rafmacalaba/data-use-mentions-extended huggingface dataset:
stolen by rebel groups. Another reason is that arms imports may lead to political repression. Blanton (1999) has indeed shown that arms imports are linked to human right violations in developing countries. A third reason is that arms purchases by governments may crowd out more development-friendly state spending. One example here is Fan et al. (2018), who showed that across countries military expenditures do crowd out health expenditures.

The recent experience of Mozambique provides an illustrative anecdote on the link between newfound wealth and arms imports, and its potential consequences. After discovering – enormous amounts of natural gas off its coast in 2009 estimated at the time to be – worth around 50 times its GDP in net present value terms the country experienced an unprecedented growth spurt and a foreign investment boom (Toews and Vezina, 2017). It ended sharply five years later when a debt crisis hit. It turned out the government had borrowed too much, and illegally, to purchase military ships. This transaction worth hundreds of millions of dollars was made possible by loans worth around 2 billion dollars, or 12.5% of Mozambique’s GDP. It inadvertently revealed cracks in the political system and that was enough to derail the entire economy (see Hill and Nhamire (2018)). Other cases of oil-for-arms episodes include Sudan’s oil for guns deal with China (Herbst, 2008), Azerbaijan wasting the wealth from its post-2010 energy boom on vast amount of weapons (Altstadt and Menon, 2016), or the 1990s Angolagate scandal, which saw Angola’s oil revenues exchanged against weapons from ex-Soviet countries (Economist, 2008). Figure 1 shows that, in those four countries, arms imports did surge after giant discoveries, in 2009-2011 in Mozambique, in 2003 in Sudan, in 2010 in Azerbaijan, and in 1996-1998 in Angola.

To examine the relationship between oil and gas discoveries and arms imports I combine data on oil and gas discoveries from Horn (2011), updated by the World Bank, with arms imports data
residential parcels that currently lack title (65% on state and 41% on customary land) and are willing to pay a median of about US$ 35 (ZMK 350 overall; ZMK 500 for state and ZMK 200 for customary land) to acquire formal document. Interestingly, levels of tenure insecurity for residential parcels are higher on customary (42% insecure and 26% very insecure) than on state land (37% insecure and 19% very insecure).

Panel B of table 4 shows that 91% of the 3,670 agricultural parcels are under customary tenure. While 30% of these can be transferred via bequest (with little difference between tenure regimes), 23% can be sold (31% on state and 22% on customary land). With 15% (43% on state and 12% on customary land), the share of parcels with any rights documentation remains limited. For these, the share with informal documents (6.2% ‘chief certificates’ and 4% other papers) exceeds that of those with formal documents (1.8% with complete and 3% with incomplete title). Even on state land 57% of agricultural parcels lack documentation entirely and 19% and 17%, have complete or incomplete title. More than 98% of parcels (81% on state and 100% on customary land) lack valid title and 47% of these would like to acquire title, pointing to high levels of unmet demand, especially on customary land. With mean and median willingness to pay of ZMK 791 and ZMK 350, implementing a systematic land titling program on a cost recovery basis could be an option in several locations if an appropriate legal and regulatory framework was in place.

Data on tenure insecurity suggest that, with tenure of some 43% or 25% of agricultural parcels perceived to be insecure or very insecure, levels of tenure insecurity on agricultural land are higher on state (52%) rather than customary (42%) land but that
1994 population census. In the second stage, twelve households per PSUs were extracted with a systematic extraction method.<sup>7</sup> For the 2007 NLSS and the quarterly LFSs, 1,848 PSUs (1,124 urban and 724 rural) of approximately 600 household each were selected from the initial set of PSUs based on the 2004 population sample. In a second stage, PSUs were first subdivided into the Secondary Sample Units (SSUs) representing about 50 households each and six of the twelve SSUs were then extracted randomly from each PSUs. In a third stage, households to interview were selected with systematic extraction from each SSU.

# **5. Empirical model**

To build a model that is both flexible and parsimonious and in line with the samples stratification, we divide the data into urban and rural areas, and model each area separately. The labor markets, sector decomposition, returns to education, living conditions, the availability and use of infrastructure and the price of transport tend to be different between urban and rural areas which may be expected to lead to different models. In what follows, it can be seen for example that employment in agriculture matters in rural (both statistically and economically highly significant) but not in urban Morocco. The reverse holds

> 7 This method implies first defining the pace of extraction by dividing the number of households in each PSU by the number of households needed for the sample. Then a number is randomly extracted between 1 and the pace number. And finally household are extracted starting with the extracted number and using the pace number to identify each successive household.

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cost are the same between genders, no wives enter the labor market.** When we equalize these parameters between husbands and wives, the wives’ leisure now has more weight in household utility and income is less valued. Moreover, as the labor market is disadvantaged for wives relative to husbands, the result of the household optimization is that both spouses only enjoy leisure and home production in the nonparticipation state.

# **8. Limitations and Future Research**

**This paper constructs and estimates a structural model to understand gender differentials in the labor market among marginalized Roma** . The model is based on the household search model of Guler, Guvenen and Violante (GGV) (2012), and extended to allow for (i) full-time versus part-time work, (ii) time inputs into home production, and (iii) household utility defined over labor income, home production and leisure. The model is estimated using data from the Regional Roma survey, which was collected in six Western Balkans countries. To the best of our knowledge, this is the first time that a model with these features has been used to study gender dynamics among the Roma population. However, many challenges remain to fully comprehend the behavior of the Roma population in the labor market.

**First, because of cultural reasons but also due to lack of economic resources, extended Roma families in many cases live together and share economic and time resources.** Therefore, exploring how extended families make choices in the labor market seems promising in the Roma population context; the current specification has 16 possible couples’ labor market states, multiplied by husband’s

32
sectoral energy consumptions and emissions are drawn from Pereira et al. (2010), as summarized in **Figure 2** , where both combustion emissions from energy-related activities and noncombustion emissions from industrial-processes are presented. For each sector, the combustion emissions are linked to the use of energy inputs through emissions factors, and this study assumes that the non-combustion emissions are proportional to the output level.<sup>12</sup> The data of emissions from deforestation, on the other hand, are from de Gouvello et al. ( 2010) and the Ministry of Science and Technology (2010). In addition, the elasticity data come from existing studies including Paltsev et al. (2005) and Timilsina and Shrestha (2007). The details are presented in **Table A1** in the Appendix.

Finally, this study assumes that Brazil‟s domestic demand for and supply of tradable goods only have minimal impacts on the world price levels. This allows us to treat the world prices of tradable goods as exogenous to the model. While to consider the depletion of the global crude oil reserves, the model assumes that the world crude oil price, which is exogenous, will grow at the rate of 10% per period (i.e., every five years) based on the median projection for the future crude oil price presented by EIA (2010), other world real price levels are assumed to increase at the rate of 5% per period (every five years) to capture the indirect effects of rising oil prices and other scarcity rents.

> 12 Since this study uses a hypothetical carbon tax to represent the burden of cutting industrial and household emissions, the dashed lines in Figure 1 show that when there is an emissions reduction target, industry and household have to pay for their combustion and non-combustion emissions.

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? Does increased trade incentivize stationary banditry by conflict actors, thus leading to a reduction in violent extractions? Or is there an entirely different reason that violence decreases when roads are more passable? We provide preliminary responses to such questions. The positive impacts of road projects are significant for one-sided violence using UCDP data, and for violence against civilians and battles using ACLED data. These findings suggest that not all forms of violence are evenly affected by road projects. Second, higher mining revenues affect differently the impact of roads on violence across the types of minerals. The completion of road projects when revenues from artisanal gold mining increase tends to increase the likelihood of violence while the opposite is true for large cobalt mining activities.

# **References**

**Ali, Rubaba, Alvaro Federico Barra, Claudia N Berg, Richard Damania, John D Nash, and Jason Russ** , “Infrastructure in Conflict-Prone and Fragile Environments: Evidence from the Democratic Republic of Congo,” World Bank Policy Research Working Paper 7273, The World Bank, Washington, DC 2015.

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# APPENDIX 2: C isa1 **with Other stlmates**

In a recent paper, Vandergaag and Vijverberg (1987 - hereafter VV) also estimate earnings equations based on the CILSS data. The comparison of their major results with ours in Table A2.1 shows differences in the magnitude and/or significance of almost all estimated coefficients. VV's conclusions hence differ both quantitatively and qualitatively from ours, and it seems worthwhile to investigate where the differences come from and which findings present the proper empirical basis for policy applications.

The differences are due to at least four factors: (1) Different dependent variable: VV use ln (hourly wage), while we use ln (monthly wage) for reasons explained in section 7 of this paper. Moreover, in this particular data set, our experience indicates that the hours and days worked daca are of questionable quality (for example, the transformation from monthly to hourly wages "creates" outliers). More important, the 'days" question asked about actual days worked in the reference week while the "hours" questions asked for typical hours. In our opinion, one should not mix actual and typical time use information to explain actual earnings.
# 1. **INTRODUCTION**

The analysis of the evolution of poverty and welfare over time in Nigeria has been difficult, because there are empirical or data considerations which make the task daunting. The social and economic data base to analyze this issue is either non-existent or missing in many Sub-Saharan countries, including Nigeria. However under the National Survey of Integrated Households (NISH) two National Consumer Surveys (NCS) were conducted, one in 1985 and the other 1992, which provide the minimum requirement towards undertaking such an analysis. It does not, however, enable us to address the detailed issues of household and individual welfare.

This paper looks at the household welfare determined by the capabilities and constraints of its members for income generation and access to publicly financed services. In the rural areas, since most households are engaged in agriculture, access to land, credit and other productive inputs, and their human capital, determine their capacity for income generation. For the nonagricultural population, particularly for urban dwellers, labor market operation and their human capital are decisive in determining their status. Human capital is generally dependent on the availability and access to a critical mass of health, education and nutritional services. Since each one of them complements the effect of the other, a critical minimum of each is required. In Nigeria, though there is a large private sector, the provision of social services, particularly education and health have been primarily in the domain of the public sector. The level and quality of provision of social services, especially for the poor, has eroded over time.

In terms of health indicators, noticeable improvements have been made since the 1970s, but a lot still needs to be done. Nigeria's health indicators such as morbidity and mortality rates, are high. The crude death rate (CDR) had declined by half from 27 per 1000 in 1965 to 15 per 1000 in 1993. The infant mortality rate (IMR) in 1993 was estimated at 83 per 1000 live births, down from 140 per
<!-- Start of picture text --> Panel (A): Public Preschools x<br>4 © Public Preschool Density & Average 2| °° 2<br>.<br>c rs) 4 0<br>a ° “|43<br>Soo&2<br>S<br>w 2|. . of 3 ¢<br>ao ® :o8©°8é 8 8 24<br>o: fi s<br>0 | 12<br><t<br>1990 1993 1996 2000 20032005 2008 2011 2014<br>Year<br>Panel (B): Private Preschools ><br>>, 40 2 Private Preschool Density *& Average 6 2<br>a 30 5 3<br>: 20 ° g<br>e ° 3 2|* 2<br>ae o<br>wo 8 =<br>% (10 3.0<br>=v<br>0<br>23<br>1990 1993 1996 2000 20032005 2008 2011 2014<br>Year<br><!-- End of picture text -->
# **<mark>4. Methodology</mark>**

<mark>Next, in an attempt to overcome the biases in the online survey, as observed in section 3.2, we construct weights based on the propensity scores to examine whether applying such weights would make online surveys nationally representative. For this, we estimated a logit model of the following type:</mark> <mark>Here, the dependent variable, y</mark> i <mark>, takes the value 1 for the online survey data (for a specific wave) and 0 for the Internet using population of the GMD. The exception is Sri Lanka where Internet usage is unavailable in the GMD, and where the dependent variable takes the value 0 for all GMD observations. X</mark> i <mark>is a vector that denotes the set of control variables that are associated with an individual’s participation in the online survey. We estimate this model for each crosssection of the online survey wave with the GMD. Equation (1) describes the possible factors that influence a person who has access to the internet and was exposed to the Internet survey, as to whether they take up the survey or not.</mark> <mark>Variables in X</mark> i <mark>include gender, age categories, education, rural versus urban residence, sector of work pre-COVID, household size categories, a variable set of assets specific to each country, and region fixed effects.</mark><sup>20</sup> <mark>We also include many interactions of these demographic variables between themselves, and with region.</mark><sup>21</sup> <mark>These demographic factors are highly correlated with an</mark>

> 20 <mark>The regional aggregations are different across countries, based on data availability. In some cases, the lowest administrative unit level where the data is available differs across
what we might credibly expect given South Asia’s development level. This is in contrast to male employment rates, which are as high as those in other developing countries, as shown in Figure 1.

Figure 1 raises an important question: _why_ are female employment rates in South Asia—particularly in Sri Lanka, Pakistan, India, and Bangladesh—far below average given their GDP per capita? This paper works towards answering this question by:

- (i) Charting trends and patterns in female employment rates across countries over time to investigate whether they have always been low.

- (ii) Using a variety of censuses, labor force surveys, and household surveys, which is complicated because definitions of “employed work” differ (explained below), to look at female employment in South Asia from a broad perspective.

> 2Majlesi (2016) uses the Mexican Family Life Survey, which asks the household decision maker respondents questions related to twelve different categories. Hence, they construct a direct measure of decision-making power for each spouse within households.

2
# Each of these children and their households face the choice of allocating

his/her time among five activities:

- going to school,

- working in the labor market outside the home,

- working in an enterprise or farm belonging to the household,

- helping with home care tasks, and

- leisure.

In the CILSS, there is direct information on the first four activities. Since the personal development needs of the child are best served by school attendance, it behooves to look first at the extent to which this time allocation is not chosen by the child, or more likely, for the child by the parents. By age 7, almost 50% of children in C6te d'Ivoire are not enrolled in school yet (Table 6 and Figure 1). The figure decreases to 32% at age 9, then rises steadily to the 40% range at ages 12-14. As of age 15, there is a sudden jump to above 60%. This corresponds to the end of the primary schooling cycle, at which point many children end their school careers. This calls for distinguishing in the analysis (as we have done so far), children in the 7-14 age range from adolescents in the 15-17 age range.

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ment assignment. In the analysis, we conduct tests to verify that treatment effects are not sensitive to Phase 1 treatment status—for example, by restricting the analysis to workers who were not in the Phase 1 sample. Second, we do not include a pure Control condition in Phase 2. Consequently, we cannot directly compare the Non-private condition with the Control condition. Since this comparison is not core to our predictions, our chosen design maximizes our ability to test our key hypothesis. In addition, we leverage our cross-randomized design to test for confounds below.

# **4.4 Auxiliary tests: Privacy, self-control and piece-rate variation**

We supplement our design with three additional sources of variation. We provide an overview here, and explain each test in more detail in Section 8. First, towards the end of the experiment, we undertake placebo tests to examine workers’ willingness to share information about their savings behavior with their network members. We again offer to send publicity SMS messages, but which advertise information about _past_ savings behavior under blocked accounts, where funds would already have been spent—obviating the scope for transfer requests against the savings. This enables us to examine the value of privacy on its own, when redistributive pressure is not a relevant force. A high willingness to share such information would suggest that privacy concerns are unlikely to drive low take-up of Non-private accounts. In addition, we supplement our analysis with several pieces of data collected through surveys, in order to better understand the mechanisms underlying our results.

Second, self-control problems in consumption could increase the value of blocked savings accounts, but this channel could not alone explain differences in Private and Non-private take-up. However, to more fully examine the relevance of self-control, we test a core prediction of time inconsistency models. Among workers who take up blocked accounts, we surprise them with the option to opt out of the direct deposit for the current paycycle only. We randomly vary whether the option is provided four days before the payday or on the
School and Vocational High School, which fall into our category of secondary education (see Section 2.3).

Formally, we define exposure to robots for plant _f_ , _ETRf,t_ , as: where: In (2), _LRi,ti,t_ 0<sup>isthenumberofindustrialrobotsshippedtoindustry</sup><sup>_i_(measuredat2-</sup> digit ISIC code) in year _t_ , divided by the number of workers in industry _i_ (in thousands). To minimize potential endogeneity concerns, as in Acemoglu and Restrepo (2017) we fix the denominator of (2) to the 2006 number of industry workers.<sup>8</sup>

# **2.2 Robots Data**

Data on imports of industrial robots are obtained from the IFR. Industrial robots are defined by ISO 8373:2012 as _an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes, which can be either fixed in place or mobile for use in industrial automation applications._ The IFR collects data from each national robotics association. Since almost all robot suppliers are members of national associations, the dataset includes virtually all robots used worldwide. An advantage of the data is that the IFR has a common protocol to count robots, so that it ensures consistency across countries and years. Information is available for each country, 2-digit industry and year.

Figure 1 shows the total number of robots shipped to the Indonesian manufacturing sector, from 2006 to 2015. Total robots’ counts from IFR are consistent with Comtrade. However, unlike IFR data, Comtrade does not provide an industry breakdown. Imports in Indonesia were roughly constant up to 2009, with approximately 350 units used every

> 8 We use 2006 as the base year to be consistent with the share of secondary education workers, which is available for the 2006 census year.

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satisfies first-order condition _dπ/dY_ = 0 and [ _p_ ( _A_<sup>_E_</sup> ) + _p_<sup>_′_</sup> ( _A_<sup>_E_</sup> ) _.E_ ] _d_ (1 + _θ_ ) = 1. In practice, POS usage acts as an extra surveillance tool to monitor tax compliance, which enhances government’s evasion detection ability _d_ . As _d_ increases, the chance of getting caught, _p_ (which is a function of _d_ ), rises. Given the convexity of _Ep_ ( _A_<sup>_E_</sup> ), _Y_<sup>_∗_</sup> will be rising in _d_ -and therefore in _p_ .

In other words, when POS device is used properly and quality audit is in place, we can also expect the magnitude of evasion _E_ also shrinks. Therefore, it reduces firms’ taxevading behavior and drives the optimum reported revenue _Y_<sup>_∗_</sup> upward—manifested in overall higher tax receipts.

# **4 Empirical Strategy**

We evaluate the impact of giving POS installations to hotels and restaurants by running balanced panel difference-in-difference (DiD) specification for each of the POS types covering a 48-month window throughout 2019 – 2022 (West Manggarai) and a 60-month one – during 2018 2022 (Gorontalo). Hotels and restaurants which received any of the POS in or before month-of-year _t_ belong to the treatment group, while the rest belong to the control group. By design, each recipient hotel and restaurant were given only one type of POS, therefore the observed impact of different types of POS on hotel/restaurant tax payment were not likely to overlap with each other.

Following the nature of our data on West Manggarai, treatment time varies among restaurants within the treatment group as POS distribution came in waves. An exception applies for the case of tapping box in the districts’ data where all restaurants in the treatment group received