rafmacalaba/data-use-mentions-extended huggingface dataset:
of such a proxy, we use the legal status of firms and the gender of the top manager. These two variables are certainly not proxies for the quality and the system of management, but they can impact them. The legal status is captured by an ordinal qualitative variable (Sole proprietorship=1; Limited partnership=2; Partnership=3; Shareholding company with nontraded shares or shares traded privately=4; Shareholding company with shares trade in the stock market=5). The gender variable is a dummy that takes 1 if the top manager is a woman and 0 otherwise.

- _Transport:_ This variable controls for the effects of constraints related to access to transport infrastructure. This constraint is measured by the subjective response given by a firm to the following question: to what extent is transport an obstacle to this establishment's activity? The response is: 0 for no barrier, 1 for minor barrier, 2 for moderate barrier, 3 for major barrier, and 4 for very major barrier.

- _Backward-looking variables:_ They represent total sales and total employment over the three years prior to the survey. As in growth models, the use of these variables makes it possible to control for initial performance and catch-up effects.

# **4.** **<u>Country-level control variables</u>**

- _Trade openness(t-1):_ This variable considers the degree to which countries are oriented towards foreign trade. We consider the KOF trade globalization index. It is the weighted average of the de facto and de jure indices. The relevance of this measure lies in the fact that we have both trade volume and trade policy dimensions.

- _Real exchange rate growth(t-1):_ This variable tracks the effect of non-price determinants of competitivity. A body of research shows that depreciating or undervaluing the real exchange rate is an effective way of promoting manufacturing exports (Rodrik, 2018; Freund and Pierola, 2012; Bernard and Jensen, 2004). According to the
- [8] Caprio, G. and Klingebiel, D. 1999. "Database of Systemic and Non-Systemic Bank Insolvencies from the 1980s Onwards." _World Bank._

- [9] Chang, R. 1994. "Endogenous Currency Substitution and Inflationary Finance." _Journal Money, Credit and Banking._ 26(4) pp. 903-916.

- [10] Clements, B. and Schwartz, G. 1992. "Currency Substitution: the Recent Experience of Bolivia." _IMF Working Paper._ Washington, DC.

- [11] Demirgiiu-Kunt, A. and Detragaiche, E. 1998. "The Determinants of Banking Crises in Developing and Developed Countries." _IMF Staff Papers._ 45 (1) 1998.

- [12] Fischer, S. 1982. "Seigniorage and the Case for a National Money." _Journal of Political Economy._ 90(2), pp. 295-313.

- [13] Guidotti, P. and Rodriguez, C. 1991. "Dollarization in Latin America: Gresham's Law in Reverse?" _IMF Working Paper._ Washington, DC.

- [14] Handa, J. 1988. "Substitution Among Currencies: a Preferred Habitat Hypothesis."' _International Economic Journal._ 2(2), pp. 41-61.

- [15] International Monetary Fund. _International Financial Statistics,_ IMF, Washington, D.C. Various Issues.

- [16] Levine, R, Loayza, N. and Beck, T. 1999. "Financial Intermediation and Growth: Causality and Causes." _Economic Growth Research._ World Bank.

- [17] Marquez, J. 1987. "Money Demand in Open Economies: a Currency Substitution Model for Venezuela." _Journal of International Money and Finance._ 6(2), pp. 167-78.

- [18] McKinnon, R.I. 1985. "Two Concepts of International Currency Substitution." in Conolly, M.D.
repeat this residual bootstrap 1,000 times and the standard deviation of the estimate, which functions as our estimate of the standard error.

Importantly, the drawing of observations from the vectors of residuals is done by weighting based on aggregate sampling weights at the subarea and area level. As such, we are taking into account the fact that sampling is informative and some (sub) areas have a higher probability of inclusion than others. Since only in-sample (sub)areas are included in the calculation of the residual bootstrap vector, this method works when only a household survey is available.

The proposed bootstrap consists of the following steps:

1. Predict an outcome (asset index or poverty) using XGBoost, BRF, or Cubist.

2. Calculate subarea residuals for in-sample subareas by differencing the prediction and the direct estimate from the survey. Call this vector of residuals _R_<sup>ˆ</sup> _sa_ = _y_ ˆ _sa_<sup>_ML_</sup> _− y_ ˆ _sa_<sup>_direct_</sup> .

3. Aggregate predictions to the area level, using estimated population from WorldPop as weights.

4. Calculate area residuals for in-sample subareas by differencing the prediction and the direct estimate from the survey. Call this vector of residuals _R_<sup>ˆ</sup> _a_ = _y_ ˆ _a_<sup>_ML_</sup> _− y_ ˆ _a_<sup>_direct_</sup> .

5. With original subarea predictions, bootstrap with replacement from _R_<sup>ˆ</sup> _sa_ for all subareas, with sampling probability determined through aggregated sampling weights.

6. Aggregate these new predictions to the area level.

7. Bootstrap with replacement from _R_<sup>ˆ</sup> _a_ for all areas, with sampling probability determined through aggregated samplign weights.

8. Repeat steps five through seven 1,000 times, saving new area estimates after each replication.

9. Calculate percentiles across the 1,000 replications.

Although
compliance. In these cases, perceptions of government services and the quality of government in general drive tax compliance, even without enforcement provisions (Doerrenberg and Peichl 2017; Bott et al. 2017; Hallsworth et al. 2017). Similarly, Besley et al. (2015) found that perceived fairness of government tax policy was important for tax compliance, which also corresponds to a sense of reciprocity.

In sum, there is now a broad body of experimental evidence that demonstrates that higher tax morale significantly affects the willingness of individuals to comply with tax regimes. However, we should note potential disconnects between laboratory findings regarding participatory decision-making and “real world” policy implementation processes. Additional research efforts have sought to address this issue through case studies, large-N cross-national surveys, and randomized, controlled trials. Schneider and Baquero (2006) closely analyzed the tax revenue collection system in Porto Alegre, Brazil, during the early days of the city’s pioneering Participatory Budgeting (PB) program. They find that government officials invested heavily in building administrative capacity to better capture taxes as they developed the PB program. The municipal government significantly reduced late tax payments as well as non-payments in the ensuing years. Part of this result could be due to improved facilitation, but Schneider and Baquero provide interview evidence from governing elites and media sources that people were more willing to pay their taxes because of government officials’ success in producing public goods through PB. Unfortunately, Schneider and Baquero did not provide any evidence that more closely linked taxpayers’ attitudes to their tax compliance behavior. Spada (2012) also evaluated Participatory Budgeting’s impact on taxation and found a long-term, positive relationship between PB and tax-share of municipal revenue, though a negative short-term relationship. Again, a lack of data prevented an examination of the mechanisms driving both short and long-term relationships.

Randomized controlled trials represent an important means to collect new data and evaluate relationships between political institutions and tax compliance. Beuermann and Amelina (2014) led an important, recent, RCT that assigned
is argued that high transaction costs in sale and purchase of land place a disproportionately high burden on the rural poor, and are likely further to depress the land sale market thereby reducing their access to private, arable land. This section examines whether rationalising the responsibilities of the RI and the tehsildar in the course of the computerisation of land records and land registration would be likely to have a positive impact on the efficiency of land administration.

While the present system of land record management owes its origin to Todarmal, it was mainly developed under British rule in the 19th century. The colonial administration relied almost entirely on revenue from land and so an efficient land records system was essential to its survival. However, regular updating of land records was rare. In _zamindari_ areas, the intermediaries were only interested in collecting rent. For this part-time rentcollectors (e.g. _guntia_ in Orissa) were hired and there was no systematic system for land records management. Most of the updating occurred during periodic settlement operations. A large proportion of Orissa's land area fell under _zamindari_ tenure and so for all practical purposes, Orissa was a 'non-land record state'.

The need to improve and strengthen land revenue administration has long been recognised. As early as 1958, the poor state of land records was identified as one of the important causes of the failure of land reforms (GOI, 1958). Thirty-five years later, the then Prime Minister remarked: Many Chief Ministers have told me that even if they do not expect any money out of land revenue, they would like to see someone to keep the land records because it is a record of rights. If nobody has a record of rights, might becomes right....Whether any land revenue is paid, whether that amount is considerable or not, whether that needs to be collected still or does not need to be collected, ... the maintenance of land records is a must and that has to be done. (Rao, 1992: 6-7) However
The Salvadoran banking sector became one of the deepest in Central America<sup>6</sup> by the end of the 1990s (see Figure 1). However, despite the financial reforms undertaken during this decade, financial inclusion remained low in El Salvador. Based on a survey of rural households, conducted by FUSADES (Salvadoran Foundation for Economic and Social Development), 8.7 percent of households had a deposit account as of 1995 and 6.8 percent had a loan from a formal financial institution.

# **III. Data and empirical methodology**

The household-level data we use in this study come from the _National Rural Household Survey_ carried out by FUSADES in 1996, 1998, 2000, and 2002. All the information gathered by each survey refers to the previous calendar year. The survey uses a questionnaire adapted from the World Bank’s Living Standards Measurement Survey and covers a stratified, nationally representative, random sample of rural households. The four waves contain information for about 937 households dispersed across the 14 departments that make up El Salvador. Within our sample, 719 households are present in more than 1 wave and 451 households have data for the four waves, allowing us to construct a panel.

The survey includes data on demographic characteristics, education, employment, economic activities, wealth, and income of households. With respect to migration, the survey has information on the number of members per household that have migrated to other countries, their kinship relationship or role in the household (i.e. head of household, spouse, son), the time they have been abroad, and the year they came back to El Salvador in case they returned. Moreover, data on whether the individuals that migrated sent remittances and the amount sent are also available.

The survey also contains information on households’ use of financial services offered by formal financial institutions (i.e., commercial banks, cooperatives, credit unions, and other financial institutions), development institutions (i.e.
# **2.2.2.1 Comparability of School Attendance before COVID-19 and during COVID-19 after School Reopening**

School attendance after school reopening was measured in October 2020, when school participation is expected to be the highest in the school year. School attendance before school closure is defined based on attendance at any time during the academic year 20192020. Therefore, we are fairly confident that school attendance before and after the outbreak of the pandemic is comparable. Nonetheless, because the reference period used to measure school attendance before and during COVID-19 is different, we explore further the comparability of our measure of school attendance between the two periods (before and during COVID-19). For example, during the agricultural period, households might withdraw their children from school to help with household farming. However, in mining the reasons for not attending school given by respondents in the COVID-19 NLPS round 6, no respondent mentioned employment as a reason. Furthermore, respondents might not attend school because they are waiting for admission. Therefore, in robustness analyses, we assume that these respondents attended school when the schools reopened (in October 2020). These results are consistent with those from our main specification.

# **2.2.3 Lockdown Indicators**

On March 19, 2020, a circular from the Federal Ministry of Education ratified the closure of all schools in Nigeria starting from March 23, 2020. At the same time, as described in Section 2.1, the federal government implemented various social distancing and mobility restrictions, and decided for the closure of business operations and regional borders. We define the baseline COVID-19 lockdown measures (labeled as _C19Shock_ in our specifications below) as a binary variable taking value one for the period after the outbreak of the pandemic and the implementation of the above-mentioned restriction measures, zero otherwise.

In addition to these interventions, the federal and state governments implemented additional lockdown measures and strict mobility restrictions in Abuja FCT, Lagos, Akwa Ibom, Borno, Osun, Rivers, Ogun, Kano,
and **1998.** Their results show that new private sector industrial investments were biased towards existing industrial clusters and coastal districts and depended on the size of the investments in the same location prior to the reforms and on the size of the investments in neighboring locations as reform proceeded. The authors infer that structural reform led to increased spatial inequality in terms of industrialization. These studies provide some interesting and **important** stylized facts about the location of manufacturing industry in India. However, due to the nature of the data used they are unable to analyze long-term patterns and determinants of '-g1iii n.-1i as we do in this paper. Bai et al. (2004) investigate the evolution of the Hoover index of :- :1 i constructed using **Cliij.** industrylevel data from **1985** to **1997.** They estimate the impact of local protectionism (i.e., the

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2012), female labor force participation (Azam, 2012) and household consumption (Ravi and Engler, 2015). We complement this literature and examine the effects of the program on schooling outcomes. Our paper does not refute these clear benefits of the program but rather shows that there is an opportunity cost.

There are two studies that are closely related to our paper. Afridi et al (2012) find that MNREGA increases mothers income and through this channel improves childrens educational outcomes. Unlike Afridi et al (2012), we do not find an improvement in schooling outcomes. There are a number of differences between our paper and their study. First, Afridi et al (2012) focus on one state, whereas we use data from the entire country from 2005 to 2008. Hence, our design allows us to understand nation wide effects of the program. Second, they use data from 2007 to 2009. By 2007, MGNREGA was already implemented in the poorest parts of the country, and was being implemented in the rest of the districts. Hence, their study only makes post introduction comparisons and uses the intensity of exposure for identification. In contrast, the strength of this study design is that it uses the roll-out timing for identification and compare outcomes pre- and post-implementation. We also examine a very rich set of schooling outcomes, whereas they focus on time spent in school. Importantly, we show that the program has an unintended effect on child labor increasing likelihood of being employed. In a subsequent study, Shah and Steinberg (2015) use test score data and evaluate the impact of MGNREGA on human capital. Their paper reinforces our findings. However, there are important dissimilarities. Their study uses the Annual Status of Education Report (ASER) data on test scores<sup>5</sup> and finds that test scores worsen for children in early roll-out districts. They focus on gender based age specific differences in labor outcomes. Our study, on the other hand, uses school level data and
two sources. The first is a long-standing panel study of health seeking behavior carried out by the Institute for Socio-Economic Research on Democracy and Development (ISERDD) in Delhi. The study followed 1621 individuals in 300 households from 7 different neighborhoods in Delhi between 2000 and 2002. The localities were chosen through initial contacts developed by the anthropologists on the team. Following these initial contacts, which were used for ―entry‖ into the locality, households within each locality were chosen randomly. In each locality, a street was chosen at random and every fourth household located on the street was asked to participate in the survey. A total of 40-45 households were approached to be part of the survey in each locality, and refusal rates were less than 4 percent in all localities. Considerable time was spent explaining to each household the purpose of the survey and care was taken to

> 6 Until recently, medical insurance for catastrophic events was limited to a small fraction of the population. Some estimates suggest that 10 percent of the urban population has access to health insurance; most of these are richer households or government employees. India recently launched a health insurance program for the poor, which is in its 3<sup>rd</sup> year in Delhi.

4
fathers have completed secondary, the algorithm decides that the most important factor determining differences in consumption is region, with higher consumption among those born in the Capital or Region 1. At this point, the branch ends because there are no more circumstance variables significantly predicting consumption with a p-value smaller than .01 in this subsample. The left side of the tree demonstrates that circumstances used in previous nodes can reappear – among those whose fathers did not complete secondary and whose mothers completed primary, the most important factor is whether the father completed primary. The “leaves” at the end of the tree represent the 6 types and their average daily consumption . This example is presented using a depth of 3, while in our estimation the maximum depth is set to 10.<sup>11</sup> CĈ A disadvantage of regression trees is that small changes to the data might change the splitting points detected and hence the level of IOp estimated. To circumvent this issue, we use random forests in our preferred specification. These are collections of hundreds

> 11 The parameters used in the estimation are detailed in the Model Appendix. As a default, we used the parameters used by a forthcoming IOp database led by LSE’s International Inequalities Institute.

7
made.<sup>17</sup> A remaining data challenge relates to transactions of apartments that tend to lack cadastral numbers for underlying land. Resolving it requires acceleration of ongoing efforts to fully implement an address register that is interoperable with the urban cadaster through an API and an expansion of existing data reporting requirements and exchange protocols for land parcels to include real estate.

While accurate property valuation will be invaluable to guide activities by the private sector, translating it into higher local revenue requires that market values change the tax base taxes on land or real estate from NMV or floor area, respectively, to value. In this context, exemptions and caps on local governments’ ability to adjust taxes may also need to be reviewed, ideally based on evidence of their impact on revenue.<sup>18</sup>

# **4. Implementing a mass valuation model for commercial agricultural land in Ukraine**

This section presents the valuation model, the public data used to estimate it, and the prices predicted by the model. Comparing predicted prices to normative values for 1.2 million of the 7.2 million parcels for which such values are available shows that, while mean estimated prices are 25% above the normative monetary value, a market-based valuation increases horizontal equity with limited increases in the country’s East and South but land values that are more than 30% above the NMV in the Center, North, and West.

# **4.1 Regression model and variable construction**

To provide a market-based valuation for agricultural land for commercial farming, we focus on some 160,000 transactions completed between June 2021 and July 2024 to estimate a hedonic valuation model (Rosen 1974). The underlying assumption is that transacted parcels’ prices are the result of an equilibrium between demand and supply functions that are differentiable in land characteristics relevant to the seller and buyer respectively (Palmquist 1989). If land markets are homogenous (Huang _et al._ 2006), knowledge of the price and
# **4 Experiment One**

## **4.1 Experiment Design**

This study took place among property owners in Congo Town, one of the tax zones in Monrovia, the capital city of Liberia. This area was identified as having many properties that could be brought into the tax net.

The first activity was creating a property database. From April to June 2014, I worked closely with the LRA to conduct a “block mapping” project to collect information on property locations and features. Enrollment agents (short-term government contractors) went doorto-door in teams of two to raise awareness of the real estate tax using brochures and flyers. They also completed a brief survey with residents, using mobile devices to obtain information about each property and its owner. The data collected in this enumeration process resulted in an electronic property database for taxation. This was the first time the government had electronic records and digital photographs of these properties.<sup>14</sup> High-value properties, such as hotels, banks, and other commercial properties, were excluded from the enumeration list because the LRA did not want to include them in the experiment, given their revenue potential. In addition, 5 percent of the enumerated properties were found to be on the tax roll already and were thus removed from the experiment sample. In all, 571 properties belonging to 464 owners made up the sample.

Properties in the sample were randomly assigned to four groups. The control group received a generic notice that informed property owners of their responsibility to pay the real estate tax and gave instructions about how to comply. The notice did not contain any information specific to the property owner but was simply addressed “Dear Property Owners.” The notice for the first treatment group (the _identification and penalty notice_ ) contained the same information about how to comply; however, it was personalized by including the owner’s name and a photograph of the property (obtained during the door-to-door enumeration). It also included the legal penalties for