rafmacalaba/datause-encoder-data huggingface dataset:
Table 1 continued

|||Mea<br>|sures o<br>|f liquidi<br>|ty needs<br>|
|---|---|---|---|---|---|
|ISIC|DESCRIPTION|I/S|I/A|CCC|wL/S|
|3823|Metal and wood wkg. mach.|0.23|0.30|1.51|0.30|
|3140|Tobacco manufactures|0.24|0.28|1.40|0.08|
|3852|Photo. and optical goods|0.25|0.24|1.71|0.30|
|3824|Special indus. mach. and eqp.|0.25|0.28|1.53|0.26|
|3853|Watches and clocks|0.26|0.36|1.42|0.20|
|3902|Musical instruments|0.28|0.35|1.89|0.28|
|3233|Leather products|0.30|0.29|1.57|0.25|
|3901|Jewellery|0.30|0.46|1.98|0.16|

– Correlations: levels ranks

|Total Inventories over assets|–|0.78|0.91|0.50|
|---|---|---|---|---|
|Total Inventories over sales|0.78|–|0.66|0.34|
|Cash Conversion Cycle|0.93|0.68|–|0.51|
|Short term debt over sales|0.50|0.34|0.40|–|

The rank correlations between the 4 measures are reported at the bottom of Table 1. It can be observed that, in general, the rankings generated by the different measures are highly correlated.<sup>20</sup>

# **5 Data**

## **5.1 Volatility**

The main data source used to determine the volatility of an industry in a given country was the Industrial Statistics Database 2000, 4 digit ISIC, created by the United Nations (UNIDO). This database contains information on nominal value added, employment, number of establishments, wages and salaries, gross output, and gross capital formation, for a set of 114 countries and 81 4 digit ISIC industries during the period 1980-1998. However, there is plenty of missing information and the real scope of the data is considerably smaller.<sup>21</sup> The final sample used in the analysis consists of an unbalanced panel including 48 countries (plus the U.S. which is the benchmark

> 20The only exception is the correlation between inventories over assets and labor costs

> 21In addition, several countries do not report data for every 4 digit ISIC industry, but only for groups of them. As the grouping varies from country to country, this data is not comparable and has to be dropped.

17
# **6.4 What determines cause of death?**

These results suggest that firms do close for all three of the theoretical reasons described above, but with closing for firm competition and firm shocks the most common. This then leads to the question of which types of firms are more likely to die for each of the theoretical reasons. To examine this, we estimate a multinomial logit model, where the outcome is firm operating status. The omitted (base) case is surviving, and the coefficients are then odds ratios for the likelihood of the firm dying for each of the three reasons (business loss, family and illness, better opportunities) relative to survival. We include the same firm and owner characteristics we used in Table 3 to enable comparability.

Table 6 presents the results. We see that firms are more likely to close because they are making a loss if they have lower baseline profits to start with, are in retail rather than manufacturing or the services sector, and if they are run by women. The chance of closing for this reason decreases with firm age, but at a decreasing rate. Firms are more likely to close for family reasons and illness if they have fewer employees and lower profits, and especially if they are run by female owners, with the chance decreasing with firm age, at a decreasing rate. In contrast, the strongest predictor of closing for reasons of better opportunity is if the owner has tertiary education, and if the firm is in the first year of business, with no significant size effect, nor a significant effect of firm age after the first year. There is no significant impact of gender on closing for this reason, and firms in manufacturing are less likely to close for reasons of better opportunity than retail firms.

Taken together with the results of the previous subsection, this highlights that the firm death experience differs by type of firm. One group of less productive and profitable firms, which are more commonly run by women, tend to die because they are making a loss, and suffer a loss in labor income for the owner when they do. Another group of less productive and profitable firms, with fewer employees, and even more likely to be run by women, tend to die because of the family reasons or illness of the owner, and result in an even larger drop in labor earnings for the owner. In contrast, a smaller group of firms, that tend to be young and run by highly educated owners, die because better opportunities come about for the owner, and there is not a large fall in owner labor earnings as a result.

# **7. Conclusions**

Firm death is extremely common among small firms, with half of the current stock of small firms in a developing country likely to die within the next six years. Yet because panel surveys 23
6 causal role in the growth process. However, these restrictions are specific to the neoclassical model, not to all equilibrium models. The next section describes an equilibrium model that is much less restrictive.

In addition to following an equilibrium strategy, the model here is essentially anaggregate model. No distinction ismade between major sectors like agriculture and manufacturing, andthe growth process faced by less developed economies is assumed to be qualitatively the same as that faced by developed economies. This second choice is partly amatter of convenience and simplicity, but it also reflects a judgment that it does much less violence to the data than is commonly supposed. It is true that the compositi **o** fdemand n changes systematically with the level of income, but this does not necessarily have strong implications for the rate of growth of output or prVodictivity. In developed countries, productivity growth or technological caange has been as dramatic in agriculture asin manufacturing. In the one part of the service sector where outputs can be adequately measured, telecommunications, productivity growt **h** as been equally impressive. Moreover the authors of a major synthesis of cross country studies of industrialization comment: Although sectoral differences inproductivity are significant, differences among countries are equally so. Total factor productivity growth tends to be higher in all sectors in countries of high growth. (Chenery, Robinson, and Syrquin, 1986.) It is this country specific element of productivity growth that the model described here seeks to address.
separately by the degree of how routine the tasks are within a given occupation, and for samples of workers who stay within an industry, switch between industries, and specifically exit manufacturing.

# **III. Data Description, Trends, and Empirical Strategy**

- A. Data Description

Our sample of US workers is taken from the Current Population Survey Merged Outgoing Rotation Groups for 1983-2002, which provides data for over 3.4 million workers who are assigned a consistent classification of their industry and occupation.<sup>6</sup> Offshore activity in each industry is measured by the total employment of foreign affiliates among multinational US firms, separated into high and low-income locations, stored by the Bureau of Economic Analysis (BEA).<sup>7</sup> The BEA sample of multi-national firms accounted for 80 percent of total output in manufacturing in 1980, suggesting that the coverage is fairly extensive. Our data on import penetration and export shares are taken from Bernard at al. (2006), which we recalculated and updated through 2002. Since relative price series for imports and exports are incomplete, we generally substitute for prices by using the share of exports in production and import penetration at the four-digit SIC 1987 level. Results using prices instead of quantities are available in the appendix materials. As a control for productivity changes that could also affect labor demand as well as wages, we use the NBER’s calculations of total factor productivity provided by Wayne Gray. This data source also provides us measures of the price of investment goods and the real price of shipments by industry and year, which we use to control for technology and industry level demand, respectively. We also match our workers to information on computer use rates by industry and occupation from CPS computer supplements

> 6 Special thanks to David Autor for providing us with concordances that provided a consistent coding scheme of industries and occupations for the period. The CPS occupation and industry codes were reclassified in 2003 to correspond to the North American Industrial Classification System, which made it difficult to compare data before and after the change.

> 7 We are unable to distinguish between arms-length trade between firms (“outsourcing”) and trade in general. Note, however, the BEA sample includes all foreign firms where a US parent holds at least a 10% equity stake. This relatively low threshold implies that firms closely aligned will be included in our analysis. It is worth noting, however, that pure firm outsourcing where no equity stake is taken will be classified as trade and not offshoring.
- 53 -

- Sur, K.B. 1988. Small Scale Entwprises In Industrial Development: The Indian Experience. Now Delhi: Sage Publications.

- Tyagi, D.S. 1981. _Growth of Agricuiturai Output and Labor Aborption In India._ Journal of Develo.mont Studles 18(1): 104-114.

- Vyas. V.S. and Mathal, G. 1978. _Frn and_ **_NonFarn_** _Employment In Rural Aros: A Perspective for Planing._ Economic and Political Weekly 13(6-7):333-347.

- Wanmall, S. 1988. _Dilham_ of _Obsmving the Poor In the Urban Fconowy: Looking Beyond the Cities In India._ Reglonal Development Dialogue 9(4):61-74.

- World lank. 1989. _Gender and Poverty In Indi Issues and Opportunities Concerning Women in the Indian Economy._ PHRW **D** raft. Washington DC: World Bank.
## (5) r _(+_ rt)*(I + )*(I + d)/(l +y) - 1

## where the f superscript indicates foreign

So that r is made up of two components: re, _whi_ h is the real foreign interest rate, and (1 + :r)*(1 + d)/(l + a), which is the real exchange rate. Thus, for example, a rea, pbsO devaluation increases the burden of servicing foreign debt and a real peso appreciation reduces it.

## 2.2.2 Fiscal Data.

Public saving is measured as the difference between current revenue and current expenditure. However, this measurement of public sector saving is inadequate if based on fiscal accounts data because the latter considers as capital expenditure items which are actually current expenditure (point i)). Besides, when computing real public saving, this measure does .ot capture capital gains derived from the inflation-erosion of domestic and foreign financial liabilities (point ii)) nor from the inflation tAx (point iii)).

## i) current Exoenditure Adiustment.

Several items which fiscal accounts compute as public investment are not strictly investment from a national accounts perspective, i.e., they don't add to the existing capital stock. These items have to be reclassified as current public expenditure which, in turn, implies lower public saving. These items are mainly military expenditure and some federal transfers which do not translate into fixed capital formation. Since the national accounts properly compute these items as current govern-ent expenditure we use national accounts data of public expenditure to estimate public saving. 3

## ii) Canital Gains From Inflatic,n on Financial Liabilities.

Nominal fiscal deficits are a misleading measure of re.l public sector financial dissaving because they don't account for the loss in real value of domestic and foreign public debt which is due to inflation. The inflationerosion of the real value of nominal debt should, thus, be subtracted from the nominal deficit. This is usually done with domestic debt, leading to the so called operational or inflation-adjusted fiscal deficit, but not with foreign debt. Only the real interest rate on domestic and foreign debt was computed as a fiscal expense. In the case of foreign debt the real interest rate was calculated as shown in equation (4) in the previous section; the real interest rate on domestic debt was obtained as follows:

# (6) r [(1 ' i)/(l + - 1I)1

## where: r (i) are the domestic real (nominal) i terest rate s _is_ the domestic rate of inflation

To avoid the problem that arose with the real foreign interest rate (average inflation different from end of period inflation) do 3astic real interast payments (and the inflation tax discussed below) were estimated _3_ Fiscal accounts **aso** compute financial investments and purchases of land/real estate as investment. Although these are clearly transactions below the line of financing, i.e., asset swaps, and thus are not public investment from a national account perspective, they don't affect our estimate of public saving since they donet require reclassifying items to current expenditure (only current expenditures are needed to estimate public saving).
- [12] Helland, Eric and Alexander Tabarrock (2003). Contingency Fees, Settlement Delay, and Low-Quality Litigation: Empirical Evidence from Two Datasets, Journal of Law, Economics, and Organization 19(2):517-542.

- [13] Kaplan, David S., Gabriel Martínez González, and Raymond Robertson (2007). Mexican Employment Dynamics: Evidence from Matched FirmWorker Data, World Bank Policy Research Working Paper No. 4433.

- [14] Kaplan, David S., Joyce Sadka, and Jorge Luis Silva-Mendez (2008). Litigation and Settlement: New Evidence from Labor Courts in Mexico, Journal of Empirical Legal Studies, forthcoming.

- [15] Lanjouw, Jean O. and Mark Schankerman (2004). Protecting Intellectual Property Rights: Are Small Firms Handicapped? Journal of Law and Economics 47(1):45-74.

- [16] Lerner, Josh and Antoinette Schoar (2005). Does Legal Enforcement Affect Financial Transactions? The Contractual Channel In Private Equity, Quarterly Journal of Economics 120(1): 223-46.

- [17] Razú, David (2006). Competencia Desleal entre Políticas Públicas en México: El Modelo de Seguridad Social vs. Programas Asistenciales, Mimeo, Instituto Mexicano del Seguro Social.

- [18] Singer, George H. (1997). Section 523 of the Bankruptcy Code: The Fundamentals of Nondischargeability in Consumer Bankruptcy, The American Bankruptcy Law Journal 71: 325-405.

- [19] Szmer, John, Susan W. Johnson and Tammy A. Sarver (2007). Convincing the Court: Two Studies of Advocacy: Does the Lawyer Matter? Influencing Outcomes on the Supreme Court of Canada, Law and Society Review 41:279-300.

- [20] Watanabe, Yasutora (2007). Estimating the Degree of Expert’s Agency Problem: The Case of Medical Malpractice Lawyers, mimeo. Northwestern University.

24
impression of the state’s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to effectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the contract.

# **5 Data and Methods**

I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively effective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoffman, 2005).

This project relies on the fourth round of Afrobarometer data that surveys Africans’ views towards democracy, economics, and civil society with random, stratified, nationally representative samples. In 2008, trained enumerators conducted face-to-face interviews in local languages with 26,513 respondents across 19 countries.<sup>5</sup> The sample is designed as a representative cross-section of all citizens of voting age in a given country.

The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-defined geographic units for which reliable population data are available and they tend to be socially homogenous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although respondents were not sampled based on their ethnic affiliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009).

Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for

> 5I excluded Zimbabwe from the analysis because of missing data on key variables.

9
mean and standard deviation for each outcome variable are shown separately for _Formosa_ - affected (i.e. treatment) and control provinces. The control group consists of all fishery workers located in Vietnam’ southern coastal provinces, from Phu Yen to Ca Mau. At the baseline, it is evident that fishery is a considerably more influential income-generating activity for individuals living in central Vietnam. On average, a central Vietnam fisher works over 62 hours a week and earns 7.4 million VND in total monthly income (approx. 335 USD), compared to the respective figures of 55 hours and 6.2 million VND of a southern (i.e. control) fisher. Saltwater fishery average monthly income is significantly higher than that of other industries in the central provinces, which is the result of a longer workload and the compensating wage differential from much harsher working environment.<sup>9</sup> Because of the arduous and labor-intensive condition, well over 90% of the fishers are male. More than a quarter of them did not have any formal educational training and only less than 3% obtained high-school or college level education; most fishers are born and raised in families that have been attached to fishery for generations.

Table A1 provides preliminary evidence indicating the negative impact of _Formosa_ on fishery earnings in central Vietnam. The table compares pre-disaster (i.e. Q1-2016) and post-disaster (i.e. the rest of 2016) average monthly incomes, separately for 3 groups: (1) the treament group, i.e. the fishery workers living in the _Formosa_ -affected provinces of Ha Tinh, Quang Binh, Quang Tri, and Hue; (2) the unaffected fishery workers living in southern provinces distant from Formosa, which we refer to as the “geographic” control group; and (3) the non-fishery workers living in the _Formosa_ -affected provinces that work in other unaffected industries, referred to as the “industry” control group. The means difference test (last column) indicates drastic and statistically significant declines in average fishery incomes of the _Formosa_ -affected individuals. In contrast, we do not observe any statistically meaningful changes in incomes of the “control” fishers.<sup>10</sup> A crucial aspect of the DiD method employed in this paper is the validity of the parallel pre-trend assumption. Figure 3 visually addresses this element; the figure plots raw average monthly income profiles for the treatment (thick line) and control groups (thin-dash line), using both LFS 2015 and LFS 2016. While the line plots exhibit strong parallel pre-trend between the two groups, they suggest an abnormal downward deviation from trend by the treatment group, right after _Formosa_ took place (in April 2016). It is also evident that

> 9 An average non-fishery worker living in Ha Tinh, Quang Binh, Quang Tri, and Hue works 41 hours per week and earns 3.9 million VND per month.

> 10 In fact, there is an expected (insignificant) increase in the average fishery earnings of the unaffected southern fishers after the first quarter of 2016. This is because the fishing season usually takes place between May and November each year, as visually seen in Figure 4.

7
- of the DA and has the objective to engage with companies and pick up early warning signs of potential problems.

- NSE Emerge has a voluntary Capital Markets Certification Program to ensure that Compliance Officers and/or CFOs of the SME exchange understand the pre- and postregulatory processes.

Exchanges can also provide courses and seminars to educate investors in investing on the SME exchange.

Table 17 shows various exchanges’ activities in the areas of outreach and training.

||Table 17. Outreach a<br>|nd Training<br>|
|---|---|---|
|Exchange|Outreach|Training|
|NSE India<br>Emerge|•<br>Awareness programs/marketing<br>effort to popularize SME<br>platform to all stakeholders<br>•<br>Roadshows to investors|•<br>Handholding through the entire process<br>of listing<br>•<br>Marketing and PR support|
|JSE AltX|•<br>Dedicated business development<br>team focusing on attracting<br>listings in South Africa and<br>Africa<br>•<br>Showcasing listed companies to<br>potential investors|•<br>Directors Induction Program in<br>partnership with Institute of Directors<br>South Africa<br>•<br>Regular current issue seminars|
|GreTai|•<br>“Corporate Day,” periodically<br>initiated and organized by the<br>exchange|•<br>Corporate governance, investor<br>relations, regulation compliance<br>•<br>Seminars on IFRS adoption for<br>companies and investors|
|Bovespa<br>Mais|•<br>Outreach programs with market<br>agents, industry, and business<br>associations to approach<br>companies countrywide, and<br>investors|•<br>Training programs, on the IPO process,<br>culture of a public company, and<br>investor relations programs.<br>•<br>Programs to introduce executives and<br>entrepreneurs to service providers<br>(investment banking, auditors, lawyers,<br>advisors).|
|NewConnect|•<br>Marketing and special programs<br>dedicated to shareholders and<br>financial community|•<br>Corporate governance, investor<br>relations, conferences and meetings<br>organized by WSE|
|BIST ECM|—|—|

_Source:_ WBG compilation of exchange information. _Note: — =_ not available.

38