rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.3.1. Common characteristics**

The firm’s size is one of our common characteristics. We categorize firms into one worker (primarily selfemployment), two workers, 3-4 workers, or 5-24 workers. Workers may be paid or unpaid. Industry, at the one-digit level (or aggregated further to present meaningful descriptives), is available in all three data sets. The 2012 and 2018 ELMPS and EcC have the calendar year the firm started, while the EsC has the calendar year if 2010 or later and then switches to decades (e.g. 2000-2009). We therefore create a harmonized firm age variable that is single years of age from 0-7 years old, and then 8-17 years old, 18-27 years old, 28+, or do not know.<sup>14</sup> We further have information on the governorate, which we present in terms of regions: Greater Cairo, Alexandria and Suez Canal, Lower Egypt, and Upper Egypt.<sup>15</sup>

# **2.3.2. Characteristics in the Economic Census**

In the EcC, we have particularly rich detail on firm characteristics. In addition to the common characteristics described above, we include indicators for having unpaid workers and wage workers. We also measure the share (percentage) of workers who are female. Since we have detailed information on value added, invested capital, and labor, we analyze the relationship between formality and the log of capital per worker and the log of value added per worker. We also include information on whether the firm is a sole proprietorship or some type of collective ownership arrangement.

# **2.3.3. Characteristics in the ELMPS**

In the ELMPS, we can identify additional detail about firm characteristics and owners. We consider the owner to be the person who worked the most on the enterprise.<sup>16</sup> We include the owner’s sex, age group
new producers, as opposed to established ones. We defined such countries as those for whom the share of oil and gas resource volume discovered between 2001 and 2018 represents at least three-quarters of the total oil and gas resource volume discovered since 1945 in said country (based on data from Rystad Energy’s Ucube). This criterion excluded all countries that were already deemed significant producers, while allowing us to include countries such as Ghana, Mozambique and Tanzania, which already had modest production but made potentially transformational discoveries. Using resources found rather than proven reserves allowed us to include countries like Liberia, whose oil finds were ultimately found to be commercially unviable.<sup>2</sup> The second criterion measured whether the resources found would, if extracted in their totality at the price average over the period, be large enough to significantly alter the country’s economic future. We measured this by multiplying the total size of the resources found between 2001 and 2018 by the average Brent oil and US gas prices from 2001 to 2018 measured in 2000 real USD.<sup>3</sup> We divided this value by 20 years to account for the fact that the GDP reported is for a single year, whereas production typically happens over an extended period of 15 to 30 years.<sup>4</sup> Where the resulting amount is equivalent to at least 5 percent of the country’s GDP in 2000 (before our study period), we included the country in our sample.<sup>5</sup> This allowed us to distinguish between countries

> 1 Our study is based on information that was public up to June 26, 2020.

> 2 This measure comprises of proven, probable, and possible recoverable economical volumes at the time of discovery, as measured by Rystad Energy’s Ucube database.

> 3 The average Brent oil price was $52.4 per barrel (bbl) and average US gas price was $3.87 per million British thermal units
labor income. As is common in many such surveys, the survey tracks a dwelling unit, so that individuals which move houses attrit from the panel.

We examine income dynamics at the household level and test whether poverty traps exist for households. The ENEU only collects data on labor earnings for each household member in their principal occupation. We add this over household members and deflate by the Consumer Price Index for the relevant quarter from the Bank of Mexico to obtain real household labor earnings. To focus only on households for whom labor earnings are likely to be a main source of income, we restrict our sample to households with heads aged 25 to 49 years old. On average two percent of the observations have household labor income of zero. Using data from the ENIGH income and expenditure survey, which does include non-labor sources of income, we calculate that labor income represents 95 percent of total monetary income for urban households with heads in the 25-49 year old age range. In Section 5 we examine the robustness of our results to using full income and expenditure from the ENIGH.

After imposing these restrictions on the age of the household head and dropping missing values, we have 54 five-quarter panels, beginning with the panel of 3,930 households which were sampled from the first quarter of 1987 through to the first quarter of 1988, and ending with the panel of 11,158 households sampled from the second quarter of 2000 through to the second quarter of 2001. This gives a total sample of 402,052 households. Ten percent of households are observed for only one quarter, while approximately 65 percent can be followed for all five quarters. For our panel data analysis we use unbalanced panels, resulting in 1,671,530 household-quarter observations in all.

Pseudo-panels are constructed on the basis of five year birth intervals of the household head interacted with three education levels (primary schooling or less, 7 to 12 years education, and more than 12 years education). For example, all household heads born between 1960 and 1964 with
# **A.2 Delhi Primary Survey**

**Travel Purpose & Distance** The survey inquires about respondents’ primary travel the a selection of work purpose during post-scheme period, offering options including (including work and job search), education, healthcare, shopping, religion, leisure, pick/drop off, and others. Additionally, the survey collects information regarding users’ travel distances and travel distances buses. It should be average average specifically by noted that the survey only asked new users but not continuous users about their average travel distance prior to the implementation of the scheme.

33
the developing world. However, expected yield gains, costs of further developing these technologies, and the political acceptance of genetically modified foods are all highly uncertain.

To quantify the extent to which the advances in crop technology can further boost agricultural yields over the next century, we first need to assess the magnitude of existing yield gaps at the global scale. In a comprehensive study, Lobell _et al._ (2009) report a significant variation in the ratios of actual to potential yields for major food crops across the world, ranging from 0.16 for tropical lowland maize in Sub-Saharan Africa to 0.95 for wheat in Haryana, India. For the purposes of this study, we employ the results of Licker _et al._ (2010), who conduct comprehensive yield gap analysis using global crop dataset of harvested areas and yields for 175 crops on a 0.5<sup>_◦_</sup> geographic grid of the planet for the year 2000. Using these estimates, we calculate the global yield gap as the grid-level output-weighted yield gap of the four most important food crops (wheat, maize, soybeans, and rice). The resulting estimate suggests that average yields are 53% of potential yields, which is close to the median estimates by Lobell _et al._ (2009). As a further robustness check we employ the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model (Jones _et al._ , 2003), run globally on a 0.5 degree grid in the parallel System for Integrating Impacts Models and Sectors (pSIMS; Elliott _et al._ 2014b) to simulate yields of the same four major food crops under best agricultural management conditions and compare simulated yields to their observed yields. The resulting yield gap estimates were not substantially di↵erent.

In the optimistic (i.e., “good”) state of advances in crop technology, we assume that yields continue to grow linearly throughout the coming century, eliminating the yield gap
including bargains with elite taxpayers, reform design to manage resistance from the general population, and politics within the tax administration is beyond the scope of this paper and is extensively discussed in the political science literature (e.g. Martin 2023, Moore et al 2018, Pritchard 2019, Brautigam et al 2008, among others). Instead, we focus the discussion below on evidence relating to factors that determine political choices to invest in tax capacity or to deploy existing capacity.

Broadly, we can consider politicians as weighing the expected benefits and costs of taxation for re-election prospects. On the one hand, higher tax revenues may support re-election by funding the provision of public services, infrastructure investments, social protection, and other public programs that citizens value. On the other, taxing citizens may harm re-election by raising citizen expectations and demands for accountability. Interestingly, this accountability effect can be triggered by simply demanding taxes regardless of whether those taxes are paid. A study of a doorto-door property tax campaign in Kananga, DRC, found higher levels of citizen engagement even among those who were visited by tax authorities but did not comply (Weigel, 2020). More broadly, there exists cross-country evidence that tax collection declines prior to competitive elections, suggesting that governments act strategically to avoid negative backlash from citizens (Prichard, 2018).

We discuss below three key factors that may shape the cost-benefit calculus of politicians in determining how much taxation to pursue: alternative revenue sources, political competition, and available technologies for reducing the salience and cost of collection. Alternative revenue sources may outright dampen the impetus to tax. Oil-rich states are a prominent example of a low-tax, lowaccountability setting, where oil revenues support a high level of public spending without the need to tax citizens. For instance, oil rich gulf countries collect on average 3.6 percent of GDP as taxes (UNU-WIDER Government Revenue Dataset 2022). At the subnational level, transfers from the central government may dampen the motivation for local taxation. In Brazil, jurisdictions hit by a
savings. Because we measure sales volatility using all available data for each firm and we only have 9 years of data, this measure is firm-specific (i.e. it is constant over the whole period of our sample).

**Cash flows** . We include cash flows to capture the cash generating capacity of the firm. In a dynamic model of Riddick and Whited (2009) cash flow is negatively correlated with cash stock because of serially correlated productivity shocks. Thus, when firms receive a good productivity shock (meaning they have good growth opportunity) their cash flow will raise. At the same time, their investment into productive equipment will also raise to allow the firm to take advantage of the higher productivity shock. As firm uses some of its stock of cash to invest into productive assets, the financial savings may fall.

We use two measures of cash flows: gross operating margin (defined as total sales, minus costs of goods sold, over total sales) and net income to assets, which is a closer proxy to the true cash flows available to firms.<sup>11</sup> Both measures could also proxy for profitability of the firm and thus can capture positive shocks to growth opportunities.<sup>12</sup>

# **5. Cross-Country Comparison**

In this section we benchmark Egypt’s savings into physical and financial assets with other countries for which we have the data. ICA data are available for a wide range of countries. We use data for the second wave of ICA surveys, dated 2006-2008 to compare with Egypt in 2007 We compare data on listed firms in 2007 with other listed firm data obtained from Datastream (we take pre-crisis year to eliminate the impact of the financial crisis that started in 2007 in the US and other industrial countries).

Figure 3 shows cash to assets in a cross-section of countries and Figure 4 shows financial savings to assets. Egypt falls approximately in the middle of the distribution on both measures.

> 11
%) Estimated difference<br>C10.3. SOSD: Social Sustainability Index C10.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.97*** 0.95***<br>H<br>Kendall tau-b 0.78*** 0.89*** 0.83***<br>Spearman 0.93*** 0.92*** 0.9***<br>A<br>Kendall tau-b 0.83*** 0.78*** 0.72***<br>Spearman 0.98*** 0.93*** 0.95***<br>M0<br>Kendall tau-b 0.94*** 0.83*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.2<br>1<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C11. Change in the indicators
a discount, on average, increases this share to 29%. Under _SDM_ , however, the uptake of LARCs at full price jumps to 35% - an increase of over 300% over _IDM_ . Interestingly, _SDM_ seems to also cause clients to become less sensitive to LARC prices: offering them discounts makes no difference to the adoption of LARCs. The difference in the effect of _Discounted_ LARCs between _IDM_ and _SDM_ is 18.1 pp (p=0.097).

In Appendix Section C, we present a model of individual utility maximization, where the clients care about the price of LARCs and the perceived returns to adopting them. This model provides straightforward implications that is consistent with _SDM_ causing a decline in the price elasticity of demand for LARCs. Importantly, follow-up surveys with clients indicate that this counseling approach maintained respect for their autonomy in decision-making: we found no differences between the two randomized counseling arms in a checklist of quality of service indicators, clients’ level of satisfaction with their counseling session, or their willingness to return to the hospital for FP services in the future (Table A10).

# **4.3 Heterogeneity of impacts**

In this sub-section, we ask, “What characterizes the client segment that is more likely to be persuaded by price discounts?” To answer this question, we first estimate the conditional average treatment effect (CATE) on LARC adoption of switching from _High_ prices to _Low_ or _Mid_ prices (as in the previous section, we binarized the treatment by pooling all the discounted prices). Then, we say an individual is relatively “strongly” affected by discounts if their estimated CATE on LARC adoption is above the median estimated CATE in the data, and relatively “weakly” affected otherwise, where we hold out folds of data in estimating the CATE so that an individual’s own outcome does not influence its subgroup assignment. We calculate the average treatment effect (ATE) for each subgroup. Finally, we compare the average value of each covariate
our panel data estimation, which raises questions about the accuracy of the point estimates.

Our first IV candidate is a dummy variable on firm’s _knowledge_ about the existence of public support programs for innovation activities offered by the Ministry of Science and Technology (Ministerio de Ciencia y Tecnología). This variable has been implemented in previous empirical studies that use information from innovation surveys in Argentina (see, for example, Brambilla and Tortarolo (2018), de Elejalde, Giuliodori, and Stucchi (2011). The condition of relevance is expected to be fulfilled because firms that know about public programs are candidates to use it, and therefore have a higher investment in innovation activities. The exclusion condition is justified considering that this dummy variable measures _knowledge_ about the program rather than firms’ participation. The latter can be correlated with other variables that may affect firm ́s productivity, given that the provision of public funds is sometimes based on firm performance. On the other hand, knowledge of the program greatly depends on public and private publicity for the program, which we can assume is random and uncorrelated with other firm characteristics.

Our second IV candidate exploits a pseudo-panel built by merging the two waves of the ENDEI survey. Although we cannot match firms across waves, we proceeded to match them at the sector-region-size level.<sup>20</sup> An essential reason for choosing this IV candidate is that there was a change in government in Argentina in December 2015.During the ENDEI 1 period (2010– 12) and the first two years of the ENDEI 2 period (2014–15), the leading political party differed

> 20 This is possible given that samples were constructed to be representative of the Argentinean manufacturing sectors (by industry-size). However, because ENDEI 1 was not constructed to be representative at the regional level, we could not divide the sample into the same regions as ENDEI 2. We therefore made an ad hoc split of the sample considering that cases were relatively balanced: “Region” is taken
Within just nine years after unification, tax morale values strongly converged, due in particular to a significant change in the level of tax morale in the East. Interestingly, older individuals in the East who were exposed for a longer time to the old regime, were less likely to change their social norms. As Kasper and Streit (1999) argue, the East Germans had to unlearn the old institutions and learn new ones, which requires some time and practice.

Looking at social interactions, one can argue that the behavior of other taxpayers is of great importance in understanding taxpayers‘ compliance. Individuals pay their taxes conditionally _,_ depending on the pro-social behavior of other taxpayers. They are more willing to pay their taxes if they perceive others to be honest. The extent to which others also contribute, triggers more or less cooperation and systematically influences the willingness to contribute. Relying on surveys from 30 European countries (EVS 1999/2000), Frey and Torgler (2007) show that if taxpayers believe tax evasion to be common, their tax morale decreases. In contrast, if they believe others to be honest, their tax morale increases. This result is consistent with the literature on conditional cooperation (see, e.g., Rabin, 1998; Falk and Fehr, 2002; Tyran and Feld, 2006). Lewis (1982, p. 144) argues for the possible existence of a ―tax subculture, with its own set of unwritten rules and regulations. Thus I am more likely to evade not only because I have friends who, I know, have got away with it (so why shouldn‘t I?) but also because evasion is ethically acceptable among my friends ... Furthermore, ̳no friends of mine can be criminals‘ ... ̳What‘s good enough for fine, upstanding citizens like Fred Bloggs, John Doe, Donald Campbell, Herman Schmitt and Hans Anderson is good enough for me‘‖.

Frey and Torgler (2007) use a non-linear
and services and services<br>Employment Total labor income Low-skilled Medium-skilled High-skilled<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: Export-employment (export-income, resp.) elasticities at the country-sector level have been estimated using employment (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. For more information on the instruments, see Appendix 1. The panel data regressions control for country-sector, country-time, and sector-time fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported. GVC taxonomy group by World Bank (2020). Skills (right panel) are determined by ILO statistics depending on their skill levels with high-skilled referring to levels 3 and 4, mediumskilled to level 2 and low-skilled to level 1. See https://ilostat.ilo.org/resources/concepts-and-definitions/classification-occupation/.

Several explanations can be offered for the skewed inverted U-shape of elasticities along the GVC taxonomy groups. Once countries upgrade from exporting mainly commodities (notably agricultural staples) to participation in limited manufacturing GVCs, this boosts export-induced demand for jobs in the manufacturing sector, as workers move out of informal (subsistence farming) into low-wage manufacturing jobs in apparel, electronics and other labor-intensive sectors. Higher exports of countries specializing in advanced manufacturing and services GVCs are linked to the highest employment growth within sectors due to additional demand for jobs alongside low-tech manufacturing jobs, especially in medium-tech manufacturing but also business services (such as trading and transport). Countries specializing in innovative GVC tasks, however, see their elasticities decline. Possible factors for the lower potential of exports to create jobs in these countries include the higher capital intensity of high-tech manufacturing production sectors, increased automation of routine tasks replacing certain types of workers, and offshoring of low-skill intensive tasks to
guaranteed by market-determined incomes unraveled the flat pre-transition skill-income link.

We explore the household surveys to examine whether (controlling for the standard demographic determinants of household savings) characteristics likely to be associated with households' relative likelihood to benefit materially from the transition to markets (and hence to experience an upward tilting of expected lifetime income profiles) and with income uncertainty are indeed systematically related to savings behavior. The next section describes the data. Section three presents some stylized facts derived from the unconditional distribution of savings rates by household characteristics. Section four presents regression results, and section five concludes.

# **2. Data**

Our results are based on three surveys contained in the _World Bank Household Expenditure and Income Data for Transitional Economies_ database. The household surveys (using stratified random samples) cover 2.466 households in Bulgaria (survey year 1995), 8.105 households in Hungary (survey year 1993) and 16.051 households in Poland (survey year 1993). The original surveys were conducted by different institutions; and were placed into a consistent format by the World Bank<sup>2</sup> to achieve comparability across the three countries, Each survey contains detailed information regarding the household's expenditure (eight categories) and its income sources (12 categories for Poland and Bulgaria, 11 categories for Hungary). Savings are defined as the difference between disposable household income and household expenditure.<sup>3</sup> In addition, the dataset contains information about asset ownership (five categories for Poland and Bulgaria, four categories for Hungary), household size, age and gender composition, location (urban/rural), sector of employment and education.

Based on this information, we construct a set of explanatory variables used in the regression reported below. The log and the squared log of the age of the head of household proxies for life cycle factors. A set of 0-1 dunmmies controls for the effect of various household characteristics on savings, comprising dummies for households located in rural