rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 18: Rail freight transport: Change in STRI by level of income**

<!-- Start of picture text --> Sector: Rail<br>BGD IND NGA PHL UKR IDN LKA TUN CRITHA URY PAN TUR MYS GRC KOR ESP FRA FINBEL<br>KENKEN ARG RUS<br>VNM NGA MEXMEX<br>JPN<br>HUNHUN ITA FRAGBRGBRFIN DNKDNK<br>IDN COL BRABRA URY CHLKAZKAZ POL LTULTU GRC CZE ESP NZL BEL DEUAUT SWESWE IRLIRL<br>PAK EGYPERPERCHNCHN ZAF TURCHL PRTPRT NLD USA<br>PAK EGY CAN AUSAUS<br>JPN<br>8 9 10 11<br>Avg income per capita (2008-16)<br>Note: STRI 2008-11 in blue, STRI 2016 in orange.<br>100<br>80<br>60<br>STRI 2008-11, 2016<br>40<br>20<br><!-- End of picture text -->

# **3.2.4  Professional services**

As indicated above, economies in all income groups have significantly cut protection in professional services, though the low-income group less so. All professional services subsectors (home country law, advisory and representation services in host country law, and accounting and auditing services) show a reduction of their restrictiveness index. Legal services in the home country law of the service provider have traditionally been less restrictive, with an average level of 50 in 2008-11. This is mainly driven by the fact that the licensing conditions, in particular for the professionals themselves, are less stringent (or even absent in many cases). Consequently, this is an area where we see less STRI reduction than in other professional services sectors covered. The STRIs for host country legal advisory and representation services were reduced more significantly (from 66 to 59). This was mainly driven by mode 4 (71 to 62), and to a lesser extent by mode 3 (61 to 55). For mode 3, the reduction is mainly driven by five economies that now allow foreign establishment in the sector, namely Germany, Korea, Pakistan and South Africa for both subsectors, and Czech Republic, for advisory services. In many economies, lawyers are subject to nationality or residency requirements to be able to practice, as well as to the limitations pertaining to the entry of services suppliers more broadly, which are usually quite restrictive in many jurisdictions.

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3 emissions. The model presented in this section was developed by Heil and Selden (1998), following Holtz-Eakin and Selden (1995) in predicting per capita gross domestic product (GDP) and using the projections to develop business-as-usual scenarios of per capita emissions.<sup>3</sup> Emissions are modeled as a quadratic function of GDP (the cubic term in levels was not significant).<sup>4</sup> Data for carbon dioxide emissions (1951-92) come from the sixth release of the DOE NDP-030 data set of the U.S. Oak Ridge National Laboratory (Boden, Marland, and Andres 1995). These data include all major sources of anthropogenic carbon emissions, except those from land-use changes which are not sufficiently developed to be included in this study.' PPP-adjusted GDP figures (1951-92) are provided by the Penn World Tables Mark 5.6 (Summers and Heston 1991). The resulting uneven panel includes 135 countries (4491 observations) representing nearly 90 percent of world population and three-fourths of carbon emissions from fossil fuel use and cement manufacture.<sup>6</sup> Denoting per capita emissions (in kgs) by p, and per capita PPP-adjusted GDP by _y,,,_ the estimated emissions model is (standard errors in parentheses): R<sup>2</sup> =0.84 n=4491 where a, are fixed country effects and d, fixed year effects (see Heil and Selden 1998 for estimation 3See Schmalensee, Stoker, and Judson (1998) for an alternative model for predicting carbon emissions based on GDP.

4The model used here for distribution analysis is the baseline quadratic in levels model. The quadratic in logarithms model yields considerably less convergence in per capita emissions over the forecasting period, but the authors believe the levels model more accurately represents future trajectories (Heil and Selden 1998).

5The omission of land-use emissions alters both the emissions totals and distributions. In general, the lower income countries currently have higher rates of emissions from land-use (such as from deforestation), which means their overall emissions appear relatively lower than they actually are. The omission of land-use emissions thereby inflates emissions inequality to a degree.

6The sample omits the former Soviet Union and Eastern Europe, as Heil and Selden believed their centrally-planned economies to be poor representatives of likely future economic systems, and hence, future emissions patterns. Omission of these high-emitters causes emissions projections to be lower than if the countries were included (the impact on inequality is uncertain).
Policy Research Working Paper 6839

# **Abstract**

Under financial constraints, exporting may have less to do with productivity and more to do with financial resources. The established relationship between exporting and productivity would differ when examined through the lens of the working capital needs of the firm. The hypothesis that working capital matters in the firm’s exporting decision is explored in two ways: first, by articulating a dynamic working capital model of the firm that incorporates the firm’s export decision. Secondly, by testing the hypothesis empirically using a unique firm level dataset from Bangladesh, where issues of financial constraints are particularly acute. The model shows that productivity determines export status of the firm as long as it is not under financial constraints. However, under financial constraints, export status is less dependent on productivity and more dependent on the availability of working capital. Empirical results support the model’s prediction. The relationship between exporting time and the need for greater liquidity is also borne out empirically as shown by a positive and significant correlation between the amount of working capital and the distance of export destination. An important policy implication from the analysis is that short term liquidity is critical in allowing productive firms to export and that access to finance may prevent the benefits of trade liberalization within a country to be fully realized.

This paper is a product of the Finance and Private Sector Unit, South Asia Region. 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 author may be contacted at rchan@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
tanker. It was more difficult to put together a comprehensive data set on public policies and private practices, a problem that has inhibited meaningful empirical research in this area. The few attempts to measure the restrictive impact of government policies have only limited coverage (McGuire et al., 2000) and there has not been, as far as we know, an attempt to use existing information on carrier agreements.<sup>7</sup> This paper draws on a database, created as part of the World Bank's services project, which contains information on both policy and private ratefixing arrangements affecting maritime trade with the United States.

These data made it possible to carry out the econometric analysis presented in this paper. Our estimates confirm, first of all, the importance of all the standard determinants of transport prices, ranging from distance to technology. More interestingly, we find that both public policy and private practices continue to exercise a significant influence on maritime transport prices. And, somewhat surprisingly, private anti-competitive practices seem to have a stronger influence on prices than public restrictions.

What are the implications for policy? The negotiations on maritime transport were the only postUruguay round services negotiations that completely failed. This failure implied an unfortunate loss of political momentum for reform of domestic policies, and, less obviously, a lost opportunity to develop pro-competitive rules. To some extent, an effort was made to develop rules that would ensure non-discriminatory access to port services.<sup>8</sup> But these rules, concerned primarily with ensuring market access, did little to protect consumers from the anti-competitive practices of international cartels. An international initiative is needed, since these practices cannot be adequately addressed only through national competition policy-given the weak enforcement capacity of small states. A further reason for developing a first-best international response to these practices is to prevent recourse to an inferior national response: recall that the cargo-sharing schemes imposed by many developing countries were primarily a response to the 7Kang (2000) uses the policy indicators developed by McGuire et al. to estimate the impact of restrictive maritime policies on bilateral shipping margins, defined as the ratio of CIF import values to FOB export values. This approach suffers from well-known data problems (import and export values are not reported by the same statistical entities) and by the undesirable property that shipping margins vary with unit values of shipped goods. The empirical approach adopted in the present paper addresses both of these problems.

8 In some respects, the approach to port services, which can be seen as "essential facilities" often controlled by "major" or monopoly suppliers, was analogous to the approach to basic telecommunications networks established in the pro-competitive regulatory principles.

_5_
For the first dimension of this survey experiment, we have a “detailed” labor module and a “short” labor module. The short module reflects the approach commonly used in short questionnaires, such as the Core Welfare Indicator Survey (CWIQ). This short module is often used to generate statistics with a high frequency, for example with annual regularity, in lieu of multi-topic household surveys that are too demanding to implement on an annual basis. In our survey experiment, the detailed module differs from the short module in two ways: the set of screening questions is longer in the detailed module and the detailed module collects information on second and third jobs. Our objective is to compare the impact of the different screening questions. The detailed module includes several screening questions about labor force participation, specifically, whether the person has worked for someone outside the household (as an employee), whether s/he has worked on the household farm, and whether s/he has worked in a non-farm household enterprise (three separate yes/no questions). These questions are asked with respect to the past 7 days as well as for the past 12 months. In the short module, there is only one question for each of the two reference periods, namely whether s/he has worked in the past 7 days (or past 12 months, respectively). From these screening questions (yes to any of the three in the detailed module; a yes to the one question in the short module), a person is identified working (employed). In the remainder of the paper, we focus on employment statistics with respect to the past 7 days of a worker’s main job. Although we expect different results depending on the reference period (seasonal activities are likely to be particularly important in the measurement of child labor), we decided to focus on the past 7 days because this is the standard ILO approach in measuring employment. Moreover, our survey was carried out over a whole year, so seasonality effects should average out between survey assignments. In both the detailed and short versions, the employed are then asked their occupation, sector, employer, hours, and wage. There are too few second and third jobs in the data to analyze those data (6 children and 1 child report a second and third job, respectively).

In the second dimension of the experiment, we vary the respondent to whom the questions are asked: directly to the child or to a proxy respondent. Response by proxy rather than self-report is a common practice in household surveys, with the household head often answering all questions. The ILO guidelines for child labor statistics are that these questions be answered by the child, without proxy, and in cases where 6
with the share in informal sector and, in particular, for parasite firms, which have a share of 80% (Figure 2, panel a).<sup>14</sup> A quarter of formal firms are in manufacturing (25%), while informal firms have a participation of more than 83% in the services and retail sectors (Figure 2, panel b). Informal firms also tend to be relatively younger than formal firms perhaps because of faster churning in the informal sector (Figure 2, panel c), while nearly half of the firms in the formal sector are established (with more than 15 years in operations). This result is consistent with the recent findings in Vietnam (Mccaig and Pavcnik, 2021) where entry and exit are relatively higher in the informal sector compared to that observed in the formal sector.

Given that the attributes of our sample from BPS data are in line with documented evidence about informal firms we are confident that our data are suitable for analysis on informality.

Figure 2: Characteristics of firms by type of informality <!-- Start of picture text --> (a) Size (b) Sector<br>100 100<br>Manufacturing<br>90 90 Retail<br>80 80 80 80 78 Other services<br>72 73 73<br>69<br>70 70<br>60 60<br>50 50<br>40 38 40<br>30 30 25<br>20 20 17 17<br>10 12 10<br>10 10<br>3 3<br>0 0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>(c) Age<br>100<br>Young<br>90 Maturing<br>Established<br>80<br>70<br>60<br>50 47<br>41<br>40 37 36 34 38 32 34<br>30 27 25 30<br>19<br>20<br>10<br>0<br>Necessity De Soto Parasite Fully formal<br>Share of firms in each sector)<br>Share of microenterprises (less than 9 employees)<br>Share of firms in each sector)<br><!-- End of picture text -->

**Notes:** Controlling by wave, country, severity of the crises and other firm-level characteristics, except the one depicted in each panel. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

14This is consistent with what we would expect for this type of firm, as these are relatively productive firms that tend to stay under the radar to take advantage of the lower costs of informality. The results do not change much if we restrict our sample only to panel firms (results available upon request).

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would be expected to have a comparative advantage in the sectors that use these factors more intensively (Levchenko (2007); Debaere (2014)). Industries that are intensive in skilled labor and capital would also be expected to have higher capabilities, which could be transferred to Polish firms through foreign investment. Data for these measures of German influence are obtained from Eurostat, OECD and UNIDO. Finally, we use data on exports from COMTRADE to construct a measure of Germany’s revealed comparative advantage (RCA) relative to Poland and to the world.<sup>7</sup> To mitigate concerns about endogeneity, all these measures are constructed using data for Poland’s pre-accession period. For example, data on Germany’s pre-accession exports and output by sector refer to the averages over the period 1994-2004. Table A.2 in the Appendix reports summary statistics on the main measures. To conduct placebo tests, we further collected data on outputbased measures for Poland itself, Russia and other neighbour countries, notably Ukraine and Lithuania.

# **5 Empirical results**

## **5.1 Effects of German acquisitions on firm performance**

Results in this section draw on the Orbis data set, and for this reason refer only to employment as the outcome variable. Unfortunately, the variable turnover presents an excessive amount of missing values, which makes it unreliable. Figure 2 plots the regression coefficients of the event-study (equation (1)) including firm and year fixed effects. Before 2004 there is no discernible difference in employment growth between domestic and to-be-acquired firms. However, after 2004 we observe a positive effect of foreign acquisition on employment growth that persists throughout the period of analysis. Overall, average treatment effects are reported in Table A.3 and indicate that German acquisition increases firm-level employment by 15%. Positive effects are also found when using an alternative measure of foreign ownership based on the number of foreign shareholders (column 2 of Table A.3) and when excluding firms that exit after accession (Table A.4). Results also show that the positive effects are not specific to German ownership. In line with the international evidence (Arnold and Javorcik (2009), Guadalupe et al. (2012), Bastos et al. (2018)), positive effects are also found for French and British shareholders, the second and third largest FDI presence, respectively (Table A.3 second and third row, and Figure A.6).<sup>8</sup> In

> 7This is constructed as the proportion of Germany’s exports in a given sector, divided by the proportion of Poland (or world) exports in that sector.

> 8We reject the hypothesis of parallel pre-trends in the case of British ownership, hence its effect should be interpreted with caution

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The transitions from the ‘other’ employment state as a fraction of the total transitions from nonemployment to employment are the same across sectors, and the sum of the flows into unemployment are equivalent to the sum of the flows out of unemployment (steady state assumption).

Table 4 contains the estimated transitions _mij_ , for all _i_ and _j_ .

Once all transitions are recovered, the probability of transitioning from state _i_ in 2012 to state _j_ in 2013 can be estimated using: where _n_ ˆ _i_ is the estimated number of individuals in the 2003 sample who were in state _i_ in 2002.<sup>68</sup> Let _N_ 2003 be the number of observations in the 2003 sample and _pi_ the fraction of the 2002 sample in state _i_ . Then: The estimated transition probabilities can be used to estimate the hazard (separation probabilities) out of sector _i_ , conditional on being in state _i_ up to period _t_ , _hii_ ( _t_ ), as follows: Table 5 shows the estimated annual transition probabilities. The probability that an individual who was unemployed in the second quarter of 2002 remains unemployed in 2003 is 72 percent. Ignoring time aggregation bias,<sup>69</sup> this statistic suggests a hazard rate out of unemployment conditional on being unemployed of 0.28.

Mobility between sectors is dominated by mobility from the formal to the informal sector. When conditioning on working in the formal sector, 81 percent of formal sector workers stay formal over the course of a year. Even if persistence is very high, 6 percent transit to informal sector jobs and only 3 percent to unemployment. These figures imply a hazard out of the formal sector of 0.19.

Transitions from informality to formality are less important, quantitatively. Only approximately 4 percent of informal workers transit to formal sector jobs. Given that persistence is very high (93 percent of informal sector workers stay informal over the course of a year), the estimated hazard out of the informal sector is 0 _._ 07, lower than in the formal sector.

Table 6 shows the estimated hazards.

This empirical evidence supports the assumption in the model of direct transitions from formal to informal, without intervening unemployment. Transitions from informal to formal are less important.

It is worth noticing that these probabilities are not instantaneous transition probabilities, since

> 68In longitudinal data I observe _ni_ , but that is not the case in cross-section data since I have a different sample of individuals.

> 69Time aggregation bias may arise because the yearly measurement may combine multiple transitions into a single aggregate transition, so transitions that occur at a higher frequency are not captured here.

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