rafmacalaba/datause-encoder-data huggingface dataset:
# **4b. Which firms and countries benefit most? Conjectures about ‘the middle’**

We have argued that trade credit is a pervasive form of firm financing, which is found in both advanced and lower income countries, and among firms of all sizes. And yet, for reasons illustrated in the short case studies of different countries above, trade credit could be relatively more important in certain contexts and among certain types of firms. Greater reliance on trade credit could be driven by a lack of reliable market-supporting institutions or underdeveloped formal financial institutions in a country, but even the early literature on trade credit in the U.S. suggested that it was better suited to the financing needs of some types of firms: “[T]rade credit represents a substantial fraction of corporate liabilities, especially for middle-market companies. For example, Mian and Smith (1994) report that ‘for the 3,550 non-financial NASDAQ firms covered by COMPUSTAT, accounts payable were 26% of corporate liabilities, at the end of 1992.’” (Biais and Gollier, 1997, pp. 903-904) Our conjecture is that trade credit may be somewhat better suited to medium-sized firms and to firms in countries that are not very poor and have a reasonable level of institutional and financial development. With respect to firm size, this is because informational frictions (related to, for example, lack of credit histories) and lack of collateral imply that smaller firms have less access to bank finance (see, e.g., Beck, Demirguc-Kunt, and Maksimovic, 2008), which makes it harder for them to extend trade credit to customers. And in the absence of reliable courts and other institutions that enable arms-length contracting, small firms are limited by their personal relationships or other informal mechanisms to signal their creditworthiness, which are likely more binding than for larger firms, and thus limit the amount of trade credit they receive. On the other hand, large firms are less subject to informational frictions and are likely to have collateral. They are more likely to receive external finance from multiple sources (but especially banks) and are therefore likely to rely less on trade credit to finance themselves. Better access to external finance might put them in a stronger position to extend trade credit than other firms, but the bargaining dynamics with customers described above may make them less compelled to do so. For example, large firms are more likely to have market power in product markets and/or have bargaining power over customers because they are the primary (or only) supplier to a customer.

For these reasons, medium-sized firms may be more reliant on trade credit than others. Using a sample of 3,000 firms from 48 countries from 1999, Beck, Demirguc-Kunt, and 22
Figure A1: Survey period in context–contd.

<!-- Start of picture text --> PAK PHL<br>100 0 100<br>0<br>-20 80 -20 80<br>-40 -40<br>60 60<br>-60 -60<br>40 40<br>-80 -80<br>-100 20 -100 20<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>POL ROU<br>100<br>0 80 0<br>-20 -20 80<br>60<br>-40 -40<br>60<br>40<br>-60 -60<br>40<br>20<br>-80 -80<br>20<br>-100 0 -100<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>RUS SEN<br>0 80<br>0 80<br>-20<br>-20 60 60<br>-40<br>-40<br>40<br>40<br>-60<br>-60<br>-80 20 -80 20<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>SVN ZAF<br>0 100 0 80<br>-20 80 -20<br>60<br>-40 60 -40<br>40<br>-60 40 -60<br>-80 20 -80 20<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

39
# ACKNOWLEDGEMENTS

A number of persons provided us with important information for this work or assisted us in preparing it in other ways. The Estimates and Projections Section of the U.N. Population Division haq en continually supportive in providing us with data, even before their publication as U.N. as;estnents. Partly as a result, nonessential differences between these projections and the biannual U.N. assessments have been minimized. We are grateful to the former acting section chief, Shunichi Inoue, and to his continually accommodating staff, as well as to the head of the division, Jean-Claude Chasteland, and hope that the collaboration will continue.

World Bank country economists, population specialists, and the few demographers in the operating divisions have also provided us with recent population estimates, bringing to our attention government reports we might not otherwise see.

This exercise was carried out using a personal computer program, ProjPC-II, written by Kenneth HiUl of Johns Hopkins University.

Both the Population, Health, and Nutrition Division and the Population and Human Resources Department have been supportive of this work, despite the time required. We are grateful to the division chief, Dr. Anthony R. Measham, and the department director, Ann 0. Hamilton.

We also wish to acknowledge the interest of the Bank's International Economics Socioeconomic Data Division (IECSE), which not only utilizes our figures--for such important Bank purposes as estimating per capita GNP-and incorporates them into the databases it maintains and publications it distributes but also helps coordinate communications with country economists. Sutekha Patel of that division has been particularly helpful in the past year.

In producing the manuscript, Inga Schweiger was responsible for some typing and coordinating the paper flow.

Rodolfo A. Bulatao Eduard Bos Patience W. Stephens My T. Vu
23 Table 5: Prospective effects of GM cotton adoption by non-adopters as of 2001 on cotton output and exports, without and with SubSaharan Africa participating (percent change from baseline)

||_Without_Su|b-Saharan Afric<br>% change in:|a adopting,|_With_Sub|-Saharan Africa<br>% change in:|adopting,|
|---|---|---|---|---|---|---|
|**_First adopters as of 2001:_**|cotton output<br>volume|value of<br>cotton<br>exports|value added<br>in cotton<br>prod’n|cotton output<br>volume|value of<br>cotton<br>exports|value added<br>in cotton<br>prod’n|
|United States|-3.8|-9.5|-2.7|-5.4|-13.7|-3.9|
|China|0.2|-0.9|-1.7|-0.1|-8.4|-1.9|
|Australia|-6.1|-8.2|-5.6|-10.1|-13.5|-9.3|
|South Africa|-4.7|-7.5|-5.0|-13.7|-14.4|-14.7|
|**_New and prospective adopters:_**|||||||
|Other high-income countries|5.0|0.9|0.0|0.5|-5.9|-4.0|
|Eastern Europe and Central Asia|2.0|0.3|-2.3|0.6|-6.4|-3.1|
|<br>Southeast Asia|0.4|-0.3|-1.6|0.0|-6.3|-1.9|
|South Asia|6.2|10.4|-2.9|5.6|3.1|-3.2|
|Middle East and North Africa|2.1|1.3|-2.7|0.2|-6.4|-4.5|
|Sub-Saharan Africa (ex S. Africa)|-7.4|-11.8|-7.2|26.7|22.2|10.0|
|Latin America and Carib.|3.0|2.0|-1.7|1.1|-6.4|-3.4|
|**World**|**1.0**|**-5.3**|**-2.7**|**1.0**|**-6.2**|**-2.9**|

Source: Authors’ GTAP model simulation results
for Occupational Safety and Health (NIOSH) found that workers with higher exposure to dioxins had, "more than 60% incidence of cancer mortality."<sup>8</sup>

- b) **Cancer and Immunosuppression** : Onozuka, Yoshimura, Kaneko, and Furue (2008) examined a sample of 1,664 patients in Japan who suffer from a disease called "Yusho.” This disease is caused by ingestion of rice oil contaminated with polychlorinated biphenyls or Dental follicle precursor cells (DFPCs). This study documents an increase in the number of patients with liver diseases and disorders of the immune system. The results show that the risk of mortality due to various cancers, but especially liver and lung cancer, significantly increased mortality compared with the mortality rates from these diseases in the general population.

- c) **Cancer and Spina Bifida** : During the Vietnam War, an herbicide called “Agent Orange” was used to defoliate the jungles of Southeast Asia. Studies of Vietnam veterans exposed to Agent Orange suggests that children have an increased risk of spina bifida, a birth defect that occurs when the neural tube - which develops in the spinal cord - does not close during the first six weeks of gestation. Children born with spina bifida often lack control of bowel and bladder, and many are paralyzed or suffer from mental retardation. The evidence that dioxins cause the defect is strong enough that Vietnam veterans are compensated if their children are born with spina bifida.<sup>9</sup>

- d) **Immunosuppression** : From studies on rats, mice, guinea pigs, rabbits, cattle, marmosets, monkeys, and humans, the Environmental Protection Agency (EPA) concluded that even low doses of dioxin can seriously harm the immune system. Dioxins directly reduce the number of B cells (immune cells developing in the bone marrow) and the number of T cells (immune cells developing in the thymus). EPA scientists believe that human embryos could

> 8 Williams, Rose Marie. Dioxina: Una toxina Universal. Revista de Medicina Medioambiental. Página 1 9 American Cancer Society

> http://www.cancer.org/cancer/cancercauses/othercarcinogens/intheworkplace/agent-orange-andcancer

11
<!-- Start of picture text --> 50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Turkey Chile Korea Spain Thailand Vietnam OECD EU 15 Australia USA UK Mexico Japan China<br><!-- End of picture text -->

**Figure 5. China’s Labor Tax Wedge in Comparative Perspective** Source: OECD, 2011.  For China, author calculation from aggregate data.

39
2 Notwithstanding the prominence of petroleum in the region, agriculture remains important to most economies in MENA. Agricultural and rural populations still account for 40-to-60 percent of inhabitants in the MENA countries and for a substantial share of aggregate employment, belying the often much lower recorded share of agriculture in gross domestic output.<sup>4</sup> Thus, the livelihood and social welfare of the largest segments of the populations of the MENA countries<sup>are still</sup> deeply rooted in agriculture and the rural economy. And<sup>as a consequence, like in most less</sup> developed countries worldwide, agriculture and rural development have<sup>potentially significant --</sup> and mutually reinforcing -- roles to play in achieving higher sustained<sup>growth and economic</sup> development in MENA.

For the last decade or more, however, many MENA countries<sup>have failed to achieve</sup> positive growth on a per capita basis (Table 2). This observation<sup>applies particularly to such</sup> populous countries as Iran and Syria in the Middle East<sup>and Algeria in North Africa.5</sup> Although agricultural growth has widely kept pace with general economic<sup>growth in MENA, agriculture in</sup> less developed countries must grow by 2-to-3 percentage points above<sup>the rate of population</sup> growth in order to contribute significantly to not only national<sup>economic growth but also rural</sup> development and welfare. By this rule of thumb, the lackluster<sup>performance of the MENA</sup> economies is discernibly mirrored in the less than robust performance<sup>of the agricultural sector.</sup>

# Agriculture and Economywide Policies in Economic Development<sup>6</sup>

That agriculture can contribute significantly to the transition of many<sup>if not most less</sup>

> 4 The share of agriculture in low-income and middle-income<sup>developing countries is often biased</sup> downward by the economic policy related repression of prices for<sup>agricultural commodities in these</sup> countries. Thus, the contribution of agriculture to total employment<sup>in such countries is frequently a more</sup> reliable indicator of the economic importance of agriculture<sup>to the national economy.</sup>

> 5 It is likely that the availability of reliable data on economic growth during<sup>1980-93 for more countries in</sup> MENA would reveal that additional countries in the Middle East and<sup>North Africa experienced negative</sup> per capita growth rates during the last decade.

6 The subsection draws heavily on Bautista and DeRosa (1996).
incremental value when it focuses on areas which are not being addressed multilaterally (Mattoo and Fink, 2004).

There are some broad caveats to our analysis. Market access in many of the countries was not predictable due to a discretionary licensing regime. From banking to transport, entry was restricted by the explicit and implicit limits on new licenses and the licensing process tended to be opaque and discretionary. In several ASEAN countries, licenses and foreign equity ownership were decided on a case-by-case basis, subject to requirements or approvals that involved several regulators and ministries. Some countries in certain sectors had no regulation at all, especially the lower income countries in the region and pertaining to the supply of services through the cross border and consumption abroad modes. In some of these cases, the supply of services was allowed in practice, while in others it was prohibited. In general, the high level of discretion and the absence of regulation created a less predictable policy environment and made it hard to define and assess the policy regime.

Section 2 describes the nature of services trade policy data as well as how it was collected and verified. Section 3 presents the ASEAN policy patterns and places them in a global context. Section 4 takes a closer look at the policy measures used by ASEAN countries, highlighting certain aspects of the regulatory environment. Section 5 assesses whether ASEAN countries liberalized their policies between 2008 and 2012. Section 6 examines instances of where ASEAN countries became more open vis-à-vis each other and provides two examples. Section 7 compares the regional and multilateral commitments of ASEAN countries with actual policy. Section 8 concludes.

# **2. SERVICES TRADE POLICY DATA AND MEASUREMENT**

A detailed description of the original World Bank Services Trade Restrictions Database— including details on the data collection process, the policy measures covered and the questionnaire used in the data collection—is provided in Borchert, Gootiiz and Mattoo (2012) and in supplementary material available at <u>http://iresearch.worldbank.org/servicetrade. The</u> global policy patterns of services trade policy emerging from the Database are presented in Borchert, Gootiiz and Mattoo (2013). Here we focus on the approach taken to updating 4
financial or business services based on the data from the WIOD and its Socio Economic Accounts. It is commonly used as a measure for labor productivity in services sectors, which should be closely linked to the levels of services development.

We also use other measures of services development to check the robustness of our results when data are available. Following the tradition in the literature, as in Rajan and Zingales (1998), we adopt two alternative measures for financial services development using the data from the World Bank Global Financial Development Database (GFDD). GFDD is an extensive data set of financial system characteristics for 203 economies from 1960 to 2010. The first measure is the bank private credit to GDP ratio, which is defined as the share of financial resources provided to the private sector by domestic banks in a country’s GDP, originally from the International Financial Statistics of the IMF.<sup>11</sup> The second measure is the share of bank private credit and stock market capitalization in GDP. Stock market capitalization refers to the total value of all listed shares in a stock market based on Standard & Poor's Global Stock Markets Factbook and supplemental S&P data.

# _III.5  Data and Some Stylized Facts on Embodied Services_

The primary data source for this study is the WIOD (2013 Version) which covers 35 industries for 40 countries over 1995-2007, so our data structure is a panel at the country-sector level over 13 years (see Appendixes 2-3C for lists of WIOD countries and sectors).<sup>12</sup> The original 2103 version of the WIOD data covers years 1995-2009, but we drop the data for 2008-2009 to avoid potential complication resulting from the 2008 global financial crisis. We consider all the manufacturing sectors (WIOD sectors 3-16), and focus on two types of modern services in this paper: financial intermediation services (WIOD sector 28) and other business services sector (WIOD sector 30).

To illustrate the importance of embodied services and to motivate our empirical analysis, we first show in Appendix 1A some data on the gross exports (X) and value added exports (VAX) of

> 11 Domestic money banks comprise commercial banks and other financial institutions that accept transferable deposits, such as demand deposits.

> 12 China and Romania are not covered by the regressions due to missing wage or employment data. Because the U.S. is used as the benchmark country to define services input intensity, it is also dropped from most of the regressions to alleviate potential endogeneity problem as explained in Section III.2.

17
- Leite, C. and J. Weidman (1999), "Does Mother Nature Corrupt? Natural Resources, Corruption and Economic Growth" _International Monetary Fund Working Paper,_ 99-85, July.

- Mauro, Paolo (1995), "Corruption and Growth", _Quarterly Journal of Economics,_ 110(3), 681-712.

- OECD (1999), "Public Sector Corruption: An International Survey of Prevention Measures, OECD, Paris.

Operations Evaluation Department (OED), World Bank (2000), OED Methodology Syndicate - Evaluation Criteria Review. Draft, June 30, 2000.

- Paldam, M (1999), _"The Big Pattern of Corruption, Economics, Culture and the Seesaw Dynamics",_ Unpublished Manuscript, _Aarhus University, Denmark, June._

- Rose-Ackerman, Susan (1997). "The Political Economy of Corruption," in Kimberly Ann Elliot (ed), _Corruption and the Global Economy,_ Washington DC: Institute for International Economics

- Sachs, J and A.Warner (1995), "Economic Reforms and Process of Globalization", _Brookings Papers on Economic Activity, 1-_ 118.

- Schacter, Mark and Anwar Shah (2000). Anti-corruption Programs: Look Before You Leap. Prepared for the International Conference on Corruption, Seoul, South Korea, December 2000.

- Segal, P (1999), "Dealing with Devil: The Hell of Corruption", _Impact,_ International Finance Corporation, Spring.

- Shah, Anwar (1999). Governing for Results in a Globalized and Localized World. Mahbub ul Haq lecture at the Pakistan Society for Development Economics, Islamabad, Pakistan, December 1999.

- Stapenhurst, R and K. Dye (1998), _"Pillars of Integrity: The Importance of Supreme Audit Institutions in Curbing Corruption",_ in New Perspectives on Combating Corruption, Transparency International and the World Bank.

- Swamy, A., S. Knack, Y. Lee and 0. Azfar _(1999), "Gender and Corruption", IRIS Center, University of Maryland, July._

- Tanzi, V and H Davoodi (1998), _"Roads to Nowhere: How Corruption in Public_ Investment Hurts Growth", in New Perspectives on Combating Corruption, Transparency International and the World Bank.

- Tomaszewska, Eva and Anwar Shah (2000). Phantom Hospitals, Ghost Schools and Roads to Nowhere: The Impact of Corruption on Public Service Delivery Performance in Developing Countries. Processed.

16
# **VI Estimating the Pass-Through Factor to Generate Estimates for 2015**

The previous sections describe in detail the methodology used to generate urban and rural estimates of extreme poverty for 2014-15, as well as a battery of validation and robustness checks designed to build confidence in the method and results. This section considers the final portion of the exercise, which is the process of determining the appropriate pass-through factors to apply to Household Final Consumption Growth that replicate these poverty rates. This portion has two main steps. The first is to calculate a growth rate, that when applied uniformly to the 2011-12 welfare distribution, generates the predicted 2014-15 poverty rate of 10.0% for urban areas and 16.8% for rural areas. The appropriate growth rates are 9.6% in urban areas and 12.6% in rural areas. The second step is to calculate these growth rates as a fraction of HFCE growth during that period, which was 17.2%. Dividing 9.6 by 17.2 gives an estimated pass-through rate of 55.9% for urban areas. Similarly, dividing 12.6 by 17.2 gives the 73.3% pass-through rate for rural areas. These pass-through rates were then applied to the 2011-12 distribution in order to project out to 2015. The final $1.90 poverty rate for 2015 is 9.5% in urban areas, 15.3% in rural areas, and 13.4% nationally.<sup>34</sup>

# **VII Conclusion**

This analysis was motivated by concerns that the standard method of applying growth in HFCE to the most recent survey measurement of per capita consumption would overstate poverty reduction in India, and therefore the world. These concerns were heightened by the age of the most recent available poverty data from India, which was collected three and a half years before the 2015 target estimate. This paper describes an alternative method for estimating poverty, which utilized a more recent nationally representative survey from 2014-15 containing some of the same demographic and socioeconomic characteristics collected in previous expenditure surveys. These previous surveys were used to estimate a model, which was then used to predict expenditure and poverty into the 2014-15 survey. This type of statistical method has generated plausible headcount poverty estimates in other countries and appears to work well in India as well, based on two validation exercises and a variety of robustness checks. The model’s predictions imply an elasticity with respect to growth that is in line with past experience, in contrast to the traditional

> 34Information on other lines can be obtained on the Povcalnet website.

20
10 Simultaneity may also arise because of possible endogeneity of district-level agricultural output prices (Pjt) and the fertilizer prices (Pfjt). That is, the district-level agricultural output prices (Pjt) are endogeneously determined by the demand for and supply cf output. We circumvent this output price simultaneity by using the district-level aggregate crop price index based on the international prices of different crops using the district-specific production weights. The aggregate international crop price index is an instrument for the district-level aggregate crop output price. Given that India is a small country in virtually all inter."tiov'al comodity markets, using internarlonal prices completely circumvents the output price endogeneity (Binewanger, Khandker and Rosenzweig, 1988).5 The endogeneity problem for the fertilizer price is minimal. The fertilizer price is a railhead price set by the government at the country level and hence does not respond to district-level demand for fertilizer.

# 1II. Data and Variable definitions

For each district Fj is an aggregate crop index of 20 major crops. No data exist on nonfarm rural wages and the agricultural wage is used as a proxy for rural wages. Wj is the daily real wage rate of male agricultural labor. The wage rate for fieldworker/rloughman is used. Nj measures rural employment. Two stock measures of rural employment are considered which both derive from the decennial population censuses. One is the 5/ Using district-level farm harvest prices, however, may not create endogeneity in the nonfarm employment equation.