rafmacalaba/datause-encoder-data huggingface dataset:
# **A.2. Detailed timeline**

Figure A.1: Detailed timeline of the intervention and surveys, including phone call reminders’ contents <!-- Start of picture text --> Baseline sample: 386 Baseline survey Conscientiousness<br>• Treatment: 193 training<br>• Control: 193 April 2019 · F2F August 2019 · F2F<br>Phone call reminder week 2<br>• "If you are running late, call ahead and Phone call reminder week 1<br>inform the supervisor"<br>• "Get to appointments with your • "Do not forget meetings"<br>supervisor on time" • "Be organized"<br>• "Try to go above and beyond what is • "Finish what you start"<br>required" • "Go out of your ways to keep your<br>• "Keep things tidy. You need to keep promises"<br>your environment neat in order to<br>work well"<br>Phone call reminder week 4<br>Phone call reminder week 3<br>• "It is important to set goals and<br>• "Make sure to wear the mandatory achieve them. Complete the tasks that<br>equipment. it is important that therules of the workplace are observed" • you start""Do not miss the bus. Be at the pick-up<br>•• "Do not let yourself get distracted""Try to carry out your obligations to • point on time""If you need to call in sick, do so before<br>your shift so your supervisor can get a<br>the best of your ability"<br>• "Keep up with the required work" • replacement""Do not forget to put things back in<br>their proper place"<br>Phone call reminder week 5<br>Phone call reminder week 6<br>• "Persist at tasks after meeting setbacks • "Return phone calls in a timelyfashion"<br>or failures"<br>• "Keep up with the required work" • "Try to work extra hard on a task to<br>• "If you are running late, call ahead to • make sure that it is done right""Do not leave a mess in your work<br>notify those who are waiting for you"<br>• "Clean up the surroundings of your • place""If you need to leave the workplace,<br>work place" ask the supervisor first"<br>Phone call reminder week 8<br>Phone call reminder week 7<br>• "Try to give the highest quality in<br>••• "Put things back in their proper place""Put your mind on the task at hand""Carefully follow the instructions •• everything you do""Get to appointments on time""Try to carry out your obligations to<br>the best of your ability"<br>• given to you by your supervisor""Do not forget meetings" • "Do not leave your belongings<br>unattended, for example your helmet,<br>safety jacket"<br>End-line survey Mid-line survey<br>(371) (344)<br>May 2020 · Phone January 2020 · F2F<br><!-- End of picture text --> _Notes_ : This figure expands on Figure 1 and shows the main conscientiousness reminders for each follow-up phone call. The number of respondents is in parenthesis. _F2F_ indicates the survey or intervention was done face-to-face, while phone surveys are coded with _Phone_ .

27
Musgrave, Richard. 1987. "Tax Reform in Developing Countries. Chapter 9.," in _The Theory of Taxation for Developing Countries_ . David Newbery and Nicholas Stern eds. Washington, DC: The World Bank Group.

Prest, A. R. 1979. "The Taxable Capacity of a Country." _Taxation and Economic Development_ . London School of Economics and Political Science: London, UK.

Rodrik, Dani. 1998. "Why Do More Open Economies Have Bigger Governments?" _Journal of Political Economy_ , 106:5, pp. 997-1032.

Rutkowski, Jan J. and Stefano Scarpetta. 2005. "Enhancing Job Opportunities." The World Bank Group: Washington, DC.

Stotsky, Janet G. and Asegedech WoldeMariam. 1997. "Tax Effort in Sub-Saharan Africa." _IMF Working Paper_ . The International Monetary Fund: Washington, DC.

Tait, Alan A., Wildfrid Gratz, and Barry J. Eichengreen. 1979. "International Comparisons of Taxation for Selected Developing Countries." _IMF Staff Papers_ , 26:1.

Tanzi, Veto. 1992. "Structural Factors and Tax Revenue in Developing Countries: A Decade of Evidence," in _Open Economies: Structural Adjustment and Agriculture_ . I. Goldin and L. A. Winters eds. Cambridge: Cambridge University Press, pp. 267-81.

Tanzi, Vito and Hamid Davoodi. 1997. "Corruption, Public Investment, and Growth." IMF: Washington, DC.

Toye, J.F.J. 1978. _Taxation and Economic Development: Twelve Critical Studies_ . London, UK.

United Nations. 2002. "Financing for Development." _Monterrey Consensus_ .

United Nations. 2002. _Report of the International Conference on Financing for Development_ . Monterrey, Mexico: The United Nations.

World Bank. 1997. _World Development Report 1997: The State in Changing World_ . Washington, DC: The World Bank Group.

World Bank. 2005. "Global Monitoring Report 2005: Millennium Development Goals: From Consensus to Momentum." 280. World Bank: Washington, DC.

World Bank. 2006. _World Development Index_ . Washington, DC: The World Bank Group.

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_L. Fox and A.M. Oviedo_ _Institutions and Labor Market Outcomes in Africa_ DB indicator or the legal origin, and employment growth could in fact be capturing other institutional aspects of importance for employment growth that are not easily measured (such as enforcement).

# **VI. Concluding remarks**

The recent evidence for many developed and emerging economies of the potentially harmful impact of labor and other regulations on economic outcomes (growth, employment, etc.) has triggered calls for regulation reform across the world. In Sub-Saharan Africa (SSA), however, little is known about the actual impact of regulations, and given the severity of the many constraints to economic development in SSA, it is reasonable to think that labor and other regulations might not be among the top “binding constraints” to development.

This paper seeks to analyze the effect of regulations –labor regulations, but more broadly, the legal system- on job creation. Our preliminary examination of firm-level data on employment growth and firm perceptions about the investment climate, contrasted with widely used _Doing Business_ (DB) regulation indicators, suggests that (1) employment growth in manufacturing is high across SSA in spite of a very “hostile” regulatory environment; (2) employment growth is no different between the “better” and the “worse” regulated countries; (3) labor regulation does not appear to be a concern to firm managers; (4) in the absence of restrictions, firms would expand, rather than contract their workforce; and (5) there seems to have been a structural change between 2003 and 2006 that allowed firms to expand employment.

We also find an ambiguous statistical relation between job creation (measured as short- and long-term employment growth), the DB regulation scores, and the origin of the country’s legal system (a proxy for regulation). In the short term, employment growth is neither related to any measure of regulation, nor to legal origin. Instead, it seems to be more driven by firm characteristics that determine productivity: having access to credit, investing in human capital, using technology, etc. In the long run, however, regulation and the legal origin seem to play a role, as they are significantly associated to employment growth over a firm’s lifetime, in addition to the firm characteristics mentioned above. Regarding the right policies to enhance job creation in SSA, our results seem to confirm that regulation reform alone might not lead to the high payoffs observed in other regions, and that improving the overall investment climate should instead be the focus.

16
# **C.  Aggregate Import Growth Decomposition**

# **C.1. Industrial Countries**

Tables 2 and 3 show the share of imports, import growth rates and their decomposition for the selected industrial and developing countries respectively.<sup>7</sup> Totals are weighted averages.

**Table 2: Global Manufacturing Imports of Industrial Countries from 1991/92 to 2001/02 and 2001/02 to 2007/08**

||Shar<br>In Do|e of World Im<br>mestic Dema|ports<br>nd (%)|Annual Imp<br>From W|ort Growth<br>orld (%)|An<br>1991/92 -|nual Import<br>2001/02|Growth due to<br>2001/02|<br>- 2007/08|
|---|---|---|---|---|---|---|---|---|---|
|Country|1991/92|2001/02|2007/08|91/92 -<br>01/02|01/02 -<br>07/08|Demand<br>Change|Market<br>Share<br>Change|Demand<br>Change|Market<br>Share<br>Change|
|Canada|32.5|47.1|46.6|6.6|8.4|2.7|3.9|8.6|-0.1|
|France|31.1|28.2|38.5|3.8|12.5|3.1|0.8|6.8|5.1|
|Germany|22.4|34.3|38.5|3.0|12.8|-1.3|4.3|10.6|2.1|
|Japan|4.0|8.9|13.0|6.7|9.1|-1.3|8.1|2.4|6.7|
|United States|12.6|20.9|23.6|9.1|7.6|3.7|5.4|5.4|2.2|
|Total: above<br>industrial co.|13,5|21.5|26.6|6.4|9.6|1.6|4.8|5.8|3.8|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Source: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

There is one important difference between the first period 1991/92- 2001/02 and the second one 2001/02-2007/08. Industrial countries’ demand growth almost quadrupled from 1.6 percent p.a. to 5.8 percent p.a. Market share changes moved in the opposite direction but only declined to 3.8 percent p.a. from 4.8 percent p.a. Average import growth, which is the sum of demand and market share changes, accelerated from 6.4 percent to 9.6 percent per annum.

Along with the acceleration of demand growth during the second period, relative contribution of demand and market share changes to import growth got reversed. During the first period, except for France, the contribution of market share changes to import growth is much larger than the contribution of demand growth. France has actually import substituted during this period and reduced its share of imports. During the second period, demand increase rate has almost quadrupled and, except for Japan, the contribution of demand growth has been higher

> 7 Table 1 in the annex also shows the impact of exports from partners that had a special trading arrangement during the 1990s and 2000s. For the US and Canada imports from NAFTA countries are presented separately to see the impact of NAFTA and for France and Germany the imports from EU are separated. In the annex tables, EU is further separated into the first 15 and 12 countries that joined later. For the developing countries, imports from EU for Turkey, and from US and Canada for Mexico, are also presented separately.

7
jobs in the textile industry from India to Morocco were destroyed since their wages were no longer competitive when compared to the cost reduction associated with mechanization.

In this article, we examine the impacts of robotization in the US on exports from Mexico to the US. As articulated in the theoretical model (forthcoming), automation in the US may affect the demand for Mexican goods through different channels. First, automation could improve the efficiency of firms in the tradable sector, which may foster the demand for complementary goods from Mexico. This increase in efficiency could, at the same time, increase the demand for Mexican goods through an income effect. Second, automation may improve the comparative advantage of the US in certain sectors, which would reduce the demand for Mexican products. Third, automation may lower the prices of American intermediate inputs imported by Mexico, which may boost the production and export levels of the latter.

Using export data at the local labor market (LLM) level in Mexico, this article studies if higher exposure to US automation through trade lowered local exports to the US from 2004 to 2014. Exposure of Mexican areas to US automation is defined as the increase in the number of robots per thousand workers by sector in the US, weighted by the initial exports of each sector at the LLM level in Mexico. The larger the increase in the number of robots and the higher the share of exports from that sector, the higher the exposure to US automation. The results indicate a strong and robust negative relationship between exposure to US automation and exports from Mexico to the US. This suggests that the substitution effect of automation seems to be stronger than the complementary and efficiency channels. These findings are consistent with automation increasing the pace of reshoring and/or lowering the pace offshoring.

Given that both economies are highly integrated, there are valid concerns about the endogeneity of the exposure to automation in the US. For instance, industries that face higher import competition from Mexico may be more likely to adopt robots to become more competitive. To overcome this challenge, we use an instrumental variable similar in spirit to 2
**Table 1** . Cumulative Installed Wind Power Capacity (MW), 1980-2007

|Year|Germany|U.S.|Spain|India|China|Denmark|Other|Global|
|---|---|---|---|---|---|---|---|---|
|1980|0|8|0|0|0|2|n.a.|10|
|1981|0|18|0|0|0|7|n.a|25|
|1982|0|84|0|0|0|12|n.a|96|
|1983|0|254|0|0|0|20|n.a|274|
|1984|0|653|0|0|0|27|n.a|680|
|1985|0|945|0|0|0|50|25|1,020|
|1986|0|1,265|0|0|0|82|n.a|1347|
|1987|5|1,333|0|0|0|115|n.a|1,453|
|1988|15|1,231|0|0|0|197|137|1,580|
|1989|27|1,332|0|0|0|262|109|1,730|
|1990|62|1,484|0|0|0|343|41|1,930|
|1991|112|1,709|5|39|0|413|n.a|2278|
|1992|180|1,680|50|39|0|458|103|2,510|
|1993|335|1,635|60|79|0|487|394|2,990|
|1994|643|1,663|70|185|0|539|390|3,490|
|1995|1,130|1,612|140|576|38|637|647|4,780|
|1996|1,548|1,614|230|820|79|835|974|6,100|
|1997|2,080|1,611|512|940|170|1,120|1,167|7,600|
|1998|2,870|1,837|830|1,015|224|1,428|1,996|10,200|
|1999|4,445|2,490|1,584|1,077|268|1,718|2,018|13,600|
|2000|6,104|2,578|2,235|1,220|346|2,300|2,617|17,400|
|2001|8,754|4,275|3,337|1,456|402|2,417|3,259|23,900|
|2002|11,994|4,685|4,825|1,702|469|2,880|4,545|31,100|
|2003|14,609|6,372|6,203|2,125|567|3,110|6,445|39,431|
|2004|16,629|6,725|8,263|3,000|764|3,117|9,122|47,620|
|2005|18,415|9,149|10,027|4,430|1,260|3,128|12,682|59,091|
|2006|20,622|11,575|11,623|6,270|2,604|3,136|18,303|74,133|
|2007|22,247|16,818|15,145|8,000|6,050|3,125|22,737|94,122|

_Source_ : EPI (2008) Over the period 1980 to 2007, growth in wind generating capacity has averaged 44.4% per annum, although it has slowed to 27.4% since 1999. It is surprising that the growth in capacity is forecast to continue at well above 15% until 2012. Installed capacity is expected to surpass 100,000 MW by the end of 2008 (Renewable Energy Industry, 2008). Although it registered very high growth rates in recent years, the current role of wind power in meeting global electricity demand is almost negligible as it accounts for only about 1% of the global electricity supply (IEA, 2008).

4
We can also use a ranked-order logit specification to investigate whether certain treatments make individuals more likely to rely on non-financial factors like brand loyalty or the (random) order in which products were received in detriment to the cost of the product (Bertrand et al., 2010). We run the following specification using pooled data Ordercijk = αj + β1TopTablecijk + β2Familiaritycijk + β3Simplifiedjk + β4Simplifiedjk ∗ TopTableijck + β5Simplifiedjk ∗Familiaritycijk + εcijk (7) where TopTablecijk takes value 1 if product chosen in order c by participant _i_ in round _j_ of session _k_ was among the first half of the products given in a round or was shown in the upper half of a comparative table in Mexico. Familiaritycijk is another dummy that takes value 1 if the participant had or had ever had a financial product from the institution of the product chosen in order _c._

# **4. Results**

Table 3 reports the coefficients from regressions in (1) (in columns 1 and 4) in (2) (in columns 2 and 5) and in (3) in columns 3 and 6. The dependent variable is a dummy that takes value 1 if the individual chose the best product. Columns 1 to 3 (4 to 6) report the results for credit (savings) sessions. In all regressions, the first rows show the coefficients associated with the disclosure treatments followed by the coefficient for the profile dummy and the participant characteristics. The table also reports the mean of the dependent variable for the omitted disclosure treatment (marketing materials) and the p- value of a t-test that two different disclosure treatments are equal.

Column 1 of Table 3 shows the results for credit sessions in Peru. The simplified KFS increases the probability that individuals chose the right loan product by 12.6 percentage points relative to the brochures (and other marketing materials) and it is significant at the 1 percent level. In contrast, KFS designed by financial institutions do not significantly improve decision-making relative to brochures. The p-value associated with the t-test that the coefficients on both disclosure treatments are equal is 0. The coefficient associated to the profile is also not significant, suggesting that individuals are equally able to choose the best loan product regardless of whether they expect to make all 15
- 50 -

GRAPH 4b: ACTUAL COMMODITY PRICECHANGES ALICPF FORECASTS AND ERROR BARS <!-- Start of picture text --> &LICPF FORECAST-=_<br>.499<br>.300<br>.209<br>1979 19B0 19B1 19B2 1§B3 i9B-4 19BS 19B6 l9B7 lB8t 1989<br><!-- End of picture text --> Forecasted Sample Period: 1979-1988
The last terms of the equation are the variables of interest, and capture the impact of China and India on other economies’ FCS. We also include an interaction with a dummy for LAC, since the impact on this region may differ from the world average.

FCS data contain negative and zero values, which might be a problem for the loglinear estimations. We deal with this issue in two ways. First, we follow Blonigen and Davies (2004) and truncate these observations to 0.1. Second, we use a Poisson pseudo maximum likelihood estimator. As discussed above, this approach proposed by Santos Silva and Tenreyro (2005) has the advantage of not requiring any transformation of the zeros in our dependent variable, which comprise 25% of our dataset. We also estimate negative binomial regressions to control for over dispersion in the dependent variable.

# **4. Data**

Data on bilateral FCS come from OECD and UNCTAD for the period 1990-2004. The OECD reports bilateral outward FCS of 29 OECD countries in 235 host economies in millions of U.S. dollars from 1982 to 2003. Data from UNCTAD covers 29 source countries in 190 host countries.<sup>6</sup> The combination of these datasets leaves us with a total of 20,949 observations, of which one fifth come from UNCTAD.<sup>7</sup> FCS data were deflated using the U.S. producer price index from the IMF’s _International Financial Statistics_ (IFS) dataset.

Unfortunately, bilateral outward FCS disaggregated by sector is not available for most OECD countries. The U.S. Bureau of Economic Analysis (BEA) does provide data

> 6 For those countries for which the UNCTAD data is reported in national currency, we transformed the figures into U.S. dollars using the end of period exchange rate, which was taken from the OECD. We then use the OECD dataset unless the observations are missing. Data for Australia for the period 1990-2000 was also taken from the UNCTAD, since OECD reports data for the fiscal year.

> 7 Missing observations in our right-hand-side variables leave us with a total of 13,765 observations.

9
sample more female-owned business would like to register than male-owned businesses – Angola, Botswana, and Rwanda. In Angola, a large portion of the informal firms would like to register – 88% of female-owned firms and 86 percent of male-owned businesses. By far, in the full sample, the most frequently cited benefit for registering is access to finance – 67 percent of firms. This proportion is the same for female-owned and male-owned informal firms. The least cited reason is less bribes to pay (26) percent. About 36 percent of firms indicated that better access to raw materials, infrastructure services, government services, and ability to issue receipts to attract customers as potential benefits for registering. However, this information is unavailable for several countries where the surveys were implemented (see table A4).

The self-reported reasons for remaining unregistered are not available for all the countries in the sample. For the 8 out of 14 countries for which these data are available, most firms cite time fees and paperwork (55%) and taxes if registered (58%) as reasons not to register, with little difference between female-owned and male-owned informal businesses. Fifty-seven percent and 53 percent of male-owned and female-owned informal businesses, respectively, cite time, fees and paperwork as the reason for remaining unregistered. Fifty-seven percent of female-owned informal firms and 58 percent of male-owned informal firms cite taxes if registered as a reason to be unregistered. Inspections and bribes are cited by a small percentage of firms, regardless of the gender of the largest owner. About 43 percent of businesses see no benefit in registering, implying that many firms are less confident about the potential benefits they cite for being registered. This varied little by the gender of the largest owner – 44 percent of female-owned informal businesses, and 42 percent of male-owned businesses. Thus, although transitions between formality and informality may be slow, there is suggestive evidence that firms would like to formalize, and the government may have a role in why they do not.

26
l POLICY RESEARCH WORKING PAPER 2347

# Summary findings

Bloch and Rao examine how domestic violence may be used as a bargaining instrument, to extract larger dowries from a spouse's family. The phrase "dowry violence" refers not to the dowry paid at the time of the wedding, but to additional payments demanded by the groom's family after the marriage. The additional dowry is often paid to stop the husband from systematically beating the wife.

Bloch and Rao base their case study of three villages in southern India on qualitative and survey data. Based on the ethnographic evidence, they develop a noncooperative bargaining and signaling model of dowries and domestic violence. They test the predictions from those models on survey data.

women who come from richer families (from whom resources can more easily be extracted). Larger dowries - as well as greater satisfaction with the marriage (in the - form of more male children) reduce the probability of violence.

In India marriage is almost never a matter of choice for women, but is driven almost entirely by social norms and parental preferences. Providing opportunities for women outside of marriage and the marriage market would significantly improve their well-being by allowing them to leave an abusive husband, or find a way of "bribing" him to stop the abuse, or present a credible threat, which has the same effect.

They find that women whose families pay smaller dowries suffer increased risk of marital violence. So do

- - This paper a product of Poverty and Human Resources, Development Research Group is part of a larger effort in the group to examine crime and violence in developing countries. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-473-3902, fax 202-522-1153, email address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Vijayendra Rao may be contacted at vrao@worldbank.org. May 2000. (29 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center