rafmacalaba/datause-encoder-data huggingface dataset:
more undernourished persons, such as those discussed by Kakwani (1988) and Ravallion (1988b). We shall only attempt to make ordinal comparisons of undernutrition, following the dominance approach.

# 3. The Data and Results

The SUSENAS tapes give data on household consumption (from both market expenditures and own production) for 50,000 randomly sampled households at each date. Following past practice for Indonesia, we shall mainly base our poverty assessments on distributions of household consumption per person, adjusted to February 1984 urban prices using the Consumer Price Index (CPI). The CPI is far from ideal for our purposes. The index is only constructed for urban areas and its composition is inappropriate for the poor. We have, however, re-weighted the CPI so as to better reflect the conaumption pattern of the poor, and have also built an allowance for urban-rural price differentials into the index.1<sup>0</sup> Appendix 1 discusses the data and their limitations in greater detail. For present purposes, the most worrying features of these data are the possibility that the rate of inflation in rural areas may be underestimated by the CPI and that SUSENAS data imply a growth rate of real private consumption per capita which is higher than implied by the national accounts. We shall return to these points later.

As noted above, the aetermination of a poverty line is always likely to be contentious, and so it is important to have some idea of how sensitive poverty assessments are to this choice. This study considers a range of poverty lines, embracing most of the alternatives found in the literature on poverty in Indonesia (see, for example, the survey in CBS,
for business regulations.

The results show a remarkable stability of the estimated coefficient for the interaction term to changes in the sample along the country or the industry dimension. The point coefficient estimates for the interaction term are always negative and statistically significant. The most sensitive coefficients are those for the largest size class - 100 or more employees - where the exclusion of Chile or Portugal leads to a stronger estimated effect of regulations. Not surprisingly given the unbalanced nature of the sample, the exclusion of finance and business activities as well as construction tend to strengthen the estimated negative effects of regulations on job reallocation.

# **6 Conclusion**

This paper exploits a rich new database with harmonized data on job flows across industries and size classes for 16 industrial and emerging economies over the past decade. We find that all countries in our sample exhibit sizeable annual gross job flows. Industry and size class effects together account for a very large share of the overall variability in job flows across country, industry and size class cells (e.g., over 50 percent of the variation in the summary measure of job reallocation is accounted for by industry and size effects interacted together). Interestingly, the most important factor here is employer size. Small businesses exhibit a substantially higher pace of job creation and destruction and this pattern is pervasive across industries and countries. Moreover, industry effects play a large role as well. Taken together, it is clear that some form of technology, cost and demand factors that are common across countries account for the bulk of the variation in job flows. Nevertheless, even after controlling for industry/technology and size factors, there remain significant differences in job flows across countries that could reflect differences in business environment conditions.

Our harmonized firm-level dataset allows us to look at two factors shaping the business environment - regulations on the hiring and firing of workers and business regulations. To minimize the possible endogeneity and omitted variable problems associated with crosscountry regressions, we use a difference-in-difference approach. The empirical results suggest that stringent hiring and firing regulations (and their consistent enforcement) reduce job turnover, especially in industry and size class cells that inherently exhibit more job turnover. To capture the latter, we use the United States patterns as a benchmark to identify and quantify industry/size class cells with inherently higher job turnover. Labor regulations also distort the patterns of flows across industry and size classes within a coun44
||**Table 5: Regions other th**|**an Latin Ame**|**rica**|||
|---|---|---|---|---|---|
||**All regions excluding**<br>**Latin America**|**Sub-Saha**|**ran Africa**|**Eastern E**<br>**Centr**|**urope and**<br>**al Asia**|
|Dependent variable: Quality<br>Certificate Y:1 N:0|**(1)**<br>**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
||**Panel A: Log od**|**ds ratios**||||
|Informal Competition (CY avg.)|2.074***<br>1.542***|4.120***|6.739***|2.206***|1.992***|
||(0.550)<br>(0.565)|(1.269)|(2.396)|(0.478)|(0.608)|
|GDP per capita (logs)|-1.038||4.139**||-0.646|
||(0.940)||(2.030)||(1.013)|
|Labor Productivity (logs)|0.190***||0.100||0.238***|
||(0.037)||(0.089)||(0.052)|
|Number of workers (logs)|0.659***||0.377**||0.663***|
||(0.066)||(0.169)||(0.087)|
|Age of firm (logs)|0.166**||0.445**||0.079|
||(0.079)||(0.205)||(0.095)|
|Manager experience (logs)|-0.143**||-0.185||-0.067|
||(0.069)||(0.155)||(0.081)|
|Herfindahl index|0.210||-0.369||0.124|
||(0.496)||(0.822)||(0.865)|
|Exports (proportion of sales)|0.768***||1.279**||0.669***|
||(0.165)||(0.525)||(0.206)|
|Foreign ownership (%)|0.007***||0.005||0.007***|
||(0.002)||(0.005)||(0.002)|
|Women top manager Y:1 N:0|0.073||-0.149||0.083|
||(0.105)||(0.297)||(0.133)|
|Annual employment growth (%)|-0.004||-0.001||-0.005|
||(0.003)||(0.007)||(0.004)|
|New Product Y:1 N:0|0.161*||0.122||0.171|
||(0.090)||(0.245)||(0.114)|
|Line of Credit Y:1 N:0|0.002***||-0.001||0.003***|
||(0.001)||(0.002)||(0.001)|
|Number of power outages (per|-0.001||-0.012||0.006|
|month)|(0.002)||(0.009)||(0.019)|
|Foreign Technology Y:1 N:0|0.005***||0.006**||0.005***|
||(0.001)||(0.003)||(0.001)|
|Website Y:1 N:0|0.009***||0.011***||0.007***|
||(0.001)||(0.003)||(0.001)|
|Total population (logs)|1.264||0.542||3.500*|
||(1.352)||(8.915)||(1.800)|
|Industry fixed effects|Yes<br>Yes|Yes|Yes|Yes|Yes|

47
**Figure 29 The Margins of Export, Annual Export Growth**

<!-- Start of picture text --> 50<br>40<br>30<br>2002-2003<br>2003-2004<br>20<br>2004-2005<br>2005-2006<br>10<br>2006-2007<br>2007-2008<br>0<br>2008-2009<br>2009-2010<br>-10<br>Firm Entry Firm Exit Country Country Sector Sector Exit Product Product Intensive Intensive<br>Entry Exit Entry Entry Exit Positive Negative<br>-20<br>-30<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

The key results of the decomposition along export margins are intriguing as it can be grasped easily by the visual inspection of Figure 29 , which plots in a chart the results from the export growth decomposition in Table 12. Firm churning, i.e. entries and exits of firms from exporting activity and their entry and exit from markets and products, seems to be associated with a positive selection effect: new entrants more than offset the loss in exports by exiting firms. This is a largely expected result, in particular for developing countries. Several papers have indeed demonstrated that export growth in developing countries takes place mostly at the extensive margin, as the reallocation of resources from less to more efficient firms and products is a main engine. Yet, a closer inspection to the various sub-components of the extensive margin suggests that performance has been rather disappointing.

Churning along the destination-country margin, i.e. entries and exits of incumbent exporters from specific destination markets was relatively stable over the 2002-2010 period. While there was evidence of exits over the same period of time, the net effect on the export growth rate was positive (21.1%). This notwithstanding, the performance is far from positive given the focus of Pakistan’s trade policy on bilateral and regional agreements. Despite the various agreements entered into force in this period of time, the contribution of the country-extensive margin to export growth did not increase, showing that bilateral and regional agreements have hardly proven superior to the previous preference for multilateral and unilateral trade liberalization.<sup>20</sup>

> 20 A World Bank policy note specifically investigates the impact of Preferential Trade Agreements on Pakistan’s export growth (World Bank, forthcoming).

39
- Thornton, Rebecca, Lauren Hatt., Erica Field, Mursaleena Islam, Freddy Solis Diaz and Martha Azucena Gonzalez. “Social Security Health Insurance for the Informal Sector in Nicaragua: A Randomized Evaluation.” _Health Economics_ , 19 (2010):181-206.

- Todd, James, Carl Armon, Anne Griggs, Steven Poole, and Stephen Berman. “Increased Rates of Morbidity, Mortality, and Charges for Hospitalized Children with Public or No Health Insurance as Compared with Children with Private Insurance in Colorado and the United States.” _Pediatrics_ 118, no. 2 (2006): 577-585.

- <mark>Ver Ploeg, Michele. “Do Benefits of US Food Assistance Programs for Children Spillover to Older Children in the Same Household?”</mark> _<mark>Journal of Family and Economic Issues</mark>_ <mark>30, no. 4 (2009): 412-427.</mark>

- <mark>Zhou, Zhongliang, Yanfang Su, Jianmin Gao, Ling Xu, and Yaoguang Zhang. “New Estimates of Elasticaity of Demand for Healthcare in Rural China.”</mark> _<mark>Health Policy</mark>_ <mark>103, no. 2 (2011): 255-265.</mark>

24
Table 1: Tariffs by region

||Applied|MFN|Bound|
|---|---|---|---|
|European Union|1.5|2.5|2.6|
|United States|1.6|2.7|2.7|
|Other Advanced Economies|2.6|4.2|14.3|
|China|3.4|3.9|4.9|
|East Asia and Pacifc|1.3|2.5|9.7|
|Europe and Central Asia|2.6|4.8|5.5|
|Latin America and the Caribbean|6.7|9.0|32.9|
|Middle East and North Africa|4.9|6.3|19.4|
|South Asia|7.7|8.0|44.7|
|Sub-Saharan Africa|7.5|9.2|36.3|
|World average|2.7|3.8|10.2|

Source: World Bank WITS database. Note: Trade weighted averages for 2015.

The model underlying the simulations is calibrated on the GTAP 9.2 database (Aguiar et al. 2016). The baseline scenario tracks the evolution of macroeconomic variables such as GDP, population and labour force growth in the absence of a policy change. Projections of these variables up to 2020 are based on the April 2017 update of World Economic Outlook (WEO) of the IMF. The policy shock (increase in tariffs) is implemented in 2018. All results are reported as the difference between the counterfactual and the hypothetical baseline scenario in 2020. The size of the tariff shocks is based on data from the World Bank World Integrated Trade Solution (WITS) database which in turn is based on Trade Analysis and Information System (TRAINS) data.

13
and 28% received 100 or more per month.<sup>89</sup> A reported 58% of companies had at least 26 employees processing a single data subject request in 2018; this is likely attributable to the multi-step process of registering the request, verifying the requester’s identity, and locating the data on multiple systems, an onerous task for organizations many of which log such information on spreadsheets.<sup>90</sup>

# **Operational Cost of Managing Data Subject Requests**<sup>91</sup>

Number of Employees Involved in Processsing a Single Data Subject Request since April 2019 <!-- Start of picture text --> Volume of Data Subject Requests<br>Processsing a Single Data Subject<br>Received per Month since April<br>Request since April 2019<br>2019<br>35%<br>35%<br>30%<br>30%<br>25% 25%<br>20% 20%<br>15% 15%<br>10% 10%<br>5% 5%<br>0% 0%<br> 0-5 6-10 11-99 100-500 500+ 1-10 11-25 26-50 51-100 101-500 500 +<br>Volume of DSRs per month<br>Number of Employees Involved in Processsing a Single DSR<br><!-- End of picture text --> The manual handling of data subject requests has placed a strain on some organizations due to the time and effort involved in servicing the requests within the required one-month window. Duties imposed by a data subject request range from locating, compiling, and providing a data subject with all the information an organization has stored on a data subject, free of charge, commonly known as “the right to access,”<sup>92</sup> to locating and deleting all the information stored on a data subject, “the right to be forgotten.”<sup>93</sup> The challenges posed by data subject requests were echoed by the IAPP’s study in which 56% of the 370 organizations surveyed reported “locating unstructured personal data” as “difficult.”<sup>94</sup> The operational costs that data subject requests impose on an organization appear to be related to the organization’s location, business model, size, and revenue.<sup>95</sup> Findings from the IAPP report suggest that the firms most likely to receive data subject requests have one or more of the following variables: headquarters in Europe, a blended business model in which both data controlling and

> 89 DataGrail, _supra_ note 73.

> 90 _Id._ at 3, 8.

> 91 DataGrail, _supra_ note 73 _._

> 92 Council Directive 2016/679, art. 23, 2016 O.J. (L 119) 1, 2.

> 93 _Id._ , art. 25, 2016 O.J. (L 119) 1, 2.

> 94 Hughes & Saverice-Rohan, _supra_ note 74.

> 95 _Id._

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31

# Table XI

# Efficiency: Links to Financial Indicators Pooled Cross-Section Time-Series: Decade Averages

# Instrumental Variables

|DEPENDENT<br>VARIABLE<br>_INV_<br>FINANCIAL INDICATOR|
|---|
|LLY|
|GYP<br>-0.027<br>0.034***<br>(0.079)<br>(0.012)<br>(0.731]<br>(0.0053|
|BANK|
|GYP<br>-0.044<br>0.041**<br>(0.080)<br>(0.016)<br>(0.8403<br>(0.0121|
|PRIVATE|
|GYP<br>-0.016<br>0.020*<br>(0.073)<br>(0.012)<br>(0.830]<br>(0.094]|
|PRIVY|
|_GYP_<br>-0.035<br>0.041***<br>(0.076)<br>(0.014)<br>(0.6483<br>(0.0031|
|(standard errors in parentheses)<br>[p-values in brackets]|
|Observations=169<br>*<br>significant at 0.10 level<br>**<br>significant at 0.05 level<br>***<br>significant at 0.01 level|
|GYP<br>=Real per capita GDP growth rate<br>INV<br>=Ratio of investment to GDP<br>LLY<br>= Ratio of liquid liabilities to GDP<br>BANK<br>= Deposit bank domestic credit divided by domestic bank<br>domestic credit plus central bank domestic credit|

PRIVATE = Ratio of claims on nonfinancial private sector to domestic credit PRIVY = Gross claims on the private sector to GDP other exolanatorv variables: log of initial income, log of initial secondary school enrollment rate, ratio of government expenditures to GDP, inflation rate, and ratio of exports plus imports to GDP.

Instruments: Decade dummy variables, log of initial income, log of initial secondary school enrollment rate, initial ratio of government expenditures to GDP, initial inflation rate, initial ratio of exports plus imports to GDP, the initial value of the financial indicator, and INV in the previous decade.
how households are able to respond to the intervention. All rounds of the RRMIMP data are used to conduct a household-level panel estimation to compare short- and long-term household effects.

Following Jalan and Ravallion (1998, 2002), the third survey round allows us to employ a dynamic panel estimation, whereby household outcomes _yit_ (such as income or expenditure, for example) depend on the lagged outcome _yit_  1, the household’s current and lagged characteristics _xit_ , project status _pit_ ,<sup>3</sup> and initial (pre-program) geographic factors _vi_ 0 interacted with year _t_ , expressed as follows: The initial (pre-program) community characteristics in 1997, _v j_ 0 , are included to account for factors that could affect program placement, such as agroclimatic features and access to public institutions (e.g., secondary schools, banks, and healthcare facilities) (Jalan and Ravallion 1998; Khandker, Bakht, and Koolwal 2009). We assume that the error term includes a household-specific unobserved effect  _it_ (which may also include unobserved geographic effects) correlated with the regressors, as well as an i.i.d. random component _uit_ , which is orthogonal to the regressors and serially uncorrelated.

If  _it_  _i_ (that is, the unobserved effect is fixed over time), taking deviations from means in equation (1) removes the unobserved effect and yields the following differenced equation: ( _yit_  _~~y~~ i_ )  ( _yit_  1  _~~y~~ i_ )  1( _xit_  _~~x~~ i_ )  2( _xit_  1  _~~x~~ i_ )  ( _pit_  _~~p~~_ )  _vi_ 0( _t_  _<u>t</u>_ )  ( _uit_  _~~u~~ i_ ) (2)

> However, heterogeneity stemming from the unobserved household-specific effect  may not be constant over the period (  _it_  _i_ ).  Changes in other conditions over time (e.g., market conditions), for example, may alter the effects of household and geographic characteristics on outcomes from year to year.

On introducing dynamics and both time-invariant and time-varying unobserved effects, the

> 3 As described in Section 5, we have various indicators of the roads program, whether described by presence of the project in the village (Y = 1, N = 0), distance of the household to the project road, and kilometers of paving completed. We examine these variants of project status in our analysis.

10
In our estimation, we are assuming that each firm takes attributes associated with each region as given and makes rational location choice decisions. For the purpose of estimation, this assumption translates into a condition where the idiosyncratic error term is independent of the regional characteristics. One of the main empirical challenges in separately identifying the effects of local spillovers (i.e. localization economies) is that the concentration of firms in location _j_ may be correlated to sources of natural advantage that are not observed in the data. If favorable natural conditions encouraged or facilitated concentration of firms in particular areas, then not addressing this correlation is likely to overstate the impact of agglomeration economies.

A standard solution for this omitted variable problem would be to employ instrumental variables. However, we use a conditional logit model to estimate equation (3), which implies that we cannot use instrumental variables. To address this problem within our estimation framework, we only analyze location decisions of firms who have started business in the four years preceding the survey. Next, we create agglomeration variables using data for establishments that were in business five or more years before the survey. By splitting the data, we hope that unobserved characteristics that matter for today’s location decisions are different from those that influenced previous concentration. Our second strategy is to include a range of variables representing sources of “first advantage” directly into the estimation. These variables are described earlier and should capture why some areas became attractive for people and establishments in the first place.

# _Variable construction_ :

<u>Infrastructure for market access: We computed travel times on the road network</u> from each GIS pixel in the country to the nearest city of 100,000 or more people. In order to extract these data, we built a raster dataset at a 500 meter resolution where each pixel records the time in tens of minutes to travel from the specific pixel (year 2000 estimate from GRUMP alpha data). Travel time is estimated using a combination of several GIS layers that are merged into a friction grid which represents the time required to cross each pixel (each pixel represents 500m2). As the pixel friction value increases, the travel time to the nearest city of 100,000 increases as well.

12 -
(Arabsheibani et al., 2000; Puri and Robinson, 2005). The difficulty has always been how to measure optimism. We construct an index OPTIMISM, which is the weighted average of the intensity measured by a 4-scale response in eight questions from the General Health Questionnaire (GHQ), related to mental health and anticipatory feelings.<sup>7</sup> Social capital is the set of social relationships an individual can draw on for various occasions. Gomez and Santor (2001) proxy for social capital using club membership, and find a positive effect on earnings from self-employment. We construct a proxy for social capital considerations, NOSOCPTL using the average of two available questions: (i) "Is there anyone you can count on to listen to you when you need to talk?", and (ii) "Is there anyone who you can really count on to help you out in a crisis?" These variables proxy for social capital in terms of the help people can get from friends, neighbors and relatives. This is an “increased security effect”, a psychological safety net in the light of risk-sharing (Ravallion and Lokshin, 2006).

The sociology literature has further stressed the role of family background in developing role models that can foster entrepreneurial aspirations and the empirical evidence is ample. However, the data does not provide us with parental occupational or educational status variables. In order to control for within family effects in selfemployment transitions we examine the impact of the presence of another self-employed member in the household, OTHERSE. About 7% of the individuals in our sample have family members that are self-employed.

# 4.4 _<u>Results</u>_

We model the decision to become self-employed in the years 2002-2004 in the probit framework, excluding individuals already self-employed in 2001. The list of explanatory variables involves demographic, psychological and sociological characteristics described in the last section and summarized in the appendix. In separate specifications, we examine the impact of household financial wealth, the relationship with financial institutions, as well as the role of labour market experience and origins.

Table 3 presents summary statistics of the key variables, and differences in the means between individuals who were not self-employed in 2001 but chose to become

> 7 See Table 3 for the list of questions.

12