rafmacalaba/datause-encoder-data huggingface dataset:
such as consumption. While data on labor market and other economic outcomes is extensive in IFLS, it was not collected in an entirely consistent manner across waves (IFLS website, 2014, <u>http://www.rand.org/labor/FLS/IFLS/study.html).</u> Table A describes key employment category variables (the first 8 variables) and employment experience variables (the last 3 variables), with notes on differences across waves. We are interested in these broad definitions of employment as there may be significant differences between them for job characteristics (and in turn, implications for job quality and possible scarring effects). While there is a breakdown of different types of self‐employment this was not recorded in wave 2. For this reason we only present some descriptive statistics relating to these differences and combine the categories when using all waves in wage estimations in section 4.

The labor market history data in IFLS covers a period of up to 20 years for each individual but this data has some limitations. Firstly, work history is based on an individual recalling up to 7 years prior, what their primary occupation was for that year. Their primary occupation may not have been their only occupation nor would they have necessarily worked there for the whole year. For example, someone who was working in the private sector 3 days a week for 7 months in 1989 may have reported “private worker/employee” as their main work experience for that year. This means that these variables are a somewhat noisy measure of experience. In addition, they could not be used to differentiate between the returns to part‐ time and full‐time experience. Nevertheless, in the absence of scarring effects, there would be no reason for these variables to be negatively related to wages. Lastly, it is important to note that as there is missing data for some individuals and the work history does not always include the first job of an individual, the experience variables do not capture an individual’s complete work history. We discuss these issues further in section 4.

**Table A: Employment categories and work experience categories**

|**Variable name**|**Variable description**|
|---|---|
|**self‐employed**|Self‐employed with no other workers (waves 1, 3<br>and 4).<br>Self‐employed, all types (wave 2 ).|
|**self‐employed with unpaid family**|Self‐employed with unpaid family<br>worker/temporary worker (waves 1, 3 and 4)|
|**self‐employed with permanent**|Self‐employed with permanent worker (waves 1, 3<br>and 4)|

13
SPECLAL Topic 1: PROJECTINQ AIDS MoRTALUy bd

# Application to Sub-Saharmn African countries

# _Data_

Estimates of HIV prevalence in 1990 for all Sub-Saharan Afican countries were made by Chin (1991) at the World Health Organization, and are summaried in Figure SI. These estimates were for males and females combined; based on the simulations, it is assumed that female HIV prevalence is 1.2 times male prevalence. HIV-1 and HIV-2 have been combined in one overall HIV estimate. Starting years of the epidemic were derived from the U.S. Bureau of the Census' AIDS data base (1990).

<!-- Start of picture text --> Figure SI. Percent HIV Seropositive, Adult Population 1990<br>f -A~~~~~~~~~~~~~~~~~~~n IVpoi<br>.,, ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. . la<br>I 1 ~~~~~~~~~~~~~~~~t fl n lVp<br><!-- End of picture text -->
local polynomial smoother. An inspection of these diagrams confirms that the point estimates yielded by the regression analysis provide a good representation of the relationship observed along the corresponding distributions.

We proceed by examining the behavior of inputs prices, in particular average wages and imported input prices, across cohorts of exporters. The results in column (1) of Table 3 confirm the well-known result that older manufacturing firms tend to pay higher wages on average (Brown and Medoff, 2003). Interestingly, the results in columns (2) and (3) show that a similar relationship holds among exporters: firms with longer spells of export activity tend to pay higher wages, on average.

In Table 4 we examine the relationship between export-age and imported inputs prices and quantities. The results in this table reveal that more experienced exporters tend to purchase larger quantities of inputs at higher prices. Like for export prices, Figure 3 provides a visual representation of these relationships, which confirm the conclusions from the regressions.

For robustness, in Appendix D we examine the extent to which our price and quantity results are sensitive to the choice of the sample period. As we noted above, the EIAS data are only available for the period 2005-2009, whereas the FTS data are available since 1990. In the main analysis, we chose to restrict the focus to the period for which we have complete information, using the the 1990-2004 data only to compute measures of export experience. Tables A2 and A3 in the Appendix report similar results using FTS price and quantity data for 1996-2009, leaving a shorter time interval (1990-1995) solely to compute measures of export experience. Reassuringly, the results reported in these tables reveal that our results are quite robust. The only qualitative difference is that the positive association between export experience and export prices is now more precisely estimated.<sup>10</sup>

# **3.3 Growth versus selection on initial size**

In the analysis above, we characterized the evolution of firm size and export performance over the life cycle using cross-sectional data. A potential explanation for the observed heterogeneity in performance across cohorts is selection on initial conditions. If firms that are initially larger or obtain more revenue when they start serving foreign markets are more likely to survive, older firms and more experienced exporters will naturally be larger and have stronger export performance even in the absence of systematic growth patterns over the life cycle.

Following Cabral and Mata (2003) we first evaluate the empirical relevance of this mechanism by identifying the universe of entrants in 2005 and tracking their performance until 2009. We then perform a similar exercise for the universe of new exporters in 2005 and following them in export markets until 2009.

> 10We have used other time periods and found similar qualitative results.

8
- 10 -

TABLE 3

# **IN"** tJSTRIAL COMPOSITION **OF** KOREAN OU3TPUT - **SELECTED** YEARS (Percentage shares **in total output)**

||1970|1975|1980|1983|
|---|---|---|---|---|
|Agricultur:|17.0|12.8|8.3|8.2|
|Mining|1.1|0.9|**0.8**|0.7|
|Manufacturing|40.3|50.4|_51.0_|_50.0_|
|Light industry|28.4|29.5|24.7|22.1|
|Food, beverages and tobacco|15.9|14.4|10.8|9.6|
|Textiles and leather|7.1|9.9|8.4|7.0|
|Lumber and wood p.iducts|1.4|1.2|1.0|0.9|
|Paper printing and publishing|1.4|1.4|1.6|1.8|
|Nonmetallic metal manufacturing|1.4|1.5|1.9|1.8|
|Miscellaneous manufacturing|1.2|1.1|1.0|1.0|
|Heavy and chemical products|11.9|20.9|26.3|27.9|
|Chemical and chemical products|5.9|10.8|12.6|11.8|
|Primary metal manufacturing|2.0|3.4|5.1|5.0|
|Metal products and machinery|4.0|6.7|8.6|11.2|
|Construction|8.6|6.2|8.0|8.2|
|Social overhead|6.7|6.7|8.1|8.9|
|Services|26.3|23.0|23.8|23.9|

Source: World Bank (1987b), Tables 1.1 and 1.2.
2

# **Introduction**

Sub-Saharan Africa trails other regions in the breadth of common access to electricity for household use. Anticipated population growth is expected to widen that gap (IEA 2010a, <mark>2010b). S</mark> everal factors contribute to the access deficit across the subcontinent (Zomers 2001). Conservative utilities have stuck to traditional policies that emphasized service to urban areas, in large part because providing service in urban areas is more profitable than extending it to remote and sparsely populated rural areas. In many cases, plans to extend electrification to rural areas have been subjected to political pressure that often prevents the utility from charging a cost-recovery tariff and amassing the investment capital needed to extend service, leaving it in a chronically weak financial position. Another reason for the slow rate of access expansion is the poor targeting of subsidies, which allows wealthier customers to enjoy subsidies they do not need (Komives and others 2005; Foster and Briceño-Garmendia 2010).

Even in urban areas where the cost of extending service to new customers is comparatively low, companies have provided service to households wealthy enough to pay the connection charge for electricity service “in full” and “in advance.” Presently, utilities lack incentives to expand electricity into areas where customers cannot afford up-front connection charges and where low load factors result in returns on investment that are too low to justify the substantial costs of extending the service.

The unfortunate reality in many Sub-Saharan African countries is that even when distribution lines are provided to increase access—whether because of a political commitment on the part of the government, an investment decision by the utility, or the availability of financing from donors—the percentage of consumers who are able to connect to the network remains extremely low. In many of these countries, the initial rates of connection in villages newly added to the electrical grid are as low as 10 to 20 percent of possible connections, and that number increases only very slowly over time.

# **Electricity Connection Assumptions and Methods**

To explore the role that connection charges play in electrification rates, this paper first examines patterns of access to electricity in Sub-Saharan Africa and reviews current practices in connection charges. Several new programs in the region and elsewhere in the world are then reviewed, focusing on how the pace of electrification can be accelerated when the barrier of high connection charges is lowered. Finally, the conclusion provides recommendations for policy makers on how to ensure wider access to electricity in SubSaharan Africa.

The data on connection charges were collected from utilities, regulatory agencies, and World Bank staff and project documents over the period 2011–13. Connection charges generally reflect the lowest rating offered by the utility or power distributor for a house with
**Table 3.  Value Added in 2000 by Russian Regional Market and by Sector**

<u>(in billions of 2001 rubles)</u><sup>a/</sup>

|Sector<sup>b/</sup>|Central|Far East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|Sector<br>total|
|---|---|---|---|---|---|---|---|---|---|---|---|
|MAR|4.7|1.3|1.4|7.2|4.1|14.7|4.0|1.7|6.9|1.0|47.1|
|AGR|110.2|17.5|7.4|84.8|125.2|155.3|15.2|11.8|8.2|15.1|550.7|
|AIR|3.6|1.3|1.0|5.3|4.5|10.0|14.9|1.6|2.5|0.6|45.2|
|CHM|13.9|0.3|1.6|10.6|9.3|35.6|7.7|2.0|0.1|4.2|85.4|
|CNM|10.3|1.1|0.7|4.6|6.7|12.2|8.8|2.4|0.8|0.7|48.2|
|COL|0.1|5.2|3.2|32.6|1.7|1.2|||||43.9|
|CON|76.4|7.1|6.1|54.7|105.2|151.6|138.6|29.7|24.1|11.4|605.0|
|CRU||4.5|16.9|9.9|37.7|97.5|||282.9|1.0|450.3|
|ELE|26.7|7.8|7.1|34.7|22.9|62.9|42.9|10.5|19.1|5.8|240.4|
|FIN|43.5|16.4|14.3|60.7|44.0|106.8|115.7|20.8|47.0|9.0|478.0|
|FME|19.0|0.5|1.6|9.0|3.9|33.8|2.3|1.1||11.3|82.5|
|FOO|24.9|9.2|3.7|17.4|24.0|28.1|33.9|17.7|0.5|4.7|164.2|
|GAS||0.2|0.4|0.5|1.5|2.8|||52.2|0.0|57.5|
|HEA|42.3|17.5|14.6|58.8|45.0|100.6|126.3|21.8|40.6|8.9|476.4|
|HOU|22.1|8.8|6.7|29.1|24.8|50.9|68.0|11.2|13.7|4.6|239.8|
|MWO|38.7|11.2|2.6|19.4|18.0|101.8|45.4|18.9|3.0|3.6|262.7|
|NFM|2.5|8.1|9.3|113.2|4.8|46.0|7.1|4.4||0.2|195.5|
|OIL|12.2|4.7|1.5|17.4|10.1|39.2|4.4|7.2|3.9||100.6|
|OTH|7.7|2.0|2.2|8.8|4.9|18.4|6.3|3.3|7.4|1.9|62.8|
|OTI|13.6|0.6|0.7|2.5|4.0|9.2|9.9|2.6|0.1|0.9|44.0|
|PIP|1.5|0.5|0.5|2.0|1.7|5.4|1.2|0.7|5.2|0.2|18.7|
|PST|2.7|1.0|0.8|3.5|2.9|6.3|6.8|1.3|2.8|0.6|28.7|
|RLW|23.5|6.9|6.3|30.2|20.5|53.8|34.5|9.4|16.1|6.2|207.3|
|SCI|8.8|3.2|2.4|10.4|7.8|22.6|16.5|4.1|9.8|1.5|87.0|
|TMS|7.6|2.8|2.2|10.1|8.3|18.7|21.1|3.6|7.4|1.5|83.3|
|TPP|6.8|4.4|15.1|13.1|2.6|13.0|6.8|7.3|0.6|4.2|73.9|
|TRD|205.4|78.2|67.0|300.6|223.3|535.3|456.6|101.6|386.8|48.2|2,402.9|
|TRK|10.2|3.5|3.1|13.6|11.3|24.6|23.0|4.7|12.9|2.2|109.2|
|TRO|4.5|1.5|1.3|5.7|5.1|11.2|10.7|2.0|5.7|0.9|48.7|
|CLI|3.8|1.6|1.2|5.0|4.4|8.6|12.9|2.0|1.9|0.8|42.3|
|Market||||||||||||
|total|747.2|228.8|202.7|975.2|790.0|1,778.0|1,241.5|305.6|962.3|151.1|7,382.4|

- a/  Value added defined here does not include taxes.

b/ Sector codes are in Table 1, oblasts in the markets are listed in Table 2.

Source: Regions of Russia, Roskomstat, and authors' calculations.

30
deal _d_ increase by 0.9 percent more following the introduction of a new establishment in the market. Column 2 of Table 2 replaces the Deal-UPC and Deal-Chain fixed effects with an even stronger Deal-UPC-Chain fixed effect. Column 3 adds various time-varying controls, such as lagged unemployment rate and lagged house price growth rate in these markets. In Column 4, we additionally consider a deal-market FE to account for any unobserved changes in the market during the particular deal. As an example, in this most preferred specification we compare the price of Diet Coke in Whole Foods in the same quarter between the winner and loser counties, after accounting for various time-varying market level factors. In this preferred specification, we find that prices of common products which were sold in both winning and losing counties around a particular deal _d_ increase by 0.7 percent more following the introduction of a new establishment in the market. Across all the very demanding specifications, our estimates remain robust.

In the next few paragraphs, we show the results of various specifications to address different concerns to our identification. The first concern is that of pre-trends: prices might already be rising in markets that attracted the MDP. In Figure 2, we show the results of specification (S2) but with the post dummy replaced by a dynamic event study design, four years before and after the MDP introduction. The figure shows that while there was no noticeable trend in prices across the winner and loser markets, prices started rising a year after the introduction of an MDP in winning compared to losing markets.

Second, if the MDP disproportionately benefits skilled workers, then it is possible that only the prices of products consumed by skilled workers increase. In that case, the rising UPC-level inflation would not matter much for worker welfare. We now divide the UPCs into two groups: products consumed primarily by skilled and primarily by unskilled workers. Using the demographics data and household transactions data from the IRI, we determine which products are primarily purchased by skilled (household head has a bachelor’s degree or greater) households. To define products consumed by skilled and unskilled households, we first compute the median expenditure by skilled households for each UPC.<sup>12</sup> If the expenditure on a UPC is greater than the median UPC-level expenditure by skilled households, we call the UPC “skilled”; otherwise, we call it unskilled. The results are presented in Table 3 and show that the UPC-level prices increased in winner compared to loser counties across

> 12The IRI data only collects demographics for 5-6 FIPS codes. We only use information from consumption expenditure by demographics for households in these counties to construct the skilled and unskilled variables

24
# Table 9 Wage Growth: Test Score Effects

Dependent: Monthly earning growth Sample: Age started>=15

|Experience|-0.0218|-0.0212|-0.0231|-0.0209|
|---|---|---|---|---|
||(0.98)|(0.90)|(1.05)|(0.90)|
|Experience * years of schooling|0.0035|0.0035|0.0038|0.0036|
||(1.32)|(1.22)|(1.42)|(1.25)|
|Experience * years of schooling * Math<br>|0.00005<br>(2.03)|0.00006<br>(1.96)|<br>||
|Experience * years of schooling * English<br>|<br>|<br>|0.00005<br>(1.64)|0.00005<br>(1.76)|
|Age started|-0.1794|-0.1633|-0.1809|-0.1686|
||(2.09)|(1.82)|(2.10)|(1.89)|
|Age started squared|0.0040|0.0038|0.0041|0.0039|
||(1.81)|(1.63)|(1.83)|(1.71)|
|Female|-00214|0.0129|-0.0160|0.0153|
||(0.70)|(0.40)|(0.52)|(0.47)|
|Asset 2000|4.01e-08|3.76e-08|4.16e-08|3.97e-08|
||(3.80)|(3.44)|(3.80)|(3.52)|
|Mother/guardian schooling|0.0021|0.0025|0.0022|0.0025|
||(1.10)|(1.28)|(1,14)|(1.30)|
|Mother/guardian age|-0.0118|-0.0064|-0.0117|-0.0064|
||(2.32)|(1.20)|(2.26)|(1.19)|
|Math|-0.0011|-0.0017|||
||(1.26)|(1.66)|||
|English<br>|<br>|<br>|-0.0012<br>(1.12)|-0.0018<br>(1.64)|
|Province dummies|yes||yes||
|School dummies||yes||yes|
|Number of observations|1487|1487|1481|1481|
|Number of provinces|45||45||
|Number of schools||101||101|
|R squared|0.1106|0.1329|0.1077|0.1349|

Numbers in parentheses are absolute t values with Huber robust standard errors. Current age is age started first job plus experience (years).

24
Table S.2: Selective Attrition - Fitzgerald et al (1998) method with Probit _Dependent variable: Probability to be traced in 2010_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Log total consumption|0.005|||||0.103|
||(0.059)|||||(0.202)|
|Log consumption expenditures||-0.033||||-0.115|
|||(0.057)||||(0.135)|
|Log consumption from stocks|||0.069|||0.042|
||||(0.044)|||(0.093)|
|Log nonfood spending||||0.037||-0.008|
|||||(0.044)||(0.051)|
|Log tropical livestock 6|||||0.500|0.384|
||||||(0.319)|(0.338)|
|CNDD+factions (_S _<sup>_CNDD_+</sup><br>_i_<br>)||||||-0.018<sup>∗∗</sup>|
|||||||(0.008)|
|FNL factions (_S _<sup>_FNL_</sup><br>_i_<br>)||||||0.011|
|||||||(0.010)|
|Violence||||||-0.020|
|||||||(0.043)|
|Sex Head||||||-0.143|
|||||||(0.196)|
|Age Head||||||0.002|
|||||||(0.005)|
|Married||||||0.479|
|||||||(0.332)|
|Divorced||||||-0.052|
|||||||(0.556)|
|Widow||||||0.135|
|||||||(0.398)|
|Primary School||||||-0.099|
|||||||(0.125)|
|Secondary School||||||0.078|
|||||||(0.161)|
|Small business||||||0.231|
|||||||(0.216)|
|Construction||||||0.098|
|||||||(0.230)|
|Extractive activity||||||0.175|
|||||||(0.534)|
|Public sector||||||0.240|
|||||||(0.305)|
|Constant|0.934<sup>∗</sup>|1.276<sup>∗∗∗</sup>|0.457|0.645|0.935<sup>∗∗∗</sup>|0.507|
||(0.560)|(0.485)|(0.380)|(0.400)|(0.061)|(0.832)|
|Province FE|No|No|No|No|No|Yes|
|Observations|1227|1227|1123|1102|1232|1007|

_Notes:_ Authors’ analysis based on data described in the text. The table reports Probit estimates. Selective attrition has been tested for the log of dependent variables in 2006. Consumption and spendings are per adult equivalent. Sampling weights are accounted for. Clustered-robust standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 35
prefecture seat as the IV for the Taiping dummy. The results are in panels A and B of Table 9.

The Taiping areas have experienced a stronger Malthusian transition than other areas. The income levels and the human capital indicators in Taiping areas are the same as the other areas. However, the Taiping areas have significantly higher fiscal capacity. Based on the OLS estimate, in 2010, one-and-a-half centuries after the Taiping Rebellion, fiscal revenue per capita in the Taiping areas is 50 percentage points (0.6 standard deviations) higher than in other areas, a huge effect. The 2SLS estimate is 50 percent larger than that of the OLS. This supports the war-induced state-capacity hypothesis. Furthermore, based on the OLS, Taiping areas have a significantly higher share of modern sectors: these areas’ share of manufacturing in GDP is higher by 4.6 percentage points (0.4 standard deviations). Based on the 2SLS, there is a positive but insignificant impact on manufacturing. However, the effect of schooling is significant and positive. The Taiping areas’ average schooling level is 13 percent higher, again a large effect. These results support the Malthusian transition hypothesis.

Two variables have robust association with the modern outcomes (see also panels C and D for the robustness when we allow more nuanced Taiping variables). The number of wars between 1776 and 1853 is _negatively_ associated with the income level in 2010; increasing it by one standard deviation (0.64) is associated with income per capita that is 9 percentage points lower. Thus, other domestic conflicts do _not_ have the same effects as the Taiping Rebellion. Interestingly, one-and-a-half centuries after the rebellion, pre-war human capital is robustly and positively correlated with income levels, fiscal capacity, modern-sector structures, and human capital. Increasing pre-war human capital by one standard deviation (1.22) is associated with higher income per capita by 13 log points, fiscal capacity by 23 log points, the share of manufacturing by 2.3 percentage points, and the average schooling by 3 percentage points. This widespread impact is consistent with the human capital view of long-term development (Glaeser et al. 2004; Galor and Weil 2000; Galor 2005).

In Panel C, we add the full set of Taiping indicators and the initial _Likin_ intensity (i.e., in 1880). Since it is hard to find even one solid IV, it is perhaps inappropriate to try to deal with multiple potential endogenous variables, and doing so would probably compromise inference too much (Young 2017). We thus only report the OLS results.

The long-term development of Taiping areas differs greatly depending on the (historically) brief encounter with the Taiping Revolution in the middle nineteenth century. First, the Early and the Late Taiping areas have vastly different long-term development. Early Taiping areas now have _lower_ GDP per capita by 22 log points; otherwise, they do not differ much with the non-Taiping areas. Thus, the war effect of the Early Taiping areas is similar to that of other 34
<!-- Start of picture text --> Labor Productivity TFP<br>1.2 1.2<br>1 4<br>5 08 i 0.8<br>56 poe<br>a a<br>04 04<br>02 0.2<br>it 2 4 & & 10 it] 2 4 6 8 10<br>Years Years<br>Employment Investment<br>o1<br>15<br>Qa<br>=E<br>8B 4<br>@-01 2<br>05<br>-02<br>0<br>2 4 6 & 10 2 4 6 & 10<br>Years Years<br>‘Consumption CPi<br>Lt]<br>08 “02<br>£06 5-04<br>8 8<br>Pos @ .06<br>02 -0.8<br>Qo 2 4 6 8 10 =i 2 4 6 a 10<br>Years Years<br><!-- End of picture text -->
# _Table 2. Means and standard deviations for selected variables_

||Benefi|ciaires|Non-ben|eficiaires|
|---|---|---|---|---|
|Variable|Mean|Std. Dev.|Mean|Std. Dev.|
|Participation|1.000|0.000|0.000|0.000|
|Woman|0.396|0.490|0.318|0.466|
|Illiterate|0.330|0.471|0.293|0.456|
|Literate|0.233|0.424|0.371|0.484|
|Education>2|0.436|0.497|0.336|0.473|
|Age<30|0.251|0.435|0.361|0.481|
|Age 30-39|0.379|0.486|0.324|0.469|
|Age 40-49|0.128|0.335|0.156|0.363|
|Age>49|0.141|0.349|0.109|0.312|
|Age unknown|0.101|0.302|0.050|0.218|
|Exp<5|0.132|0.339|0.153|0.360|
|Exp 5-10|0.194|0.396|0.227|0.420|
|Exp>10|0.674|0.470|0.620|0.486|
|Agriculture|0.300|0.459|0.221|0.416|
|Tailoring|0.273|0.447|0.371|0.484|
|Electronics|0.427|0.496|0.408|0.492|
|Abengourou|0.093|0.290|0.078|0.268|
|Abidjan|0.176|0.382|0.159|0.366|
|Biankouma|0.300|0.459|0.221|0.416|
|Bouaké|0.159|0.366|0.184|0.388|
|Daloa|0.053|0.224|0.044|0.205|
|Man|0.145|0.353|0.171|0.377|
|Yamoussoukro|0.075|0.264|0.143|0.351|
|0 employees|0.106|0.308|0.165|0.372|
|1-3 employees|0.493|0.501|0.452|0.498|
|4-6 employees|0.247|0.432|0.143|0.351|
|7-9 employees|0.040|0.196|0.044|0.205|
|10+ employees|0.115|0.319|0.196|0.398|
|CA bef. PAFPA|125843|192137|115042|139576|
|Revenue bef. PAF|71886|154679|69443|98888|
|CA aft. PAFPA|136666|202277|114286|155341|
|Revenue aft. PAF|100512|195761|90081|153686|
|N|227||321||

In table 3 the simple DID-estimates of the impact of PAFPA are presented (see equation 10). The estimated effect CFA for CA is around 11.600 and 8000 CFA for revenue in the full sample, meaning that there is a positive effect in the overall sample. This result also shows when we split the sample across gender. However, when we split the sample according to sectors, the result is that in terms of CA there has been a negative effect from PAFPA in the agricultural sector, and this is also the case in the tailoring sector, when the variable of interest is revenue.

13