rafmacalaba/data-use-mentions-extended huggingface dataset:
“world” instead of from a specific country. Its GHG footprint, regardless of being animal food that has country-specific footprint data or not, will be from the world average.

4) The item can be matched to FCT, or their nutritional values can be found elsewhere.

In this way, all spices were excluded. HIES 2000 and 2016 both have 17 food categories, and each category contains one “Other (specify)” item. We manually assigned specific TM items to 11 out of 17 “Other” items based on the characteristic of the food group (i.e., mixed food group like “Dining out” or a narrow and specific group like “Fish”), high potential in GHGe, and the lowest possible nutritional values to measure the upper bound in environmental cost and the lower bound in nutrient intake. Following these rules, 30 and 37 HIES food items were excluded, accounting for 3.8 percent and 4.5 percent of total consumption by weight, in year 2000 and 2016 respectively (see Annexes 1 and 2). Matching results are shown in Annex 3.

# **2.2 Scaled consumption patterns**

The FBDG-scaled diet was modeled to show changes in each food item consumption after assigning a required amount to each of the 10 food groups classified in the FBDG while controlling for individuals’ original dietary preferences. Creating FBDG-scaled diet requires two pieces of information: 1) total grams of food by 10 food groups recommended by the FBDG (see Annexes 4 and 2) actually consumed food items’ shares of their food groups. We summed items’ recommended amount by 10 groups and assigned these totals to households. Multiplying these assigned amounts with each food item’s actually consumed share yields scaled consumption by food items. For example, in the ten-group version, according to the FBDG (see Annex 4), 30 grams of egg was assigned to per AME per day. In both HIES 2000 and 2016, egg category consists of hen egg, duck
the plot, in view of the possibility of non-labor input applications not having been finalized at the time of the post-planting interview.

Conversely, control group households received a single, post-harvest visit during which they completed a unified agricultural questionnaire, including the same self-reporting of plot size,

> 4 A parcel is defined as a continuous piece of land that is not split by a river or a path wide enough to fit an oxcart or vehicle. A plot is continuous piece of land on which a unique crop or a mixture of crops is grown, under a uniform, consistent crop management system.

> 5 After walking the perimeter of a given plot with the plot manager to identify the boundaries, the enumerators measured the area with the GPS unit. The NSO enumerators were experienced users of the handheld GPS technology, which was adopted by the NSO for land area measurement in 2010 in the context of the Third Integrated Household Survey and the Integrated Household Panel Survey (IHPS).

> 6 Once the GPS-based plot area information was imputed into the Survey Solutions Computer-Assisted Personal Interviewing (CAPI) application, the measurement error was calculated and displayed automatically.

> 7 We cannot gauge the extent to which the sampled farmers trusted the GPS-based plot area measures and therefore cannot test whether mistrust of objective evidence might help explain highly imperfect updating of self-reported plot area.

> 8 The post-harvest visit was scheduled according to the households’ harvest calendars, as the primary purpose of this visit was to harvest and weigh the crops on pre-designated crop cut sub-plots on the selected maize plot.

6
growth in agricultural output as a factor of production we use the FAO’s index of agricultural production (both food and non-food) measured in real terms.

While this approach to agriculture may appear artificial, it exactly parallels how timber production is treated in TFP growth estimation. The wood itself is considered to be the natural resource, and the growth rate of the quantity of timber produced is calculated year on year based upon timber production measured in cubic meters. The share of timber production in value added is the associated resource rent on timber harvest – which, arguably, could also be viewed as a type of land rent.<sup>4</sup>

# _Labor and the self-employed_

In developing countries, particularly low-income countries, self-employment is a large share of total labor inputs– in Sub-Saharan Africa, for example, the share of formal wages in value added can be as low as 10%. For the share of labor in the equations above, s� as measured in the SNA is too low because it includes only formal employment. The Penn World Table data on total compensation of labor add other components of value added (part of mixed income in developed countries, agricultural value added in low-income countries) to the SNA figures for the compensation of employees. As a result, the sum of returns to capital, labor and natural resources

> 4 Note that there is some risk of double-counting the contribution of forests to TFP growth because, according to SNA principles, _plantation_ forest outputs are measured as part of production in the agriculture sector.

9
form of intimate partner violence**|0.492<br>0.535|{.128}|
|**_B. Empowerment and Aspirations_**|||
|**Gender Empowerment Index [0-100]**|.175<br>.145|{.497}|
||[.204]<br>[.209]||
|**Sexually active**|.696<br>.671|{.364}|
|**If active: Age at sexual debut**|14.5<br>14.6|{.223}|
||[2.21]<br>[1.84]||
|**If active: Uses contraceptive**|.425<br>.408|{.383}|
|**(any, excluding condoms)**|[.495]<br>[.492]||
|**If active: Ever used condoms**|.093<br>.098|{.584}|
|**Leisure activity: Men (weekly hours)**|4.89<br>5.20|{.380}|
||[5.27]<br>[5.19]||
|**Time use: Learning Activities (weekly**|29.4<br>27.2|{.804}|
|**hours)**|[34.2]<br>[30.1]||
|**Unwanted sex over past year**|.121<br>.086|{.138}|
|**Transactional sex over past year**|.039<br>.043|{.941}|
|**_C. Human Capital and Economic A_**|**_ctivities_**||
|**
351,946,619|129,962,760|224,652,419|89,170,854|103,383,673|18,704,086|122,087,759|
|2004-05|375,871,197|150,098,317|246,782,858|107,059,771|121,870,827|26,147,004|148,017,831|
|2007-08|389,035,480|163,874,365|262,370,352|120,148,883|111,853,912|23,526,797|135,380,709|
|2009-10|398,066,826|173,753,871|274,142,334|129,531,026|103,722,684|25,164,653|128,887,337|
|2011-12|404,974,104|181,536,553|281,815,810|135,890,458|100,942,252|27,915,420|128,857,672|

Source: Authors’ own estimate based on NSSO and census data as noted below. Note: As described earlier, we extrapolated census data from 1991, 2001 and 2011 in order to project the total female population above 15 years of age for 1993-94, 1999-00, 2004-05, 2007-08, 2009-10 and 2011-112, across rural and urban locations. We then use the FLFP rate as calculated from NSSO data for the same years to determine the total FLFP.

Our calculation from NSSO survey data across rounds suggests that FLFP rates for the age group of 15 years and above had declined from 42.6 percent in 1993-94 to 31.2 percent in 2011-12 (Table 2). The decline had been continuous during the last two decades, except for the year 2004-05, when it rose to 42.7 percent. While the FLFP rate had initially been higher in
2283 PoLicy RESEARCH WORKING PAPER New Tools and New Tests in. Comp arative. Politicalto. E ; conomy The Database of Political Institutions Thorsten Beck AlbertoSn CarGro Philip Keefer Patrick Walsh The World Bank DevelopmentRegulation and Research CompetitionGroup Policy February 2000 Some say that democracy more likely to survive under parliamentary governments. That result is not robusi to the use of different variables fram the Database of Politicai Institutions, a large new cross-country database tnat may illuminate many other issues affecting and affectea by political institutions.

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# **4.6. Administrative Data**

We received a list from JUCEMG of all firms which registered with the chamber of commerce in Belo Horizonte between October 25, 2011 (the day the communication treatment started) and September 19, 2012. This list contained the official business name, fantasy name, street address, phone number, and CNPJ of the business, and also contained registrations for MEI status as well as SIMPLES. In addition, we obtained a list from PBH of all firms registering for an ALF license during this same period. We used a matching algorithm and manual checking to match this to the listing survey data, described in Appendix 4. For each formalization measure, we define “definite” matches as cases where there are sufficient data in both data sources to make it almost certain we are matching the same firm, and “definite or probable matches” to also include cases where it seems very likely to be the same firm, but less data are available for confirming the match. We also construct an overall measure of whether a firm has formalized which measures whether they are a definite or probable match for at least one of these three forms of formalization (MEI, SIMPLES, or ALF).

We cross-checked the accuracy of this matching procedure with two subsamples of data. The first was reports from the inspectors, who recorded the CNPJ and ALF of firms which registered between the inspectors’ first visit (when they issued warnings to informal firms) and their second visit (where they came back to see whether firms had formalized or were taking steps towards doing so). Secondly, in November 2012 the survey firm attempted to re-contact firms that were in the follow-up survey and ask them for their CNPJ and ALF numbers and registration dates.<sup>11</sup> Both these checks revealed the matching process to be accurate at matching firms which had formalized – it was only a handful of cases where individuals had registered at different addresses that we had not detected.

# *
We first discuss the omitted variables problem and explain the large number of controls that we include in our panel estimations of (5) to deal with it. We then turn to the reverse causality problem and detail our instrumentation strategies. Table 8 in Appendix B.1 summarizes our main variables and provides descriptive statistics.

> 10One may be worried that identification in (5) comes from the _within_ variation in the data. The latter could be small given yearly data, especially for the spatial variables. This point has been made in other studies (e.g., Ellison _et al._ , 2010, p.1200), but those studies usually use more aggregated measures of agglomeration. Those measures change much more slowly over time than the _K_ -densities, especially at short distances. The reason is that our microgeographic measures are constructed from geocoded data, and that there is a lot of churning (entry and exit) at short distances. This is not picked up by spatially more aggregated measures, but it is by ours. Substantial plant-level churning creates a tension. One the one hand, there is _a lot of year-on-year variation_ , which allows for identification using within variation (see Table 3). On the other hand, there is also _a lot of noise_ at a small geographical scale, which makes the estimates less precise. The _K_ -density cdfs are kernel-smoothed across distances, which reduces the noise due to plant-level churning (see our cepr discussion paper version for additional details). Smoothing is also a standard procedure when working with the asm longitudinal files (e.g., Statistics Canada usually uses three-year moving averages to filter noise in these series).

12
1 See e.g. Thomas, 1990, Haddad, Hoddinott and Alderman, 1997, Duflo, 2000, Quisumbing and Maluccio, 2000, Attanasio and Lechene, 2002, Katz and Chamorro, 2002, Schady and Rosero, 2007.

> 2 The World Bank defines the investment climate as _the set of location-specific factors shaping the opportunities and incentives for firms to invest productively, create jobs and expand_ (World Bank, 2005, p19). De facto, any factor that affects firm performance and decision making can be considered part of the investment climate. This has led some (e.g. Easterly, 2002) to criticize the concept as being devoid of any meaning. Instead, we take the view that it is important to clearly specify which aspects of the investment climate we are considering.

3 For example, in the Amhara region in Ethiopia, where the Ethiopian Rural Investment Climate Survey data used in this paper were collected, the belief that the harvest will be bad if women work on the farm is prevalent (Zwede and Associates, 2002, Bardasi and Getahun, 2007).

4 Gender differences in the performance of male and female entrepreneurs in developed countries are relatively well documented, but the evidence is mixed. Some studies report evidence of female underperformance, whereas other do not find gender differences (see e.g. the literature reviews in Parker, 2009 and Sabbarwal and Terrel, 2008).

5 In addition, Sabbarwal and Terrel (2008) find some evidence that female firms in Eastern Europe and Central Asia have higher returns to scale, but the differences in returns to scale between men and women are small, and could be an artifact of a rather restrictive production function specification. For example, differences in technology between sectors are modeled as being additive in TFP, rather than as interacting with the returns to capital, labor and material inputs.

6 Some studies suggest women indeed have more difficulty accessing finance than men (e.g. Brush 1992, Carter, 2000), while others
they are in overall sales. Analogously, the exit of poorly performing firms should raise productivity. And reallocating resources in the sector toward more productive firms will also raise sector productivity growth.

Over 1998–2013, most of the increase in productivity—two-thirds—came from the entry of new firms (Figure 9).<sup>11</sup> To help put this in perspective, the rate of entry of new firms averaged 10 percent a year. The other primary source of growth was improving productivity of incumbents, which contributed 40 percent.

**Figure 9 Decomposition of annual TFP growth in manufacturing, 1998–2013**

(percent a year) <!-- Start of picture text --> 2.00<br>1.63<br>1.28<br>1.22<br>1.11<br>0.95<br>0.78<br>0.52<br>0.37<br>-0.15 0.05 - 0.10 0.04 -0.16 -0.04<br>1998~2013 1998~2007 2007~2013<br>Overall Within<br>Between Entry<br>Exit<br>2<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> Source: Authors’ estimates based on NBS’s Industrial Enterprise Survey.

Note: Calculations exclude firms that switched between sectors in the sample period. The number of such firms was small.

Over the entire period, firm exit contributed negligibly to manufacturing productivity growth, reflecting one of several possibilities. Poorly performing firms either did not exit or exited but accounted for only a small percentage of aggregate output. Or the productivity of some firms that exited was average or better. Similarly, there were no gains from reallocating resources (labor, capital, and intermediate inputs
# **1. Introduction**

Sound fiscal policy is central for macroeconomic stability and growth, as well as for poverty and inequality reduction. This can happen both through its influence on macroeconomic conditions (economic growth, employment, and inflation) and through the distributional implications of taxes and public spending. For instance, public spending that is well-targeted to poor and vulnerable households can effectively alleviate the incidence and depth of poverty, support broader distribution of the benefits of economic growth (de la Fuente et. al 2017), while at the same time leaving scope and resources for public interventions in other areas. Fiscal policy thus plays a direct role in poverty and inequality reduction through redistributive effects and how resources are collected and spent influences whether the fiscal toolkit achieves povertyreducing and equalizing goals. In the case of Cambodia, so far a comprehensive analysis of the distributional implications of fiscal policy has been missing and this study aims to fill this gap.

In the decade to 2019, Cambodia sustained macroeconomic stability, including prudent fiscal management, and robust growth. Strong and inclusive growth contributed to substantial poverty reduction and shared prosperity. According to official statistics, poverty nearly halved nationally from 33.4 percent in 2009 to 17.8 percent in 2019/20. Inequality remained moderate in international comparison with a Gini coefficient estimated at 0.32 in 2019/20, although this level of inequality represents a slight increase since 2014.<sup>5</sup> This paper assesses the redistributive effects of Cambodia’s 2019 fiscal policy and its individual elements. We apply an internationally recognized methodology developed by the Commitment to Equity (CEQ) Institute to assess how taxes and social spending affect poverty and inequality.<sup>6</sup> The present analysis relies on the 2019/20 Cambodia Socio-Economic Survey (CSES) and 2019 administrative data on taxes, transfers, and social spending. The CSES is collected by the National Institute of Statistics (NIS) and the fiscal administrative data is compiled by the Ministry of
between legal and illegal activities, leaving aside factors capturing the relative costs such as the efforts to prevent and fight criminal activity. Among the costs indicators, the most common ones refer to expenditure in security, the number of police officers, the prison sentences associated with commission of crime, and detention rates as percentage of committed crimes (Dilis, Miron, and Summers 2010). These are important elements determining the evolution of violence. But, in view of how limited is the systematically collected information of this type, including them would significantly reduce the number of observations in our analysis and introduce an information-availability bias.

The set of controls included in the empirical exercise below are the following: hourly labor income (Gould, Weinberg, and Mustard 2002); income inequality (Bourguignon 1999 and Enamorado et al. 2014); unemployment rate among the population aged 35 to 44 (Freeman 1999); high school dropout rates (Anderson 2014 and Chioda 2015). In addition, we control for the number of youth aged 15 to 24 as a share of the total population, the proportion of workers in the informal sector, and the proportion of total employment accounted for by employees in the manufacturing industries.

# **_3.1 DATA SOURCES_**

In Mexico, like in most developing countries, measuring violence and criminal activities is a challenging task. The ideal measurement would capture all illegal activities occurring in a country during a given period. However, this is impossible in practical terms. By their nature, illegal activities are hidden and, in contexts of weak institutional capacity and a limited application of the rule of law, the great majority of crimes go unreported by victims. For instance, according to the most recent victimization survey, approximately 33 million crimes were committed in Mexico in 2013 of which only 10 percent were reported.

However, there are two crimes that are measured with some degree of accuracy: homicide and car theft. Unlike other offences, for which the opening of a ministerial investigation requires a formal complaint
We use two institutional measures to capture the differences in investor rights across countries. In the regression specifications, we interact country level institutional variables with firm level governance measures to examine how the effect of governance on maturity varies across different institutional environments.

Our first country level governance measure is investor protection ( _investor_ ). This variable measures the extent of minority shareholder protection against expropriation by the controlling shareholders. It ranges from zero to 10 and higher values imply better protection of minority shareholders. We use the “strength of minority investor protection” variable from “World Bank Doing Business Surveys” to construct this measure. The data for constructing this variable comes from surveys administered to corporate and securities lawyers and includes sections on countries’ disclosure requirements for related-party transactions, extent of director liability in lawsuits, shareholders’ ability to initiate lawsuits, shareholders’ rights in major corporate decisions, existence of governance mechanisms controlling excessive board control and the transparency of corporate decisions.<sup>6</sup> The survey for this measure begins in 2006. We use the value at year 2006 to back fill the missing values for each country. Within-country variation of this variable is highly persistent. Hence, filling missing values with most available data would not create a large bias. Moreover, our focus in this paper is on cross-country variation in the debt-maturity relationship rather than within-country variation.

Our second variable is the legal environment. We use a dummy variable ( _common law_ ) that takes a value of one for common-law countries (the United States; the United Kingdom; Australia; New Zealand; Canada; Hong Kong SAR, China; Ireland; and Singapore) and zero for civil law countries. This variable comes from Djankov, McLiesh, and Shleifer (2007) and is similar to the one described in La Porta et al. (1998). We use this variable to instrument investor protection.

# **2.4. Short-Term Debt, Leverage, and Control Variables**

We use Compustat-Global to measure short-term debt and to construct
the historic record of a set of variables related to the composition of the household and to the cash transfers it received.

We have constructed our treatment variable using seven available variables in the ENCEL panel:<sup>36</sup> (1) the length of exposure for households (period in which the households appear as beneficiaries of the program), (2) the total amounts transferred into a household up until 2003, 2007 and 2017, (3) the number of people comprising of a household (excluding “temporary” members), (4) the number of youths in a household of primary school age, (5) the number of youths in a household of secondary school age, (6) the number of youths in a household of high school age, and (7) the number of elderly adults in a household.

It is important to note that estimating the degree of exposure of the households to the program in this manner is similar, in spirit, to the strategies that use deterministic functions of exogenous variables.<sup>37</sup> The main difference of this study with other efforts to estimate the effects of the program is that, instead of calculating years of eligibility according to the individual’s age or potential transfers in accordance with family composition, it is estimated empirically, taking advantage of ENCEL’s complete panel, allowing a better partitioning of the working sample in order to differentiate the types of households that are statistically homogeneous, according to their historic domestic arrangements and cash transfers received from the program.<sup>38</sup> We consider that the empirical correlation between the households’ composition and the temporal distribution of the transfers hardly reflects the disposition of certain type of families with some parents being more concerned over their children than others, or children with having greater cognitive abilities or being better adapted to school life. Rather, correlation is a result of households’ observance of the operational rules of the program over time. Therefore, we