurance claim will be denied without recourse. With 1994-2003 data from 70 countries and the World Bank Governance indicators, they find that institutional quality, and the levels of transparency and uncertainty it entails, explains insurability (which they measure as insurance penetration) better than income level for non-life insurance, and suggest that “an examination of life insurance penetration across countries along the lines of the present paper is left for future research.” This is the point of departure for the present paper, which establishes correlations of the different drivers of insurance market development discussed in the literature with insurance penetration for both life and non-life insurance based on a data set that is much more extensive in terms of countries and years of observation.

# **3. Data**

We use 4 main sets of cross-country data in our analysis: income per capita, insurance market data, national governance indices and financial market development measures. All the data are available yearly and most of the variables span roughly 180 countries for a period of 20 years. Two of the three measures of financial market development are more limited in terms of geographical and time coverage, as described below. Summary statistics are presented in Table 1. Online Appendix Table 1 shows summary statistics for low and lower-middle income countries and higher-middle and high income countries separately.

<u>Income.</u> The income measure used is annual GDP per capita provided by the World Bank for 194 countries for the period of 1996-2016. The values are in U.S. dollars of 2011 in PPP terms
to GDP. The impact is stronger in countries with greater trade openness and capital account openness. These findings are robust to the use of alternative informality measures and various indicators for financial development.

Confidence in a causal effect of financial development supplies policy makers with the information needed to direct policy action. It suggests that efforts to broaden access to the financial system can help reduce informal activity. This is likely to be particularly helpful when combined with other policies.

12 See the Appendix for details regarding calibrating the model of Elgin and Oztunali (2012) to survey-based estimates of self-employment.

13
# **3. Method and data**

Our quantitative analysis is based on MAMS, a recursive dynamic computable general equilibrium (CGE) model, and a (linked) microsimulation model. Taking as reference a baseline scenario for 2004-2015 that assumes continuation of major economic trends, simulations are conducted to analyze different aspects of MDG achievement. MAMS innovatively incorporates an MDG module that links specific MDG interventions to MDG achievements. The relatively detailed treatment of government activities in MAMS makes this link possible. Specifically, MAMS has a relatively detailed treatment of (1) government and private sector activities in MDG-related areas, in education extended to include higher levels and (2) MDG outcomes as a function of relevant services (provided by the government and private sectors) and other determinants. A sequential ―top-down‖ approach is followed to link the labor market results of MAMS to the microsimulation model, which is used to quantify the evolution of poverty and inequality. For a detailed description of MAMS and the microsimulation models, see Lofgren et al. (2012) and Vos and Sanchez (2010), respectively. In this section, we describe the Yemen data used for the two models.

MAMS was calibrated to a 2004 Social Accounting Matrix (SAM) and other data for Yemen.<sup>7</sup> The main sources of information for the construction of the Yemeni 2004 SAM were the supply and use tables for the same year, complemented by data on the balance of payments and government finance statistics as well as the 2005 HBS. Table 3.1 shows the accounts in the SAM, which determine the size (i.e., disaggregation) of the model.

> 7 See Pacheco (2009) for details on the construction of this SAM.

15
Fig. 10. Firms’ responses during the COVID-19 crisis (% of firms) <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Permanently Temporarily Sales decrease Policy support Start/increase Start/increase Start/increase<br>closed closed (%) online sales delivery remote work<br>MENA LMI UMI<br><!-- End of picture text --> _Source:_ the authors’ calculations based on COVID-19 Follow-up Enterprise Surveys. _Note_ : The MENA region is represented by Jordan, Lebanon and Morocco.

In the following, we aim to shed light on how firms’ outcomes and responses during the COVID-19 crisis have varied by firms’ management quality, past innovation and digitalization, as well as by ownership and trading status. We examine which firms closed temporarily or permanently, which saw larger drops in sales or employment relative to 2019, as well as which firms were more likely to receive government support. We then examine firms’ responses and adaptations, such as whether they started or increased business activity online, delivery or carryout or remote work, as well as looking at the share of online sales in total sales and the share of the workforce working remotely. Figure 10 reports some stylized facts for economies in the MENA region. Given that only three MENA countries are covered by the COVID-19 Follow-up Enterprise Surveys, the econometric analysis is based on a larger sample of more than 18,000 firms in 32 economies in MENA, Europe and Central Asia.<sup>13</sup> Figure 11 illustrates differences in predicted probability of starting (or increasing) online business activity and remote work according to different firm characteristics, while controlling for firm size and age. We find that firms’ pre-COVID-19 characteristics and performance influence both firms’ adaptability and the severity of the outcomes
theory assumes that households maximize the welfare of all household members, and that the household functions collectively and has a single, cohesive objective. Neoclassical economic theory also assumes that individuals maximize their own personal utility, including, for example, deciding whether and how to share any personal income gains or agreeing to share tax burdens. Household-based surveys do not yet systematically capture how the income-based benefits received and the income losses from tax payments are shared among individual members of the household.<sup>10</sup> Understanding how the income gains and losses generated by transfers and taxes are allocated among individual household members is necessary to understand how fiscal policies create individual impacts. A class of empirical models has attempted to capture intrahousehold resource allocations using information on exclusive or assignable expenditures; an example of the former is spending on toys exclusively for children and of the latter is expenditure on clothing for boys, men, girls, and women (Lewbel and Pendakur 2008; Dunbar and others 2013; Chiappori and others 2002; Bose-Duker and others 2021; Bargain 2022). In describing how the intrahousehold allocation of resources creates inequality within the household, or varying levels of resource poverty for different members of the same household, these models allow a description of how intrahousehold inequality changes from pre-fiscal to post-fiscal income levels.

However, three practical disadvantages of empirical models of intrahousehold allocations make it hard to apply them widely and transparently to gendered fiscal incidence analysis. First, in low-income settings, and for lowincome households in low- and middle-income settings, assignable expenditures are infrequent and account for small shares of total household expenditure. Infrequent, marginal expenditures might provide unreliable estimates of how (total) income gains and losses are shared.<sup>11</sup> Second, available evidence suggests consumption magnitudes (for individual items) are themselves often imprecisely estimated. In Mexico in 2016, for example, the coverage rate – defined as the cumulative consumption expenditure magnitude for an item covered in a household survey divided by cumulative consumption expenditure (for the same
We set the discount factor _β_ at 0.99, implying that the annual real interest rate is 4 percent. The value of the risk aversion parameter, _γc_ , is equal to 2. Following Bernard et al. (2003), we set the elasticity of substitution across product varieties, _θ_ , equal to 3.8. Following Ghironi and Melitz (2005), we set the elasticity of substitution across Home and Foreign goods, _φ_ , equal to _θ_ , and the dispersion of rm productivity _kp_ equal to 3.4. We normalize _zmin_ to 1. We set iceberg trade costs _τ_ equal to 1.7, following the estimates of trade costs reported by Anderson and van Wincoop (2004). We calibrate the xed export costs _fx_ so that the shares of exporting plants in the developed and emerging country are respectively equal to 21 percent and 18 percent, consistently with data reported in Bernard et al. (2003) for the United States and in the World Bank Enterprise Survey for Brazil.<sup>15</sup> To ensure steady-state determinacy stationarity of net foreign assets, we set the parameter _ψ_ measuring asset adjustment costs equal to 0.0025 as in Ghironi and Melitz (2005). Following Ebell and Haefke (2009), we set entry costs, _fe_ , so that regulation costs amount to 5.2 months of per capita output. To pin down the rm exit rate _δ_ , we target the portion of worker separation due to rm exit equal to 30 percent in the United States and to 37 percent in Brazil: these values fall within the range of estimates reported by Haltiwanger et al. (2008). Empirical evidence indicates that informal rms are less productive than formal ones. We normalize the productivity parameter in the informal sector to unity and we assume that the productivity in the formal sector is 30 percent higher than in the informal sector.<sup>16</sup> Regarding the parameters specic to the search and matching framework, the gross replacement
improvement as was the case in education. Information on the distribution of performance based on all available past changes and based on the information conditioned on characteristics relevant for the different police districts can help inform those negotiations.

For example, homicide data may be available for all police districts in a state (as was the case for Minas Gerais). The target that may be negotiated between the principal and the agent may be for improvement in homicides at the state level, but if the target is to be met, it will be up to individual police districts to improve the outcomes in each one of their districts. It is often the case that a sector will set targets not only at a high administrative level but also for sub state administrative units. Setting the same state-level target for each sub state administrative unit implies greater or less demands on performance improvement, given that the police districts are starting from different initial conditions. Comparing the targets to some of the changes that have taken place in the recent past may help provide some check on the reasonableness of the performance improvements which are being sought.

**Figure 6  – Unconditional relative performance on homicide reduction from 2000-2007, by Police Regions**

<!-- Start of picture text --> Trend 2000/2008<br>Barbacena<br>Trend 2004/2008 100<br>Vespasiano Belo Horizonte<br>Target 2008/2009<br>80<br>Unaí Contagem<br>60<br>Uberlândia 40 Curvelo<br>20<br>Uberaba 0 Divinópolis<br>Teófilo Otoni Governador Valadares<br>Patos de Minas Ipatinga<br>Montes Claros Juiz de Fora<br>Lavras<br><!-- End of picture text --> Source: Author’s calculations using data from Peixoto et al (2010) Figure 6 represents a simple way to present comparison for many police districts simultaneously. This type of figure is useful when the number of districts is limited, but would (obviously)
enabling framework for an expansion in mode 4‐trade. What matters of course at the end of the day is that actions are taken to implement the reforms that have been identified by governments as priorities.

In many cases implementation will involve a need for investments: in training, in infrastructure, data and information systems, etc. The multilateral aid for trade initiative that was launched at the 2005 WTO ministerial in Hong Kong provides a mechanism through which low‐income developing country governments can obtain financial and technical assistance to implement regulatory improvements. To date most aid for trade assistance has focused on infrastructure investments and trade facilitation – not on regulation or service sector policies, but there is no reason why aid for trade would not be provided for services policy reforms is these are identified as priorities by developing country governments (Hoekman and Mattoo, 2007).

# **_Dealing with data gaps and improving transparency_**

The types of cooperation discussed above should extend to – and benefit from – a concerted effort to enhance transparency of policies and address data lacunae. Statistics on services trade and investment flows are generally very limited, greatly constraining the scope for analysis of the impediments, determinants and impacts of such flows. For most countries services data are highly aggregated, often not available on an origin‐destination (bilateral) basis, and generally reported on a less timely basis than are data on trade in goods. Data on applied policies are not reported to or by the WTO on a comprehensive basis as a result of the positive list approach to scheduling in the GATS. As noted above, few WTO members have fully complied with notification requirements for scheduled sectors. In principle, this is a task that should be undertaken by and through the WTO. Ideally a final Doha package would include a commitment by WTO members to report on applied policies in scheduled sectors and give the WTO secretariat the mandate to collect and report data on all applied policies, whether scheduled or not. But independent of whether this can be achieved, better data
objective of this paper is to analyze the simultaneous impact of technological change and globalization on the demand for different types of skills and aggregate employment indicators in a developing country context. We focus our analysis on Indonesia over the period 2005-2015, when ICT use increased markedly and there was a large shift in exports, from the commodity boom between 2005 and 2010 to a period of declining exports between 2011 and 2015.<sup>2</sup> The commodity boom started in the early 2000s and led to an increase in the export shares of mining and mineral products at the expense of manufactured products (World Bank, 2016). The end of the commodity boom around 2011 increased the participation of manufactures in total exports. Although our interest is on goods trade and technology and their impact on labor markets, we recognize that trade in services, such as tourism and e-commerce, can affect the demand for skills depending on the skill intensity of these sub-sectors.

In our empirical analysis, we compare annual changes in the demand for different types of skills across provinces and industries, and in aggregate labor market indicators at the province level, with changes in the industry value of goods exports and changes in a province measure of technology use. We perform our analysis for different demographic groups –high- and loweducated workers, men and women. We apply an instrumental variable strategy to account for the

> 2 ICT adoption is still low in Indonesia, but measures such as cellphone use, internet, and computer access exhibited a threefold increase over the analyzed period. See Section 4 for more details.

2
reclassified in industry) and 200 countries (Gaulier and Zignago, 2010). The information on whether firms export and GVC participation is obtained from the Enterprise Survey, while the information on bilateral trade flows varies across industries. Our results confirm the key standard finding of gravity equations, namely the evidence on the negative association between trade flows and geographic distance (Table 6). This negative association is stronger for MENA than peer countries in Eastern Europe and Central Asia (Pal et al., 2022). Our estimates also show that innovative firms and GVC participants (as opposed to exporters that do not import) tend to trade more than other firms.

# **IV. Firm performance during the COVID-19 crisis**

The COVID-19 crisis disrupted economic activity across the globe. The spread of the pandemic forced governments to impose strict containment measures, generating international supply and demand shocks across many countries. A growing literature has documented severe impacts of the COVID19 crisis on firms as disruptions in production and depressed demand resulted in revenue losses, business closures, mass layoffs and liquidity shortages (Adams-Prassl et al., 2020; Bartik et al., 2020; Humphries et al., 2020; Revoltella et al., 2020). While global value chains remained so far quite resilient, it is an open question whether COVID-19 will have a long-term impact on international trade, global value chains and their organization.

Numerous studies have also pointed to differences across enterprises, with smaller firms being especially hard-hit (Apedo-Amah et al., 2020; Bartik et al., 2020). Building on the large literature emphasizing the importance of management practices for firm performance (including during crises), recent work has in turn also documented that better managed firms experienced smaller drops in sales and were more likely to implement changes in the organization of labor, and in particular to use remote work more intensely (Schivardi et al., 2021; Cette et al., 2020; Giorcelli, 2019; Bartz-Zuccala et al., 2018; Bloom et
# **vaccinated.**

This hypothesis is underpinned by descriptive surveys in low- and middle-income countries that show people are more likely to be accepting of COVID-19 vaccines if they trust it has benefits. In addition, the nascent experimental literature on this topic (almost exclusively in high-income countries) suggests that in some instances information treatments can lead to people being more accepting of COVID-19 vaccines. Prior to field work commencing, when this hypothesis was pre-registered, we were agnostic on which specific types of information about the benefits from COVID-19 vaccines would lead to greater willingness to get vaccinated.

The third and fourth hypotheses build on the previous two hypotheses by inferring how the impact of information about the benefits of COVID-19 vaccines on people’s willingness to get vaccinated is likely to vary based on intra-household dynamics. These hypotheses are as follows:

**Hypothesis 3 - Respondents who receive an information treatment and have household members who are willing to be or have been vaccinated will be more (less) likely to state they are willing (unwilling) to get vaccinated.**

**Hypothesis 4 - Respondents who receive an information treatment and have a role in making household decisions will be more (less) likely to state they are willing (unwilling) to get vaccinated.**

Both hypotheses are founded on the premise that the elasticity of people’s willingness to get a COVID-19 vaccine is influenced by the attitude and behavior of others in their household. In the case of the third hypothesis, we suggest that the treatment will have a relatively smaller impact on people in households where others are unwilling to get vaccinated because their attitudes will be less malleable to outside sources of information that are 10
can construct a variable  = proportion of households in village  residing in settlement . Given this school allocation mechanism, we expect (|)  0 The validity of excluding  from equation (5) depends on the extent of intra-village mobility. If those households with a relatively strong demand for education tend to relocate to settlements already having schools or set up new settlements next to existing schools, then settlement population shares could reflect schooling preferences, rendering them inadmissible as instruments. While the establishment of new settlements is far-fetched in our context given long-standing land ownership patterns, the possibility of intra-village migration in response to school availability cannot be as easily dismissed.<sup>9</sup> However, in our analysis of village census data in the Appendix, we fail to find evidence of increased migration into settlements with newly established primary schools. This result supports our strategy of treating settlement population shares within a village as exogenous with respect to school availability.

To facilitate IV estimation and comparability across estimators we employ the linear probability model with standard errors clustered at the settlement, the lowestlevel sampling unit in the household survey.<sup>10</sup> However, for the final results in the paper we present the corresponding probit estimates as well.

# 4.2 Results

To investigate the endogeneity of school location, as just discussed, the first four specifications in Table 3 exclude distance to school. In the first column, we see that, conditional only on village fixed effects and child age, the presence of a school in the settlement increases girls’ enrollment rate by 18.6 percentage points, which is very close to the conditional gender gap in school enrollment. In other words, girls with a school in their settlement have practically the same enrollment rate as boys.

> 9Andrabi, et al. 2007, follow a related strategy of using information on village population in rural Pakistan to instrument the availability of