rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8400

# **Abstract**

Small-scale entrepreneurs are ubiquitous in emerging market economies, yet very few graduate to become larger businesses. This paper asks whether such entrepreneurs aspire to grow and, if so, on which dimensions of the business? What factors influence these aspirations, how realistic are they, and do entrepreneurs dynamically update them based on realized outcomes? A unique panel data set of small-scale retailers in Indonesia is used to show that the average business has strong short- and long-term aspirations for growth in shop size, number of employees, number of customers, and sales. Yet, more than 50 percent of the businesses report no aspirations for growth in the next 12 months, and 16 percent fail to imagine an ideal business over the long term. Entrepreneurs with low profits, business skills, and agency beliefs, as well as those who are older, female, and less educated have significantly lower aspirations. Analysis from a year later shows that most entrepreneurs fail to set realistic aspirations at baseline, but significantly adjust their aspirations to realistic levels with realized outcomes. The analysis also shows that baseline aspirations are a strong predictor of measures of business expansion and innovation, as well as performance outcomes a year later.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at bzia@worldbank.org.

_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 represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Trade Reform and Regional Dynamics: Evidence From 25 Years of Brazilian Matched Employer-Employee Data<sup>_∗_</sup> Rafael Dix-Carneiro Brian K. Kovak<sup>_‡_</sup> Duke University<sup>_†_</sup> Carnegie Mellon University and NBER

# **_JEL codes—_** F14, F16, J46, J61 **_Keywords—_** Trade Liberalization, Transitional Dynamics

> _∗_ This project was supported by an Early Career Research Grant from the W.E. Upjohn Institute for Employment Research. The authors would like to thank Peter Arcidiacono, Penny Goldberg, Gustavo Gonzaga, Guilherme Hirata, Joe Hotz, Joan Monras, Enrico Moretti, Nina Pavcnik, Mine Senses, Eric Verhoogen, and seminar and conference participants at the Bureau of Labor Statistics, Dartmouth, EIIT @ Oregon, FGV-EPGE, George Washington, Hitotsubashi, INSPER, IPEA-RJ, Johns Hopkins SAIS, Keio, Kyoto, MIT, NBER ITI, NBER Summer Institute (Labor Studies and Development), Oregon, Princeton, PUC-Rio, University of Pennsylvania, World Bank, and Yale for helpful comments. Marisol Rodriguez-Chatruc provided excellent research assistance. We thank Data Zoom, developed by the Department of Economics at PUC-Rio, for providing codes for accessing IBGE microdata, and Brooke Helppie-McFall for information on minimum wages in Brazil. Dix-Carneiro thanks Daniel Lederman and the Office of the Chief Economist for Latin America and the Caribbean at the World Bank for warmly hosting him while part of the paper was written. Remaining errors are our own.

> _†_ rafael.dix.carneiro@duke.edu

> _‡_ bkovak@cmu.edu

1
# **I.  Introduction**

Recent literature has emphasized the importance of trade costs for international trade. Anderson and van Wincoop (2003) estimate that trade costs are on average nearly twice as large as production costs, implying that trade costs are important determinants of comparative advantage, perhaps even more important than production costs. Obstfeld and Rogoff (2000) show that trade costs can explain six of the major empirical puzzles that international macroeconomists have struggled with for 25 years. One well-known but little understood trade cost is imposed by the distance a product must travel to reach a market. Understanding how the effect of distance on trade has changed over the last 1520 years will shed some light on why distance is so important, and also on how much technological change can do to reduce the impact of trade costs.

The standard explanation for why distance matters for trade is that transport costs are increasing in distance. Less tangible costs of trade, related to accessing information about foreign markets and finding trade partners, also tend to depress distant trade (Rauch 1999). This suggests that large reductions in transport costs and major improvements in communications technology, such as have materialized since 1980, should generate substantial trade growth and reduce the importance of distance. Indeed, the volume of trade has trebled since 1980 compared with only a 75 percent increase in world real GDP; however, evidence implies that, if anything, distance has become more important for trade.

One way of identifying movements in the effect of distance on trade over time is by comparing estimates of the elasticity of trade to distance from gravity regressions in different years. But results from the large number of international trade studies that 2
online sales system to extract transaction records to be passed on to tax authorities. It is designed for larger users which typically have their own online sales system like chain hotels or restaurants. In contrast to RPOS and TMD that require both hardware and software installation, WS procurement is much simpler and cheaper as it merely requires installing WS software into users’ system.

Lastly, PAC Hotel refers to an application specifically for recording hotel transactions in Gorontalo. The application is installed on the taxpayers’ device which then can be used for recording transactions. To record the transactions, taxpayers need to input the transaction data manually into the application. Local tax authorities usually give this application to small hotels who already have a computer but do not have their own transaction recording system. This treatment is equivalent to RPOS given to restaurants, only PAC does not come with the hardware. Similarly, PAC Hotel also sends transactions data to local tax authorities in real-time basis.

Table 1: Targeted taxpayers before and after being assigned POS machines

|**Taxpayer Before POS Machines**|**Taxpayer After POS type**|
|---|---|
|Manual recording or bookkeeping<br>Electronic recording (cash register)|RPOS (West Manggarai)<br>RPOS or RPOS-Grab (restaurants-Gorontalo)<br>PAC Hotel (hotels-Gorontalo)|
|Ofine system POS|TMD|
|Own online sales system|WS|

# **2.4 Data**

To study the effect of POS on tax payment, we constructed a unique administrative data set on restaurant and hotel tax receipts in the regency of West Manggarai and district Gorontalo. Our data on restaurant tax receipt was paired with the list of restaurants in the two districts that received any of the POS installations during the program as well as the time they received it. Extracting from the information, we can thus identify treatment group, control group, as well as intervention time for our analysis purpose.

Given the rudimentary stage of the POS distribution program’s scalability, the number of POS recipients is relatively small compared to those who have not received one–as the Data section shows. That results in small-sized treatment groups in our estimations, which may compromise the statistical power, and either mute or exaggerate the estimated effects. This could be a caveat, however with the near-complete administrative data on such a program, such a limitation is rather natural and leads to a minimum bias in the results. Therefore, the policy insights that this study offers remain valid.

8
# **S1: STROBE Statement for cross-sectional, observational studies, with information for this study**

||**Item**<br>**No**|**Recommendation**|
|---|---|---|
|**Title and abstract**|1|(a) Indicate the study’s design with a commonly used term in the title or<br>the abstract<br>_In the abstract, we include the following text indicating this is cross-_<br>_sectional data analysis: “With nationally representative, cross-sectional_<br>_data from ten countries in Sub-Saharan Africa, collected using clinical_<br>_vignettes, unannounced visits, and visual inspections, we assess whether_<br>_health providers are available and have sufficient knowledge and means to_<br>_diagnose and treat patients suffering from conditions amenable to primary_<br>_health care.”_|
|||(b) Provide in the abstract an informative and balanced summary of what<br>was done and what was found<br>_In the abstract, we include data on methods and on results._|
|**Introduction**<br>Background/rationale|2|Explain the scientific background and rationale for the investigation being<br>reported<br>_We include the following text: “Measuring quality at various levels of the_<br>_health system—especially where the system fails the people in need—_<br>_remains rare in LMICs. Existing indicators of structural quality, such as_<br>_availability of medicine, equipment, and qualified health attendants, are_<br>_not sufficient to adequately capture the quality of care being offered.1 The_<br>_lack of quality measurement can help to explain both why there is limited_<br>_research on how to effectively transform low-quality health systems into_<br>_high-quality ones, and why there are few such successful reforms in LMICs._<br>_Actionable and credible data on quality, if properly disseminated, can play_<br>_an important role in holding country health systems accountable for_<br>_effective and safe care and can form the basis for quality improvement.”_|
|Objectives|3|State specific objectives, including any prespecified hypotheses<br>_We include the following text: “We use the data to measure dimensions we_<br>_would argue are essential for providing quality care: What do providers_<br>_know? Are providers available at health facilities? And what means do they_<br>_have at their disposal?”_|
|**Methods**|||
|Study design|4|Present key elements of study design early in the paper<br>_We lay out the design: “Our paper reports on the first large scale_<br>_assessments of clinical knowledge, health provider absenteeism, and_<br>_means to provide care in Sub-Saharan Africa. We use data collected_<br>_through clinical vignettes, unannounced visits, and visual inspections of_<br>_over 20,000 providers in over 8,000 facilities from ten Sub-Saharan African_|

36
**Table 4. High-skilled emigration rates. 1990 and 2000**

||**Gross hi**|**gh-skilled emig**|**ration rate**|**Net high-**|**skilled emigr**|**ation rates**|
|---|---|---|---|---|---|---|
||To all|To OECD|To non-OECD|Total|Men|Women|
|**Year 2000**|||||||
|WORLD|8.1|5.9|2.2|0.0|0.0|0.0|
|OECD|4.8|4.4|0.4|-5.5|-5.4|-5.7|
|HIGH|4.8|4.4|0.5|-6.6|-6.8|-6.4|
|DEV|12.0|7.7|4.3|8.0|7.0|9.6|
|LOW|20.3|13.0|7.3|16.0|13.7|21.2|
|LDC|19.9|12.9|7.0|16.6|14.9|21.0|
|SIDS|40.9|39.3|1.6|34.8|29.3|40.8|
|USA|0.6|0.5|0.1|-11.6|-12.0|-11.2|
|CANZ|7.2|6.9|0.2|-30.9|-32.4|-29.5|
|EU27|9.7|8.8|0.9|2.4|2.4|2.3|
|GCC|14.3|3.6|10.7|-104.9|-230.7|-32.2|
|LAC|12.2|11.5|0.7|10.8|9.8|11.9|
|SSA|15.6|12.3|3.3|10.2|8.6|13.7|
|CIS|16.1|3.2|12.9|2.8|2.2|3.6|
|INDIA|6.0|4.3|1.7|5.4|4.7|7.0|
|CHINA|5.2|3.7|1.5|5.2|3.7|9.2|
|MENA|17.5|11.2|6.4|9.0|10.3|6.6|
|**Year 1990**|||||||
|WORLD|6.8|5.2|1.5|0.0|0.0|0.0|
|OECD|4.6|4.3|0.3|-4.0|-3.8|-4.3|
|HIGH|4.8|4.4|0.4|-4.7|-4.7|-4.7|
|DEV|9.6|6.4|3.2|6.8|6.0|8.1|
|LOW|23.2|11.8|11.4|18.9|17.7|21.8|
|LDC|23.2|11.7|11.6|20.4|19.2|24.1|
|SIDS|42.2|40.7|1.4|38.0|33.0|43.8|
|USA|0.7|0.5|0.1|-10.0|-9.4|-10.8|
|CANZ|6.6|6.3|0.3|-28.8|-30.5|-27.0|
|EU27|9.2|8.5|0.7|3.9|3.8|4.1|
|GCC|12.9|3.1|9.8|-104.1|-188.2|-36.4|
|LAC|10.7|10.1|0.6|9.3|8.3|10.5|
|SSA|17.2|13.1|4.1|10.6|9.0|15.2|
|CIS|7.6|1.6|6.0|1.8|1.6|2.1|
|INDIA|4.3|2.8|1.5|2.6|2.1|3.9|
|CHINA|4.5|3.0|1.6|4.5|3.2|10.5|
|MENA|19.9|12.9|7.0|13.7|13.9|13.2|

Notes. Column ‘Stock’ gives the aggregate stock of emigrants in millions ; ‘College’ gives the percentage of high-skilled emigrants ; ‘Women’ gives the percentage of female emigrants.. For high-income (HIGH). developing (DEV) and low-income countries (LOW). we use the World Bank classification. Least developed countries (LDC) and small island developing states (SIDS) are defined by the United Nations. EU27: 27 countries of the European Union. USA: United States of America. CANZ: Canada + Australia + New Zealand; CIS: Commonwealth of independent States of the former USSR. MENA: Middle East and Northern Africa. SSA: Sub-Saharan Africa. Each country only belongs to one geographical group.
# **Table 3a.  Trade Flows of Kenya**

|||Imports|||Exports||
|---|---|---|---|---|---|---|
||USD in|||USD in|||
||<br>billion|% of total|% of supply|<br>billion|% of total|% of output|
|**Business Services**|||||||
|Air Transport|0.1|0.8|17.5|0.6|9.6|69.1|
|Communication|0.1|1.4|12.6|0.3|4.8|40.5|
|Insurance|0.0|0.5|12.4|0.0|0.5|10.2|
|Business Services nec|0.2|2.2|9.8|0.0|0.6|2.4|
|Financial Services nec|0.0|0.4|3.9|0.0|0.5|4.3|
|Transport nec|0.1|0.7|4.5|0.1|1.8|7.3|
|Water Transport|0.0|0.2|10.2|0.1|2.2|58.9|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|2.2|23.7|53.3|0.8|12.2|33.8|
|Energy and Minerals|0.8|8.9|91.8|0.1|0.9|68.6|
|Food Products|0.6|5.8|3.7|0.7|10.9|4.9|
|Petroleum and Coal Products|0.9|9.1|44.0|0.1|1.7|11.1|
|Other Manufacturing|2.9|30.4|57.1|0.3|4.0|14.6|
|Textile and Apparel|0.5|5.5|23.1|0.5|7.2|22.7|
|Wood and Paper Products|0.3|3.4|21.1|0.1|1.8|9.1|
|**CRTS**|||||||
|Agriculture and Forestry|0.4|3.7|3.8|1.8|27.3|16.7|
|Other Services|0.2|2.5|2.4|0.9|13.9|8.6|
|Trade|0.1|0.5|2.6|0.0|0.1|0.3|
|Utilities|0.0|0.0|0.3|0.0|0.2|1.1|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

47
The CHNS collects detailed information on household demographics, education, health and nutrition, occupations and labor force participation, housing and asset ownership, time use, and incomes. One major advantage of the CHNS is that it contains detailed information on individual hours spent on various activities. For example, it records how many hours per day, days per week, and months per year each individual worked in the garden (vegetable plots near the house), on crop production, livestock, and fisheries; it asks about hours worked in wage labor, handicraft and small commercial household businesses. These questions about labor time allocation all refer to "the past year." We will however, refer to individuals’ activities by the year of the survey round.<sup>7</sup> In terms of domestic activities, the CHNS collects time allocation during the preceding week to various “household chores” in which are included buying and preparing food, doing laundry, getting water, and cleaning house; hours spent in child and elderly care are also recorded separately.

The detailed information on time allocation across market, farm and household production allows us to map out women’s behavioral responses to a household members’ out-migration. However, time allocation information is not collected by means of complete time diaries so that measurement errors can arise in calculating leisure. A priori, there is no reason to believe that any existing measurement error is systematically related to a household member’s migration status.

The CHNS was not originally conceived to study migration. The migration status of household members in CHNS must be built up from the household roster. If an individual who was in a previous round of the CHNS is not in the current round of the survey, a question is asked regarding the location of this individual. However, it is only from the 1997 survey round on that the reason for being absent was also recorded. This allows us to distinguish “labor” migrants who left to seek work from individuals who left for reasons such as marriage and schooling. For this reason we use the 1997 and later rounds only. We will consider any individual who has left the home county for work reasons between two waves to be a migrant. Another drawback of the CHNS is that we are unable to tell whether the migration is to an urban or other rural area. Although most migration of rural Chinese is to urban areas, some could well be to rural destinations.

> 7  For example, if an individual reported in the 1997 survey round that she worked in a family business during the past year, we will refer to this as working in a family business in 1997.

8
Does this re-classification affect the overall characterization of the poor population in any significant manner? To see this, we run standard poverty profiles of the poor based on whether FAFH is accounted or not. First we compare demographic and socioeconomic characteristics between those groups that actually did change poverty status: those who leave poverty versus those who fall into poverty when FAFH is included. Then, we test whether the change in the composition of the poor population translates into a different poverty profile. We do this by comparing mean characteristics of the entire poor population under each of the two scenarios. Results are shown in Table 5.

All differences are calculated going from the ‘FAFH poor’ to the ‘at-home poor’, and therefore reflect the fact that the former group eats relatively more at home than the latter. In other words, the ‘FAFH poor’ are classified as poor when FAFH consumption is accounted for, while the ‘at-home’ poor are classified as poor when FAFH consumption is not. Overall, we find a number of statistically significant differences between the two population groups that change poverty status. These differences also remain statistically significant when we compare the overall poor populations under each scenario.

Looking first at extreme poverty a few differences emerge. In terms of demographic characteristics, the ‘FAFH poor’ have a different household structure – smaller household size and fewer prime-age males; the household head is more likely to be indigenous; and the household head is less likely to be illiterate or have primary incomplete. There are no statistically significant differences in the distribution of dwelling ownership, though there are a few in the access to services: the ‘FAFH poor’ are more likely to have electricity and a bathroom. In terms of geographic location, once FAFH is accounted for there are fewer poor households in rural areas. Finally, marked differences arise when looking at labor market outcomes. Results are consistent with those households that are extremely poor when FAFH is accounted for but not otherwise having lower income per capita and fewer household members employed, in particular males. Differentiating across types of employment, it is fewer employees, as opposed to self-employed or employers, what drives the results. Overall, the results suggest that households with more prime-age adults and working members are more likely to consume food outside the home and therefore more likely to be classified as extreme poor if those resources are not properly accounted for.

The difference in the profile of the moderately poor is more pronounced. In this case, almost all characteristics are different across the two scenarios. In terms of household structure, the ‘FAFH 23
_GLOBAL FOOD INSECURITY OUTLOOK_ _25_ with an R<sup>2</sup> of 0 _._ 97 using historical data only. Even though prediction errors may possibly be even smaller when making the vast number of possible subtleties explicit in the model through high-dimensional data, it will be more difficult to understand the types of errors and biases that are likely to emerge from the more complex methods and make the model less easy to sustainably support.

# _B. Possible missing data dimensions_

While the model produces statistically good results, it is still useful to consider what data dimensions may possibly be missing as this helps understand what factors may typically drive prediction errors.

Ideally, food price inflation would be made explicit in the model. This is mostly complicated by data availability. Historically, the food component of CPI is available for many countries, and it would make sense to add it to the model as a description of food price developments compared to overall price developments would provide a vastly superior interpretation of food insecurity pressures stemming from price development compared to what can be inferred from CPI inflation alone. However, in highly food insecure countries and in fragile and conflict-affected countries particularly, the availability of detailed sub-indexes has remained rather limited. This is pointed out by (Andr ́ee, 2021a,b) whose efforts have recently sought to produce food price inflation data from alternative sources to fill important data gaps. Such approaches are however not yet fully mainstreamed and would introduce a complex dependency into the model’s data pipeline. More importantly, since the application is interested in projecting future food insecurity, there would also be the need to have an outlook for food price developments. The WEO, which is central to the empirical application here, only provides forward guidance on CPI inflation rates and not food price inflation rates, which complicates the issue further.<sup>28</sup> There is also some scope to argue that CPI inflation rates alone should provide a reasonable predictive signal. The earlier references already highlighted that a deterioration in inflation captures broad macro-economic deterioration that has preceded major historical food crises. Second, some degree of substitution is possible within household expenditures so that households can offset increased food prices by lowering expenditures in another part in their consumption basket. Only when price increases are also broad-based, and there is less flexibility to cope with food price rises by adjusting other expenditures, will households be forced to reduce food intake. Third, from a statistical perspective, CPI inflation and food price inflation tend to be strongly correlated so much of the statistical information about the position of one of the variables is contained in the other from a model’s perspective.

> 28Forecasting commodity prices is notoriously difficult. Anyone who would have solved the problem of knowing future prices beforehand would likely also be able to solve poverty and malnutrition by redistribution of personal wealth.