rafmacalaba/data-use-mentions-extended huggingface dataset:
conclusions drawn by Leeson (2008) is that low media freedom is associated with low political participation and poor political knowledge. And countries with less-regulated and more privately-owned media have larger politically active and knowledgeable population.

Furthermore, existing media research pays particular attention to the power of media to encourage political accountability. For instance, Voltmer (2009) discusses how the role played by media in both recently established and older democracies can be an indicator of a democracy’s strength, especially with regards to encouraging government accountability and public awareness. Results of this research demonstrate that although media is often criticized for having close ties to governments, it can serve as an effective watchdog for political accountability and good governance. Similarly, Francken et al. (2009) study the relationship between media and capture of public expenditures on education, using 2002-2003 survey data on the level of reach of the public spending on education to local schools. The study demonstrates that media – conditional on the population characteristics and their levels of literacy – can have an effect on the extent of corruption in a country. In turn, Olper and Swinnen (2013) focus on the influence of mass media competition on agricultural policy. These researchers find a significant relationship between the increase in the media diffusion and the changes in agricultural policies in both developed and less developed economies. Furthermore, the authors demonstrate that the larger the share of an economy’s population that is affected by a certain policy, the larger is the mass media coverage of this policy.

Notably, substantial research explores the theme of media ownership and free press. Particular attention to this subject is drawn by the research of Djankov et al. (2001), investigating the relationship between media ownership and press freedom focusing on political, economic and social outcomes. The authors find that countries where more media is state-owned have less free press, lower level of governance and market development as well as fewer political rights for citizens and substantially lower outcomes in the areas of education and health. Moreover, the authors argue
||(0.010)|(0.015)|(0.027)|(0.020)|
|---|---|---|---|---|
|Number of observations|19,979|8,068|5,469|6,442|
|R2|||||
|Adjusted R2|0.448|0.454|0.366|0.394|

_Note:_ *** p<0.01, ** p<0.05, * p<0.1. Marginal effects at mean are presented.

# _5.3 Alternative way of modeling the influence of income_

Model (1) includes the income variable, which is an important explanatory factor for ICT adoption in general according to the literature. Individuals’ income level could affect their decisions to purchase an Internet-enabled phone, and afford mobile data packages to access internet, which jointly have an impact on the decision of mobile internet adoption. To further unpack the impact of income on affordability, the analysis tries an alternative specification with inclusion of a few affordability dummies while other covariates remain the same. To understand how the prices of mobile data packages affect the adoption of mobile internet, the analysis considers a country’s average cost per 1 gigabyte (GB) of mobile data, and constructs a dummy variable that equals 1 if the country’s average cost per 1 GB of mobile data is more than 2 percent of the individual’s monthly income. Moreover, since the affordability of an access device is often cited as an obstacle to internet usage, the analysis uses data about a country’s average cost of an internet-enabled phone from International Data Corporation (IDC) and constructs a dummy variable that equals 1 if the average phone cost is more than the individual’s monthly income. Due to collinearity concerns, the inclusion of these affordability measures precludes the incorporation of the income variable directly
regulators are essential in terms of “shaping” domestic competitive markets after services liberalization has taken place. Merely opening up a services sector would not suffice due to the many market failures from which services sectors suffer and which therefore requires additional design of regulations in order to create markets that are _effectively_ competitive. Setting this regulatory framework should ideally be done by competent regulators. Consequently, countries that are institutionally strong are most likely to be well‐placed to have these qualified regulators. In his work, Van der Marel (2016) uses rule of law as a proxy for institutions from the World Bank governance indicators database following standard practice.

In a second work, Beverelli _et al_ . (2015) take a similar stand but from the trade perspective. They argue that the institutional capacity of countries is important in order to attract foreign services suppliers into the domestic market. Most services are supplied through a foreign affiliate that carries along a so‐called proximity burden in which the foreign services provider needs to be on the domestic market. Only the best services firm will be able to detect institutionally strong countries as a sort of natural self‐selection, which in turn has an effect on downstream productivity for firms using services from those firms. In their work, the authors use the level of corruption from the World Bank governance indicators database. Interestingly, in our regressions we use the FDI restrictiveness index which could test for such channel of proximity by foreign affiliate as the former is closely associated with foreign affiliate sales.<sup>17</sup>

> 17 Note, however, that FDI is an imperfect variable for foreign affiliate sales, and so therefore FDI restrictiveness is an imprecise variable for regulations in services trade through foreign affiliates, which would not directly confirm the results of Beverelli _et al_ . (2015). Yet, since the majority of FDI takes place in services it could stand for a rough proxy variable. Note that their regulatory services variable is for one‐year only which is therefore cross‐sectional.
(0)), the relative mean deviation, and the standard deviation of logarithms. The second panel presents the growth rate in mean hourly wages ( _γAGR_ ) and the average of quantile-specific growth rates, across quantiles, denoted Mean _GIC_ .

– Table 3: Inequality measures hourly real wages (HRW) US and Brazil

||Fac|tual|US<br>Counter Factual|Fac|B<br>tual|ra<br>C|zil<br>ounter Factual|
|---|---|---|---|---|---|---|---|
||1995|2007||1995|2007|||
|Gini|0.355|0.383|0.381|0.539|0.490||0.473|
|Theil Entropy|0.205|0.260|0.258|0.536|0.457||0.444|
|Theil mean log deviation|0.218|0.254|0.250|0.511|0.411||0.383|
|Relative mean deviation|0.257|0.274|0.272|0.404|0.366||0.348|
|Standard deviation of logs|0.682|0.710|0.703|0.963|0.852||0.814|
|Growth of mean wage (_γAGR_)|0.1|73|0.101|0.0|17||-0.150|
|Mean _GIC_|0.1|27|0.063|0.1|38||-0.016|

26
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(EU) members, evidence suggests that support to agriculture is disproportionately distributed to larger farms (Moreddu 2011). This is a natural outcome when agricultural support is tied, as it often is, to production or related factors such as land (FAO, UNDP, and UNEP 2021). If inputs and outputs are subsidized in proportion to quantities used and produced, then larger and richer farmers will always receive a disproportionate share of the benefits. This can also have knock-on, policy-relevant consequences for other areas of society. For example, one study finds that female-headed farms, which tend to be smaller, are less likely to benefit from agricultural subsidies than male-headed farms (FAO 2011).

In addition to household distribution, the spatial incidence of subsidies may also be of interest to policy makers. Output subsidy distribution is often based on producing a specific crop. By setting price floors or trade restrictions on certain crops, governments are (possibly inadvertently) supporting particular regions of their countries that are more suitable for growing those crop types. This will have social, economic, and political implications that extend beyond how progressive or regressive the subsidy is at the household level. For example, if they disproportionately benefit poorer regions, even if captured by richer households within those regions, subsidies may have positive distributional impacts through regional multiplier effects. Thus, from a regional development perspective, the spatial distribution of subsidies matters.

> 1 This includes the EU-28 as a single, aggregate entity. From here on, where we talk about _countries_ , we are referring to _countries/regions_ , as the data include the EU-28.

2
:_ Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

The contribution of nonagricultural income to total household income has not increased, either for the poor or the non-poor (Figure 13). In fact, on average, households were slightly less reliant on nonagricultural incomes in 2014 than in 2009. Rural Sudan has therefore become more reliant on agricultural wage incomes (possibly a form of tenant farming) and less reliant on nonagricultural 14
expenditures (also depicted in Figure 3) – we do not observe a lower level of health expenditures for Green Card holders compared to those without.

Similarly, we also estimate the additional protective impact of the Green Card against impoverishing health expenditures to have been relatively modest. Health expenditures can have an impoverishing impact if near-poor households have to switch away from buying other necessary goods in order to cover urgent health costs. A near-poor household that covers health expenditures and therefore has to reduce spending on food would then become a poor household in terms of its living standards (O‘Donnell et al. 2004). In Turkey, national poverty measures are calculated using a consumption basket that _includes_ health spending. With health expenditures included in household nominal expenditures, a household that might be poor without health expenditures may record non-poor levels of expenditures if we include health spending.<sup>7</sup> Table 3 quantifies the impoverishing impact of health spending. We calculate this impact as the percentage of individuals who were poor _without_ taking into account health expenditures (and measured against a poverty line similarly net of health expenditures<sup>8</sup> ) but were non-poor if health expenditures are taken into account. We find that, with 0.8 percent, the impoverishing impact of health spending was modest at the onset of program expansion (2003) due to limited out-of pocked spending even in the absence of the Green Card program. We observe a mild decline in the poverty impact of health spending in Turkey between 2003 and 2008 from 0.8 to 0.3 percent, although the decline is not statistically significant when the confidence interval around the poverty headcount estimates are considered.

Lastly, we examine the incidence of catastrophic health expenditures. These are defined as the share of total expenditures dedicated to health above a certain threshold level (Xu et al.

> 7  We apply the following calculation to assess the impoverishing impact of health spending: Given published poverty rates by TURKSTAT at the national level, we derive poverty lines
concerns. We do the same for a measure of financial constraints of the firm. We control for all time invariant country specific factors (country fixed effects) and so our results are immune from endogeneity problems that plague much of the cross-country evidence on corruption issues. We go beyond and argue that if our results our indeed causal and not spuriously driven, we should find that better access to finance alleviates the harmful effects of corruption more in countries where the enforcement mechanisms for implementing laws and rules are weak. The assumption here is that weak implementation of the laws allows public officials to favor those who have the money to pay bribes, paving the way for the corruption-finance nexus in the sense discussed above. If our results are indeed picking up the effect of the corruption and finance nexus on employment growth, we can expect them to be stronger when law enforcement is weaker. However, if our results are spuriously driven, there is no reason to expect the corruption-finance relationship to vary with the quality of law enforcement. Our results easily pass this test. To further raise our confidence against endogeneity concerns, we contrast the effects of corruption with other aspects of the business environment that are correlated with corruption but there is no reason to expect their effects to depend on access to finance. These business environment elements include power outages, burdensome regulations, and enforcement quality proxied by how well the courts function.

Fifth, we use micro-level data which allow us to capture within-country variations in the level of corruption and regulatory burden. Most of the existing studies use macro-level data which cannot capture these variations. As Svensson (2003) notes, firms facing similar institutions and policies may still end up paying different amounts in bribes for the same amount of services received. This is particularly important when considering the role of financial constraints since financial constraints tend to vary significantly across firms within the country.

7
retrieve household income with a high degree of precision. However, as with any restricted sample, these surveys are usually representative only nationwide or across provinces or states.

The universal coverage of the population can be approximated through population censuses, which typically provide relevant inputs to measure several dimensions of well-being at high levels of disaggregation (for example, in Mexico, CONEVAL’s social backwardness index or the marginalization index of the National Population Council).<sup>3</sup> This greater geographical detail, however, comes at the cost of a lack of robustness in the information on household incomes. Censuses are not designed to collect comprehensive data on income. They provide an incomplete picture of household monetary circumstances, which, at least for the purposes of this analysis, represents a main weakness.

To address the dilemma between precision and geographical detail and, hence, to make the relevant dataset available for empirical analysis, the Elbers et al. (2003) small area estimation technique is used to impute household per capita income from surveys to corresponding households in censuses. This is accomplished by predicting, from an income model in the survey, the parameters and distribution of errors, which are then used to simulate the income distribution in the census dataset from which to compute poverty and inequality indicators.

Two critical steps are necessary to make this model work properly. The first step consists of considering the household survey as a random sample of the total population found in the sample frame of the census. The second step is to identify a set of potential explanatory variables that are common between the survey and the census and that satisfy a conceptual and statistical equality criterion. This means, respectively, that these variables should measure the same phenomenon in both datasets and that the respective distributions of the variables are statistically indistinguishable, that is, the sample mean is statistically equal to the population mean. The variables that satisfy this criterion are then candidate regressors in the modeling of household per capita income in the survey dataset.

> 3These indexes,
has been neutralized at post-fiscal income stages, which indicates (among other things) that the incorporation of older-age dependents into the household is likely negotiated by the household’s adults. Both the starting positions of the negotiating members and the outcomes are likely affected too by the nature of the contribution as well as the gender of the older-age dependent. A single policy, therefore, may create impacts at different decision or negotiation points, and intrahousehold bargaining models that completely describe interactions and the incentives that constrain those interactions at each of those points will lack generality.

# 5.2 Jordan

To create household types in the Jordan Engendered CEQ assessment, Rodriguez, Wai-Poi, and Woodham (2022) explored whether households could be grouped into commonly occurring categories that exhibit demographic or organizational differences salient in the allocation of care and social reproduction responsibilities on the one hand and the incidence of taxes and spending on the other.<sup>33</sup>

> A relatively small number of categories are needed for tractable analysis. Each category should neither represent too many people so as not to obscure differences between them nor too few people so as to lack representativeness of the entire population. The statistical tradeoff between large groups with precisely estimated average characteristics and smaller groups that would likely show greater deviation from a national average but exhibit less precise estimates should also be taken into account.

22
# **6 Firm capabilities in Mozambique**

Using the 2018 Enterprise Survey data, the analysis suggests that the firm capabilities in Mozambique are weak. When compared with other countries with Enterprise Surveys tracking management practices<sup>10</sup> , Mozambique’s overall management practices are 16 percent below firms in Kenya, and 11 percent below those in Gambia, after accounting for size of the firm (Figure 5). The gap to more developed Latin American countries is even larger. These results go in line with the assessment done in 2014 in the manufacturing sector for Mozambique (Lemos and Scur, 2014).

Mozambique’s average management practices mask still significant differences in the distribution of enterprises (Figure 6). Compared to countries with higher income and management practices (like Colombia), where the proportion of firms with “bad” management practices is relatively low, Mozambique has a high concentration of enterprises with weak management practices.

**Figure 5. Average management practices scores, controlled by number of employees**

<!-- Start of picture text --> Colombia N=989<br>Peru N=1000<br>Paraguay N=364<br>Ecuador N=361<br>Argentina N=982<br>Uruguay N=340<br>Suriname N=230<br>Kenya N=1000<br>Bolivia N=363<br>Gambia N=151<br>Mozambique N=650<br>Lao N=332<br>0 0.5 1 1.5 2 2.5 3<br>Mozambique LAC SSA EAP<br><!-- End of picture text --> Source: Enterprise Survey data, authors

> 10 This is preliminary and will be updated with more countries when compared with the World Management Survey.

18
4.5 Weather events**

Variables describing the occurrence of weather events, such droughts, floods and storms allow to measure the impact of weather shocks and natural disasters. A recent study for Vietnam uses monitored data on riverine floods and cyclones to identify natural disasters and assess their welfare impacts (Thomas et al., 2010). Notwithstanding the limitations of self‐reported data, which often suffers from subjective judgements and is highly correlated with welfare outcomes, other studies from Vietnam use self‐reported data to assess the welfare impacts (Arouri et al., 2015; Bui et al., 2014).

17
2020). A large strand of literature shows that men and women have different preferences (Andersen et al., 2013), also during the COVID-19 time (Fan, Orhun and Turjeman, 2020). For example, women have been found to be more risk averse (Dohmen et al., 2011; Eckel and Grossman, 2008), more pro-social (Eckel and Grossman, 1998), more patient (Dittrich and Leipold, 2014), and more conducive to family welfare (Ashraf, 2009; Rubalcava, Teruel and Thomas, 2009). During the COVID19 crisis, women are more risk-averse, worry more about their and others’ health, and take more preventive actions (Fan, Orhun and Turjeman, 2020). In Kenya, women predominate in agriculture but their contributions are mostly unpaid. Moreover, they are less predominant in formal employment and women’s property rights are often through men (Ellis et al., 2007). It is important to understand the gender differences in the COVID-19 impact for the future policy design attempting at alleviating gender inequality as well as for post-COVID relief interventions.

This study pools the first five waves from a nationally representative survey conducted 1
22 First, the extent to which parties convey a credible programmatic policy stance to voters depends on their ability to impose penalties on members who stray from the policy and to choose candidates who adhere to it, making the policy stance credible. Nondemocracies that seek to mobilize support with ideological and not merely coercive appeals might also exhibit a programmatic stance; such a stance would not be credible, however, if leaders did not give scope to members of the ruling party to act collectively in the event that the leader reneged on this commitment, since in this case the leader could not make credible commitments to party members to pursue the programmatic goal. The Database of Political Institutions has variables recording whether the three largest government parties in a country and the largest opposition party can be recognized as espousing economic policies that are left (more redistributionist), right, or centrist; or whether instead the party has not stance on these issues and, instead, seems to exist to further the ambitions of the party leader. The coding is based on descriptions of party policy stances in source documents (political handbooks and yearbooks).

The analysis below captures the degree to which parties have programmatic stances in two ways. One is a variable that equals the fraction of all parties (up to four in the database) that have a programmatic stance. The other focuses more directly on the largest government and opposition parties: while the capacity to make credible commitments to pursue policies in the broad public interest are a function of whether a party is programmatic, the incentives to do so are strongest when both incumbent and challenger parties are programmatic. The analysis therefore estimates the effects of a dummy variable, which equals one if both the largest government and largest opposition party are programmatic and zero otherwise.