rafmacalaba/datause-encoder-data huggingface dataset:
in the overall level of business regulations may have a negligible link to growth, moving from the lowest quartile of improvement in business regulations to the highest quartile is associated with a significant increase in annual per capita growth of around 0.8 percentage points.

The next section describes the datasets used and presents the summary statistics. Section 3 presents the econometric models. Section 4 discusses the results and section 5 concludes.

# **2. Data description**

To test the link between business regulations, firm creation and growth we use primarily the World Bank Doing Business indicators. Published annually, they measure the efficiency and strength of laws, regulations and institutions relevant to domestic small and medium-size companies throughout their life cycle.<sup>2</sup> We use nine of the different areas included in the Doing Business indicators as follows:

- The starting a business indicator evaluates business entry regulations. The indicator is based on the methodology developed by Djankov _et al._ (2002). It measures all procedures required to start up and formally operate an industrial or commercial business, as well as the time and cost to complete them and the paid-in minimum capital requirement.

- The dealing with construction permits indicator records the procedures required for a business in the construction industry to build a warehouse. These procedures include the submission of project-specific documents – such as building plans and site maps – to the authorities and obtaining all necessary clearances, licenses, permits and certificates. Other procedures relate to completing all required notifications and receiving all necessary inspections.

- The registering property indicator captures the process necessary for a business to purchase a property from another business. The procedures necessary to transfer the property title to the buyer’s name so that the buyer can use the property for expanding its business, use the property as collateral in taking new loans or, if necessary, sell the property to another business are also measured.

- The getting credit indicator covers two areas relevant for access to credit for businesses and is based on the methodology developed by Djankov _et al._ (2002). The first area covered consists in the legal rights of borrowers and lenders with respect to secured transactions. In particular the indicator measures whether certain features that facilitate lending exist within the applicable collateral and bankruptcy laws. The second area measures the extent to which credit information is shared by looking at the coverage, scope and accessibility of credit information available through public credit registries and private credit bureaus.

- The protecting investors indicator assesses the strength of minority shareholders protection against directors’ misuse of corporate assets for personal gain and is based on

> 2 The Doing Business database is available at www.doingbusiness.org.

3
It is also worth noting that, beyond any direct link between laws and formality, legal reform may also exert influence on the social norms that may have historically blocked women out of the private sector. Aldashev et al. (2012) discuss how changes to formal laws can have a “magnet” effect, thereby, drawing informal laws in the same positive direction as legal reforms. Laws facilitating female entrepreneurship may also provide a useful backstop mechanism to women who wish to establish formal enterprises in the face of informal laws that would prevent them from doing so.

In the second part of our analysis, we consider the relationship between starting informally and subsequent firm performance. The results displayed in tables 6 to 8 suggest that, holding other observable characteristics fixed, firms that began operations informally have lower sales, lower labor productivity and, for firms with female owners are less likely to be engaged in exporting activity. We find no evidence that firms that began informally and survived through to the time of the survey possess a higher level of productivity that allowed them to formalize and survive. Overall, it seems that the negative relationship between beginning informally and subsequent performance is more robust for firms that are fully owned by men; however, the coefficients between this group of firms and those firms with female participation in ownership is not generally statistically significant. The one exception is firms in South Asia; in this region, it appears that beginning informally is only associated with lower performance for firms with female owners. Informality is notoriously high in South Asia—over 90 percent of businesses in the region are informal (Bussolo et al., 2020). In this region, the Enterprise Survey data a show much lower prevalence of firms that began informally with female owners relative to firms that are fully owned by men. We know from ILO data that women are more likely than men to be engaged in the informal economy in the region; the fact that we see fewer of them formalizing (and, thus, represented in the Enterprise Survey data) suggests that there are characteristics associated with these firms that make them both less likely to formalize and less productive if they manage to do so. Obtaining a deeper understanding of the gender and informal entrepreneurship in the region would be an interesting avenue for further research but is beyond the scope of this study.

# **5. Conclusion**

In this paper we show that a legal environment that discriminates against women as employees and entrepreneurs is positively associated with the probability that a firm with female participation in ownership began operating as an informal enterprise. The association is strongest for firms in South Asia, but also holds for firms in Sub-Saharan Africa and in the Middle East and North Africa. We also document a similar, although less strong, relationship between legal equality and the duration for which firms 31
# **Figure 11(a): Vietnam: Return to skill by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 0.6<br>0.4<br>0.2<br>Agriculture/Mining<br>0<br>1992 1998 2004 2006 Manufacturing<br>-0.2<br>Trade/Services<br>-0.4<br>-0.6<br>-0.8<br>Return<br><!-- End of picture text -->

**Year**

**Figure 11(b): Vietnam: Proportion of skilled workers by sector over time (Senior Secondary and above)**

<!-- Start of picture text --> 50<br>40<br>30 Agriculture/Mining<br>Manufacturing<br>20<br>Trade/Services<br>10<br>0<br>1990 1998 2004 2006<br>% skilled<br><!-- End of picture text -->

**Year**

51
39 Ministry of Health officials. For example, throughout the project area, measlL., malaria, and infant malnutrition--all epidemiologically major causes of death and disease--were not viewed as serious; and tapeworms were often viewed as beneficial, since they were believed to inhibit or kill other intestinal parasites. Beliefs about the causes of many diseases were also often mistaken. For example, malaria was widely viewed as caused by vapors from the ground or by spirits; measles by spirits; venereal diseases by exposure to crowds of people (and, in the case of one ethnic group, by overworkl); and infant diarrhea during teething by worms infecting the gums (for which the local treatment is unhygienic extraction of the milk teeth, often leading to severe mouth infections).

Important information wan obtained about the determinants of behavior as well as actual practices. It was discovered that a strong belief was held among all the ethnic groups that it was shameful and a sign of unfitness to be a wife for a woman to show any sign of weakness or sickness during pregnancy--a critically important explanation for the low utilization of antenatal services in the area. It was also generally believed that the consumption of white foods during pregnancy can cause shameful stains on the baby at birth; these foods were therefore carefully avoided, depriving both mother and baby of important sources of protein during this key period. Other beliefs and behaviors were rarticular to one ethnic group. The Amhara tribe, for example, delay the beginning of breastfeeding for several days after birth beeause they believe that colostrum (the highly nutritious, thick milk produced by mothers in the first days after birth, which also passes on the mother's immunity to diseases to the child) is bad for the baby.

Such findings represent critical qualitative information for project planners. With this kind of knowledge, it should be possible for MOH officials to design IEC strategies and messages to combat specific mistaken beliefs; to differentiate IEC strategies for a given ethnic group where their beliefs and behavior differ importantly from others; and hence increase the practice of preventive health care and the use of modern health services. Without such knowledge, it would be easy for the belief to be perpetuated that the main cause of Ethiopia's high levels of mortality and disease is the low coverage of rural health facilities.

Lessons for the future. In an informal evaluation of the assessment, the Bank staff involved drew a number of conclusions for the future:

# Specific to EthioRia

- The information was valuable, was obtained in a quick and costeffective way, and could not have been gained from a traditional KAP survey--because, given existing levels of ignorance about the determinants of behavior, no one would have known the relevant questions to ask in a questionnaire survey.

- A number of quantitative questions had been asked about numbers of children, etc., to which the answers were not statistically
Taken together, we view this evidence as suggesting that, while not perfect, the matching was competently done, and lack of an effect is not simply a result of poor implementation.

# _3.4.3 Binding minimum wages?_

In a previous experiment with female community college graduates, Groh et al (2013) find that one reason these individuals had difficulty finding jobs was that the minimum wage of 150 JD per month appeared to be binding, with 85 percent of those employed through a wage subsidy program receiving exactly this minimum wage. Jordan subsequently raised the minimum wage to 190 JD per month, beginning 1 February 2012. This raises the possibility that one reason for the lack of hiring is that firms would like to hire workers for lower wages, but find the minimum wage binding.

The minimum wage appears to be much less of an issue for our current sample, of mainly university graduates. Only 1.7 percent of those employed in the May 2013 follow-up survey are earning 190 JD<sup>5</sup> , with a median monthly salary of 300 JD for females and 350 JD for males in our sample. Moreover, the job offers that candidates received and turned down typically had salaries above the minimum wage. It therefore does not appear that the main reason for a lack of impact is binding minimum wages.

# _3.4.4 Perhaps search costs are not the main reason firms are not hiring more workers_

Our experiment was premised on the assumption that one reason firms do not hire more workers is that the search and matching costs are high for them, especially for small and medium firms. Survey results from the baseline survey of firms find many firms giving responses that make it seem like they have difficulty matching: 60 percent say they find it difficult to distinguish between good and bad workers; 35 percent say hiring a new young worker is more a question of chance than skill; and 39 percent agree or strongly agree that it is difficult to find qualified employees. Nevertheless, it is unclear how costly this is for them in practice. Only 6.5 percent of firms surveyed at baseline say they would be willing to pay a reasonable price to learn more about the quality of job candidates. Moreover, we saw only a minority of firms responded to the opportunity offered by our matching service to lower search costs. In a follow-up survey of firms which had participated in the matching services, 25 percent said they can usually fill a position

> 5 4.4 percent are earning less than 190 JD, which could reflect part-time work, or informal employment.

17
Figure A.3. Refugee Characteristics before and after moving to Tajikistan <!-- Start of picture text --> .5 1<br>0.46<br>0 .8 3<br>.4 0 .3 8 .8 0 .7 7<br>.3 .6<br>0.55<br>.2 .4<br>0 .1 6<br>0.29<br>0 .1 0<br>.1 Female Male .2 Female Male<br>Tajikistan Afghanistan Tajikistan Afghanistan<br>50 40000<br>4 6.0 7<br>30843.08<br>45 30000<br>40 4 0.0 2 20000<br>150 77 .52<br>35.83<br>35 10000<br>31.43<br>30 Female Male 0 79.70 Female 496.90 Male<br>Tajikistan Afghanistan Tajikistan Afghanistan<br>.45 7<br>6.86<br>0.41 6.77<br>.4<br>0.38<br>6.5<br>.35<br>.3 0.29 0.29<br>6.03<br>6<br>5.92<br>.25 Female Male Female Male<br>Tajikistan Afghanistan Tajikistan Afghanistan<br>Employed [=1]<br>Labor Contract in Afghanistan [=1]<br>Weekly Hours Work in Afghanistan<br>Monthly Wage in Afghanistan (Somoni)<br>Student [=1]<br>Household Size<br><!-- End of picture text --> _Notes:_ The data comes from the Afghanistan Welfare Monitoring Survey conducted for this study in the fall of 2021. The Figure includes the 5,592 individuals using in the main regressions. Red lines represent 95% confidence intervals for the mean. Employed is [=1] if the individual has worked for remuneration during the past 7 days. Student is restricted to the population aged between 6 to 24 years old.

50
36 Table 3

# Maximum Coverage Rer DORositOr

|Country|Year|Local Currency<br>Value|US$ Value|1988<br>US$ Value|
|---|---|---|---|---|
|Argentina|1988|A<br>81,000|8,416|<br>8,416|
|Austria|1986|AS<br>200,000|13,100|<br>14,116|
|Belgium|1985|BF<br>500,000|8,421|<br>9,248|
|Brazil|1988|OTN<br>3,500|16,500|<br>17,780|
|Canada|1986|C$ 60,000|43,181|<br>46,530|
|Chile|1977|t.u.<br>100|1,000|<br>1,949|
|Chile (comp.)|1981|t.u.<br>250|5,061|<br>6,576|
|Colombia|1988|Col$ 200,000|668|<br>668|
|France|1985|FF<br>400,000|44,518|<br>63,829|
|India|1980|Rs.<br>30,000|3,815|<br>5,470|
|Japan|1986|Y 10,000,000|59,340|<br>63,942|
|Kenya|1985|Kshs. 100,000|6,086|<br>6,684|
|Netherlands|1986|Fi<br>35,000|14,286|<br>15,394|
|Nigeria|1988|N<br>50,000|10,860|<br>10,860|
|Norway||unlimited|u|nlimited|
|Philippines|1988|P<br>40,000|1,896|<br>1,896|
|Spain|1981|ptasl,500,000|16,248|<br>21,111|
|Switzerland|1984|SF<br>30,000|12,768|<br>14,521|
|Turkey|1983|TL 3,000,000|13,306|<br>15,779|
|UK|1982|pds.<br>10,000|17,505|<br>21,424|
|uS|1980|US$ 100,000|100,000|<br>143,379|
|Yugoslavia||unlimited||unlimited|

# NOTES:

The year listed is the latest year that the ceiling was modified (to the amount specified in the first column). This amount was converted to US dollars using the average exchange rate for that year. Finally, this was reflated to 1988 dollars using the US CPI. Thus, the last column specifies the 1988 dollar value of the coverage ceiling at the last time it was changed, and no the dollar value of the ceiling in effect in 1988. Thus, if one takes the US as an example, the ceiling was set in 1980 to what would now be $143,379. Since then, the value of the ceiling has depreciated by 30S. The source for exchange rates and the CPI is the IMF's International Financial Statistics.
regarding Finland’s PISA performance, directed mostly toward other OECD countries with lower scores, for instance the United States. Vietnam’s superlative performance points to a similar future stream of research, with the added advantage of relevance for developing countries. Section 5 provides concluding ideas that might be among the first of many more such ideas for future investigations of Vietnam’s performance.

# **2 Endowment**

Utilizing the categorization of explanatory factors presented in Figure 3, this section analyzes mean differences in explanatory factors on students, parents, teachers and schools. All variable means presented in the tables are statistically different at the 95% significance level, unless otherwise noted in the footnotes and figures in parentheses represent standard deviations. PISA documentation, especially the technical report - [OECD, 2014a] provides rich definitions and explanations of the variables used. Appendix tables A2, A3 and A4 of this paper accordingly provide references mapping the variables used in this paper and the original PISA variable names.

## **2.1 Student Characteristics**

Table 1 begins an exploration of differences in mean values between Vietnamese and Dev7 student characteristics. The absence of differences is sometimes as important as the presence of differences. Table 1 indicates no differences by age or gender of students. The PRESCHOOL variable shows the first instance of a large statistically significant difference. While 78.88% of Dev7 students reported attending pre-school, the number of students attending pre-school from the Vietnam sample was 91.20% - a sizable difference that is both statistically and economically significant. The relationship between pre-school and later educational outcomes has been studied very closely over the years. Longitudinal impact evaluation studies regarding the Perry Pre-school project and Head Start in the US are among the most cited studies in the economics literature<sup>1</sup> . We can also see from the numbers of REPEAT in Table 1 that PISA takers in Vietnam were three times less likely to have repeated a grade in the past (6.79% compared to 19.15%).

> 1For detailed meta-analysis, see [Barnett, 1995] and [Schweinhart et al, 2005]

5
Page 2 of 3 Table 13 ... Perturbatlrons of Germany's Real Govermnent Expenditures CR CA CR CR GR CR Li LI Li Li Li Li CMP PGUP lS ER PXN CURBAL CUP PUP RS ER PXN CURBAL STN(+1,-1) 0.008U 0.0212 0.0050 0.0048 0.0216 0.0477 0.0157 0.0246 0.0082 0.0131 0.0208 0.0455 SVN(+5, 5) 0.0609 0.0806 0.0366 0.0471 0.0875 0.2331 1.0437 0.0435 0.1282 0.0598 0.0541 0.1979 Lo(.+S5.l) 0.0953 0.0001 0.0028 0.0000 0.0001 0.0241 0.1118 0.0002 0.0027 0.0000 0.0002 0.0033 LjIt5,-1) 0.2621 0.2141 0.2436 0.1200 0.2802 0.0767 0.6575 0.2921 0.3097 0.0828 0.2745 0.1017 ... Perturbations of the United States Target Monetary Base

||- -<br>UP|CR<br>PGNP|CR<br>aS|CR<br>ER|CR<br>PXN|CR<br>CURBAL|Li<br>CGP|Li<br>PGNP|Li<br>RS|LI<br>ER|Li<br>PXN|Li<br>CURBAL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|STNCtl,l)|0.0308|0.2241|0.0628|0.0075|0.0372|_0.0077_|0.0827|0.1341|0.0748|0.0070|0.0349|0.0230|
|SYT(M5.*5)|0.0419|0.1756|0.0518|0.0059|0.0953|0.0045|0.0283|0.154|0.0537|0.0057|0.0945|0.0595|
|SYN(C40. 10)|0.3253|1.5077|0.6781|0.0356|0.6645|0.0256|0.1956|0.9623|0.4073|0.0335|0.5857|0.0913|
|LIC1O1,)|1.5671|1.5395|1.5974|1.5908|1.6034|1.6441|1.8818|1.6582|1.7612|1.5785|1.4998|1.9566|
|LIU(-10.-1)|1.6620|1.9140|1.7937|1.6010|1.7650|1.6362|1.8970|1.8327|1.8413|1.5884|1.6572|1.9595|
|LIN(*10,+5)|0.1954|0.1154|0.1626|0.0475|0.0519|0.0470|0.1513|0.0926|0.1084|0.0462|0.0546|0.1971|
|LIN(+5,+1)|1.4427|1.4910|1.5458|1.5730|1.5845|1.6286|1.8639|1.6289|1.7357|1.5607|1.4760|1.9498|

Un
respondents—corresponding to 18 percent of the sample—were surveyed before the devaluation (the control group), while 984 respondents were surveyed after (the treatment group). However, pre-treatment respondents are predominantly from the capital of Botswana—Gaborone—and from urban areas more broadly. Specifically, 63 percent of the pre-treatment respondents were from Gaborone; 85 percent were from urban areas. In the two days following the devaluation, only 10 percent of the respondents were from urban areas. Therefore, the treatment coincides with a shift in the sampling of respondents from urban to rural areas, which is also likely to correlate with respondents' evaluations of their living conditions and well-being. Part of the treatment effect might therefore be due to preexisting differences in subjective well-being between people in rural and urban areas, or may arise if, for example, more confident, optimistic, or resourceful individuals selfselect into cities and urban areas (cf. Cook and Campbell 1979; Achen 1986).

Despite this initial imbalance between the control group and the treatment group, there are at least two reasons to believe that we can plausibly mitigate the consequences of nonrandom assignment. First, since we can identify the source of nonrandom treatment assignment— geographically imbalanced sampling—with relative precision, we can also go a long way towards making the treatment and control groups comparable by adjusting for the relevant covariates. As we explain in more detail below, we do so in a number of ways; most importantly by controlling for whether respondents live in urban or rural areas; by excluding respondents in the Gaborone area; and by zooming in on the discontinuity in the data generated by the devaluation. Second, since the imbalance between the pre- and post-treatment groups is a result of the fact that the _Afrobarometer_ simply _happened_ to conduct interviews mainly in Gaborone and urban areas prior to the devaluation, we can rule out other sources of nonrandom treatment assignment caused by the actors generating the data. First, it is highly implausible that the _Afrobarometer_ ’s timing of the survey was related to the central bank’s decision to devaluate in any way, or vice versa. Second—and more 10