rafmacalaba/data-use-mentions-extended huggingface dataset:
Française Développement, and Ministère de l’Économie et des Finances: <u>http://www.cepii.fr/institutions/en/ipd.asp) for 2012. The database provides several country-level</u> measures of land governance, which we summarize as an index measuring “land tenure insecurity of rural populations”.<sup>4</sup> Finally, we use the 2017 overall indicator in the Enabling Business of Agriculture dataset (https://eba.worldbank.org/en/eba), which captures the ease for farmers to operate their businesses. We use the overall score that aggregates the following sub-indicators supplying seed, registering fertilizer, securing water, registering machinery, sustaining livestock, protecting plant health, trading food, and accessing finance.

# **Section 2.  The global distribution of cropland and its dynamics**

The MODIS product allows us to assess patterns of geographic cropland distribution and its evolution over time. Table 1 below provides a breakdown of global cropland by region (using the World Bank definition) at the beginning and the end of our study period, showing that 65 percent of global cropland is located in the Europe and Central Asia region and Asia. North America has over 15 percent of the stock of cropland.

> 1 The data contains 1,455 global lakes and rivers which total 870,000 km2

> 2 The seven crops are cassava, maize, oil palm, sorghum, soybean, sugarcane, and wheat.

> 3 World Bank, Doing Business 2004–2020 (database), https://archive.doingbusiness.org/en/doingbusiness. Doing Business was discontinued by the World Bank in September 2021 due to certain irregularities which do not affect the data used in this paper.

> 4 The index is given by the first axis in a principal component analysis summarizing the information from the land governance variables in the Institutional Profile Database: b6030 “diversity of land tenure regimes”, b6050 “recognition of land
|N (obs.)|134174|134174|134174|134174|134174|134174|134174|
|N (frms)|18355|18355|18355|18355|18355|18355|18355|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. RCA is a standard measure of comparative advantages of Germany versus Poland and the world. All measures of German influence refer to the period 1994-2004. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level. TFPR is estimated using the method proposed in Ackerberg et al. (2015).

35
alternative energy sources might have some background in the environmental concerns, it also reflects the interest in the broadening of alternative energy sources in a context of future energy supply constraint.

# **3 THE SOCIAL ACCOUNTING MATRIX INCLUDING BIOFUEL SECTORS**

The basic data for the model are obtained from a social accounting matrix (SAM) that in this case also isolates sectors related with biodiesel and bioethanol production from the other accounts.

Here we summarize the most critical aspects of data collection and treatment. The initial matrix of intermediate purchases is based on the 1997 data (INDEC, 2001); it was updated in Chisari _et al._ (2009). For this study, we used sectoral information to update it as of 2006. The distribution of the factor income across income groups is based on the distribution observed in Argentina in 2006 according to household income surveys. The distribution of the consumption basket per type of goods and services is based on aggregates from a new household consumption survey for 2005.

In both the input and output matrix and the household consumption, consistent data on consumption and production were obtained through the cross-entropy method (Robinson, Cattaneo y El-Said, 2001). As for the government expenses, distribution between goods and services data are available for 2006 for the national and provincial governments. Municipal expenditures are assumed to be distributed in the same proportion as the average for the two other government levels.

Aggregate demand and supply in the SAM are consistent with national accounts.

The model includes 29 production sectors, four for agriculture, one for petroleum and mining, 16 for goods and eight for services. In addition to the usual activities, the SAM identifies sectors related to the production of biofuels as separate sectors: soy, corn, sugar cane, soy oil, industrial sugar, refined gasoline, diesel, biodiesel and bioethanol.

Four factors of productions are accounted for: labor, land, physical capital and financial capital. Both labor and financial capital are mobile across sectors while physical capital is sector specific. Land is
Pe/T|38.5|19.8|1.95|53.1|
|Spending per school-aged childi||||3.51|100|

Source: columns 3 and 4 are simulated from regression estimates of the relation between the corresponding indicator and per capita GNP, based on the data for some 125 countries in 1993; the last two columns are authors' computation, following procedures explained in the text.

a/ Proxied by the school life expectancy which is defined as the expected number of years of schooling given the current structure of enrollment rates across the three levels of schooling.

b/ This is the product of the components on the right-hand-side of equation (1"'), i.e. the product of the first item in the next column and the inverse of the second item.

c/ In the framework of equation (1"'), B's advantage is computed, for the first item listed, as the ratio of its indicator to that of country A; and for the second item, as the inverse of its ratio to that of A.

Because equation (1"') is an identity, we expect the product of the components of both sides of it to have the same value. Yet based on the data in table 4., the product on the right-hand-side is 3.51 (=1.80 x 1.95), which is only about 83 percent of 4.22, the corresponding figure on the left-hand-side shown in the previous table. Data flaws and incompleteness are a possible source of the discrepancy, but the aggregation of data for the three levels of educationon coverage, teacher pay and pupil-teacher ratio-into a single synthetic indicator representing the system as a whole, may also have contributed to it. To avoid this problem, we repeat the disaggregation exercise below, focussing on only one level of education, the primary level, for which the available data are more complete.

> 27 These percentages are calculated
_Figure 22: Structure of household expenditure by decile_ <!-- Start of picture text --> 100% taxes, insurance, debt servicing<br>90% alimony, help to relatives<br>80% construction and purchase of real estate<br>deposits and savings<br>70%<br>other goods and services<br>60%<br>healthcare<br>50%<br>sports, leasure, hotels, restaurants<br>40%<br>transport and communcations<br>30% purchase of a car<br>20% durables<br>10% utilities<br>0% clothes, footwear<br>1 2 3 4 5 6 7 8 9 10 food products<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

As food is a main line of households’ expenditures and their share in total expenditures depends on the well‐ being of the household, food consumption was most affected by the crisis, with households either cutting their expenditures on food items, or switching to cheaper items. In relative terms, the downward adjustments in food expenditures were the largest for the bottom decile, and become progressively smaller (although still negative) as we move up the income distribution (Figure 23).

In addition to cutting food expenditures, the recession years are also characterized by reductions in expenditures on utilities across the whole distribution, but with higher income households adjusting their utilities expenditures to a greater extent, as many households in the bottom decile are either already consuming the minimum necessary energy amounts for basic needs, or lack the ability to make energy efficiency increasing alterations.

The population vulnerable to the risk of absolute poverty reduced mainly food expenditures, not adjusting expenditures on utilities, public transportation, communication and other basic services in contrast to other population groups. Households from the richest decile, for the most part, did not have to reduce their expenditure on food
Learning from Developing Country Power Market Experiences: The Case of Peru Renewable generation has had modest but increasing contributions to power supply in Peru and is expected to continue growing in the future. Wind, solar and biomass power plants jointly contributed 2.8% of total net generation in Peru in 2017. Installed renewable generation capacity (other than hydro) increased up to 408 MW in 2017, with 243 MW of wind power, 96 MW of solar power and 69 MW of biomass power (see Figure 5-7). The share of renewable generation in Peru is expected to increase further, following the commissioning of new renewable generation facilities.

These and other expected renewable capacity additions have been driven by renewable auctions, which have resulted in total capacity additions by winning bidders of 537 MW of hydro power plants, 394 MW of wind power plants and 227 MW of solar power plants (see Table 5-2). Note that capacity additions awarded through auctions are yet to be fully materialized.

<!-- Start of picture text --> MW<br> 450<br> 400<br> 350<br> 300<br>243 243<br> 250<br>146 146<br> 200<br> 150 - -<br> 100 84 84 96 96 96 96<br> 50<br> - - 23 - 23 - 61 61 61 61 69 69<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Biomass Solar Wind<br><!-- End of picture text --> Figure 5-7 Evolution of renewable generation capacity in Peru (other than hydro).

Source: COES Table 5-2 Capacity additions (MW) per technology in renewable auctions in Peru.

|**Auction**|**Solar**|**Wind**|**Hydro**|**Biomass**|
|---|---|---|---|---|
|
recently by Bairagi et al. (2020) to estimate a demand system with 15 major food items in Vietnam (see tables 1 and 2 for detailed items). Their main contribution consisted of using three waves of the VHLSS (2012, 2014 and 2016) and to consider income and urbanization effects. Bairagi et al. (2020) found a large variation in expenditure and price elasticities across rural/urban areas and different income groups. Moreover, Vietnamese households prefer animal proteins and fruits and vegetables to the traditional staple rice. This tendency is stronger in higher-income urban households, where rice is already an inferior good, and Bairagi et al. (2020) explain it as an effect of the economic growth of Vietnam and the consequent increase in income and urbanization. The authors aknowledge the limitations of Deaton’s method, however, in absence of actual market data, they prefer it over other methods.

6
of informal employment throughout all data sources, which is in line with the majority of Russian literature and deemed more appropriate for transition economies. Throughout the analysis for this paper, in most cases the results are qualitatively consistent across the three surveys under study.<sup>23</sup> Importantly, we find that a statistically significant wage gap between formal and informal workers starts to be statistically significant around mid 2000s and has widened since. Hourly wages of informal and formal workers started out at around parity and maintained similar growth rates through the mid 2000s, but a clear divergence is observed after that. Moreover, while real wages

> 23 The main exception to this is that we find inconsistent results using the LiTS when measuring the impact of job and life satisfaction. Specifically, we find empirical results that are opposite from those obtained using the RLMS. For example, results from the LiTS suggest that informal workers are more satisfied with their jobs and their lives. Given that the RLMS includes more information to explore other aspects of informality, some of which are used to explain the differences in satisfaction levels, these <u>are the results we report here. We note however that the discrepancy with the LiTS remains an unresolved puzzle.</u>

25/67 Informal Employment and Worker’s Wellbeing in Russia
]***|[0.028]**|[0.022]|[0.406]*|
|Obs|21,411|3,144|21,411|21,411|3,222|
|R2|0.233|0.383|0.202|0.286|0.399|

Sources: World Bank Enterprise Surveys and World Value Surveys. Notes: The sample includes 61 countries, of which 28 are classified as more traditional and 33 as less-traditional when using median of the WVS indicator as threshold. All models control for manager experience, indicators of firm age, legal status, size, whether the firm exports, whether it has financial statements certified by external auditors, the percentage of the firm that is privately owned, and industry, country, and year fixed effects. For the model of credit application rejection, controls also include firm leverage. Models in columns (1), (3) and (4) use all firms in our sample; model in column (2) restricts the sample to firms reporting the result of their most recent credit application; model in column (5) restricts the sample to firms with open lines of credit that report the value of their debt. Standard errors are clustered at the country level. *** significant at 1%, ** at 5%, * at 10%.

if these firms are riskier, less profitable or less productive. Estimations presented in Table 3 suggest that there is no evidence of gender differences in leverage, that is, the debt-to-capital ratio, a measure of a firm’s risk. These results are generally consistent in both more and less traditional countries (Panel B).<sup>19</sup> While female-managed firms have comparable risks, they are relatively more profitable (column 2) and have no discernible differences in TFP (column 3) in both more traditional and less traditional countries. Female-managed firms have, on
# **Table 7. Poverty dynamics based on synthetic panel data, bound estimates for nonparametric joint probabilities (percent)**

||Lower Bound|Upper Bound|
|---|---|---|
|Poor, Poor|10.59|3.21|
|Poor, Non-Poor|12.48|15.15|
|Non-Poor, Poor|0.08|7.46|
|Non-Poor, Non-Poor|76.85|74.18|
|All|100.00|100.00|

Note: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

**Table 8. Poverty dynamics based on synthetic panel data, bound estimates for nonparametric conditional probabilities (percent)**

||Lower Bound|Upper Bound|
|---|---|---|
|Poor to Poor|45.91|17.49|
|Poor to Non-Poor|54.09|82.51|
|Non-Poor to Poor|0.10|9.13|
|Non-Poor to Non-Poor|99.90|90.87|
|All|100.00|100.00|

Note: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

Finally, the conclusions derived from tables 5 and 6 may be substantiated by taking a comparative view of Bhutan’s case. Estimates of joint transition probabilities based on synthetic panels are available for a number of countries: a selection of the most relevant to the context at hand is shown in Table 9. Bhutan’s recent experience emerges almost unequivocally as the most favorable in the bunch
Table A2: Patient Information Implementation

||(1)<br>Patient Info<br>Mean|(2)<br>N|
|---|---|---|
|Watched any video (constructed)|0.795<br>[0.404]|1002|
|Watched any video: patient survey|0.722<br>[0.448]|1002|
|Watched any video: admin records|0.978<br>[0.146]|686|
|Watched full video: admin records|0.892<br>[0.310]|686|
|Share of video watched: admin records|0.943<br>[0.192]|686|

_Notes:_ Standard deviations in brackets. Information on whether the patient video was watched from two sources. Administrative records were kept by intervention officers and contain information whether the video was partially watched. Patient survey records were collected by surveyors based on observed participation in the intervention. The constructed variable is coded as 1 if at least one record lists the patient as receiving the intervention and the other record agrees or is missing for this patient (but 0 if neither record lists the patient as _yes_ or one record list the patient as _no_ ).

Table A3: Selection into Sample by Treatment

||(1)<br>Full S|(2)<br>ample|(3)<br>Selected:|(4)<br> Home Survey|
|---|---|---|---|---|
||Took<br>Home<br>Survey|Took<br>Home-<br>Based<br>RDT|Took<br>Home<br>Survey|Took<br>Home-<br>Based<br>RDT|
|Extended Training|-0.0260|0
national sales in food and drug stores and 32% of national sales in mass merchandise stores. The finest location of each store is given at the county level. We only use stores that appear throughout the entire sample period such that store entry and exit do not affect results. Among the stores in the sample in 2006, 84% remain throughout the entire sample period. A large number of products from all Nielsen-tracked categories are included in the data: 2.6 million universal product codes (UPCs) in total aggregated into around 1,100 product modules, which are further aggregated up to 125 product groups.

Although alternate price indices released by government agencies do exist, they have limitations that render them less suitable for our analysis, especially due to sampling error. These limitations are outlined in Beraja et al. (2019). Therefore, this paper uses price indices constructed from micro data.

The advantage of using the retail scanner data as opposed to the Nielsen Consumer Panel is that a wider range of goods is observed at higher frequencies and quantities. Scanner price indices are constructed as in Beraja et al. (2019). We briefly describe the approach they adopt in Appendix Section D and refer interested readers to their paper for details. Leung (2021) also investigates the behavior of the constructed indices and finds grocery store price indices are quite similar to the CPI city-level food-price indices, which are published for around 20 sample areas in the US. We construct a range of additional price indices using alternative methods, which give nearly identical results.

Retail stores need to apply to the FNS to accept SNAP payments. We obtain a panel of SNAP participating retail stores from the FNS and are able to match this panel with Nielsen stores. Our string-matching algorithms match over 90% of Nielsen stores to SNAP participating stores across store types, which implies that almost all stores in our data participate in SNAP.<sup>23</sup> However, we focus on results from grocery stores for several reasons. First
65 government spending is that by Bahl and Pillai.53 Using data for 17 l..dian states in a cross-sectional analysis of 1970-71 expenditures they concluded that after accounting for stage of development factors, "A one rupee higher level of per capita aid Is associated with a more than four rupee higher level of per capita expenditures."S4 That analysis, therefore, indicates that grants can be stimulative.

At least two analyses of transfers in developing countries have oonsidered both the stimulative/substitutive effects of grants as well as whether different grant achemes differentially affect investment (capital) and recurrent expenditures of looalities. Spending impacts of the two major grant programs in the Philippines--the Bureau of Internal Revenue Allotment (BIR) and the Specific Tax Allotment (STA)--have been considered by Bahl, Schroeder and Wasylenko. 5 5 Their model reflects the fact that flows from the two grants are channeled into two different local funds--the BIR into the General Fund and the STA into the Infrastructure Fund. The former is primarily a recurrent spending fund while the latter is generally, but not exclusively, devoted to capital spending. The model encompasses the fact that interfund transfers ocour such that each grant scheme has both a direct effect on the fund into which it flows and also an

> 5 3 Roy W. Bahl and Velayudhan Pillai, "The Allocative Effects of Intergovernmental Flows in Less Developed Countries: A Case Study of India," Public Finance/Finances Publiques, Vol. XXXI, No. 1 (1976), pp. 7389.

5 4 I bid, p. 83. _lb_ 55 Roy Bahl, Larry Schroeder and Michael Wasylenko, "The StimulativaB Effects of Intergovernmental Grants in Developing Countries: The Case of the Philippines," unpublished manuscript (1987).
effects of reform episodes related to the oversight of international goods trade. Martincus _et al._ (2015) show that inspections impede exporting in a study of the risk management system that governs Uruguayan exports. Fernandes _et al._ (2021) conduct an evaluation of the effects of reduced inspections of import shipments by Albanian customs during a period of substantial reform. Fernandes _et al._ (2017) evaluate preliminary steps towards a reform undertaken by the Food and Veterinary Agency of North Macedonia. Cariolle _et al._ (2019) and Chalendard _et al._ (2019) show how external information on trade flows can be used to forecast tariff evasion in Gabon and Madagascar, respectively.

In this paper we isolate a channel through which risk management reforms contribute to better border management. We argue that the rarity of many types of import shipments makes untenable a presumption that inspecting agencies are fully informed about the conditional probability that a shipment does not comply with import regulations. It is likely that modern IT systems substantially reduce ambiguity by improving the agency’s conditional forecast of the probability that a shipment complies with import regulations. To motivate our focus on ambiguity, we introduce a new stylized fact - shipments that were less frequently observed in prior years are conditionally more likely to be inspected. We apply tools from decision theory to understand inspecting agencies’ likely response to ambiguity, and the manner in which ambiguity-reducing investments in IT are likely to shift inspection resources. Finally, we calibrate the model to illustrate quantitative relationships between ambiguity, search intensities and firms’ compliance with import regulations.

The rest of the paper is organized as follows. Section 2 briefly describes the operation of modern risk management systems as they apply to oversight of imported goods shipments. Section 3 motivates our focus on ambiguity with stylized facts from the Serbian customs data. Section 4 develops the theoretical framework incorporating ambiguity, describes the equilibrium, and derives comparative static predictions of the model. Section 5 calibrates a numerical version of the model and conducts counterfactual analysis. Section
# _Table 2 Summary_ _<u>Statistics of Vietnam’s Workers aged 15-59 from the VHLSS 2004</u>_

||||ByGe|nder|By|Skill|
|---|---|---|---|---|---|---|
|Ownership||Total|Male|Female|skilled|unskilled|
|Total|Number of Obs.|21022|10599|10423|3981|17041|
||Share of female (%)|.50|||.46|.51|
||Age|35.0 (11.7)|34.8 (11.8)|35.3 (11.6)|35.0 (10.7)|35.0 (11.8)|
||Education years|7.7 (3.7)|8.0 (3.6)|7.3 (3.8)|12.8 (1.5)|6.5 (3.0)|
||hourly wage<sup>*1</sup>(VND 1,000)|5.3(4.7)|5.5 (5.1)|4.9 (3.9)|7.4 (6.3)|4.2 (3.0)|
||Hours worked per week<sup>*2</sup>|34.8 (15.8)|34.9 (15.4)|34.7 (16.3)|38.6 (15.8)|33.9 (15.7)|
|Foreign|Number of Obs.|278|106|172|134|144|
||Share of female (%)|.62|||.57|.67|
||Age