rafmacalaba/datause-encoder-data huggingface dataset:
Overall, these results support the argument that the informal sector consists of different types of business owners. Some operate informally due to stringent regulation and simplifying regulation can entice them to register their business. Others run informal businesses while they are looking for a job and they switch to being wage earners when more job opportunities arise. The evidence in this paper does not exclude the possibility that there could be more than two different species of informal business owners. For example, there may be a third group of individuals that does neither want to formalize nor become a wage earner. In my data, a large number of firms continue to operate informally even after the business registration reform. In fact, evidence from Bolivia and Indonesia suggests that not all informal firms benefit equally from registering and for some firms in Bolivia, formalization lowers profits (McKenzie and Sakho, 2010, and McCulloch, Schulze, and Voss, 2010, see also Perry et al, 2007).

This paper is also related to Hsieh and Klenow’s (2009) argument that low aggregate productivity in developing countries is in part due to misallocation of resources across firms and that complex regulation is one factor than can contribute to this misallocation. The findings of this paper suggest that business registration reform allows individuals to better sort across occupations, thus promoting reallocation of resources and potentially raising productivity. More broadly, the results imply that studies of regulatory reforms may need to go beyond measuring average effects since reforms can have important effects on productivity through reallocation.

The remainder of this paper is structured as follows. Section 2 describes the data and the classification of informal business owners into wage worker and formal business owner species. Section 3 discusses the business registration reform and hypotheses for how this reform affects different species of informal business owners. Section 4 lays out the identification strategy and summarizes transitions into different occupations in the pre-reform period. Section 5 presents the impact estimates of the reform on firm formalization and transitions on wage work. Section 6 concludes.

# **2. Mexican Employment Survey Data and Species Classification**

The main data source used in this paper is the Mexican National Employment Survey (ENE), the survey that the Mexican government relies on for calculating unemployment statistics and the size of the informal sector. The ENE was conducted quarterly starting in 2000-II and covers a 3
**Lo**

Table 12 Effects of PROBECAT Training for MEN Corrected for SamVIe Selection - Unweighted & Choice-based Weights

|Explmnatory<br>Variables||OLS Esdmates||Weig|hted Leas Squares Esima|tes|
|---|---|---|---|---|---|---|
||Log Monthly Salary|Weekly Hours<br>Worked|Log Hourly Wage|Log Mothaly Salary|Weekly Hours<br>Worked|Log Hourly Wage|
|Constant<br>|5.726**|42.587**|.574*|5.711**|43.561*|.541**|
|General experience<br>|.023 *|.134|.019 *|.027**|.163**|.021**|
|Experience-squared<br>|-.000**|-.004 **|-.000**|-.000|-.003**|-.000**|
|Schooling attainment<br>|||||||
|No Education<br>|-.203t*|-1.024|-.194|-.279 *|-2.055|-.239 *|
|Primary-incomp.<br>|-.005<br>|-.797|.003|-.041|-1.298|-.018|
|Secondary-inconip.<br>|.062|.430|.026|.084 a|.802|.035|
|Secondary-comp.<br>|.165 *|-1.790|.186**|.194*|-1.328 *|.200**|
|Highschool<br>|.279 e|-.704|.295*|.324**|.096|.316**|
|College<br>|.571 *|-.455|.555**|.644**|1.042|.581|
|Training x Schooling<br>|||||||
|No Education<br>|-.058|14.820|-.485|.055|18.035|-.472|
|Primary-incomp.<br>|-.063<br>|9.101*|-.270 **|.031|11.755**|-.261|
|Primary-comp.<br>|.212**<br>|8.409**<br>|-.002|.311|11.306 *|.002|
|Secondary-incomp.<br>|.267 *<br>|5.424**<br>|.161|.366 *e|8.052**|.175|
|Secondary-comp.<br>|.199*|10.132**|-.039|.319 *|13.112|-.019|
|High School<br>|.171*<br>|7.848**|-.029|.277|10.502**|-.013|
|College<br>|.045|2.769|.032|.138|4.805|.053|
|Lamda (sed. corr'd)|-.179**|4.650 **|-.049|-. 197 **|-5.216 **|-.044|
|R-squared<br>|.157|.054|.145|.206|.060|.191|
|Sample size|2330|2271|2271|2330|2271|2271|

Notes: I. Regressions included duration of ptevious unemployment, self employed status, and time dummy variables. 2. * and ** denotes statistical significance at the 5 percent and I percent levels, respectively.
# **Table 7: Country Policy Responses**

|**Country**||**Econo**<br>|**my-wide Policies**<br>||||**Social Protection Programs**<br>||
|---|---|---|---|---|---|---|---|---|
||**Reduce taxes**<br>**on foodgrains**<sup>**2**</sup>|**Stock**<br>**management**|**Export restrictions**|**Pricing policies**|**Cash**<br>**transfer**|**Food for**<br>**work**|**Food ration/stamp**|**School feeding**|
|Afghanistan*|√|||||√|||
|Bangladesh*|√<br>|√<br>|√<br>Imposed ban on rice<br>exports|√<br>|√<br>|√<br>Long history<br>and being<br>expanded|√<br>Food Rations eliminated but millions<br>of poor households get Vulnerable<br>Group Feeding (VGF) cards which<br>entitle them to 15 kg. grains per<br>month.|√<br>Was replaced by<br>cash grants but is<br>being restarted.<br>|
|Bhutan|√|√||√|√|||√|
|India|√<br>|√<br>|√<br>Imposed ban on wheat<br>and non-Basmati rice<br>exports, and high taxes<br>on Basmati rice exports|√<br>||√|√<br>Maintains a pretty active public food<br>distribution system|√<br>|
|Maldives|√|√||√|√<br>For specially<br>vulnerable<br>groups|||√<br>|
|Nepal*|||√<br>Imposed ban on rice<br>exports|||√||√<br>Limited coverage|
|Pakistan*||√|√<br>Imposed ban on wheat<br>exports|√<br>|√<br>||food ration/stamp programs have not<br>been implemented (the caretaker<br>government announced that food<br>ration cards would be introduced, but it<br>never happened)<br>|√|
|Sri Lanka*|√||NO explicit ban on rice<br>exports although trade<br>protection<br>and<br>other<br>measures<br>have<br>kept<br>domestic prices above<br>internationalprices.|√|√||√|School feeding<br>program has very<br>limited reach.|

_Source: Country authorities_ 21
50 _Note_ : Reporting to the first decimal point is for formatting purposes and does not imply that numbers exceeding 10 percent have three or four significant figures.

Table A1.9: Distribution of consumption effects by income and region among net oil importers, 1999–2009 (percentage of countries in each category except for the median)

|**Category**<br>|||∆**P**<br>|||||∆**C**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
|(number of<br>countries)|**Med**|**<**<br>**-5%**|**-5 to**<br>**0%**|<br>**0%**|**>0%**|**Med**|**0–**<br>**2.5%**|**2.5–**<br>**5%**|**5–**<br>**5.5%**|**>7.5%**|
|**Income**|||||||||||
|Low (29)|0.0|3|7|86|3|2.3|3|52|28|10|
|LM (36)|0.0|3|25|64|8|2.3|6|44|39|6|
|UM (31)|0.0|0|29|65|6|2.5|6|39|42|10|
|High (35)|0.0|0|51|40|9|0.9|0|83|6|6|
|**Region (exclu**|**ding high-**|**income c**|**ountries)**||||||||
|EAP (14)|0.0|0|29|57|14|3.3|0|36|57|0|
|ECA (15)|0.0|0|40|33|27|0.7|27|53|13|0|
|LAC (21)|0.0|5|19|76|0|3.3|0|24|57|14|
|MENA (5)|0.0|0|20|80|0|3.2|0|40|40|20|
|SAR (7)|0.0|0|43|57|0|1.7|0|86|0|14|
|SSA(34)|0.0|3|6|91|0|2.4|3|50|32|9|

_Sources:_ Table A1.8 and author’s calculations.

_Notes_ : Oil import status in 2009. Med = median in percentage of GDP, LM = lower middle, UM = upper middle, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MENA = Middle East and North Africa, SSA = Sub-Saharan Africa.

Table A1.10: Distribution of production and consumption effects by income and region among net oil exporters, 1999–2009 (percentage of countries in each category)

|**Category**|||∆**P**|||||∆**C**|||
|---|---|---|---|---|---|---|---|---|---|---|
|(number of<br>countries)|**Med**|**<20%**|**-20 to**<br>**-10%**|**-10 to**<br>**0%**|**>0%**|**Med**|**<0%**|**0**–<br>**2.5%**|**2.5**–<br>**5%**|**>5%**|
|**Income**|||||||||||
|Low (2)|-19.3|50|0|50|0|0.4|0|100|0|0|
|LM (11)|7.5|0|9|27|64|2.2|9|45|18|27|
|UM (17)|-3.6|6|6|65|24|1.6|29|41|24|6|
|High (9)|-2.9|11|0|78|11|1.6|11|67|11|11|
|**Region (exclu**|**ding high-i**|**ncome co**|**untries)**||||||||
|EAP (2)|3.2|0|0|50|50|4.7|0|0|50|50|
|ECA (5)|0.0|0|20|20|60|-3.3|80|20|0|0|
|LAC (7)|-3.0|0|0|86|14|2.0|0|71|29|0|
|MENA (7)|-2.2|14|0|43|43|4.5|0|14|43|43|
|SSA(9)|-0.8|11|11|44|33|0.7|22|78|0|0|

_Sources:_ Table A1.8 and author’s calculations _Notes_ : Oil export status in 2009. Med = median in percentage of GDP, LM = lower middle, UM = upper middle, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MENA = Middle East and North Africa, SSA = Sub-Saharan Africa. No country in South Asia was a net oil exporter. Retention of three figures for medians exceeding 10 percent is for formatting purposes and is not intended to imply that the number of significant figures is three.
# lb) Time - Series

<!-- Start of picture text --> .6660+02 1 I | I , 1 I , 1 I 1<br>180|< 1~~~~~8S7 1867<br>E ~~~~~~~~~~~~~~~~~~19i64<br>EE UNITED KINGDOM<br>u - ENGLAND 19<br>_ Wa WALES S.\DE<br>Co X A 5 WAGES 1908A. 1935A SWEDEN<br>12 1759 9RUSS,IA 6<1 ; - UNITED STATES<br>o.4670+02 - '\< ;a<br>U. WFST GERMA _ NY /*... 1949** *~~~~~91 .......<br>__ _ 195~9DENMARK 195"<br>/D ~ ~ 19747NETHERLANOS 1979<br>. 19779_<br>.2680+021 1 1 1 ' 1 1 -- 1 t t f g I t<br>.5866+01 .1214+01 .8175+01 .9136+01<br>Log Income per capitE (R970$)<br><!-- End of picture text -->

<!-- Start of picture text --> FIG. 2. The Kuznets curve: historical time series from five European countries and America (source: See Appendix).<br><!-- End of picture text --> Source: Lindert and Williamson<sup>(1985).</sup>
- 26 -

mean and OECS XL rates were similar at 48%. Primary rates in Jamaica were at record highs showing a percentage standard deviation of 54%, in part due to both the effects of hurricane Andrew in 1992, and previously hurricane Gilbert in 1988 which directly hit Jamaica.

The above analysis shows the large volatility in insurance pricing which affects the Caribbean. Following hurricane Andrew, additional reinsurance capacity was created in the Bermuda market as traditional major reinsurers such as Munich Re exited the Caribbean market and world reinsurance prices increased. This additional capacity along with the global development of capital market instruments such as catastrophe bonds financed in the capital markets, have helped stabilize volatility. Nevertheless, future volatility cannot be ruled out, and even a fraction of that seen in the past would be destabilizing to Caribbean insurance markets and the availability of coverage needed against natural hazards.

Within this context, it is observed that a type of 'contagion' effect occurs globally when major disasters use up significant portions of reinsurer capacity. While this contagion does not occur in the same manner as emerging market jitters occur during economic crises, it has a similar effect and essentially reflects supply constraints which result in higher pricing to reach a new supply/demand equilibrium. Such adjustments are widely recognized by insurers and reinsurers alike, and reflect a 'recovery' effort by both insurance and reinsurance providers for capital lost or depleted due to large events. In this sense, catastrophe reinsurance contains elements of risk financing ('finite insurance') to fund the insured party, with compensatory payback obtained through temporarily higher rates. It is widely stated in the industry, therefore, that due to large catastrophe exposures, the low rates seen in the last two years will not continue.

**Supply Shift and Demand Effect after Catastrophes**

<!-- Start of picture text --> Post Event Supply Function<br>Pre-Event Supply Function<br>N = New Price &<br>Capacity Equilibrium<br>Demand Function<br>Quantity (Reinsurance Capacity)<br><!-- End of picture text --> Another factor affecting the stable supply of catastrophe reinsurance relates to the manner in which the market is organized. As expressed in Goldman Sachs' Fixed Income Research on Insurance Linked Securities: "Natural catastrophes such as hurricanes and earthquakes reduce private wealth just as do economic losses on diversified portfolios of stocks and bonds. Nevertheless, a substantial portion of this exposure is borne by private corporations (privately held reinsurance companies) and small numbers of wealthy individuals (Lloyds unincorporated names). This inefficient sharing of risk has resulted in high reinsurance rates and has motivated many primary insurance companies to maintain very large retained exposures and to cease issuing new
8 TABLE 2: Return and Risk Statistics for Selected Stock Markets - Dec. 1984/June 1992

||Average Return(%)|Standard Deviation<br>(%)J|Coefficient of<br>Variation|
|---|---|---|---|
|DEVELOPED<br>MARKETS||||
|United States|1.42|<br>4.85|3.42|
|United Kingdom|1.43|<br>5.75|4.02|
|Japan|.66|6.60|10.0|
|Germany|1.29|<br>7.22|6.0|
|Switzerland|1.10|<br>5.76|5.24|
|France|1.63|6.62|4.06|
|Hong Kong|2.68|7.92|2.96|
|EMERGING<br>MARKETS||||
|Taiwan (China)|3.33|15.61|4.69|
|Korea|1.91|8.73|4.57|
|Mexico|4.65|13.90|2.99|
|Thailand|2.61|8.58|3.29|
|Brazil|2.97|22.28|7.50|
|India|2.24|10.19|_4.55_|
|Europe|1.67|5.64|3.38|
|EAFE (Europe,<br>Asia & Far East)|1.12|6.78|6.05|
|World|1.15|5.03|4.37|
|GSCI (Goldman|1.42|4.80|3.38|
|Sachs Commnodity<br>Index)||||

Sources: Developed Markets--MSCI (Morgan-Stanley Capital Markets International). Emerging Markets--EMDB (Emerging Markets Database) of the IFC.
on beneficiaries. Third, a cutoff is determined based on the observed coverage rate. Beneficiaries are selected by counting off households ranked from highest to lowest propensity score until the cutoff is reached. The selected households are then used to calculate the targeting errors. Here we carry out such an analysis using the CHIP survey data, with households as the unit of analysis.

Tables 11, 12 and 13 contain descriptive statistics for dibao and non‐dibao households. Both ex post and ex ante incomes of dibao households are, on average, lower than those of non‐ dibao households. A smaller share of the income of dibao households is from wage employment, and in 2007 and 2008 (but not 2009) dibao households are less likely to have a member engaged in migrant work than non‐dibao households.

Household size is smaller for dibao households, and they contain markedly higher shares of members who are elderly, in bad health, or with a disability. In 2007, for example, 20% of dibao households contained a family member over the age of 60, 41% contained a member in bad health, and 35% contained a member with a disability, as compared to 10%, 14% and 12%, respectively, for non‐dibao households. Differences also exist in ownership of physical assets. Housing conditions, as measured by whether housing is multi‐storey and the presence of piped water and flush toilets, are poorer for dibao households. Ownership of durable goods such as household appliances and motorized vehicles is lower.

Tables 11‐13 show that the communities in which dibao households live are somewhat different from those of non‐dibao households. In general, a higher share of dibao households live in villages that experienced a natural disaster, do not have a paved road, and are distant from the nearest township government.

Probit regressions reveal that many of the characteristics in Tables 11‐13 are statistically significant predictors of dibao status. Table 14 reports the estimated marginal effects of the probit regressions. Specification 1 uses ex ante income calculated using village average dibao transfers, and specification 2 uses ex ante income calculated using county average dibao expenditures, as the income variable.<sup>10</sup>

> 10 We also estimated the probits using ex post income because of concerns about measurement error in calculation of ex ante income using village and county average dibao transfers.  We found that the estimated coefficients on ex post income were in fact smaller and the standard errors relatively bigger than those on our estimates of ex ante income.  We concluded that our estimates of ex ante income, despite their possible weakness, are useful for this analysis.

19
which would not change earnings conditional on employment.

Finally, certification increases by 2 percentage points the probability of having a written contract, Statistics South Africa’s definition of a formal job (Table 1, column 5). This effect is entirely explained by the higher employment rate (Table 2, column 4). Consistent with this result, 4 percentage points of the 5.2 percentage point increase in employment are in wage employment, and only 1.2 percentage points into self employment. Wage and self employment status are not prespecified outcomes.

The effects on employment and earnings are substantial and easily exceed the cost of the program. The employment effect is almost three times larger than the mean standardized short-run effect size of active labor market programs reviewed by Card et al. (2018), larger than the effect of an intervention that helped similar South African workseekers get reference letters from past employers (Abel et al., 2020), and similar to the effect of a program that subsidized firms to hire South African workseekers from similar backgrounds (Levinsohn et al., 2013). The average earnings gain in the first three months after treatment is 778 South African rand (USD 130 PPP) – 5.6 times the average variable cost of certification alone and 2.3 times the average variable cost of assessment and certification (details in Appendix B). The average weekly earnings gain is equal to 17% of the weekly adult poverty line in South Africa (details in Appendix D.2).

# **4 Separating Workseeker- and Firm-Side Information Frictions**

Certification may increase employment and earnings by providing information to firms, to workseekers, or both sides of the market. This distinction matters for modeling information frictions and designing government or market-based remedies to information frictions. In this section, we show that both sides of the market change behavior in response to new information. Our argument proceeds in three steps. First, we show that public certification changes workseekers’ beliefs and search behavior in ways that are consistent with the existence of either firm- or workseeker-side information frictions, or both. Second, we discuss another arm of our workseeker experiment that reveals information only to workseekers. The results of this intervention, compared to the effects of public certification, are consistent with both firm- and workseeker-side information frictions existing. The results are not consistent with only one-sided frictions, either for workseekers or for firms. Third, we discuss an audit-style experiment that reveals information only to firms. The results of this experiment are consistent with firm-side information frictions.

## **4.1 Certification Changes Job Search and Beliefs**

We document three patterns in the effects of certification on workseekers’ beliefs and job search behavior. First, certification shifts workseekers’ beliefs about their skills closer to their measured skills. We ask candidates if they think they scored in the top, middle, or bottom third on each of the six assessments compared to other candidates assessed by Harambee. Certification increases 16
posterior mean model size: the posterior probability‐weighted average number of regressors (across all possible models) is just 1.73.

While these posterior model probabilities, and associated inclusion probabilities for individual variables, are a useful way of summarizing the relative importance of particular models and variables, we do not want to overinterpret the precise magnitude of these probabilities and their concentration across models. This is because, as noted in the previous section, the concentration of posterior probability mass across models is sensitive to our choice of prior parameter g. When g is chosen to be small (which we do in order to mimic standard frequentist inference for a given model), the BMA algorithm is more sensitive to small differences in model fit when assessing the relative probabilities of models. As a result, posterior probabilities are more concentrated across models, and similarly, posterior inclusion probabilities are more strongly concentrated on fewer variables. Instead, we simply emphasize the _ranking_ of models and variables by their posterior probabilities. In particular, in Table 4A we have highlighted the top three variables ranked by their PIPs for each outcome variable. This allows us to identify at a glance the relatively most important determinants of each outcome without reference to the precise magnitude of the variables’ inclusion probabilities, which in some cases is quite small. We also think that this exercise of picking the top few variables as ranked by PIP is analogous to the kind of exercise that a policymaker interested in allocating scarce political capital across a few high‐impact reforms might do. In what follows we will refer to these variables with the highest PIPs as the most “important” or most “significant” for a given outcome variable even though this terminology is somewhat imprecise.

In the second and third column for each outcome variable we report the posterior mean and standard deviation of the slope coefficient corresponding to that variable. Note that these are unconditional means and standard deviations, i.e. averaging across all models including those in which the variable does not appear and for which the slope coefficient is then by definition zero. To obtain the posterior mean conditional on inclusion, we need to scale the reported mean by the inclusion probability. Returning to the CPIA as a specific example, the variable with the highest PIP, PQ2Elections, has a posterior mean for the slope coefficient equal to 0.44.<sup>18</sup> This is the expected impact of this variable on the CPIA, averaging across all models. Considering only models in which this variable

> 18 To interpret the magnitude of these coefficients, note all variables are scaled to run from 0 to 1.  So a chance in the value of PQ2Elections from its worst possible value of 0 to its best possible value of 1 would lead to an increase in the CPIA of 0.44 (also on a scale from 0 to 1

19
- Tobias, J.L. and Li, M. (2004) “Returns to Schooling and Bayesian Model Averaging; A Union of Two Literatures,” _Journal of Economic Surveys_ , 18: 153–180.

- Verdinelli, I. and Wasserman, L. (1995) “Computing Bayes Factors Using a Generalization of the SavageDickey Density Ratio,” _Journal of the American Statistical Association_ , 90: 614-618.

- Zellner, A. (1971) _An Introduction to Bayesian Inference in Econometrics_ , New York: Wiley.

- Zellner, A. (1986) “On assessing prior distributions and Bayesian regression analysis with _g_ -prior distributions,” in _Bayesian Inference and Decision Techniques: Essays in Honour of Bruno de Finetti_ , eds. P.K. Goel and A. Zellner, Amsterdam: North-Holland, pp. 233–243.

- Zellner, A. and Siow, A. (1980) “Posterior odds ratios for selected regression hypotheses,” (with discussion) in _Bayesian Statistics_ , eds. J.M. Bernardo, M.H. DeGroot, D.V. Lindley and A.F.M. Smith, Valencia: University Press, pp. 585–603.

32