rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. Conclusions**

In conclusion, this research examined the gender implications of tax policies, firm characteristics, and variations in tax declaration behaviors and payments for businesses in Addis Ababa, Ethiopia. Using data collected from 2,320 taxpayers for 2011 and 2012, the study uncovered valuable insights into the dynamics of the tax system.

The findings suggest that while the tax policy itself is not explicitly biased, gender disparities in tax declaration and enforcement processes have emerged. These disparities could contribute to an unequal tax burden, which could have significant and far-reaching consequences for the affected parties.

One particularly notable finding is that women report significantly lower sales, with a substantial 27-percentage point difference in reported sales compared to their male counterparts. Similarly, women report reduced business income tax liabilities, with a 21-percentage point difference compared to men-owned businesses. These differences in reporting appear to be due to truthful representation of their businesses, as women-owned businesses do not engage in zero or negative tax liability reporting, which is a well-known tax avoidance behavior in the African context.

Despite reporting lower tax liabilities, there are no discernible gender differences in terms of business income tax payments. This is because menowned businesses have higher expenses and costs of goods and services, which reduce the difference in net profit between male and female-owned businesses. As a result, women end up paying nearly identical amounts in taxes as men, despite initially submitting lower tax liabilities.

Although we do not claim that this difference we see is solely due to gender (as there are factors correlated with gender that we have not controlled for or addressed in our methodology), we believe that the finding that women shoulder a higher burden is still noteworthy, with far-reaching policy implications. This suggests potential disparities in business expenses and challenges faced by women entrepreneurs that warrant further investigation and policy 28
To answer this question, we collected the available administrative data for all the programs studied and from other similar programs in the region.<sup>33</sup> Table 8 shows the targeting costs per screened households (e.g., for each interview, for PMT, regardless of whether the household will be selected). It also shows the targeting cost per final beneficiary. The cost per beneficiary is higher, since not all households screened will become beneficiaries. In addition, mechanically, programs with a lower selection rate (including a smaller share of total, screened households) will have higher targeting costs per beneficiary as a share of program budget. This would be an additional argument for high selection thresholds. The last column of Table 8 indicates the targeting costs as a share of total transfers made to households by the program.

The results from each program must be taken with caution since costs are not recorded systematically and in a consistent manner. Besides, targeting costs are not always easily separable from other program costs (e.g., staff time). Finally, targeting operations often serve other purposes, such as registration of households for future payments (which needs to be conducted even for non-targeted transfers). This means that eliminating targeting is unlikely to eliminate the full amount indicated in Table 8. Despite these caveats, Table 8 shows consistent figures and findings.

The cost to screen each household is found to be similar across methods: around $5-$7 per household, whether PMT, HEA or CBT is employed. It is higher in Chad ($9.5) and lower in Senegal 2 ($3.2), and also lower when self-targeting is employed. This indicates that a reasonable, conservative rule of thumb in the Sahelian context, consistent with Beaugé et al. (2018), would be $5 to $10 per screen household. Given that selection rates vary greatly across programs, these costs result in a cost per beneficiary household ranging from $
scale in financial transactions: while shoe-leather costs may not vary with the amount involved in a financial transaction aimed at protecting assets from inflation, the benefits do. This would remain the case even if there were no barriers to entry into certain asset markets. (ii) But these barriers to entry are widespread, and restrict access to some assets that are particularly effective in avoiding the inflation tax, to larger depositors. Neri presents revealing evidence about the incidence of ownership of overnight deposits and credit cards across the distribution of income. (iii) Tighter

> the rise in inequality.

> 34 Easterly and Fischer’s results suggest that an increase in inflation from zero “to hyperinflation” would decrease the income share of the poorest quintile by 1.7 percentage points (from an average of 6.2%). They also study opinion poll responses from some 32.000 households across thirty-eight countries, and find that “inflation aversion” declines with self-reported socio-economic status, even after controlling for education.
sup> As another indicator of firm quality, we perform an Abowd, Kramarz, Margolis (AKM) style decomposition of wages (Abowd, Kramarz and Margolis, 1999) and calculate firm wage 19The monthly labor force status and wage indicators originate from the Hungarian Social Security Administration. The demographic indicators originate from the National Health Insurance Fund Administration of Hungary. The firm-specific indicators originate from the National Tax and Customs Administration of Hungary.

> 20We focus on firms with double-entry bookkeeping as most quality measures (e.g. TFP) are only available for them. In addition to that we exclude from the benchmark analysis seven firms which have more than 10,000 workers—very large and unique firms in the Hungarian context—to avoid outliers driving the results. Appendix Table B2 shows that our results are robust to the inclusion of the largest firms and single-entry bookkeeping firms—the estimated employment effects and heterogeneity results by firm quality are stronger under the extended

21 We use the _prodest_ Stata module of Rovigatti and Mollisi (2020) and apply the estimation procedure of Wooldridge (2009). We regress the logarithm of value added (gross revenue minus the cost of goods sold) on year effects, the logarithm of firm size (variable input) and the logarithm of subscribed capital (state variable), while using material and service costs as proxies for unobserved productivity. The TFP is the residual estimated from this regression. Finally, we take the firm-specific average of the TFP indicator over 2010-2015.

15
there is one additional margin to test for the impact of the program: households with larger extended families may be more affected by SR.

They use the Colombian Household Surveys 1986-2005 (before and after reform); it is a bi-yearly data from 1986 to 1996 and yearly from 1997 to 2005. It is representative of 10 cities and provides repeat cross-sections. They find a 2-5% increase in informality post-SR. The effects of the SR are strongest in households with no vulnerable members and attenuated in household with vulnerable members and also in household without potential extended beneficiaries. They also estimate their results in a panel of poor individuals matched twice with similar results.

Identification of these effects of SR comes from time variation across cities as to when a means test of eligibility for SR was implemented. In their panel estimates, the variation is also across eligibility to SR or not. However, with their data (biannual data for household surveys or two waves for the panel of poor individuals), it appears there is only one round of data difference in the introduction of the program across cities. Thus, most of the identification comes from pre and post comparisons. This makes the results particularly susceptible of being influenced by other changes affecting labor markets that were occurring at the same time, e.g. a broader package of labor market reforms were introduced to make labor markets more flexible and there were external shocks that affected labor demand. The simultaneity of these changes makes it difficult to ascribe all of the effects to the SR program.

The papers reviewed, both in Mexico and outside of Mexico, suggest that social programs that put incentives on informal work can have an effect on the level of informality. However, the papers are not conclusive and there is room to improve the measurement of the effects. The next section describes the data and the identification strategy proposed for this task.

# **4. Expected Effects of** **_SP_**

_SP_ provides health benefits to households provided that the individuals are not in formal
The macroeconomics literature on gender inequality and development has grown rapidly since the early 1990s, when cross-country income data for a large sample of developed and developing countries became available. The papers reviewed in this section identify the effect of gender inequality on both development and economic growth using cross-country (or, sometimes, cross-country and cross-time) variations in gender inequality. The following subsections summarize findings from a large body of literature that aims to identify the effect of gender inequality on educational attainment and economic outcomes for both development and economic growth.

# **_2.1. Inequality in educational attainment_**

The seminal paper by Mankiw et al. (1992) highlights the cross-country correlation between human capital (measured by educational attainment), income, and growth. As a natural follow-up to this analysis, subsequent papers have attempted to separate the effect of male as compared with female educational attainment and to provide evidence on the relationship between gender inequality in schooling and economic growth.

Hill and King’s (1993) study is among the first to estimate the correlation between female education and the gender gap in primary and secondary enrollment on GDP per capita between 1975 and 1985. The estimated correlation is statistically and economically significant. Controlling for capital stock, the level of female education and the size of the labor force, countries with a female/male enrollment ratio lower than 0.75 have up to 25 percent lower GNP compared with similar countries with a lower level of gender inequality.

Knowles et al. (2002) is one of the few cross-country studies to estimate theory-based specifications. Following Mankiw et al. (1992), the authors augment the Solow model to incorporate female and male human capital separately and to estimate the effect of these types of human capital and of the gender gap on the steady-state level of income using long averages between 1960 and 1990. Their findings indicate a negative correlation between the size of the gap and income: controlling for male educational attainment, a lower level of female educational attainment is associated with lower steady-state
_Page 3_ _PENSIONREFORMiNLATINAMERiCa_ While not every country has the same degree of problems with regard to contributions, benefits and coverage, the preponderance of these concerns suggests that these problems are inherent in pay-asyou-go, defined benefit plans.

# B. ESSENTIAL **DATA FOR** ASSESSING **THE** HEALTH OF PENSION SYSTEMS

In most countries worldwide, pension reform typically occurs in the context of a fiscal crisis or impending fiscal crisis affecting the solvency of the pension system. This occurs in part because of the political resistance to changing current pension systems. At the same time, surpluses in a largely unfunded system may not be an indicator of financial health. A second reason pension reform often comes too late is the absence of good data on the fiscal implications of the _status quo_ as well as of policy options to reform the system. This problem is especially critical in developing countries. Basic and reliable information on the number of contributors and pensioners is essential to forecast future revenues and payouts but is not always readily part of the data that is brought to policy makers' attention. Appropriate models are needed to estimate future fiscal implications.

## Demographic and Economic Data

Detailed demographic information is basic and essential. Data on present population, mortality rates, fertility rates and immigration are among some of the factors required to understand the size and pace of the aging population in relation to younger cohorts. The age structure of the population is important to understand dependency ratios. Dependency ratios are defined as the number of younger people to support the population of older persons who are retired.

Labor market conditions are another factor whose trends are important to guide pension policy. The number of employed individuals determines the portion of the younger population that is contributing (in pay-as-you-go systems) toward retirement payments to older individuals. Contractions in ernployment will affect contributions. So, too, will the definitions in the pension plans as to those who are covered. Military and civil servants are among some groups that may be exempted and have their own pension plans. The retirement
changes are estimated. Fourth, by assembling detailed tariff data varying by country-product-year for the period 1997-2017, we consider the trade policy shocks not only in terms of country-product eligibility for duty-free treatment but also in terms of the magnitude and dynamics of the preference margin offered to beneficiary and non-beneficiary countries.

Our study also complements the handful of studies that assessed the impact of the special provisions in AGOA for textile and apparel articles, but, in contrast to our study, they focused only on early impacts, up to 2008 at most. Two studies anticipate some of the concerns we raise.<sup>10</sup> Edwards and Lawrence (2010) assess whether AGOA’s apparel provisions led to a sustained effect using a theoretical model. They find that MFA quotas on China helped AGOA countries, Lesotho in particular, to increase exports of low value-added apparel products to the US. But this increase was not associated with sustained benefits since the ROO - which allow countries to import fabric from any origin - did not encourage significant local value-addition.<sup>11</sup> Rotunno et al. (2013) also emphasize the role of simplified ROO (single transformation) under AGOA in fostering rapid growth of African LDCs’ apparel exports to the US in the first preference years. Importantly, they show this rapid growth did not represent true export success but instead reflected those countries’ imports of apparel from China being transshipped duty-free to the US without any value addition, a pattern which stopped after the MFA ended.<sup>12</sup> The restrictiveness of ROO may affect not only the immediate impact of trade preferences but also for its sustainability when preferences decline. Our firm-level analysis for Mauritius in Section 6 will demonstrate that the ROO liberalization led to a significant increase in its apparel exports to the US. In the long-run, restrictive ROO may limit preference utilization but, by

> 10 Mattoo et al. (2003) also anticipated difficulties for African countries’ apparel exports under AGOA after the MFA end
# **1 Introduction**

Despite evidence that women’s political preferences differ from those of men, women are less likely to participate in political and social discussions on Twitter and other social media. Given evidence that social media can affect offline behavior (Alatas et al., 2019, Bianchi et al., 2023, M ̈uller and Schwarz, 2020), women’s lack of participation on Twitter may have important ramifications for policy, especially around gender. Following recent evidence that in-person social support (in the form of self-help groups) matters for women’s political participation (Prillaman, 2023), we hypothesize that women can form similarly supportive communities online.

We test this hypothesis using data from Twitter. We collected data on 451 hashtags, which we categorize as relating to various (non mutually exclusive) topics: social, gender, racial, LGBTQ, religion, youth, education, economic, health, COVID, climate, political, security, entertainment and lifestyle, and the Middle East and Northern Africa (MENA). We use data on interactions (retweets, quote tweets, and likes) between users to construct two key network statistics that proxy the support women tweeters might benefit from: clustering (the fraction of closed triangles, i.e. how often are two of someone’s friends themselves friends?) and degree (the number of connections).

We find that women are more likely to participate and more likely to be influential when their networks are larger and more clustered. This finding supports the potential role of mutual support in bolstering women’s participation in important debates.

2
was carried out for a single year, and full details of the calculation of the scores used for ranking were not provided, this result does not speak strongly to the superiority of private sector ownership versus state ownership.

A further level of analysis can be provided for LESCO and KE the two utilities whose performance was examined in greater detail. In particular, the T&D losses in excess of an efficiency benchmark of 5 percent and collection losses for these two utilities are quantified in monetary terms and expressed as a percentage of utility revenues, allowing their financial magnitude to be compared. Data for LESCO are limited to only six years (2009-2015), however throughout this period the

> 38 Performance Evaluation Report of all Distribution Companies for the Year 2015-2016. NEPRA.

37
in percent of all production workers)|-4.421***|0.860|-3.561|74.53***|34,803|0.005|
|Firms with formal training to employees (%)|0.244***|-0.179***|0.065|0.259***|68,631|0.063|
|Firms with a female top manager (%)|-0.0105***|-0.0516***|-0.0621|0.137***|69,038|0.014|
|Foreign ownership (%)|2.414***|5.464***|7.878|5.220***|68,478|0.008|
|Public ownership (%)|-0.121**|-0.0668|-0.1878|0.705***|68,512|0.000|
|Sole proprietorship(%)|-0.0332***|0.134***|0.1008|0.376***|69,075|0.007|
||*** p<0.01, *|* p<0.05, * p<|0.1||||

_Source:_ Calculations based on World Bank Enterprise Surveys (2018).

_Note:_ The regression equation is specified as follows: x� �α�p�β�FCS �p�γ�δ� �ε� where x� indicates the characteristics of firms _i_ ; p� is a binary indicator for pioneering; FCS is a binary indicator for operating in a fragile state, and δ� are sector fixed effects. Since the specification includes an interaction term with two binary indicators, coefficient β captures the average
(0.010)|(0.088)|(0.016)|
|Product Innovation|-0.0214|||||
||(0.121)|||||
|Process Innovation|<br>0.0811|||||
||(0.122)|||||
|ICT Index||0.0922***|0.1057***|||
|||(0.015)|(0.015)|||
|RD+/L||<br>0.0076*|<br>0.0166***|||
|||(0.004)|(0.004)|||
|Log(age)||<br>-0.0133|<br>0.0097|0.1893|-0.0522**|
|||(0.015)|(0.015)|(0.120)|(0.022)|
|Informal sector competition||<br>-0.0151|<br>0.0550***|<br>-0.0950|<br>-0.0064|
|||(0.020)|(0.021)|(0.077)|(0.011)|
|Education obstacle||<br>0.0213|<br>-0.0169|||
|||(0.024)|(0.024)|||
|Exporter||0.0124|-0.0178|0.1197|-0.0426|
|||(0.025)|(0.025)|(0.249)|(0.036)|
|Demand increasing||0.0348*|0.0298|||
|
Policy Research Working Paper 8960

# **Abstract**

The paper revisits the traditional transport-agricultural growth nexus. Connectivity is particularly challenging for small island developing states, such as the Comoros, where domestic markets are limited and transport and transaction costs tend to be high. Using household survey data from Comoros, the paper shows that significant untapped economic potential exists in the domestic market. The analysis shows that better transport connectivity increases crop production and market sales. Accessibility to Moroni, the primary market in the country, and inter-island connectivity are of particular importance. Not only transport infrastructure, but also services are important. Access to intermediary services, such as cooperatives and collectors, is also found to have a positive impact on crop production and market sales.

This paper is a product of the Transport and Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
## **b. Fully owned infrastructure SOEs**

<!-- Start of picture text --> 5<br>0<br>-5<br>-10<br>-15<br>Power Airlines and airports Roads Railways Average<br>Return on average assets Adjusted return on average assets<br>Percent of average assets<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18. Partially privatized SOEs refer to firms that are majority-owned or controlled but not fully owned by the state.

Figure 3 highlights the differences between fully owned and partially privatized infrastructure SOEs.<sup>7</sup> Among partially privatized SOEs, ROAA is highest in the road and power sectors (figure 3, panel a). In fact, the performance of roads is much better for partially privatized SOEs than for fully owned ones (figure 3, panel b). Power SOEs exhibit no major differences between the two groups. Overall, performance is worse for fully owned SOEs, especially after adjusting for subsidies.

# **_4.3 Subsidies_**

Subsidies of infrastructure SOEs average about 12 percent of operating income (figure 4). They are highest for the worst-performing sectors. Power companies receive only 4 percent of their income in subsidies; in contrast, the roads sector receives about 47 percent. In the airlines and airports sector, about 10 percent of the sample receives transfers, which are used mostly to finance improvements and repairs at airports, especially small regional airports in Eastern Europe, which are expensive to maintain and upgrade.

> 7 Partially privatized SOEs are firms that are majority owned or controlled but not fully owned by the state. Full ownership is defined as state control of at least 99.5 percent of the share capital.

10