rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>types of surveys such as consumption and labor force surveys (e.g., Douidich et al. 2016). Newhouse</mark> et al. (2014 <mark>) and Dang et al. (2019) offer recent summaries of previous imputation studies that highlight the main advantages, debate different approaches, and provide useful insights about welfare imputation practices. More recently, Dang and Verme (2019) is the first study to propose the use of a cross-survey imputation method in the context of the Syrian refugees living in Jordan, using survey and administrative data provided by the</mark> United Nations High Commissioner for Refugees <mark>(UNHCR). Their findings suggest that in the absence of actual household consumption survey data, their cross-survey imputation method can provide a secondbest alternative to address the data challenge.</mark> <mark>In this paper, we make several new contributions to the nascent literature on poverty measurement for refugees. First, we apply the cross-survey imputation method to a sample of refugees based in Chad. Since this country is in Sub-Saharan Africa and one of the poorest countries in the world, its differences from Jordan in both geographical location and income levels represent a new setting to apply and further validate this imputation method. Second, we exploit a richer and more diverse set of data than the Jordan study, which includes r</mark> egistration data, census-type targeting data, and a household consumption survey. We also rigorously examine the imputation method against different poverty lines, including the food poverty line, the national poverty line, the international poverty line, and various other simulated values. Finally <mark>, in addition to the poverty imputation exercise, we test the performance of the proposed method for targeting purposes. In particular, we compare the targeting performance of our method with the targeting method currently used in Chad to administer cash assistance to refugees as well as with the global experience.</mark> <mark>The estimation results
</sup>

# **_4.2 Correlates of (in)formality_**

What types of firms choose to be formal? With a single cross section, it is only possible to carry out an exploratory analysis to identify variables that are corelated with formality since inference about causal effects is not possible. Table 7 reports the results of probit regressions where the dependent variable is a dummy that takes the value 1 if the firm is legally registered either at the national registrar general or country-level revenue authority, or at the local authority or municipality, or both. The table reports average marginal effects. The baseline model is reported in column 1: log productivity, having electricity and having a young woman owner (relative to having an older woman owner) are positively correlated with formality, with positive coefficients significant at the 1 percent level. In the baseline specification, having a young woman owner is associated with a 22 percentage points higher probability of being formal, relative to having an older woman owner. These variables remain significant when additional covariates are included in the model. Being in an urban area is positively associated with formality but only at 10 percent level of significance. The second column adds having customers abroad as a covariate. Its coefficient is positive but again statistically significant only at the 10 percent level, and, as can be seen in

> 17 The results discussed in the text were obtained using the sampling weights in the RIA data set. The IPW approach was also used on the data without sampling weights and the results were overall similar. The main differences were that the formal-informal gap for log productivity was reduced less, to 0.38, which is significantly different from zero only at the 10 percent level. Also, the final ATET for employment of 2.31 is a bit lower than the gap in the unweighted data. A further robustness check was carried out by using a propensity score matching approach rather than inverse probability weighting, using the “teffects psmatch” command of Stata. The results are
over the life cycle, again repeated for both subsamples. As discussed in the previous section, the results are broadly robust to this alternate definition.

Taken together, Tables X, XI and XII support our belief that none of these main results are being driven by any particulars of our baseline measure of family management.

_Including Alternative Legal Forms of Firm Registration._ Our baseline sample includes only private limited liability companies. These account for a large majority of 68.8% of firms in our sample, with the remainder comprising of joint stock companies (12.7%), limited liability partnerships (4.2%), sole propiertarships (4.1%), public companies (1.4%), general partnerships (0.13%) and other (8.7%). In Table XIV, we assess how our main results change when we consider all legal forms. In column (2), we see the relationship between employment, trust and family management is robust to including all legal firms (compared to the baseline result repeated in column 1). In column (8), we find the management mode decisions of large firms are less affected by trust for this alternative sample in the same way as in the baseline specification (repeated in column 7).

_Including Alternative Size Cutoffs._ which we prefer since the coverage for smaller firms is inconsistent across countries in the BvD database.<sup>27</sup> One question is whether our results wash out once we consider the full sample of firm sizes. In columns (3) and (9) of Table XIV we repeat our two main specifications without applying this firm size cutoff. Our results are statistically and economically unchanged in this wider sample.

An additional concern is whether we incorrectly measure the location of multi-plant establishments. For example, a multi-plant establishment in the US might produce across many locations but register in Delaware. The level of trust in Delaware would therefore be an inaccurate measure of the
011|2,011|
|R-squared|0.775|0.777|0.576|0.579|
|Mean of dependent variable|0.233|0.233|0.070|0.070|
|Sd. of dependent variable|(0.416)|(0.416)|(0.238)|(0.238)|
|Mean of right to bequeath land|0.724|0.724|0.724|0.724|
|Sd. of right to bequeath land|(0.447)|(0.447)|(0.447)|(0.447)|
|Mean of right to sell land|0.656|0.656|0.656|0.656|
|Sd. of right to sell land|(0.475)|(0.475)|(0.475)|(0.475)|

_Notes_ : IHPS data are used and the sample includes owned parcels only. Household fixed effects and parcel size are controlled for throughout. Parcel characteristics include soil type (sandy, between sandy and clay, or clay), land slope (flat, slight slope, moderate slope, or hilly), and whether land is in swamp/wetland. Robust standard errors are clustered by district. Figures in square brackets are p-values from wild cluster bootstrap (Roodman, 2018) with Rademacher weights and 1000 replications. *** significant at 1%; significant at 5%; * significant at 10%.

18
86||-0.237|-0.68||
|Female*Years of schooling|0.021|0.7||0.018|0.59||
|Female*TEEP cohort|0.578|3.91|***|0.566|3.72|***|
|Mother’s years of schooling|-0.044|-4.9|***|-0.039|-4.22|***|
|Mother’s age|-0.006|-1.69|*|-0.005|-1.55||
|Log(consumer durables)|-0.161|-7.84|***|-0.175|-8.23|***|
|Height|0.005|1.77|*|0.005|1.73|*|
|Number of students per classroom in high school|0.002|1.48||0.001|1.24||
|Constant|-7.100|-3.19|***|-6.988|-3.02|***|
|Number of observations|2,962|||2,761|||
|Pseudo R-squared|0.237|||0.238|||

Source: TEEP Tracking Survey. Notes: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

43
#  **_Premise 1: Higher levels of OGD make government more accountable._**

Under this premise, OGD provides an evidentiary basis through which citizens can be better informed on how government is performing. In particular, for this information to be effective—for it to be a type of “clear” transparency as noted by Fox (2007)—two elements would need to be present. In line with Khemani’s earlier findings,<sup>106</sup> this would first include information on sectoral performance, both in terms of provision of services (e.g. number of teachers, number of vaccinations, etc.) as well as service delivery outcomes (e.g. completion rates, infection rates, etc.). Second, in order for the efficiency of such service provision and outcomes to be determined, transparent information is needed both on how much was budgeted for these services as well as how much was actually spent on these services (e.g. execution rate). Only by having both types of data can “demand-side” consumers—that is, citizens, civil society organizations, researchers, journalists, and other “infomediaries”—make informed judgements as to whether the government is adequately executing its mandate and meeting its responsibilities.

However, it should be noted that transparent access to sectoral and budgetary data is just one element; in order to hold government officials accountable, citizens need mechanisms to exercise their “voice”, either formally or informally. In line with the earlier work of Fox (2007), such voice channels could provide a degree of “answerability” and “enforceability”, which is crucial to ensuring a principle-agent element in the citizen-state interface. Formal citizen voice channels would include electoral mechanisms in the form of free and fair elections, which are held at regular intervals in line with legal provisions, and with no _de jure_ or _de facto_ impediments to participation. Other non-electoral, or social accountability mechanisms, could include, _inter
: Household surveys (Table 1). _Note_ : Urban apartment sector. Space is in square meters. Comparable data for Lithuania are not available.

Without detailed rental sector surveys and with inappropriate census methodologies for tracking rentals, it is hard to capture the quality effects with data. However, results have been compiled for the quality dimension of crowding (Table 5). As expected, the Armenian and Russian urban rental stocks are the most crowded, followed closely by the residual public rental sector in Romania.<sup>40</sup> Tenants in Poland have a considerably larger supply of space and rooms than their counterparts elsewhere in the region. Owner-occupied units, in turn, are considerably more spacious than rental units.

A more detailed empirical analysis of quality issues affecting the rental housing stock is beyond the scope of this study. However, the situation in Lithuania, which was recently evaluated more closely, may be considered quite representative of the region.<sup>41</sup> The private rental stock in Lithuania falls into three levels of quality: (i) few hundred units in newly constructed private rental housing for the upscale market, serving mostly foreigners; (ii) larger market for prewar housing stock in inner cities, mostly restituted buildings; and (iii) “retail” micro rental market in the privatized postwar apartment stock. A majority of private renters live in the postwar stock, mostly in small one-room (studio) apartments. According to the Vilnius city government, the remaining 4,000 public rental buildings, all of them in the postwar apartment stock, exhibit “slumification” tendencies.<sup>42</sup>

# **Supply Changes – Stock Conversions and New Construction**

# **_Existing Stock Conversions_**

# _Conversion Mechanisms_

At the inception of reform in 1989 there was almost no formal, private rental housing supply in the studied countries. Private rental supply in transition countries was thereafter established

> 40  World Bank (2004a) reports evidence on the positive
to a terrestrial backbone network affects the Internet speed available at the location. The provision of 3G or 4G coverage also enables access to high-speed Internet through mobile phones, while 2G coverage only provides for limited Internet browsing and applications.

The analysis concludes that mobile broadband coverage is associated with greater household consumption and with reduced poverty status. These relationships are robust after the study controlled for household demographics and other spatial characteristics, as well as for access to other, complementary digital infrastructure such as 2G coverage or fixed broadband Internet. The welfare effects, however, do not appear to be uniform across subgroups. The positive welfare effects of 3G coverage are more evident for households in urban areas or headed by men or younger cohorts. In addition, although there is a positive correlation between proximity to fixed broadband infrastructure and welfare, this relationship becomes insignificant after one controls for other geographical characteristics, such as region fixed effects, road density, nighttime lights, or elevation above sea level.

This paper makes two distinct contributions to the literature. First, it is among the first papers to measure the effects of mobile broadband and fixed broadband infrastructure on household welfare. Most studies focus on cellphone (2G) access, while much less is known about the welfare impact of fixed or mobile broadband Internet. The studies that do look at the Internet tend to focus on fixed broadband rather than mobile broadband Internet (Hjort and Poulsen 2019).<sup>4</sup> This distinction is important because mobile broadband Internet is the main technology used to access the Internet in Senegal. By considering both technologies, the study sheds light on the infrastructure gap and the complementarities that exist between the technologies.

Second, this appears to be the only paper that relies exclusively on the rich data available from household consumption surveys to measure the welfare implications of various types of digital technologies, that is, mobile phone access, fixed broadband Internet, and mobile broadband Internet.

The analysis applies a robust methodology that can be used to assess the impact of mobile
unemployed.<sup>2</sup>

# **3.1.2 World Bank Enterprise Surveys (WBES)**

The WBES are countrywide, representative samples of businesses and firms from all sectors of activity with at least five workers. They include information on enterprises’ establishments, assets, operations, sources of funding, structure of the workforce, and type of activities. We exploit data that were collected over the 2005-2018 period for coastal nations that had survey cycles before and after the date when submarine internet was acquired. These countries are Ghana, Kenya, Mauritania, Nigeria, Senegal, and Tanzania. We limit our sample to small- and medium-size enterprises (SMEs) that employ a maximum of 99 individuals. It is also important to note that while digitalized road maps are for the period before 2014, our household and firm-level data extend beyond 2014. Using household and firm-level data that extend beyond 2014 maximizes the number of observations, but this is justified because the African road networks have not changed much in recent years.<sup>3</sup> The WBES provide information on the location of firms that we first match with the location of cities and then from which we compute both the indicator of whether the establishment’s location is connected to internet, and the distance to the closest major road. After pooling all observations together, we obtain a sample of 15,033 firms. We define internet connectivity as being within a radius of two kilometers of an optical-fiber node. Firms’ hours of operation range between 10 and 84 hours, averaging 35 hours a week. They employ, on average, 16 full-time workers, and an average of 6 female workers. More than a quarter of firms, 26.85 percent, reported that telecommunications limitations are

> 2The characteristics of individuals in the sample are summarized in Table (A2) in the Online Appendix. Skilled occupations are distributed as follows: sales (38 percent); manual labor (18 percent); agricultural employee (
obtained from the multilevel logistic model. Thus, I am confident that I did not lose substantial statistical information by dichotomizing this variable.

# **6.2 Independent Variables**

## **6.2.1 Socio-Demographic Variables**

I control for standard socio-demographic variables that can affect citizens’ acceptance of government’s right to make people pay taxes. A question probing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their income can be problematic in the context of developing economies, where individuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also affect household resources.

## **6.2.2 Experience with Paying Taxes or Fees**

It is difficult to assess just how ubiquitous taxes are in ordinary Africans’ lives. There has not been any systematic effort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states’ revenue raising capacity is generally low. On average, the tax-to-GDP ratio in Sub-Saharan Africa is around 21 percent, compared with the OECD average of about 32 percent. In Tanzania and Uganda, the total tax share drops to about 10 percent. Historical data suggests that the tax share of many European countries did not reach 15 percent of GDP until World War II when incomes were substantially higher than they are in many African countries (Fjeldstad and Rakner, 2003, 3).

The types and amount of taxes citizens pay varies both within and between countries. We do know there are taxes on agricultural crops, but the rates and processes of collection vary within countries (Kasara, 2007). User fees from electricity, water, sanitation, and other services comprise the majority of local revenue
HDDS) for Measurement of Household Food Access: Indicator Guide (v.2)_ . Washington, D.C.

- Thome, K., Taylor, E. J., & Filipski, M. (2014). _Local Economy Impacts of FISP. Discussion paper, Evaluation of the 2013/14 Farm Input Subsidy Program, Malawi_ . London.

- Tirivayi, N., Knowles, M., & Davis, B. (2016). The interaction between social protection and agriculture: A review of evidence. _Global Food Security_ , _10_ , 52–62. https://doi.org/10.1016/j.gfs.2016.08.004

- Tiwari, S., Daidone, S., Ruvalcaba, M. A., Prifti, E., Handa, S., Davis, B., ... Seidenfeld, D. (2016). Impact of cash transfer programs on food security and nutrition in sub-Saharan Africa: A cross-country analysis. _Global Food Security_ , _11_ , 72–83. <u>https://doi.org/10.1016/j.gfs.2016.07.009</u>

- Vaitla, B., Coates, J., Glaeser, L., Hillbruner, C., Biswal, P., & Maxwell, D. (2017). The measurement of household food security: Correlation and latent variable analysis of alternative indicators in a large multi-country dataset. _Food Policy_ , _68_ , 193-205

34
Figure 11. Average annual GDP growth per capita by country group <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>RR Non-RR Rest of the World RR<br>-1<br>Pre boom Boom Post boom<br>%<br><!-- End of picture text --> Note: RR= Sub-Saharan African resource-rich countries listed in IMF (2012), nonRR= rest of Sub-Saharan African countries, RR RoW= rest of the world resource-rich countries listed in IMF (2012) Source: author’s calculations based on World Bank data.

This result is striking. While the boom period represented a prosperous period of growth for resource-rich Africa, the subsequent slump in growth rates underscore how resourcedependent this was and how little economic transformation had occurred, to develop other sources of growth during the boom. The same period saw a slowing of growth across the entire region, reflecting the overall higher level of regional resource-dependency compared to before the boom.

# **What about the rest of the economy?**

It is important to consider not just total GDP growth performance but also how nonresource GDP has fared. This is because the resource sector is such a large driver of growth in most of the region, and particularly during the boom, that a focus on GDP growth numbers is largely reflecting the price-driven increase in resource sector activity in this period. To appreciate the broader growth experience, and particularly the sustainability of economic growth we need to look beyond the resource sector.

Figure 12 separates the average GDP growth rate (left panel) and compares it to the nonresource GDP boom – using sectoral GDP data that subtracts GDP attributable to resource extraction. It shows that resource-rich Africa saw a boom in both GDP and non-resource GDP during the period of higher prices - lying above the non-resource-rich countries for much of this period. During the boom, by the end of
11 and after the quake, so that it is unlikely that the population living in the treated towns as of 1981 had changed significantly and in a different direction compared to towns lying just outside the border.

Finally, we explore whether the quake had different effects on labor market outcomes of adults in treated and control towns. We exploit a retrospective question on employment status 5 years before the census, as of 1976, to compute town-cohort specific changes in employment rates between 1976 and 1981. Figure (5) reports the cohort-specific estimated difference between treated and control towns along with the 95 percent confidence bands. None of the difference turns out to be statistically significant at conventional levels and the null that they are jointly zero cannot be rejected. Failure to detect statistically significant differences further supports the assumption that the population from sampled towns belonging to the quake region was not affected by quake-related interventions and that the direct effects of the quake, if any, were similar in the two groups of towns.

# 4 Short and long-run effects of avoiding military service.

In this section we explore the short- and long-run effects of avoiding military service. We start by documenting how exemption status granted while in high school or shortly after affected a set of relevant current and subsequent outcomes. We then turn to difference-in-difference estimates of the effects of exemption status before formal eligibility for service.

## 4.1 Preliminary evidence

Table (1) reports unconditional comparisons of schooling and other labor market outcomes of youths from towns on the inner border of the quake region to those of similar youths from towns just outside the region in the aftermath of the quake. Throughout the paper the observational unit is the cell town and year of birth and all estimates are weighted by the number of individuals in the cell. We focus separately on two groups of cohorts, youths born between 1963 and 1965 and between 1959 and