rafmacalaba/datause-encoder-data huggingface dataset:
well as provide them with a better understanding of products demanded in different export markets. One can divide the services offered by EPAs into four broad categories: 1) country image building (advertising, promotional events, but also advocacy); 2) export support services (exporter training, technical assistance, capacity building, including regulatory compliance, information on trade finance, logistics, customs, packaging, pricing); 3) marketing (trade fairs, exporter and importer missions, follow-up services offered by representatives abroad); and 4) market research and publications (general, sector, and firm level information, such as market surveys, on-line information on export markets, publications encouraging firms to export, importer and exporter contact databases).

The economic justification for government involvement in export promotion is based on the theory of asymmetric information and other market failures. There are important externalities associated with the gathering of foreign market information related to consumer preferences, business opportunities, quality and technical requirements, etc. Private firms alone will not provide foreign market information, as companies hesitate to incur research and marketing costs that can also benefit competitors. The same applies to pioneer exporters, who make a considerable investment in attempts to open foreign markets, cultivating contacts, establish distribution chains and other costly activities that can be used by their rivals (Hausmann and Rodrik, 2003). Higher uncertainty associated with trading across borders in markets with different legislation have also been put forward as a justification for export insurance schemes supported by the public sector.<sup>4</sup> From an economic perspective the argument for public funding of EPAs needs to be based on an assessment of the social costs and benefits associated with the activities of the EPA. Social benefits are likely to be larger than the social costs if there are large positive externalities associated with higher current exports across firms, sectors or time and within the exporting country.<sup>5</sup>

> 4See Greenaway and Kneller (2005) for a recent survey of the literature on trade and externalities. For a more skpetic view, see Panagariya (2000).

> 5Note that some of these externalities may travel across borders. It is clear that some of the benefits from export promotion activities can be captured by consumers in the importing country for whom search costs are reduced. This undermines the case for national government funding of export promotion programs and calls

2
Table 6: Count of districts where the industry <u>produces specialization value</u>

|NIC|IndustryDescription|1989|1994|2000|2005|2010|Average|Change|
|---|---|---|---|---|---|---|---|---|
|15|Food products and beverages|37|41|38|42|57|43.0|-0.04|
|16|Tobacco products|20|17|24|29|29|23.8|0.43|
|17|Textiles|31|29|29|41|28|31.6|0.13|
|18|Wearing apparel; dressing and dyeing<br>of fur|16|5|20|18|2|12.2|1.05|
|19|Leather; luggage, handbags, saddlery,<br>harness and footwear|33|29|36|20|23|28.2|-0.08|
|20|Wood and wood products, except<br>furniture; straw and plating|34|44|34|37|6|31.0|-0.08|
|21|Paper and paper products|23|20|18|18|2|16.2|-0.14|
|22|Publishing, printing and reproduction of<br>recorded media|12|14|19|16|21|16.4|0.38|
|23|Coke, refined petroleum and nuclear<br>fuel|18|17|16|25|19|19.0|0.17|
|24|Chemicals and chemical products|14|13|13|12|4|11.2|-0.14|
|25|Rubber and plastic products|11|12|7|10|12|10.4|-0.23|
|26|Other non-metallic mineral products|34|36|37|39|58|40.8|0.06|
|27|Basic metals|4|16|16|20|19|15.0|0.85|
|28|Fabricated metal products, except<br>machinery and equipments|25|17|18|12|21|18.6|-0.27|
|29|Machinery and equipment, n.e.c.|26|22|17|9|9|16.6|-0.47|
|30|Office, accounting and computing<br>machinery|8|9|9|13|2|8.2|0.35|
|31|Electrical machinery and apparatus,<br>n.e.c.|12|12|7|12|14|11.4|-0.21|
|32|Radio, television, and communication<br>equipment and apparatus|15|13|13|12|7|12.0|-0.14|
|33|Medical, precision and optical<br>instruments, watches and clocks|9|11|11|10|44|17.0|0.15|
|34|Motor vehicles, trailers and semi-<br>trailers|12|14|16|11|7|12.0|0.04|
|35|Other transport equipment|15|16|14|7|7|11.8|-0.35|
|36|Furniture, manufacturing n.e.c.|20|22|17|16|38|22.6|-0.13|
||Correlation to previous survey||0.88|0.86|0.83|0.55|||
||Total count for Traditional|297|286|297|300|299|295.8||
||Total count for Modern|132|143|132|129|130|133.2||

Notes: See Table 5. Change compares the 1989-1994 period to the 2000-2010 period.
**_Reduction of policy mistakes across countries._** Hence, though external circumstances have been favorable, there is also strong evidence that African countries have increasingly learned to avert economic mistakes and have avoided bad leadership, conflict, corruption, and macroeconomic instability. Such reduction of policy mistakes is central to preventing growth collapses—a key point from the growth analysis described earlier.

To look at how widespread the phenomenon is, table 11 compares the unconditional probabilities of growth acceleration and deceleration by country groups during 1995–2005 against the averages for the entire sample, 1975–2005. The likelihood of growth decelerations has clearly declined by half across the board for different types of countries to about 12–14 percent, which is the average for Africa during 1995–2005. In addition, the probability of major conflict has declined the most: from 17 to 6 percent. Likewise, the likelihood of growth acceleration has improved for different types of countries. That said, oil exporters and resource-rich countries have experienced more growth accelerations than all other country subsets—well above the mean for the entire period or all countries during same recent period. This finding suggests that better commodity prices and terms of trade were still significant factors; for the recent period at least, possessing mineral resources has not been a curse to growth.

**Table 11: Frequency  (country-years) of growth acceleration and deceleration,** **<u>by country category, 1995-2005 versus 1975–2005</u>**

||**Growth a**|**cceleration**|**Growth d**|**eceleration**|
|---|---|---|---|---|
|**Country category**|**Likelihood**<br>**1975-2005**|**Likelihood**<br>**1995-2005**|**Likelihood**<br>**1975-2005**|**Likelihood**<br>**1995-2005**|
|All countries' mean|0.25|0.42|0.22|0.12|
|Coastal|0.26|0.44|0.22|0.12|
|Landlocked|0.23|0.34|0.22|0.14|
|Coastal without resources|0.24|0.38|0.23|0.14|
|Landlocked without resources|0.22|0.34|0.22|0.14|
|Oil exporters|0.29|0.49|0.23|0.12|
|Non-oil exporters|0.24|0.40|0.22|0.12|
|Resource countries|0.30|0.55|0.21|0.08|
|Non-resource countries|0.23|0.36|0.23|0.14|
|Major conflict|0.16|0.35|0.17|0.06|
|Minorconflict|0.19|0.32|0.32|0.13|
|Source:Arbache andPage (2007a)|||||

**_Economic fundamentals before and after 1995._** If growth is robust, economic fundamentals should have become stronger in the past decade. Table 1.12 compares sample means of economic fundamentals during 1995–2005 for all countries and all episodes (growth acceleration, growth collapses, and normal times), as well as for the subset of countries undergoing growth accelerations with sample means of the same

32
- We impute the main activity of each respondent based on the yearly number of hours reported working in each activity in the household questionnaire. Variables nf8 and nf9 define the number of hours worked in a year in the first household non-farm business.<sup>18</sup> Similarly, nf17 and nf18 refer to the second business, and nf26 and nf27 to the third business. Variables fm29 and fm30 define instead the number of hours worked in a year in the household farm.<sup>19</sup> Finally, ws6year defines the number of hours worked in paid jobs in the previous year.<sup>20</sup> Based on this, we define each job category as follows:

- Self-employment: if business hours _≥_ 240, wage work hours _<_ 240, and agricultural hours _<_ 240.

- Wage worker: if wage work hours _≥_ 240 (with industry code different than agriculture, plantations, livestock, ag. services, forestry, and fishing), business hours _<_ 240, and agricultural hours _<_ 240.

- Agricultural worker: if agricultural hours _≥_ 240, business hours _<_ 240 and no old age pension; or if wage work hours _≥_ 240 (with industry code equal to agriculture, plantations, livestock, ag. services, forestry, or fishing), and business hours _<_ 240.

- Not working: if business hours _<_ 240, wage work hours _<_ 240, and agricultural hours _<_ 240.

- Income: computed as sum of annual non-farm business income, wages and salary, and net agricultural income. Annual wages and salary are available at the individual level (questions 6.7-8 “How much was [NAME] paid in cash for this work”). Non-farm business income and net agricultural income are instead aggregated at the household level. We then impute individual business income household business income the number of hours the

- by weighting by respondent spent working in the business in a year (questions 7.8-9, 7.17-18, 7.26-27) over total hours spent by the household in the business. Similarly, agricultural income is computed by weighting household agricultural income by the number of hours the respondent worked in the activity (questions 4.29-30) over total household farm hours.

> 18Reference question: “Does anybody in this household run their own business, however big or small? Does anybody make something for sale, such as cloth or some food like pickles? Or does anybody sell something in a market or to customers of any sort? Or does anybody provide a service to others for a price, either a skilled service like a doctor or an unskilled service like a barber?”

> 19Reference question: “Now I would like to ask about people in your household who helped to work on the farm the last 12 months”.

> 20Reference question: “Now, [besides work on the household farm or in any of the household’s businesses,] what work for pay or goods did [NAME] DO LAST YEAR?”.

50
# **Table 3 Number of active public-private partnerships, by sector and total size of investment, in sample countries**

|||||_Number o_|_f projects_|||
|---|---|---|---|---|---|---|---|
||_Investment_|||||||
|_Country_|_(millions of US$)_|_Total_|_Electricity_|_Airports_|_Railroads_|_Roads_|_Seaports_|
|Albania|2,747|14|11|1|0|1|1|
|Argentina|30,853|96|76|5|6|4|5|
|Bhutan|243|1|1|0|0|0|0|
|Brazil|326,753|685|557|11|18|54|45|
|Bulgaria|2,833|34|29|1|0|0|4|
|Burundi|16|1|1|0|0|0|0|
|Ethiopia|131|1|1|0|0|0|0|
|Georgia|1,060|6|4|0|0|0|2|
|Ghana|6,716|9|7|0|0|0|2|
|Indonesia|61,686|74|56|0|1|16|1|
|Kenya|3,791|19|15|0|0|4|0|
|Kosovo|548|4|3|1|0|0|0|
|Peru|31,182|108|69|4|6|19|10|
|Romania|6,236|28|27|0|0|0|1|
|Solomon Islands|236|1|1|0|0|0|0|
|South Africa|28,075|90|85|0|1|3|1|
|Ukraine|4,177|41|38|0|0|0|3|
|Total|507,284|1,212|981|23|32|101|75|

_Source_ : PPI database and authors’ data collection and calculations. _Note_ : Dollar amounts are in 2020 dollars.

Brazil has the largest active portfolio in terms of both the number of projects and total investments, with 685 PPP projects and $327 billion of investment commitments. It is followed by Indonesia, Peru, Argentina, and South Africa in terms of investment commitments. The next tier of countries consists of Albania, Bulgaria, Georgia, Ghana, Kenya, Romania, and Ukraine with active portfolios of $1–$7 billion and fewer than 41 active projects. Bhutan, Burundi, Ethiopia, Kosovo, and Solomon Islands have only one to four active PPP projects.

Most active projects in the sample countries are in the electricity sector (981 out of 1,212 active PPPs). Every country in the sample has at least one active electricity PPP. Most of the active transport PPPs in our sample are in Latin America. Indonesia is the only country in our sample outside Latin America that has more than five active PPPs in transport.

In Argentina and Brazil, a substantial number of active PPPs have been implemented through equity sales (32 and 24 projects, respectively). All of them are in the electricity sector, with most of them in the distribution subsector. As the investment commitments of these projects are based on quality improvements rather than specific sets of investments, their contribution to the 18
**Figure 2: Russia: Inflation and Nominal Depreciation**

<!-- Start of picture text --> CPI Inflation average, yoy change Nominal Appreciation, RUR/US$<br>225<br>50<br>200 Nominal<br>175 0 appreciation,<br>150 -50 19931995199719992001200320052007 RUR/USD, ave<br>125 -100<br>100 -150 Nominal<br>75 -200 appreciation,<br>50 -250 RUR/USD, end of<br>25 -300 period mom<br>0<br>-350<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text -->

**Figure 3: Russia: Real Effective Exchange Rate (REER), 1995=100**

REER, ave (1995=100) <!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> Eventually, a major devaluation (Figure 2, 3) coupled with debt restructuring and, finally, substantial fiscal adjustment brought the overvaluation to an end and set the Russian economy on a path towards sustainable growth.

# **B. But since 1999, Russia was set on a path of rapid recovery and growth**

**_After the 1998 crisis, Russia experienced remarkable economic recovery and rapid growth that continued until the arrival of the global crisis in mid-2008._** High oil prices provided strong winds in the sails of the Russian economy. But prudent fiscal policy with continuous fiscal surpluses, declining public debt and rising reserves accumulated in the Stabilization Fund have helped strengthen policy fundamentals and improved underlying policy environment for growth (Figure 4). Indeed, the Russian economy expanded by an annual average growth rate of almost 7 percent during 1999-2007 (Table 1). In 2007, real output grew at 8.1 percent with signs of the economy “overheating.” Russia’s nominal dollar GDP went from $300 billion in 2001 to about $1.3 trillion in 2007, making it one of the dozen largest economies in the world. Initially, output growth was driven by strong export growth and import compression; but from 2000 onwards, domestic demand was the driver of Russian growth, in particular buoyant household consumption and business investment (Figure 5). Comparing the 1998-09 and 2008-09 crises, it is clear that external

8
<!-- Start of picture text --> Policy, Research, and External Affairs<br>Wlre and Human Resouroes<br>WPS 656<br><!-- End of picture text --> This paper a joint product of the Welfare and Human Resources Division,<sup>Population and Human</sup> Resources Department and the Agricultural Policies Division,<sup>Agriculture and Rural Development</sup> Department - is part of a larger effort in PRE to identify factors underlying<sup>rural changc and rural</sup> economic performance. Copies are available free from the World<sup>Bank, 1818 H Street NW, Washington,</sup> DC 20433. Please contact Maria Paz Felix, room S9-109, extension 33724 (42<sup>pages).</sup> Stark and Taylor examine the importance of absolute income and relative deprivation incentives for internal and intemational migration in developing country households.

Empirical results, based on Mexican villas data, support the hypothesis that households' relative deprivation in the village reference group is significant in explaining migration by household members to destinations where a reference group substitution is unlikely and the returns to migration are high.

Independent of relative deprivation, village households wisely pair their members with the labor markets in which the returns to their human capital are likely to be. greatest. The rcsults suggest that a specific type of migration constitutes a response to a specific conriguration of variables, and the role of relative deprivation appears to differ for internal and international migration.

Taking relative deprivation into account when studying migration is shown to have important implications for development policy. For example, economic development that does not redress intravillage income inequalities may become associated with _more_ migration.

The PRE Working Paper Series disseminates the findings of work under<sup>way in the Bank's Policy, Research, and Extemal</sup> AffairsComplex. Anobjectiveof theseries is to get thesefindings out quickly,<sup>even ifpresentations are less than fully polished.</sup> The findings, interpretations, and conclusions in these papers do not nccessarily<sup>represent official Bank policy.</sup> Produced by the PRE Dissemination Center
In summary, understanding the evolution of the unorganized sector in developing and emerging economies is a very important task for regional scientists, development economists, and policy makers. Our inquiry provides some initial steps towards understanding its evolution for India. One strong conclusion emerges from the Indian experience—single, grand theories about India’s unorganized sector and its transitions are likely to be inadequate. The persistence of the unorganized sector across so many different states and industries is too great to afford a single, unifying explanation. Taking somewhat smaller steps, our work highlights that growth of the organized sector does reduce the unorganized sector share. There are also some key establishment size distribution properties for India in how the transition occurs.

The plan of this paper is as follows. Section 2 discusses our data and the levels of unorganized activity in the Indian economy. Section 3 estimates production functions for Indian manufacturing establishments. In both of these sections, we make comparisons to U.S. establishments where appropriate. Section 4 presents the decomposition of state-industry changes, and Section 5 analyses industrial traits in the organized sector and changes in unorganized sector activity. The final section concludes and discusses implications from this work.

# **Indian Data for the Organized and Unorganized Sectors**

We employ cross-sectional establishment-level surveys of manufacturing and service enterprises carried out by the Government of India. Our manufacturing data are taken from surveys conducted in fiscal years 1989, 1994, 2000, and 2005. The service sector has only more recently been surveyed in fiscal years 2001 and 2006. In all six cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This section describes some key features of these data for our study.<sup>2</sup> It is important to first define and characterize the distinction between the organized and unorganized sectors in the Indian economy. These distinctions in the Indian context relate to establishment size. In manufacturing, the organized sector is comprised of establishments with more than ten workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register

> 2 For additional detail on the manufacturing survey data, we refer the reader to Kathuria et al. (2010), Fernandes and Pakes (2010), Hasan and Jandoc (2010), Nataraj (2011), and Ghani et al. (2011b). Dehejia and Panagariya (2010) and Ghani et al. (2011b,c) provide a detailed overview of the services data and its important characteristics.

5
# **1. Introduction**

Hsieh and Klenow (2014) have documented that plants in the U.S. grow more over their life cycle compared to plants in Mexico and India.<sup>1</sup><sup>_,_2</sup> Less developed countries also face widespread informality.<sup>3</sup> This paper argues that informality, arising from incomplete enforcement, distorts incentives for growth over the life cycle.<sup>4</sup> Since tax compliance increases with plant size, informal plants may optimally choose to keep operating at smaller scales to escape detection. Hence, we study the role of informality in explaining the life cycle of plants. We ask how aggregate economic outcomes such as output and plant-size distribution, as well as the life cycle of plants, would change if informality were reduced by improving enforcement or reducing the burden of formality.

One of the key challenges in studying how informality distorts plant life cycles is the limited availability of plant-level panel data containing information on registration status. This study overcomes this challenge by analyzing Mexico’s census plant-level data from Instituto Nacional de Estadística, Geografía e Informática (INEGI). The data tracks plants over 20 years, from 1998 to 2018, and includes information about establishments’ registration with different tax authorities. Therefore, unlike Hsieh and Klenow (2014), this allows for analysis of a balanced panel of registered and unregistered plants.

In our analysis, an informal establishment is a plant which is not registered with either the central tax authority or the social security administration. In contrast, we refer to plants as formal if they are registered with either the central tax authority or the social security administration. We first show that young formal plants are bigger in terms of employment than young informal plants. Formal establishments in Mexico start operations with 6.1 workers on average whereas their informal counterparts report only 1.9 workers on on average. Second, formal plants grow faster over their life cycle compared to informal plants. While formal plants grow by a 136% during their life cycle, informal plants are only 1.77 times bigger when old compared to young informal plants.

In order to quantify the losses of informality from distortions to the life cycle growth

> 1We use the terms establishment and plant interchangeably throughout the paper.

> 2Eslava et al. (2022) shows that Colombian plants grow less than plants in the U.S.

> 3ILO estimates 57.4% and 89.1% of the employment as informal employment in Mexico and India, respectively whereas 77% of plants operate informally in Mexico according to INEGI census data.

> 4Compliance with taxes varies with plant size: the effective tax rate is higher among large establishments compared to smaller plants (See López and Torres (2020) for Mexico).

1