rafmacalaba/data-use-mentions-extended huggingface dataset:
challenges associated with firms evading taxes in Indonesia are similar to those in other middle-income countries where tax revenue to GDP has remained stubbornly low (Alm, 2019; Basri et al., 2021). This study is particularly valuable for revenue authorities in these settings that face sizable challenges in accurately estimating levels of tax evasion and identifying non-compliant taxpayers. The results show that substantially more revenue could be collected if the government increased compliance by registered firms.<sup>2</sup> This study may also assist revenue authorities with their compliance activities by identifying the dimensions where the most significant heterogeneity exists in tax evasion between firms. For example, the higher rates of tax evasion among non-exporting firms suggest that many firms may perceive that the revenue authority lacks third-party information about economic activities that do not cross international borders. Furthermore, the notably higher rates of tax evasion by firms that see tax administration as a major obstacle to their business activities provide suggestive evidence that efforts to simplify the tax system may lead to increases in compliance.

This study contributes to two strands of the existing literature about tax evasion by firms and methods for surveying sensitive topics. Regarding research about tax evasion by firms, we make three main contributions. First, we focus on measuring levels of tax evasion in a middle-income country in a novel way compared to previous studies that have typically relied on tax administrative data for a subset of firms (e.g., see Pomeranz, 2015; Carrillo et al., 2017; Basri et al., 2021; Waseem, 2022; Waseem, 2023; Artavanis et al., 2016)<sup>3</sup> or survey questions that either directly ask about compliance or focus on tax morale (both of which are likely to underestimate tax evasion) (e.g., see Ali et al., 2014; Luttmer and Singhal, 2014; Hoy, 2022). Secondly, our focus is on the tax compliance behavior of almost exclu-

> 2However efforts by the authorities to
strategy is to use mobile phone data and combine the two types of targeting. On average, given the existing budget available for this type of activity, the cost- eective strategy using mobile phone data performs over 50% better compared to the next best strategy that only relies on incidence in the previous year.

My empirical design accounts for confounders correlated with movement. Using rainfall proxies and month xed eects, I control for seasons and holidays, which drive a large amount of migration in Senegal. I also test that it is not an unobservable correlated with both migration and malaria driving the results, but instead the combination of movement and the malaria levels at origins and destinations. Additional checks show that the relationship between imported incidence and malaria incidence is not driven by some other relationship between origins and destinations as well as to ensure that the relationship holds only for malaria and not for other health conditions. I also test the impact of future imported incidence on malaria incidence in the current month and nd no relationship.

This paper builds on previous health literature that has established travel as a risk factor for contracting malaria, (Montalvo and Reynal-Querol 2007, Lynch et al. 2015, Osorio, Todd, and Bradley 2004, Siri et al. 2010, Littrell et al. 2013), by estimating the size of the causal impact from an expected imported case, which makes it possible to conduct policy simulations and compare dierent targeting strategies. Tatem et al. (2009) and Le Menach et al. (2011) use three months of cell phone data to estimate the malaria importation rate to Zanzibar using a static model that does not account for seasonality due to the limited time frame of their mobile phone data. Similarly, Wesolowski, Eagle, et al. (2012), Enns and Amuasi (2013), Chang et al. (2018) and Ihantamalala et al. (2018) among others, do not incorporate seasonality in incidence and focus on identifying potential sources and sinks based on
8c: Share of various trade partners in the global machinery exports to Russia, 2001, 2005 and 2010, % total <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25 2001<br>20 2005<br>15 2010<br>10<br>5<br>0<br>Belarus Ukraine EU China<br><!-- End of picture text --> Source: World Bank staff calculations on WITS/UN COMTRADE data.

**The decline in the market share illustrates the difficulties faced by Belarus companies’ to maintain their participation in the expanding Russian market.** In the heavy truck segment, Belarus’s sales to Russia were stagnant for the second part of 2000s, despite the fact that Russian market for heavy trucks was growing on average by 11 percent per year between 2000 and 2008. Demand for more sophisticated products in Russia has increased. This demand cannot be served by Belarus more traditional product lines, and instead has been filled by imports of western-made higher quality trucks (see Table 1.9 and Figure 1.9).

12
sections have demonstrated that this method results in consumption measures quite divergent from the “benchmark” and may also significantly over-estimate the degree of inequality in the distribution. Given both its poor relative performance and onerous time imposition, this method is not a recommended choice for this study setting.

Turning to dollar costs as a way to assess the resource utilization of the different diary modules with respect to a recall survey, the exact costs involved to administer each of the questionnaires and diaries depends on a number of context-specific factors, such as the price of labor, literacy levels, costs of travel between the sample EAs, printing costs, overheads, and so on. The main drivers of cost differences, however, are relatively easy to pinpoint. They are (i) differential interviewer-days needed to complete a household, and (ii) differences in time needed to enter the data into electronic format.

Table 9 summarizes these differences and their cost implications under a set of assumptions about time to walk between households, review diaries, and implement recall modules. We are not considering a large multi-topic survey, but a short questionnaire with a complete consumption module (either by diary or recall). In the most labor-intensive set-up, for an interviewer doing personal diaries with daily visits, six households can be interviewed in 17 days. The alternatives to the personal diary are household diaries with varying visits in combination with a second enumeration area (to avoid days with no work in the enumeration area, given that the length of the diary is 14 days). For the analysis in Table 9, no distinction is made between the different types of recall questionnaires as they all yielded around four interviews per interviewer per day, which is three times less intensive than in the ‘light’ fieldwork set-up of household diaries with infrequent visits.

Once the diaries and questionnaires come back from the field, they go through several steps before being available in electronic format: administration and filing of forms, coding of items, double blind entry, reconciliation of the two entries
013|
||(0.021)|(0.021)|(0.032)|
|Dummy year 2014|0.039*|0.015|0.089***|
||(0.022)|(0.024)|(0.033)|
|Dummy year 2016|0.078***|0.025|0.088***|
||(0.023)|(0.024)|(0.032)|
|Observations|3,863|1,718|2,145|
|Pseudo R2|0.029|0.072|0.038|

This table reports the marginal effects from the logit regression of child care attendance on the instrumental variable and control variables of women. The observations in these regressions are women of children age 1-6. Heteroskedasticity-robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation at the commune level. *** p<0.01, ** p<0.05, * p<0.1. Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

34
, Illinois.

http://faculty.chicagobooth.edu/john.romalis/Research/Draft_v7.pdf Deaton, Angus. 1997. _The Analysis of Household Surveys: A Microeconometric Approach to Development Policy_ . The Johns Hopkins University Press for the World Bank: Washington, DC.

Deaton, Angus. 1989. “Rice Prices and Income Distribution in Thailand: A Non-Parametric Analysis.” _The Economic Journal_ 99(395): 1-37.

Deaton, Angus. 1988. “Quality, Quantity, and Spatial Variation of Prices.” _The American Economic Review_ 78: 418-430.

Dix-Carneiro, Rafael. 2011. “Trade Liberalization and Dynamics.” Mimeo, Princeton University, Princeton, NJ.

Durán Lima, Jose, Alfonso Finot, and Marcelo Lafleur. 2011. “Analysis of the Effects of Trade Opening on Household Welfare: An Application to Chile, 1999-2006.” Mimeo, United Nations Economic Commission for Latin America and the Caribbean (ECLAC), Santiago, Chile.

Fereira, Francisco H.G., Anna Fruttero, Phillipe Leite, and Leonardo Lucchetti. 2011. “Rising Food Prices and Household Welfare.” World Bank Policy Research Working Paper No. 5652, Washington, DC.

14
>2</sup> εεNNaa a is is

γa is the shrinkage factor due to conditioning the random effect on the sample mean for area a. Na is the shrinkage factor due to conditioning the random effect on the sample mean for area a. N is the shrinkage factor due to conditioning the random effect on the sample mean for area a. Na is is the number of sample observations for area a. The shrinkage in variance due to conditioning the random effect on the sample will be higher when Na is large or when the variance of the residual ε is low, because in these cases the sample contributes more information about the unexplained component of welfare. Shrinkage falls as the estimated within-area correlation in welfare σ<sup>2</sup> , declines, because in those cases a larger share of unobserved welfare is idiosyncratic to households rather than common to areas, so the sample residuals convey less information about the true area mean. Similarly, ηη the shrinkage factor γ declines as the area sample size Na declines, because the sample contributes less information about the welfare in the area.

This basic structure is appealing in part because it is similar to the Elbers, Lanjouw, and Lanjouw (2003) model that has been used extensively for survey-census poverty mapping for decades, with the main difference being the conditioning of the random effect on the mean sample residuals. The concept of shrinkage in the nested error EBP framework is well-established in the small area estimation context (Battese, Harter, and Fuller, 1988, Jiang and Lahiri, 2006, Molina and Rao, 2010, Van der Weide, 2014). In addition, unlike Bayesian spatial autocorrelation models, nested error random effect models do not impose additional smoothness in model errors across neighboring areas. Spatial correlation is instead modeled entirely through the area random effects, which are conditioned on the sample residuals for that area.

As noted above, it is not clear that Empirical Best methods are preferred in the traditional poverty mapping setting where a household census serves as the
9 2005). On the other hand, about 700,000 Tutsi people returned to Rwanda from exile in Uganda shortly after the genocide (Newbury 2005). This group of _old caseload refugees_ either fled Rwanda during waves of ethnic violence against Tutsi since independence or were the offspring of Rwandan exiles.

Grasping the demographic imbalance in numbers, Fig. 1 depicts sex ratios for five-year age groups calculated from the (pre-genocide) 1991 Census and the (post-genocide) 2002 Census for Rwanda. The graph allows comparing the relative distribution of men and women across age at the two points in time. Clearly, in 2002 there are shortages of men that _may be_ attributable to genocide-related excess male deaths (i.e. shortages of men even larger than prior to the genocide for some age groups). The shortage of men is most pronounced in the groups of 20-45 year olds and the elderly older than 55 years. An immediate implication that follows from the unbalanced sex ratios is the reduced chance of women to get married to men of similar age for women in the age group most affected by genocide or to remarry after being divorced or widowed. This is hence a topic that we will investigate in more detail below.

# **4 Data**

## **4.1 Rwanda Demographic and Health Surveys**

The analysis builds on three cross-sectional Rwanda Demographic and Health Surveys (RDHS) collected in 1992 (before the genocide) (ONAPO and Macro International 1994), 2000 (after the genocide) (ONAPO and ORC Macro 2001) and 2005 (INSR and ORC Macro 2006). The data in each survey is representative of households at the national and in 1992 and 2005 at the provincial level, based on a stratified survey design. In the 2005 RDHS, each of Rwanda’s twelve provinces was divided into an urban and a rural stratum, resulting in 23 strata (the province of Kigali City only consists of urban areas). In a first stage, primary sampling units were drawn from a
still include covariates that are not significant under the fitted model, all non-significant (at the 1% level) covariates are removed sequentially.<sup>36</sup> Finally, we check for multicollinearity and remove covariates with a variance inflation factor (VIF) above 3.

Fitted models using the first sample and the described model selection are presented in Tables 3 and 4 in the appendix. Since in small area estimation models are used for prediction, assessing the predictive power of the model is important. This may be measured using the coefficient of determination, _R_<sup>2</sup> (Tzavidis et al., 2018). For the household level model used in this exercise the _R_<sup>2</sup> is close to 0.45 while that of the unit-context model centers around 0.25.

Figures 9, 10, and 11 represent checks on the normality assumptions using normal _Q-Q_ -plots of household level residuals and estimated area effects for the onefold unit level model with random municipality location effects. The resulting plots for the unit-context models can be seen in the appendix figures 26, 27, and 28. The natural logarithm transformation (Fig. 9) presents evidence of deviation from normality. Marhuenda et al. (2017) notes in applications that use real data the exact fit to a distribution is barely met; however the Box-Cox and log-shift transformations provide considerably better normal approximations (see Figures 10 and 11).<sup>37</sup>

# **4.3 Results**

Once the models to be used have been selected and _L_ = 500 samples under two-stage sampling described in section 4.1 have been taken from the “census” created using the Mexican Intra Censal Survey of 2015 we obtain estimates using the different considered models. The target parameters for this simulation are mean welfare and the Foster et al. (1984) - FGT class of decomposable poverty measures for municipalities present in the census. The true values of the target
may be unable to afford, a small fraction of the associated increases in land values is captured _ex post_ via land taxes (Hawley _et al._ 2018).

A second challenge is that, even if a registry is successfully established, failure to reduce the cost of registering transactions will make subsequent registration unaffordable for most and may lead to a return to informality.<sup>6</sup> High registration cost undermined sustainability in the past (Atwood 1990; Pinckney & Kimuyu 1994) and continues to do so even for interventions with otherwise positive effects (Agyei-Holmes _et al._ 2020; Fabbri & Dari-Mattiacci 2021). In Peru, a well-functioning low-cost registry was closed down in response to pressure by local notaries, triggering a marked increase in the cost of service delivery and, as a result, informality (Gutierrez & Molina 2020), suggesting that low-cost land service delivery is feasible but may run counter to powerful political economy forces.

This points to complexity paper-based registration procedures and standards for surveying, planning, and valuation as key issues that may increase the cost of registering rights and the associated corruption risk. Addressing these through a regulatory framework and performance standards that are monitored can go a

> 5 While policy makers placed enormous hopes in non-transferable documents to improve credit access in rural (Stein _et al._ 2016) or urban areas (Kironde 2006), these were bound to be disappointed and there were several cases where banks that had lent against such licenses were unable to liquidate, partly due to poor quality and maintenance (Kusiluka & Chiwambo 2019).

> 6 Evidence of high fees leading to ‘deformalization’ comes from Jamaica (Barnes & Griffith-Charles 2007), the Philippines (Maurer & Iyer 2008), and-for agricultural land-Rwanda (Ali et al. 2021). In urban Argentina, Galiani and Schargrodsky (2016) report that registering an inter-family transaction cost 27% of property values or more than 7 times average household income while registering a within-family transfer such as an inheritance or divorce cost about
14 poverty analysis seek to capture these interactions, and revealed preference seems to suggest that they are of interest to policymakers in many developing countries.

Despite this widespread agreement on the essential fact that poverty is multidimensional, there has been a lively debate on whether that implies that one should construct scalar indices that summarize information on those various dimensions in a single number, or provide multiple indices, one for each dimension, in a “dashboard approach”. Drawing entirely on the existing literature, we have argued here that such a dichotomous view misses the point. The most interesting aspects of the multidimensionality of poverty arise from the _interdependence_ among dimensions. The joint distribution of dimensions over the population contains more information than the corresponding marginal distributions, and the correlation patterns in that joint distribution may change how we compare poverty across two countries or time periods. This dependency structure is missed entirely by the dashboard approach, and often obscured by scalar indices.

Drawing on examples from both developed and developing countries, we illustrated three alternative approaches that allow researchers and policy analysts to focus on the dependency structure of a joint distribution. The first was stochastic dominance analysis, which permits partial orderings across joint distributions that are robust not only to poverty lines and welfare weights (as in the unidimensional case) but also to dimension weights. The second was a representation of the overlap of deprivations over the population, by means of simple tabulations or Venn diagrams. Given agreement on the identification criterion along each dimension, this extremely simple tool can complement the “dashboard” statistics on the marginal distributions in informative ways. And the third was the use of copula functions to study the multivariate association among different components of well-being across two or more joint distributions.

Finally we have argued that multidimensional poverty indices, like most other tools, can be accommodated in the economist’s toolkit, and that the risk of serious injury decreases with reliance on relative prices and the focus on a few core, truly irreducible dimensions. If such indices are used, plenty of fodder will
regional boundaries that can fuel sovereignty demands. In a homogenous country such as Japan, regions’ ethnic compositions are roughly the same with a single group dominant in all regions. Demand for sovereignty does not arise in this setup. The same is true if there are ethnically heterogeneous regions in an equally ethnically heterogeneous country (the United States offers examples of states with no substantial difference between regional and country-wide level of ethnic heterogeneity). What matters in both cases, is that regional distinctiveness is small. Demand for sovereignty arises when regional distinctiveness is high, as for example when a fairly ethnically homogeneous region is different from the rest of the country. _Prima facie_ support of this hypothesis is found in state efforts in multi-ethnic countries to dilute the ethnic homogeneity of regions by gerrymandering internal borders.<sup>27</sup> Thus, we hypothesize that: _H2: Regions that are ethnically different should demand more sovereignty._

# **_Inequality_**

The distribution of resources among groups in any state is a source of conflict. A common claim is that a high degree of economic inequality leads to crime and violence. The usual measure of inequality is the Gini coefficient, measuring inter-personal income inequality at the country level. In this study, we use, for the first time, a measure of inter-personal income inequality at the regional level. This information is derived from household survey data and allows us to compute inter-personal inequality for almost 900 regions from 48 countries, for one year in the period from 1998-2003.

High within-regional income inequality ( _Gi_ ) may be considered in two different ways.

First, it may hamper efforts to collectively organize a regional demand for sovereignty because nation (see Darden 2010). It follows that ethnic distinctiveness need not be a precondition for nationalism.

27 Dilution can also occur through assimilation (as in Greece’s strategy toward Slavic-speaking groups in the post-war period) or through forced population movements (Stalin’s deportations of Chechens). It follows that internal administrative borders are endogenous to the risk
|0.26|0.31|
|High-skilled|0.43|0.37|0.24|0.33|
|Low-skilled|0.95|0.30|0.16|0.17|

Note: Real weekly wages in USD and prices of 2011. Author's estimates using labor force survey data from Bangladesh.

**Table 1b. Coefficient of variation of real weekly wages across sub-districts in India India**

|||A<br>|ll sectors<br>|||
|---|---|---|---|---|---|
||1999|2004|2007|2009|2011|
|All workers|0.64|0.59|0.55|0.62|0.54|
|Males|0.60|0.54|0.50|0.61|0.52|
|Females|0.96|0.81|1.32|0.78|0.78|
|Young|0.64|0.65|0.53|0.65|0.50|
|Old|0.65|0.56|0.57|0.63|0.60|
|High-skilled|0.47|0.47|0.46|0.59|0.47|
|Low-skilled|0.87|0.59|0.62|0.62|0.43|

|||Textil<br>|es & Apparel<br>|<br>||
|---|---|---|---|---|---|
||1999|2004|2007|2009|2011|
|All workers|1.01|0.83|0.93|0.92|1.
59 5. The pairs of similar firm names identified in Step 4 are then sorted alphabetically to identify neighbor names (i.e., names that appear in consecutive rows after sorting) and correct any potential similarities that may have been missed in Step 4.

6. The algorithms in Step 4 generate “big groups” of firm names that are considered similar because they share several common words. To break up these “big groups”, firm names are sorted alphabetically within each group and, if two consecutive firm names do not have sufficiently similar pre-processed names (as captured by a cutoff in the Levenshtein distance), the group is “broken” at that place, resulting in two separate groups of similar firm names.

7. After similar firm names are identified from the steps above and assigned temporary unique numeric identifiers, firm names are then sorted alphabetically and based on firm address. If two consecutive names have similar addresses and similar names and did not have the same unique numeric identifier, they are joined in a new final unique numeric identifier.

The steps above are applied to clean the names of Sri Lankan firms. A large number of transactions, especially in the import data, are made by firms whose names indicate they are courier companies. Since these transactions are made for a third party, frequently individuals, and since their total trade value is negligible, we dropped all observations whose names are of courier firms.<sup>97</sup> Finally, we also drop from the sample observations for which the firm name corresponds to an individual making personal imports under a permit.<sup>98</sup> Such observations account for a very small total trade value.

# **A.C Agricultural Production, Cultivated Area, and Yields**

We rely on both administrative data and remote sensing estimates.

## **A.C.1 Administrative Data**

For rice, we obtain data on production and cultivated area in each district from the ‘Paddy Statistics’