rafmacalaba/data-use-mentions-extended huggingface dataset:
from those countries, as described in the _Temporary Trade Barriers Database_ (Bown, 2015b).

Second, we adjust the baseline Estevadeordal et al. (2008) approach by examining changes in the variables defined as long differences. Long differences allow us to take into consideration the accumulation of a number of newly imposed TTBs that may have arisen over time, given that our main motivation is on the question of whether, at the ends of these periods, the accumulated effect was of a building block or stumbling block.<sup>20</sup> Specifically, the estimation sample uses the changes in bilateral import protection (potential preferential liberalization) taking place from 1990-2000, which we also split into two sub-periods: 1990-1994 and 1994-2000. The 1990-1994 period captures MERCOSUR as an FTA,

> 20 The Estevadeordal et al. (2008) approach involves a panel structure at the annual frequency. Since they are interested in exploiting the cross-country aspects of their data in ways that are less relevant for the questions under investigation here (which are focused on Argentina and Brazil), the higher frequency data allow them to utilize industry fixed effects. By examining long differences, we do not need to control for other determinants (e.g., macroeconomic shocks, trade volume shocks, political-economic shocks) that the literature has shown affect intertemporal variation in TTB use at higher frequencies. See, for example, the discussion in Bown and Crowley (forthcoming).

13
is, selectively dropping a random subset of those who migrated and are from the wage information treatment group, in order to balance attrition, produces an estimate not too different from the effect on the basic measure (Migrated-B). However, selectively dropping a random subset of those who did not migrate and are from the wage information treatment group to balance attrition produces an estimate different from the effect on the basic measure and very similar to the effect on the preferred measure (Migrated-P). This also suggests that attrition is likely to be more among migrants than non-migrants.

## **5.3 on other outcomes**

I also investigate the effect of the information treatments in other outcomes that were collected using the full follow-up survey.<sup>31</sup> I find that the information treatments did not affect whether the potential migrants choose the same country or region (Persian Gulf versus others) as they did during the initial survey. Furthermore, none of the information treatments changed whether households, particularly those of inexperienced migrants took out new loans, paid back old loans, or bought new assets (bottom panel).

However, I find that wage information increases the chance that inexperienced potential migrants seek new manpower companies. The inexperienced migrants receiving wage information are 6 percentage points more likely to consult different manpower companies after the initial survey. This effect is 26 percent of the likelihood in the control group. This probably reflects an action that inexperienced potential migrants can take upon realizing that they had been misinformed. However, none of the information treatments affect whether they consult with family members or friends. Similarly, none of the information treatments affects any of these outcomes for the experienced potential migrants.

# **6 Estimates of VSL**

Since the information treatments are effective in changing the expectations of inexperienced potential migrants concerning both earnings and mortality rate associated with migration, I estimate the value of a statistical life (VSL) for this group by using the information treatments as instruments. The first part of this
||‐101.552**||
|X Other worker ill X Female||||[45.910]||
|Highest usual hours of work in household|||||24.664|
|X Other worker ill|||||[46.347]|
|Highest usual hours of work in household|||||‐89.162|
|X Other worker ill X Female|||||[53.808]|
|Mean dependent variable|3,948|3,766|3,948|3,948|3,948|
|Observations|116.4|113|116.4|116.4|116.4|

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Assets are measured by the first principal component from an index of household assets including bed, wall clock, watch, player, radio, tv, sewing machine, fan, air conditioning, fridge, freezer, gas stove, shovel, landline telephone, cell, bike, motorbike, car, computer, livestock, farm implements, generator, land; then normalized to have mean zero and standard deviation one. Usual earnings and usual hours of work from reports in baseline survey. All specifications include individual fixed effects. Estimation uses least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
**Table 6 - Direct Effect of Price Increase through Taxes (%), by Decile**

|_Price shock scenario_|_1_|_2_|_3_|_4_|_5_|_6_|_7_|_8_|_9_|_10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Complete pass-through|-0.09|-0.08|-0.06|-0.07|-0.07|-0.09|-0.06|-0.06|-0.05|-0.03|
|Low-bound elasticity|-0.03|-0.04|-0.03|-0.04|-0.04|-0.05|-0.04|-0.04|-0.04|-0.02|
|Medium elasticity|-0.01|-0.02|-0.02|-0.02|-0.02|-0.03|-0.02|-0.03|-0.02|-0.02|
|Upper-bound elasticity|0.01|0.00|0.00|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|-0.01|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ The table shows the share of total consumption for each decile. Complete pass-through refers to elasticity equal to zero; consumers pay all the increased prices, and this does not affect the quantity purchased.

**Figure 2 – Income Gains: Direct Effect of Tobacco Taxes (Increase in Expenditure because of tobacco taxes), by Decile**
of fa&rness in health financing, since the way a country finances its health care-and in particular the balance it strikes between pre-payments and out-of-pocket payments-has implications not just for how people pay for health care (the focus of the FFC index) but also for whc uses health services, how often and how much. The FFC index does not capture-and _does not purport to capture-how_ fair a financing system is in terms of its impact on the distribution of access to and utilization of health services. The limited objective of the FFC index-and the alternative suggested below-needs to be borne in mind.

## _LINKING PAYMENTS TOABILITY TO PAY-WHY?_

The WHR argues that a key dimension of performance in a health care system is how fairly it protects households financially. Underlying the FFC index is one particular interpretation of the term "fairness in financial protection", namely that _households ought to be required to pay for health care in line with their ability-to-pay_ (ATP). There appears, in fact, to be widespread support amongst policymakers and the public at large for the principle of linking health care payments to ability to pay [3]. There has, however, been relatively little discussion of the rationale underlying this. As Culyer [4] notes, one obvious, reason that might be advanced in support of the principle is that policymakers are concerned that payments for health care affect people's ability to seek health care when ill. This concern stems from a more fundamental concern of policymakers with the distribution of health service utilization and ultimately with the distribution of health itself. But this simply provides a rationale for de-linking payments and utilization-it does not provide a rationale for linking payments to ATP [5]. A flat-rate tax, for
and 98 percent in the United Kingdom). But there is a sharp difference in account penetration in the poorest income quintile: in the United States 26 percent of adults in this group report having no formal account, while in the United Kingdom only 3 percent do. A 2009 FDIC survey found a similarly large gap in account penetration between rich and poor households in the United States.<sup>7</sup> A comparison with account penetration in the poorest quintile in Australia and Canada—two other countries with Gini coefficients and legal traditions broadly similar to those of the United States— adds further support to the suggestion that factors beyond income inequality help explain the variation in the use of formal accounts.

## **A strong correlation between inequality in the use of formal accounts and inequality in income**

Account penetration in the richest quintile as a multiple of that in the poorest <!-- Start of picture text --> 15<br>• •<br>• •<br>• PARAGUAY<br>10 •<br>• •<br>•<br>• •<br>• • • • •<br>• ••• ••<br>51 • • ••• •• • • ••••• • • • • •• •• • •• •• • • • •••••••• •• ••• • • • •• • •••••• • • • • • • •••• •• • ••• • •••• • ••• • •<br>SWEDEN<br>20 30 40 50 60 70<br>More equal Income inequality Less equal<br>(Gini coefficient)<br><!-- End of picture text --> _Note:_ Data on Gini coefficients are for 2009 or the latest available year. _Source:_ Demirguc-Kunt and Klapper
# **Introduction**

The role played by small and medium enterprises (SMEs) in employment generation and economic recovery is a key question for policy makers. Multi-billion dollar aid portfolios across countries are directed at fostering the growth of SMEs. However, there is little systematic research/data, informing the various policies in support of SMEs, especially in developing countries. Moreover, the empirical evidence that exists on the firm-size growth relationship has been mixed and we do not know whether SMEs or other firms are significant contributors to the creation of jobs, and how this varies across countries.

In this paper, we first present comprehensive statistics on the contribution of SMEs to total employment, job creation, and growth in the formal sector across 104 developing economies. The data compiled are more comprehensive and more comparable across countries than existing cross-country SME databases (e.g. Ayyagari, Beck, and Demirguc-Kunt, 2007). We then examine the relationship between firm size, employment, and productivity growth and how this varies with country income.

Our sample consists of 49,370 firms in 104 countries, surveyed over the period 20062010. We focus on the formal sector since studies (e.g. La Porta and Shleifer, 2008) have shown that while informal firms account for a large portion of economic activity in developing countries, growth and development comes from the creation of highly productive formal firms.<sup>1</sup> Informal firms have been shown to be small and extremely unproductive compared to even small firms in developing countries.

> 1 This empirical evidence fits in with the dual economy view originally associated with Harris and Todaro (1970) that suggests that the informal economy provides a social safety net until the establishment of formal productive firms that provide jobs and contribute to growth and development.

2
period considered is of interest since it covers the pre- and post-Moroccan Arab Spring, and the subsequent political and economic changes set in motion by the protests.

A preliminary analysis of the two sets of variables already indicates that they do not go at par. Although Moroccan households experienced an improvement in their living standard between 2001 and 2013 and escaped from poverty and vulnerability to graduate into the middle classes – more than 70 percent of Moroccan households indicate that their well-being did not improve compared to the last 10 years. There is, however, a lot of variation depending on social status. While most of the poor, vulnerable and lower-middle-class households experienced few improvements compared to the last 10 years, middle-class households (but mostly those in the upper class) appear to have benefitted the most. When compared to the responses of the 2001 survey, the result is clearer. The upper classes appear to have experienced a significant increase in the share of those seeing their well-being improved; however, the share of those other classes experiencing an improvement did not vary much, with the only positive change being an increase in those declaring that conditions had not changed for them, as well as a reduction in the share of those seeing a decline.

To further confirm the general perception that improvements in welfare had not been uniformly distributed—a trend captured in household perception – we looked at the perceived status (poorer or richer than neighbors) and compared the results from the 2006 and 2013 surveys, as this section of the questionnaire was not available in the 2001 survey data. Despite these improvements, in both rounds most households did not consider themselves richer than the households living in their neighborhood; however, as in the previous case, the perception of relative poverty is higher among the poorest and declines quasi-monotonically through to the top one. What is striking is finding how much this perception worsened among the lower and middle classes along with their absolute shares: in 2013 almost 50 percent of those in the lower middle class, and 30 percent of those
area (in ‘000 hectares) and crop productions (in ’000 tons) and crop yields (in kg/ha), for the main crops in the country. This includes maize and rice. These allow us to create a measure of crop yields and shares.

# **Soils: NSS Land and Livestock Survey**

Recall that the NSS land and livestock survey collected information on the soil texture. These data are only available in Round 59 (which covers year 2002), but as texture tends to be stable over time, this suffices for our purpose of generating a district level control variable on soil conditions. Soils, at the field level, are classified into the following texture categories: sand - 1, loam - 2, silt - 3, light clay - 4, heavy clay - 5, others - 9. We use a district-level (weighted) vector which indicates the percentage of soils into each soil category.

# **Distance: Digitization of road maps and computation of the minimum travel time between districts**

We obtained scanned paper maps of India from 1996 and 2011 via the work of Allen and Atkin (2022). These maps included different types of roads which are distinguished by color. For instance, a highway has a different color than a local road. Different road types imply different travel times: for example, it would typically take less time to travel 1 km on a highway than on a local road.

Using GIS software, we superimposed the road maps onto district shape files of 2001 and 2011 (geo-coding, in GIS parlance). We then converted the road map into color-coded pixel data (raster data, in GIS parlance). Using information on the location of each district, and the roads connecting them, we computed a measure of the minimum travel time between districts.<sup>33</sup> This calculation attributed a “cost of distance” to each road type: the highways were attributed a cost of one, medium roads
than new processes (Levin _et_ al. 1987). As such, patent data may understate changes in the nature of innovation as countries shift their environmental policy focus from end-of-the-pipe to integrated solutions leading to modified production process.<sup>8</sup> Despite these caveats, patent data offer several advantages when studying technological change and its effect on the environment, and have been widely used in studies of eco-innovation.<sup>9</sup> For instance, Johnstone _et al_ . (2010) show that patenting activity for renewable energy technologies, measured by applications for renewable energy patents submitted to the European Patent Office (EPO), has increased dramatically in recent years, as both national policies and international efforts to combat climate change begin to provide incentives for innovation. Figure 1 illustrates these trends for five technologies: wind, solar, geothermal, ocean power, and electricity from biomass and waste. With the exception of biomass and waste, each technology experiences an increase in innovation after signing of the Kyoto Protocol in December 1997.

Whereas patents represent an innovation that is ready to be commercialized, scientific publications can be used to measure research at earlier stages of technology development. In particular, data on scientific publications can be useful for research carried out by public sector or non-profit organizations that place less emphasis on obtaining patents. As there is no equivalent to a patent classification system for publications, keyword searches of titles and abstracts are often used to identify relevant publications. This search strategy may introduce a biased towards research from English-speaking countries that should be considered when comparing publication trends across countries.

> 8 In some cases, process changes may be captured by patent data, particularly when third-party suppliers provide relevant equipment or materials.  For an example, see Popp _et al._ (forthcoming), which discusses process changes in pulp and paper production.

> 9 Popp (2005) provides an introduction to the use of patent data for studying environmental innovation.

8
9 summarizes Japan’s experience to promote energy efficiency, using a mix of objective setting, benchmarking, norm and regulations, R&D, and direct investments.

These types of industrial policies may usefully complement price-based policies, even though there are risks associated with them. In particular, the role of government failures (see discussions in the climate domain in Anthoff and Hahn, 2010 and Helm, 2010) needs to be accounted for in the same way than market failures are included. Activist policies create a risk from capture and rent-seeking behaviors (Laffont, 1999) that need to be considered. For a comprehensive survey of industrial policies in developing countries, see Harrison and Rodriguez-Clare (2010).

# **Box 9. Japan’s energy efficiency strategy: an example of sector-scale policy**

Building on its excellent track record in energy conservation since the first oil shock (the energy use per unit of GDP has decreased 26 percent between 1980 and 2009), Japan has pledged to go further with the “Energy Conservation Law” enforced in 2006. The law seeks to improve energy efficiency (already one of the highest in the world) by 30 percent in 2030 relative to 2006. The aim is double: protecting the environment, and improve the efficiency of the economy (represented by the term in Equation (2)).

To reach such a target, the plan outlines a strategy to foster energy conservation technologies and develop and disseminate a **benchmarking approach** in order to correctly monitor energy conservation. More precisely, the strategy seeks to (i) introduce the most advanced technologies for replacing equipment in the energy sector, (ii) make net-zero-energy houses available by 2020 (and the norm nationwide by 2030), and (iii) introduce new integrated standards for energy consumption in all buildings.

Interestingly, the national strategy also contains the **Top Runner Program** , which is a very innovative policy to raise energy efficiency standards. The program sets standards for 21 products (e
</sup>|-0.03<sup>_∗∗∗_</sup>|0.04<sup>_∗_</sup>|0.04<sup>_∗_</sup>|
||(0.01)|(0.01)|(0.02)|(0.02)|
|Present bias (male)||0.01||-0.00|
|||(0.02)||(0.04)|
|Present bias (female)||0.01||-0.03|
|||(0.02)||(0.04)|
|Risk aversion (male)||-0.00||-0.04|
|||(0.02)||(0.03)|
|Risk aversion (female)||-0.00||-0.01|
|||(0.02)||(0.04)|
|Self-effcacy (male)||0.01||-0.01<sup>_∗_</sup>|
|||(0.01)||(0.01)|
|Self-effcacy (female)||-0.00||0.00|
|||(0.00)||(0.01)|
|Outcome mean|0.05|0.05|0.56|0.56|
|R-squared|0.043|0.046|0.124|0.128|
|Observations|1254|1254|1254|1254|

Note: Robust standard errors are reported in parentheses. All regressions include village fixed effects and the controls listed in Table 1. Self-esteem measures are corrected for acquiescence bias. Significance levels are denoted as follows: * p<0.
production processes among young entrepreneurs in Bosnia and Herzegovina; and Drexler, Fischer, and Schoar (2010) find that a rule-of-thumb training for micro-entrepreneurs in the Dominican Republic increases the likelihood that they keep accounting records and calculate monthly revenues.

This compendium of studies suggests that while financial literacy programs may be effective to some extent, measuring the impact of such programs is quite challenging. By and large, the findings mentioned above are based on behavioral outcomes such as opening a savings account, keeping financial records, investing in a business, or computing business earnings. Yet, if financial literacy is a precursor to financial behavior, then determining improvements in the former is necessary to understand the channels leading to changes in the latter. Furthermore, if improvements in financial literacy are required to change financial behavior, then it would be difficult to detect behavioral outcomes, since these would be situated at the end of the causal chain. Overall, the literature has overlooked a critical component in evaluating financial education programs: the intermediary impact on financial literacy levels following the intervention.

Many financial literacy surveys in high-income countries exhibit two approaches to measuring financial literacy (OECD, 2005). The first measures respondents’ understanding of financial terms and their ability to apply financial concepts to particular situations, and the second asks respondents for a self-assessment of their financial understanding and knowledge, as well as their perceptions and attitudes towards financial instruments and decision. The survey questions that have by now become the standard measure of financial literacy, however, are those developed by Lusardi and Mitchell (2009) on numeracy, inflation, and diversification.<sup>2</sup> These questions have been included in various surveys to evaluate financial literacy in many corners of the world, including the US, the Netherlands, Italy, Germany, and New Zealand. They have likewise been

> 2 The financial literacy questions developed by Lusardi and Mitchell (2009) are the following: (1) Suppose you had $100 in a savings account and the interest rate was 2
71.5**|**112.2**|**668.3**|

Source: Authors calculations For the model, labor is specified in two broad categories, temporary labor and family labor. Temporary labor is assumed to be hired daily at a wage rate given by the minimum wage law, which for 2018 was Le500,000 per month or the equivalent of US$3.38 per day.<sup>11</sup> Family labor is priced at a daily “reservation wage” that is higher than zero but lower that the daily rate for the hired-in labor. From the 2015 Population and Housing Census, the average household size is 5.6, with rural household generally larger (6) than urban households (5.1). The average farm family is assumed to have six (6) members, of whom 2 are adults and deliver a total of 20 person-days of labor per month. The family labor constraint may be relaxed through the hiring of temporary labor on a daily or weekly basis. Although the family worker is paid the reservation wage, which is assumed to be lower than the wage for temporary workers, all workers are assumed to have the same level of productivity.

# _Fertilizer availability and use_

The use of fertilizer is well below the requirement. In 2015, only 3.8 percent of farmers reported applying chemical fertilizer and 9.7 percent of farmers reported applying organic fertilizer. Most farmers (41

> 11 It is of course recognized that the actual wages may in fact be lower than the minimum wage, given the high level of unemployment. If this is the case as is likely then _a priori_ the model would be likely to underestimate the level of employment.

13