rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5. Intention-to-treat effects on total, paid wage, and unpaid employments and days worked**

||Total em|ployment|Paid wage e|mployment|Unpaid em|ployment|Days w|orked|
|---|---|---|---|---|---|---|---|---|
||All|Existing|All|Existing|All|Existing|All|Existing|
|Post|||||||||
|Cash|-0.242*|-0.200|-0.043|0.008|-0.063|-0.043|0.160**|0.081|
||(0.112)|(0.134)|(0.094)|(0.108)|(0.089)|(0.097)|(0.058)|(0.051)|
|Matching|-0.053|-0.074|0.013|0.010|0.141|0.146|-0.145|-0.188*|
||(0.232)|(0.262)|(0.114)|(0.121)|(0.084)|(0.094)|(0.097)|(0.088)|
|ANCOVA|||||||||
|Cash|-0.091|-0.091|0.010|0.010|-0.039|-0.039|||
||(0.149)|(0.149)|(0.088)|(0.088)|(0.098)|(0.098)|||
|Matching|0.022|0.022|0.052|0.052|0.135|0.135|||
||(0.286)|(0.286)|(0.126)|(0.126)|(0.092)|(0.092)|||
|Control group mean|2.271|2.302|1.106|1.106|0.507|0.506|5.663|5.707|
|Standard deviation|2.290|2.327|1.881|1.908|1.236|1.246|1.585|1.559|
|S.E. clustered|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Block fixed-effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2 (Post)|0.093|0.096|0.097|0.100|0.032|0.030|0.078|0.080|
|R2 (ANCOVA)|0.226|0.226|0.243|0.243|0.064|0.064|||
|N (Post)|1,067|989|1,042|969|1,042|969|851|796|
|N (ANCOVA)|989|989|969|969|969|969|||

Note: ***p < .01, **p < .05, *p < .1. The dependent variables are the total number of employees, the number of paid wage employees, the number of unpaid employees, and the average number of days worked in a week (only available at midline). All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head and whether a member in the owner household is a community leader. The control group mean refers to the follow-up survey. The detailed regression results are provided in Table A.6 and Table A.7 in the appendix.

22
and inequality, and iv) economic transformation. In section 3 we consider the legacy of the boom and prospects for the future. Section 4 concludes.

# **Section 1: The commodity price boom**

The boom period 2004-2014 marked a critical decade in Africa’s post-independence economic history.<sup>4</sup> This decade spawned much optimism, centered on the ‘Africa Rising’ narrative<sup>5</sup> ; associated with increased rates of economic growth, higher levels of inward investment, and large, sustained revenues from natural resource exports and taxation. This period also constituted the longest sustained period of high commodity prices on record.

Overall, this gave governments large increases in their fiscal space, derived in part from the receipts from resource taxation alongside debt relief for some countries post-2000. For example, at least 8 Sub-Saharan African countries obtained revenues from natural resources representing on average more than 10% of their GDP during the boom – a significant fraction by both regional and global standards (see Figure [fig:resrev_countries]). Many chose to use this time to undertake significant expansions of public service provision, such as educational enrollment, as well as increased infrastructure investment. For example, according to IMF’s Investment and Capital Stock Dataset, public investment in physical assets, including economic infrastructure – like roads, and airports – and social infrastructure – such as hospitals and schools – rose from roughly an average of US$200 per capita during the pre-boom period (1998-2003), to an average of over US$435 per capita during the boom, and US$270 after the boom period in resource-rich countries in Africa. Similarly annual spending on health services rose from 4.6% of GDP to 4.9% on average, and expenditure on education rose from an average of 2.8% of GDP before the boom, to 3.5% of GDP after the boom.

Policymakers sought to capitalize on the boom to translate investment and revenues into broader economic transformation. This included the opportunity to boost growth of manufacturing exports and other non-resource sectors of the economy. This economic diversification, emphasizing export diversification, was a popular economic objective during this period. Despite this, there was little change in the overall sectoral composition of exports of resource-rich economies beyond a boost in the resource sector and a squeezing of agricultural exports (see Figure 3).

> 4 Previous booms had occurred between 1949 and 1952, 1972 and 1975, and 1992 and 1995 (Varangis, Takamasa, and Mitchell 1996; Radetzki 2006) However, as noted by Deaton (1999) (p. 28), while price cyclicality was present, there was little evidence of a long run increase in commodity prices.

> 5 Africa Rising, The Economist, December 2011, 3rd edition, https://www.economist.com/printedition/2011-12-03

7
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# **1. Introduction**

The informal sector provides limited opportunities. It is the home of millions of businesses run by the poor. Many of them are reluctant entrepreneurs. They may exert tremendous effort and make a lot out of little, but the rewards are few. Often, informal businesses are too small in scale to improve livelihoods. Informality is widespread in developing economies, accounting for approximately one-third of GDP and 70 percent of employment (Loayza, 2016; Ohnsorge and Yu, 2021). Informal firms add only 15 percent of the value per employee of formal firms (La Porta and Shleifer, 2014). They do not provide a stable source of income. They are vulnerable to external shocks and poor infrastructure (Islam, 2019). And they rarely become formal. Thus, the choice of starting out in the informal or formal sector may chart the destiny of a business.

Women are well represented in the informal sector. In poorer developing economies, 92 percent of female workers are employed informally, which is higher than the percentage of male workers (87 percent). Further, in a majority of countries (56 percent), the percentage of female workers in informal employment exceeds the percentage of male workers.<sup>3</sup> Over 80 percent of women in non-agricultural jobs in South Asia are in informal employment. The corresponding figures for Sub-Saharan Africa and Latin America and the Caribbean are 74 percent and 54 percent, respectively.<sup>4</sup> In developing economies, the prevalence of self-employment is high; in low and lower-middle income economies, 55 percent of female employees are in self-employment; this compares to 9.7 percent in high-income economies.<sup>5</sup> The selfemployed in developing economies can, for the vast majority, be categorized as self-employed without employees or unpaid family workers. As such, this high prevalence of self-employment may be indicative of limited opportunities in the formal sector and, thus, a large informal economy. A key question is what factors determine a woman’s choice to engage in the informal versus the formal sector. More specifically, what influences the choice to start a business in the informal or the formal sector. This choice is likely tied to a woman’s motivations for starting the business, as well as the economic, cultural, and legal environment in which she finds herself.

Evidence suggests that institutions matter. There has been burgeoning research showing that unequal or discriminatory laws affect the economic status of women, using data from the World Bank’s Women

> 3 --- --- --- <u>https://www.ilo.org/wcmsp5/groups/public/ ed_protect/ protrav/ travail/documents/publication/wcms_711798.pdf</u>

> 4 https://www.unwomen.org/en/news/in-focus/csw61/women-in-informal-economy

> 5 World Development Indicators [accessed: July 2, 2021].

2
# **4 Methods**

## **4.1 Calibration**

The raw Purple Air sensor data can have significant biases. Therefore, the data needs to be calibrated. Different calibration equations have been developed for this purpose, including one from the U.S. EPA (Barkjohn _et al._ , 2023) and one from Barkjohn _et al._ (2021). More recently, Jaffe _et al._ (2023) find that while these calibrations perform well under certain conditions, such as urban aerosols and smoke aerosols, they largely underestimate PM under high dust pollution. They present an alternative calibration that involves identifying potential dust events and further adjusting the estimate by a factor of 5.6. We test both the Barkjohn _et al._ (2021) and Jaffe _et al._ (2023) calibrations.

The Barkjohn _et al._ (2021) calibration applies the following equation: The Jaffe 2023 calibration uses a two-step method that applies either the Barkjohn _et al._ (2021) calibration or a variation of it depending on the value of the 0 _._ 3 _μm/_ 5 _μm_ ratio. This is the ratio of the concentration of particles with a diameter of 0.3 micrometers to those with a diameter of 5 micrometers.

- If 0 _._ 3 _μm/_ 5 _μm >_ 190, use the original Barkjohn _et al._ (2021) calibration

- If 0 _._ 3 _μm/_ 5 _μm <_ 190, use the Barkjohn _et al._ (2021) calibration _×_ 5 _._ 6

As one of our low-cost monitors lacks data on levels of particles of size 0 _._ 3 _μ_ m and 5 _μ_ m, a substitution is made. An alternative criterion for identifying dust events is when the PM1/10 ratio < 0.25 (Jaffe _et al._ , 2023). Comparing the two Jaffe specifications, we find that approximately 86% of hourly values are the same. Since the monitor that lacks the 0 _._ 3 _μ_ m and 5 _μ_ m values is one of the only ones functioning in 2020, we use this alternative method for generating the Jaffe calibration in order to be able to conduct the analyses of the COVID-19 mobility policies implemented in 2020.

These calibrations both use data from the U.S. where conditions may differ substantially from Dakar, leading to a different relationship between the data from low-cost monitors and regulatory-grade monitors. For example, the lack of paved roads may affect the amount and type of dust particles in the air, along with different fuel standards, high levels of construction, and the proximity to the Sahara Desert. Therefore, we also produce a correction based on calibrating the Purple Air data using data from local regulatory-grade monitors. We co-located two of the PA monitors with two regulatory-grade monitors in the city. One was located in Bel Air and provided overlapping data from July 21, 2020, to April 17, 2021.<sup>11</sup> The second was located in Pikine and provided overlapping data from April 17, 2023, to December 15, 2023.<sup>12</sup> To generate the calibration formula, we use a lasso method with a linear model and a five-fold crossvalidation. We include the Purple Air PM2.5 value along with humidity and temperature averaged across

> 11Some additional dates are missing within this period, most notably October 26-December 31, 2020.

> 12Again, there are some missing dates within this period including all of August and October.

11
forgiveness rules – that the loans are forgiven if the funds are used as intended for 8 weeks – suggest that the PPP is presumably designed to be short-term in nature. As discussed below in Section 7, the PPP almost surely aided the banks as well and helped contribute to the “financial surprise” of their remarkable resilience during the COVID-19 crisis.

In contrast to the limits we placed on our review of the TARP research, we review to the extent possible all of the empirical research on PPP to date, recognizing that we are likely missing a number of contemporaneous working papers that are coming out at this time. The empirical PPP research literature does address a surprisingly large number of important questions, given how recently the program was initiated. However, as will become clear, there is too little attention to date on most of these issues, requiring additional future research.

Some research investigates the extent to which PPP recipient firms, their employees, and communities benefited from the program. Starting with the PPP firms, one paper finds that the PPP helped alleviate the financial and economic constraints of relatively small recipients – those with nine or fewer employees – leaving the owners of these firms more satisfied with their access to credit and better able to hire and pay their employees (Berger, Freed, Scott, and Zhang, 2021).<sup>27</sup> These authors also find that these results hold only in the short-term, a month or two at most, consistent with the presumed short-term design of the program. Other research suggests that PPP had mixed results on firm credit scores, and helped only modestly with medium-term survival (Hubbard and Strain, 2020), which another study finds improvements in medium-run firm survival, but only for microbusinesses (Bartlett and Morse, forthcoming).

Those that investigate employment effects at PPP recipient firms have positive outcomes of different degrees. Some papers suggests that PPP loans increased employment at small

> 27 The study finds no effects for somewhat larger small businesses with ten or more employees, but it does not cover relatively large firms close to the 500-employee limit for the program.

35
relationship can emerge between migration decisions and health/education outcomes. Since the data come from similar household survey data, parallel IV approaches can be used, as discussed in the previous section on poverty and income.

# **Labor Market Impact**

Migration for employment can have several effects on the economy beyond the direct change in location of some workers. In countries of origin, at the household level, migration may change the household labor composition as well as labor supply decisions of family members who stayed behind. At the macro level, departure of a group of workers, especially if they are concentrated in certain skill, occupation and age categories, will have implications for absolute and relative wage levels as well as other labor market outcomes. These same issues are relevant in destination countries. Labor market implications receive extensive attention in OECD countries, where there is significant public and political opposition to migration flows on the grounds that migrants take jobs away from natives.

Different data sources can be used, depending on the questions asked. Multi-topic surveys are well suited to studying the impact of emigration on wages in specific sectors, labor mobility across sectors, and whether or not skills acquired through migration foster upward mobility among individuals. Aside from migration variables and data collected for potential identification strategies, surveys should include employment status, wage rates, job types and human capital variables in the employment module. For people with migration experience, it is particularly useful to collect information on occupations prior 30
https://doi.org/10.1016/j.worlddev.2009.11.022 Wei, S. J., & Shleifer, A. (2000). Local corruption and global capital flows. _Brookings Papers on Economic Activity_ , _2_ , 303–346.

Weill, L. (2011). How corruption affects bank lending in Russia. _Economic Systems_ , _35_ (2), 230–243. https://doi.org/10.1016/j.ecosys.2010.05.005 Wellalage, N. H., Locke, S., & Samujh, H. (2019). Corruption, Gender and Credit Constraints: Evidence from South Asian SMEs. _Journal of Business Ethics_ , _159_ (1), 267–280. https://doi.org/10.1007/s10551-018-3793-6 Wellalage, N., Locke, S., & Samujh, H. (2020). Firm bribery and credit access: evidence from Indian SMEs. _Small Business Economics_ , _55_ (1), 283–304. https://doi.org/10.1007/s11187-019-00161-w World Bank. (2008). _Finance for all? Policies and Pitfalls in Expanding access_ . _World Bank Policy Research Report_ .

35
**Table 3: Can Human Experts Predict Profits After 3 Years?**

|||Non-Winner|s||Wi|nners||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Judges|McKenzie|Fafchamps|Judges|Judges|McKenzie|Fafchamps|
|Business Plan score|-0.0041|-0.0059|-0.0068|0.0009|-0.0055|-0.0077|-0.0070|
||(0.0180)|(0.0187)|(0.0183)|(0.0127)|(0.0130)|(0.0143)|(0.0139)|
|Female||-1.9060<sup>***</sup><br>|-1.5232<sup>**</sup><br>|||-0.9084<sup>*</sup><br>|-0.7493<br>|
|||(0.4940)|(0.4889)|||(0.4427)|(0.4437)|
|Age||0.1241<sup>**</sup>|0.0856<sup>*</sup>|||0.0488|0.0454|
|||(0.0413)|(0.0408)|||(0.0357)|(0.0350)|
|Graduate Education||-0.9355||||-0.2196||
|||(0.8028)||||(0.6096)||
|Worked abroad||<br>0.8023||||<br>-1.2460<sup>*</sup>||
|||(0.7827)||||(0.6162)||
|Lottery choice||-0.0858<br>||||-0.3335<br>||
|||(0.3742)||||(0.2986)||
|Wealth||<br>-0.0250||||<br>0.0983<sup>*</sup>||
|||(0.0741)||||(0.0467)||
|Grit||-0.4430<br>||||-0.1569<br>||
|||(0.3611)||||(0.2745)||
|Ability||0.6697<sup>**</sup>||||-0.1657||
|||(0.2152)||||(0.1714)||
|Agriculture||-0.4077<br>||||-0.1922<br>||
|||(0.4580)||||(0.3703)||
|IT||-0.8747||||-0.4450||
|||(0.6561)||||(0.4687)||
|Manufacture||0.2694|0.5862|||0.1297|0.3557|
|||(0.4920)|(0.4386)|||(0.3850)|(0.3560)|
|Retail|||0.8505||||1.4675<sup>**</sup>|
||||(0.9435)||||(0.5256)|
|Self-confidence|||0.2508<sup>*</sup>||||0.1727|
||||(0.1164)||||(0.0959)|
|Motivation|||0.0803||||-0.1730|
||||(0.1394)||||(0.1035)|
|Happiness|||0.1224||||-0.0216|
||||(0.1550)||||(0.1238)|
|Optimism|||0.4247<sup>*</sup>||||-0.0293|
||||(0.1744)||||(0.1322)|
|Ability|||0.4466<sup>*</sup>||||-0.2251|
||||(0.2021)||||(0.1561)|
|Credit|||<br>0.2527||||<br>-0.1028|
||||(0.1453)||||(0.1971)|
|Existing|2.7561<sup>***</sup>|2.4015<sup>***</sup>|0.9045|1.0744<sup>***</sup>|1.0263<sup>***</sup>|0.8252<sup>*</sup>|0.6637|
||(0.4717)|(0.5047)|(0.5662)|(0.2971)|(0.2964)|(0.3534)|(0.4177)|
|Log(Total award paid)|||||<br>0.4332|<br>0.3865|<br>0.3941|
||||||(0.2969)|(0.3008)|(0.3027)|
|Regional Indicators|No|Yes|Yes|No|No|Yes|Yes|
|Sample Size|1,059|1,059|1,059|1,047|1,047|1,047|1,047|
|P-value||0.000|0.000|||0.058|0.001|
|AdjustedR2|0.035|0.083|0.125|0.011|0.020|0.030|0.035|

Robust Standard Errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively. See Appendix A.1 for variable definitions. Dependent variable is inverse hyperbolic sine of profits 3 years after applying, coded as zero for applicants without an operating firm. P-value is for testing that set of controls added by MacKenzie (2015) or Fafchamps and Woodruff (2017) (abbreviated as FaWo) are jointly zero.

33
# **Table A.3. Descriptive Statistics, Capital Distribution, by Owner and Firm Characteristics**

||All|Bot. decile|1st q.|2nd q.|3rd q.|4th q.|Top decile|
|---|---|---|---|---|---|---|---|
|**Owner characteristics**||||||||
|Male|36.42|21.93|22.49|31.35|39.62|58.51|64.65|
|Age owner|37.8|38.44|37.71|38.26|37.33|37.23|38.18|
|No diploma|7.73|16.67|33.71|27.18|26.63|12.48|2.58|
|Primary completed|16.53|12.52|30.31|26.51|25.93|17.25|5.13|
|Some secondary|58.82|10.71|26.33|25.28|23.89|24.5|9.45|
|Post secondary|14.31|7.81|18.65|17.18|27.71|36.46|18.11|
|Other post primary|2.62|1.84|10.69|27.14|27.29|34.88|14.37|
|Can read|88.76|82.72|85.77|86.12|91.88|95.36|96.25|
|Can write|87.82|80.50|83.73|84.76|91.50|95.65|97.06|
|Poor|61.13|13.37|30.34|26.16|22.99|20.5|7.06|
|**Firm characteristics**||||||||
|Annual value added|4304138|981232.9|1275135|1438029|1901095|3226659|3782644|
|Physical capital|145255.6|1469.54|3215.51|11281.62|35296.27|532807.8|1137005|
|No capital|0.005|0.05|0.02|0.00|0.00|0.00|0.00|
|Value added per hour worked|20697.02|5340.53|6780.46|9360.12|9523.56|12800.06|14938.48|
|Gross operating profit|125703.4|59326.88|85340.87|108583.8|139324.8|184766.5|198145.5|
|Workers, total|1.30|1.11|1.13|1.2|1.4|1.74|2.13|
|Paid workers|0.14|0.041|0.035|0.06|0.16|0.41|0.77|
|Wage worker, male|0.23|0.202|0.204|0.24|0.19|0.23|0.32|
|Wage worker, female|0.13|0.16|0.12|0.13|0.1|0.14|0.22|
|Water|8.45|3.95|10.19|14.70|25.1|50.01|31.38|
|Electricity|12.01|2.33|6.41|8.74|20.62|64.22|37.92|
|Land line, phone|15.34|3.44|9.00|15.74|27.25|48.02|24.58|
|Fixed location|57.97|10.11|24.12|22.54|25.45|27.89|10.9|
|Mobile location|40.51|11.37|28.42|27.36|23.73|20.49|8.47|
|Other location<br>_Note:_Means and percentages are we<br>upper panel. For the bottom panel, t<br>regarding endowments in infrastruct<br>_Source_: Authors' calculations using|1.52<br>ighted using sa<br>he first segment<br>ure and location<br>1-2-3 survey dat|8.69<br>mpling weights<br>presents mean<br>s of firms.<br>a.|21.44<br>. Percentage<br>s of firms ch|10.58<br>s of characte<br>aracteristics|42.16<br>ristics of inte<br>while the sec|25.82<br>rest are pres<br>ond shows p|13.35<br>ented in the<br>ercentages|

# **Figure A.4. Location of Activities, Informal Firms**

<!-- Start of picture text --> 25<br>21.65<br>20 18.81 18.14<br>15 12.78<br>10.62<br>10 8.49<br>5 3.25 2.38 2.26 1.53<br>0<br>Home Fixed Improvised Home with Mobile Improvised Customers' Fixed Shop, Other<br>without location on sellpoint in equipment location location on house location in a restaurant,<br>equipment streets a market streets public hotel<br>market<br><!-- End of picture text --> _Source:_ Calculations using 1-2-3 Survey data 30