rafmacalaba/data-use-mentions-extended huggingface dataset:
</sup> The data consists of weekly pricing, volume, and store merchandising conditions generated by participating retail store point-of-sale systems across the US from 2006-2015.<sup>22</sup> Data is included from approximately 35,000 participating stores and include store types such as drug, grocery, and mass merchandise stores, covering around 53%-55% of

> 19This measure uses Shapley and Owen values, which calculate the average marginal contribution of each regressor over all possible orderings.

> 20As discussed in Section 4.1, this occurs given that the benefits series contains a large disturbance term, muting the observable first stage; the housing-price control helps to net out this disturbance term, improving relevance.

> 21Information on access to the retail scanner data as well as the consumer panel data below is available at `http://research.chicagobooth.edu/nielsen/` .

> 22The data measures the weighted average weekly price of each good at the store-level, which includes retailer discounts such as loyalty cards and coupons. We find minimal economically meaningful changes to shopping behavior of households, suggesting that discounts and coupons do not explain our findings of price incidence (see Appendix Table N13).

12
on these existing strategies to design a blocked savings account to help shield savings from redistributive pressure.

# **4 Experimental Design**

We design an experiment to test whether redistributive pressures distort labor supply—and therefore output and earnings—among full-time factory workers in Côte d’Ivoire. Workers are paid piece rates for output, and receive their wages twice a month in cash. We provide an overview of the design in this section, summarize the implementation protocols in Section 5, and provide more details in Appendix A.4.

## **4.1 Blocked savings accounts**

We seek to construct a design that mimics the approach in the tax schedule in Figure 2B above: a tool to lower redistributive pressure on income gains. To approximate this approach, we introduce a blocked savings account into which workers can transfer earnings _increases_ . Workers who opt in choose a threshold, which must be weakly higher than their baseline earnings. In each biweekly paycycle, any amount they earn _above_ this threshold is automatically deposited by the factory into the blocked account; the remainder of their earnings is paid in cash as usual on payday.

Using administrative data from the factories, we compute baseline earnings to be the worker’s average earnings per paycycle in the past 3 months. The funds in the account cannot be accessed until the end of the blocked period (3-9 months). The savings product is administered by one of the largest banks in Côte d’Ivoire, Banque Populaire (BPCI).

Because the funds deposited into the blocked savings account cannot be accessed, they cannot be used to fulfill transfer requests—at least not until cash is available after the unblock period. This enables workers to potentially accumulate a large lump sum of savings. Consequently, if workers decide to increase their labor supply, the incremental earnings from that effort (which then get deposited into the account) will potentially be “taxed” at a lower rate than if the account were not available. In other words, the accounts
14

# 2. Preferences and parties

For most political economy questions, one is interested in the structure of political decision making and also in the policy preferences or ideological leanings of decision makers. Preferences are difficult to discern even in a small sample of countries, In the US literature, the most rigorous attempts at assessing preferences have relied on voting records of legislators. In the literature on European politics, impressive progress in comparative politics has been made on the basis of labor-intensive efforts to review and classify the platforms of political parties (see, for example, Laver and Shepsle). For many countries of the world, though, such information is not available, not germane, or not a reliable indicator of decision maker preferences.

A starting point for any analysis that requires preferences of policy makers is the party identification of decision makers. Our data set identifies the party of the prime minister and/or president, the three largest parties in the government coalition, and the largest party in the opposition. In addition, our sources provide useful information for some countries about the policy preferences of key decision makers on five different dimensions: economic, nationalistic, regional, rural, religious. These dimensions were chosen because they do not necessarily correlate with each other: religious or nationalistic parties adopt both left and right wing economic policies; some religious parties are not nationalistic and some are; etc. That is, we intended, where possible, to allow for a multi-dimensional policy space. Coverage is far from exhaustive, unfortunately, but constitutes the most comprehensive characterization of parties and decision makers that can be extracted from the sources upon which we relied. Detailed information on party platforms (even on the existence of written party platforms) is unavailable for most countries from our primary sources (Political Handbook of
as being less secure when compared to large and medium firms as shown in Figure 1a. On probing deeper, we find that there is significant variation across sizes even within the same country, i.e., holding all legal and political institutions constant. For instance,

> 13 According to TI, “International surveys on perceptions serve as the most credible means of compiling a ranking of nations”. See Batra, Kaufmann and Stone (2003) for a detailed analysis of the sampling methodology and findings of the survey

> 14 The original database consists of 10032 observations in 81 countries. We lose 2243 observations because of missing data on the dependent variable and the four firm level variables, size, ownership structure, organizational form and industry sector. On removing variables with missing institutional variables, we lose 1country (West Bank and Gaza) which has 29 firms. This leaves us with 7760 observations in 80 countries.

> 15 Employment is typically the most reliable figure in developing countries. Hence number of full time workers is used as a measure of firm size by the World Bank Group and other international survey teams including RPED and the Oxford Centre for the Study of African Economies

13
|Temporarily away from<br>work|Although you did not work last<br>week, do you have a job or your<br>own business to which you will<br>soon return?|Although you did not work last<br>week, do you have a job or your<br>own business from which you are<br>temporarily away and to which you<br>will soon return?|
|---|---|---|
|Unemployed|During the last week, did you<br>search for a job or try to establish<br>your own business or farm?|Did you search for a job or try to<br>establish your own business or<br>farm in the last week or the last<br>four weeks?|

Note: Translated by the authors from the questionnaires of the EPHPM 2015 and 2018.

A comparison of the 2015 and 2018 employment outcomes for rural women is reported in Table 7. Assuming negligible changes in female labor force participation between the two years, we can compare the results from the two surveys to draw some inferences on how changes in the questions affected the measurement of female employment. The comparison between the two survey years shows that removing the explicit exclusion of housework did not change how women reported employment in response to the initial question. The addition of the follow-up question which lists different types of underreported activities increases the share of women who report paid employment, but only by 2.1 percentage points. In addition, it is also probable that it increased the share of women who report having unpaid employment (25 percent of the women who reported an employment activity in the follow-up question then identified it as unpaid work). The share of women reporting unpaid work increases from 5.8 percent to 7.2 percent. With these changes, the share of employment increases from 37.4 percent to 41.2 percent.

Table 7.
(267)|12,397|
||Survei Tahunan Perusahaan Industri|Badan Pusat Statistik (BPS-||Districts and||
|Indonesia|Pengolahan(Annual Manufacturing Survey)|Statistics Indonesia)|2000-2015|metros (291)|34,366|
|Ethiopia|LargeandMediumManufacturingSurvey|CentralStatisticalAgency(CSA)|=2000-2009|Towns(91)|3.465|

Notes: For each country, the statistical agency listed in the source column implements an annual survey of manufacturing establishments. In Chile, although we have access to the data from 1996-2007, we used only the period 2001-2005 because product categories changed in 2001 and the correspondence between the old and new product classification is not perfect, while for the year 2006-07, establishment location is missing.
decrease relative to the size of the economy (Figure 1.3).

<!-- Start of picture text --> Figure 1.3 Costa Rica: Current Account Deficit in US Dollars and as a Percentage of GDP<br> 1983-2003<br>0 -1.0%<br>-100 -2.0%<br>-200 -3.0%<br>-300<br>-4.0%<br>-400<br>-5.0%<br>-500<br>-6.0%<br>-600<br>-7.0%<br>-700<br>-800 -8.0%<br>-900 -9.0%<br>-1000 -10.0%<br>1983 1985 1987 1989 1991 1993 1995 1997 1999 2001<br>Current Account Deficit CA/GDP<br>Export Promotion FTZ Law Reform Intel’s Arrival<br>Import Liberalization<br>US$ millions<br>Capital Account Liberalization<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

The latter is a very important result, in view of the fact that the opponents of the liberalization process forecast that the trade balance would be wrecked as a consequence of import liberalization. Thus, the performance of Costa Rica’s economy during the last two decades demonstrates the profound economic understanding of the designers of the reform, as they took into account the general and not merely the partial equilibrium effects when designing and implementing the process of economic liberalization. In fact, 7
for which VAT rates are defined.<sup>10</sup> Note that as in most household surveys, total consumption of households in HBS using the weights provided by GUS amount to 49 percent of total household consumption reported in National Accounts. Since we do not make assumptions about informality, the amount of VAT calculated using the HBS constitutes 67 percent of total tax collections paid by the households. For excise taxes, we apply statutory rates to consumption of alcohol, tobacco, fuel and automobiles (net of VAT) identified in the HBS to estimate the direct burden of these excises on households. However, since fuel is an important intermediate product we use a costpush model and the 2010 input-output matrix to estimate second round effects of excises on fuel to all other products in the economy.

On the spending side, the HBS provides detailed information on who received payment from contributory and non-contributory social protection programs. Family Allowance, Social Assistance, Nursing Allowance, Nursing Benefit, and Housing Benefit are directly identified in the survey and since they are not taxable, the reported net value can be directly used.

The approach to estimate the incidence of public spending on education followed here is the socalled “benefit or expenditure incidence” or the “government cost” approach. In essence, we calculate per beneficiary input costs by level of education from government spending at the voivodship level and the number of pupils in each level and voivodship. This approach is also known as the “classic” or “non-behavioral approach”, and it amounts to asking the following question: how much would the income of a household have to be increased if it had to pay for the free or subsidized public service at the full cost to the government? Since the HBS does not provide information on educational enrolment by public versus private institutions, we identify children going to public schools based on household expenditures on tuition, and assign a lower public benefit to those households based on the standard subsidy amount per student in private institution (75 percent for those in
<!-- Start of picture text --> EU-15<br>90<br>All HICs<br>80<br>Other HICs<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-10<br>percent<br><!-- End of picture text --> Figure 7. RRAs to agriculture, including decoupled supports, among EU-15, all high-income, and non–EU15 countries, 1955–2010 (%).

Source: Authors’ compilation, from estimates in Anderson and Nelgen 2012b.

57
is, given an initial employment shock _x_ 0, thereafter _xt_ +1 = _xt × ρ_ . Based on employment data from across the OECD, we calculate a proportional factor of _ρ_ = 0 _._ 7143 (see Web Appx. Section D).

Of course such an economy would never fully recover: even though lim _t→∞ xt_ , we would still have that _xt >_ 0 _∀t_ . Having a date at which the recovery is complete is necessary to distinguish cleanly between the direct impact of the shock through loss of employment and the long run impact through lost human capital. Based on these criteria, we proceed as follows. First, we assume that the full recovery takes 10 years. Assuming a decay factor of 0 _._ 7143, by ten years there would be very little residual deviation from steady state GDP (0 _._ 7143<sup>10</sup> ≈ 0 _._ 03).<sup>32</sup> Then, we subtract this deviation from _xt_ for all _t <_ 10 – so the last factor is now zero. We set _xt_ = 0 for _t ≥_ 10. Finally, we multiply all the factors by a number (slightly larger than one) to ensure that the initial

> 32In addition, the employment recovery from the Great Recession of 2008 and onwards, which was a smaller employment shock, took over ten years to be completed: see Web Appx. Section D.
government spending on output. These short-run responses of output to government spending are potentially consistent with a variety of longer-run responses of output to aid.

The rest of this paper proceeds as follows. Section 2 sets out the empirical methodology and identification strategy, and Section 3 describes the construction of the instrument based on loan-level data from the DRS database. Section 4 contains my core results, and Section 5 subjects them to a variety of robustness checks designed to explore the plausibility of the identifying assumption. Section 6 investigates a number of potential sources of heterogeneity in multipliers, and Section 7 concludes.

# **2.  Empirical Strategy**

I estimate variants on this simple empirical specification to assess the short-run effects of government spending on output: (1) Here, and denote GDP and total government spending in country and year , both measured in constant local currency units; and the composite error term denotes all other sources of GDP fluctuations, such as other fiscal or monetary policy changes, terms of trade shocks, changes in productivity, natural disasters, and many other shocks. I sweep out the country-specific and yearspecific components of the error term, and , by including a full set of country and year effects in all specifications. The key parameter of interest is , which captures the short-run government spending multiplier, i.e. the contemporaneous change in output due to a change in government spending. As noted in the introduction, cannot be interpreted as a deep structural parameter. Rather, it should simply be thought of as a reduced-form empirical summary of the contemporaneous relationship between annual fluctuations in government spending and output.

The standard difficulty in statistically identifying is that changes in government spending are likely to be correlated with other contemporaneous shocks to output captured in the error term, so that OLS estimation of Equation (1) will be inconsistent. For example, if government spending increases endogenously in response to an economic downturn, perhaps due to the role of automatic stabilizers, then OLS estimates of the multiplier would be biased downwards. On the
Note: Controls are not exhaustive yet. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The table contains the results from a single, exactly-identified OLS instrumental variable regression for 2009, 2014, 2019, and 2020 Cambodia Socioeconomic Survey (CSES) data.

# <u>Hours worked</u>

In terms of hours worked, we also find results that support our hypotheses (Table 4.2). First, in the long run (2009–2019), increases in exports per worker correlate with increase in mean weekly work hours for male, female, and overall workers. For females, a foreign demand shock of US$100 per workers leads to an increase in mean weekly working hours by 54 minutes, higher than nearly 41 minutes increase in mean weekly hours for male workers. For a shorter period of time (2009–2014), only the coefficient for female workers is statistically significant at a 10 percent significance level. This is not surprising since Cambodia specializes in female-intensive exports, and hence increases in trade exposure in Cambodia are likely to affect female working hours more.

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## **Platform observations**

We generate a measure of the typical conditions a rider would face on a trip at a given location and time, independent of their choice of space. Through the app we recruited and tasked men commuters to collect data on crowding and enforcement of the gender reservation policy from the platform across the entire SuperVia network. For this purpose, we divide all SuperVia lines into segments of several stations and designate half-hour blocks of the rush hour periods (6-9AM, 5-8PM). We then offered tasks such that, over a period of about three months, our platform observers collected at least three observations from each such (halfhour _×_ line segment) combination in the direction of rush-hour traffic (i.e., in-bound in the morning, out-bound in the evening). Each task specifies where and when to collect the data. Observers stand on the platform at the designated time and place and record the percentage of male riders in both public and reserved spaces as well as the share of commuters who can sit. We impute the mean observation for each of these station-time period bins for each ride in our data.

We take two additional steps to validate these data. First, we confirm that data collected by the platform observation team are strongly correlated with what riders themselves observe on their rides (Table A3). Second, we obtain administrative data from the SuperVia as an alternative measure of crowding.<sup>15</sup> Figure A2 shows crowding reports from our platform observations are highly correlated with SuperVia administrative records.

# **3.2 Social Norm Survey and Implicit Association Tests**

To measure the attitudes women face while traveling in the public space, we administer a social norm survey and IATs on a random sample of men and women commuters on the train platform. The survey includes questions on commuting behavior, stated preferences and willingness to pay to use the reserved space, perceptions about harassment and attitudes

> 15The estimates are generated by SuperVia transport planners, based
In most HCES, the largest questionnaire is the HH questionnaire which collects detailed information on housing welfare and consumption. Demographic characteristics such as employment, education, health, gender, or age are collected at individual level. Most HCES, including living standard measurement surveys, household budget surveys, or household income and expenditure surveys also include a housing module, which covers a wide range of variables on housing conditions and physical characteristics of the housing unit such as the construction material used, the number of rooms the house has, or the availability of electricity. The module also collects information on housing-related expenditures such as utility bills or expenditures on maintenance and repairs. A complete list of surveys included in our analysis is displayed in Annex 2.

Despite the common focus on the determinants of consumption, expenditure, and living standards in developing countries, household consumption and expenditure surveys exhibit a diversity of structures and designs, with their own coding standards for variables, making cross-country comparison cumbersome. Given the specificities of each country, response options for variables within the housing module as well as standard questions on housing characteristics differ across regions and countries. While speaking to the same concept such as access to water or access to electricity, the way that concept is measured, which answer categories are provided, and how answers are coded across surveys might differ. In order to ensure comparability of housing adequacy across countries, the survey-specific variables on the seven adequacy dimensions need to be harmonized across HCES surveys.<sup>8</sup> We adopt harmonization practices followed by GMD and others in considering the harmonized adequacy dimension as a bivariate dummy at the household level on whether their response choices can be categorized as meeting adequacy criteria.

# 3.2.2 DHS

In order to complement and check the robustness of the coding and harmonization of the seven adequacy dimensions, we revert to Demographic and Health Surveys (DHS) which are nationally representative household surveys that provide data for a wide range of indicators in the areas of population, health, and nutrition and
else (or almost nowhere else) in the data set. This is because Bitcoin prices are volatile and the unit of account for most crypto-currency trades is the underlying fiat currency (e.g., where someone sends dollars from the United States to relatives elsewhere). The third section presents the main results, which we interpret as compelling evidence that Bitcoin are being used both for domestic payments and for international transmission of funds (including the large global pool of international remittances). The next section discusses how our methodology can, in principle, be applied (or adapted) to other cryptocurrencies and exchanges, including many classes of stablecoins. This analysis can perhaps guide regulators in identifying the selection of off-chain transactions to focus on when requesting IP addresses for audit. It also highlights how individuals or institutions, who may not have access to any private information, might still be able to draw inferences on the transaction use of cryptocurrencies and cross-border transfers, including capital flight. The final section concludes.

# **I:  Local Bitcoins, and off-chain exchange**

The core data set, described in detail in the Data Appendix (Appendix A.7), makes use of data from LocalBitcoins.com, the world’s largest peer-to-peer (P2P) Bitcoin exchange, which allows Bitcoin to be traded in 135 different fiat currencies. The data encompass more than 45 million trades over the period March 15, 2017 – July 23, 2021.<sup>4</sup> To contextualize, the universe of trades that occurred on-chain over the same period, encompasses 452 million transactions.<sup>5</sup> LocalBitcoins.com is able to operate in such a large number of countries because it only offers deposit services for members’ Bitcoins, but does not involve national fiat currencies, thereby side-stepping national banking regulations. Although other P2P exchanges

> 4 LocalBitcoins has existed since 2012. However, we limit our analysis to the period since March 2017, when the website revamped the exchange’s back-end