rafmacalaba/datause-encoder-data huggingface dataset:
# **V. Descriptive statistics**

Since its inception, _Doing Business_ has been steadily gaining substantial political clout. Given the reports’ power to shape regulatory agendas and inspire business regulatory reforms worldwide, the project’s global media coverage has been growing exponentially (see figure 1). From 2004 to 2018, _Doing Business_ has been highlighted by countries’ local media more than 17,400 times.<sup>6</sup> Countries with the highest media coverage of _Doing Business_ during that time frame include Pakistan (1,337 articles; 97 media outlets<sup>7</sup> ), India (1,028 articles; 720 media outlets), Mexico (965 articles; 141 media outlets), and Russian Federation (959 articles; 400 media outlets). Not surprisingly, these four economies have also been active reformers on the _Doing Business_ indicators. Since 2004, Pakistan implemented 19 business regulatory reforms, India – 37, Mexico – 26, and the Russian Federation – 36.

**Figure 1:** _Doing Business_ media coverage has been steadily growing over the years

<!-- Start of picture text --> 3,000<br> 2,500<br> 2,000<br> 1,500<br> 1,000<br> 500<br> ‐<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Doing Business year<br>Number of articles, Factiva local press<br><!-- End of picture text --> _Source:_ Factiva _Note:_ The time frame for Factiva article count is determined by the interval between (a) the press release date of the _Doing Business_ report for a specific year and (b) the day prior to the press release date of the _Doing Business_ report for the subsequent year. For instance, for _Doing Business_ 2017, the time period for counting Factiva articles is between 10/27/2015 (the day of the launch of _Doing Business_ 2016) and 10/24/2016 (the day prior to the release of the _Doing Business_ 2017).

Most of the media articles that discuss _Doing Business,_ especially in India and Russian Federation, tend to highlight increases in countries’ rankings, comparing them either with the preceding years or with those of neighboring economies. Many articles also focus on recent accomplishments of incumbent governments. India’s media, for example, discusses the ambitious plans and strategies of the government to secure a position on the _Doing Business’_ top-performers list. In Mexico, _Doing Business_ related media coverage often outlines recent reforms and investment attractiveness of the economy, while also highlighting remaining

> 6 Primary data collection on media coverage of _Doing Business_ has been done through the use of the Factiva database. Factiva is an online comprehensive database primarily designed to provide data to business communities, researchers and specialists, aggregating content from both private and public sources. It pulls data from more than 32,000 information sources, including newspapers, journals, magazines, television and radio transcripts. Factiva has an almost universal reach, allowing to filter data by local and international sources.

> 7 Media outlet is considered to be a written publication either in print or electronic. If the same outlet produces both print and online outputs, only one is counted.

12
- 1.12 -

Tabt LL.: NI& r _of_ Farmers ok Cooa aWd 01 **Palo**

|||COCOA|||OL PALM||
|---|---|---|---|---|---|---|
|Region|Number|%Of<br>Souwm<br>Total|% Of<br>S<br>ySo<br>Tot|uem<br>Number|% Of<br>Totbl|% ot<br>Survey<br>Total|
|Ash"nX|91|17.3|13.0|12|2.3|1.7|
|Brong Ahao|27|5.1|3.9|6|1.1|09|
|CenVl|37|7.0|5.3|33|6.3|4.7|
|Ea*rn|42|8.0|6.0|14|2.7|2.0|
|VoIl|5|1.0|0.7|13|2.5|1.8|
|_Wee Ae_|35|6.7|5.0|34|6.5|4.8|
|Ohatr'|237|45.1|33.6|112|21.3|16.0|

Tabr 6: AV **Ae** trbuluon of Tracltuil Cocoa Trm" by Region

|Region|0.4|5,9|YEARS<br>10.14|(S)<br>15.19|20-29|30 plus|Al Ages (Aores)|
|---|---|---|---|---|---|---|---|
|Ashanl|1.39|0.00|4.18|11.68|36.55|46.20|214.5|
|BronoAhab|3.21|4.59|6.42|23.85|23.85|36.08|109.0|
|Cenmi|5.78|5.29|1.65|20.66|30.58|36.04|1210|
|Eastmn|2.18|0.35|0.65|2.46|0.77|1.50|89.5|
|Volta|0.00|0.00|100.00|0.00|0.00|0.00|1.0|
|Welern|3.30|0.00|6.35|14.21|4.82|71.32|31 5|
|Ghana|5.21|2.82|5.21|19.I6|27.0|35.89|566.5|

Table LB.7: Mean Age Dstrbuton of Hybrid Cocoa Tres by Region

|Region|0-4|5.9|Y EAAR<br>10-14|8I()<br>15-19|20.29|30 pFu|All Ages (Acres)|
|---|---|---|---|---|---|---|---|
|AehanU|58.06|12.62|12.28|7.77|3.11|1.16|257.5|
|BrongAhab|28.91|1.99|12.63|1.33|31.23|25.91|150.5|
|CenVa|37.50|25.86|2.59|26.02|6.03|0.00|118.0|
|E_sn|49.55|13.43|8.71|11.69|7.96|8.46|201.0|
|Volt|72.73|0.00|18.18|9.09|0.00|0.00|5.5|
|_WoeeSn_|81.89|0.00|0.00|13.39|1.57|3.15|127.0|
|Ghana|51.44|10.79|9.91|11.18|9.33|7.35|857.5|
- Ossa, Ralph. 2011. “A ‘new-trade’theory of GATT/WTO negotiations.” Journal of Political Economy 119(1): 122-52.

- Ossa, Ralph. 2012. “Pro...ts in the “new trade” approach to trade negotiations.” American Economic Review: Papers and Proceedings 102(2): 466-69.

- Ossa, Ralph. 2014. “Trade Wars and Trade Talks with Data.” American Economic Review 104(12): 4104-46.

- Palmeter, David and Petros C. Mavroidis. 2004. Dispute Settlement in the World Trade Organization: Practice and Procedure. Cambridge, UK: Cambridge University Press.

- Panagariya, Arvind. 2000. “Preferential Trade Liberalization: The Traditional Theory and New Developments.” Journal of Economic Literature 38(2): 287-331.

- Park, Jee-Hyeong. 2011. “Enforcing International Trade Agreements with Imperfect Private Monitoring.” Review of Economic Studies 78(3): 1102-34.

- Petersmann, Ernst-Ulrich. 1997. The GATT/WTO Dispute Settlement System: International Law, International Organization and Dispute Settlement. London: Kluwer Law International.

- Pierce, Justin and Peter K. Schott. 2015. “The Surprisingly Swift Decline of U.S. Manufacturing Employment.”Mimeogr., Yale University.

- Potipiti, Tanapong. 2012. “Import tari¤s and export subsidies in the WTO: a small country approach.” Mimeogr., Chulalongkorn Univ.

- Prusa, Thomas J. and Susan Skeath. 2002. The economic and strategic motives for antidumping ...lings.” Weltwirtschaftliches Archive 138(3): 389-413.

- Prusa, Thomas J. and Robert Teh. 2010. “Protection reduction and diversion: PTAs and the incidence of antidumping disputes.” NBER Working Paper No. 16276.

- Richardson, Martin. 1993. “Endogenous protection and trade diversion.” Journal of International Economics 34: 309-24.

- Richardson, Martin. 1995. “Tari¤ revenue competition in a free-trade area.” European Economic Review 39(7): 1429-37.

- Riezman, Raymond. 1985. “Customs unions and the core.” Journal of International Economics 19 (3-4): 335-65.

- Riezman, Raymond. 1999. “Can bilateral trade agreements help to induce free trade?”Canadian Journal of Economics 32(3): 751-66

- Romalis, John. 2007. “NAFTA’s and CUSFTA’s impact on international trade.”Review of Economics and Statistics 89(3): 416-43.

116
calculate the percent change in real exchange rates between 2009 and 2012 in two ways. We first take the percent cumulative change over the period from 2009 to 2012 and, as an alternative, the average of annual percent changes for 2012, 2011 and 2010. Since the two methods produce very similar series, we report only those for the latter.

Financial market size is measured by total external private financing—i.e. inflows of equity, bonds and loans (these are data for 2010-2012 from the IMF _Global Financial Stability Report_ , transformed into logarithms). Alternatively, we measure financial market size as the portfolio liability stock from Lane and Milesi-Ferretti (2012), as stock market capitalization, and as aggregate GDP. Reassuringly, use of these alternatives had little material impact on the results, since most of the alternative measures are fairly highly correlated. Similarly, there are several common measures of reserve adequacy: reserves in months of imports, reserves as proportion of M2, reserves as a share of GDP, reserves relative to total external debt and reserves relative to short term debt. Below we report results for the ratio of reserves to M2. Results using other measures are similar.

**Table 1: Effect on Measures of Market Conditions in BRICS countries, Indonesia and Turkey, April-July, 2013**

||% Change<br>in Nominal<br>Exchange<br>Rate|%<br>Change<br>in Stock<br>Indices|%<br>Change<br>in<br>External<br>Reserves|Basis<br>Points<br>Change<br>in Bond<br>Yields|Basis<br>Points<br>Change<br>in CDS|Capital<br>Market<br>Pressure<br>Index I|Capital<br>Market<br>Pressure<br>Index II|
|---|---|---|---|---|---|---|---|
|Brazil|12.52|-8.92|-1.69|55.78|64.06|3.46|5.00|
|Russia|4.63|0.42|-3.32|24.95|35.95|2.74|2.69|
|India|9.98|4.04|-4.77|n.a.|n.a.|7.15|6.57|
|Indonesia|3.58|-10.01|-13.61|64.75|64.06|5.06|6.47|
|China|-0.85|-6.49|0.38|23.54|51.78|-2.71|-1.80|
|South Africa|8.96|3.07|-5.42|57.68|48.17|3.98|3.26|
|Turkey|7.61|-12.16|-8.20|40.85|66.97|3.26|4.63|

Note: An increase in nominal exchange rate is depreciation; increase in capital market pressure indices I and II imply a larger nominal depreciation or reserve loss; and a larger nominal depreciation, reserve loss, or decline in stock market index, respectively.

Table 1 offers an overview of the behavior of these measures of market conditions in the summer of 2013, displaying their values for the BRICS countries, and Indonesia and Turkey, well known cases on which much commentary focused. We see that with the exception of China their exchange rates all depreciated (China of course being known for its policy of seeking to stabilize its currency against the dollar). Similarly, reserves fell in six of seven cases (again China being an exception). But equity prices fell in just 4 of 7 cases (including in China), in a first hint of the heterogeneity we document below. Our composite indices show a negative 4
**Appendix III.C Income Sources of the Elderly, France 1984**

Percentage of households with income from the following sources:

|Age|Eamings<br>only|Public pension<br>only|Private pension<br>only|Eamnings<br>&<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|62.8|37.2|-|-|-|
|65-74 years|5.1|94.9|-|||
|75+ years|2.7|97.3|-|||
|65+ years|3.9|96.1|-|**-**|**-**|
|uintile (65+)||||||
|1|4.9|95.1|-|-|-|
|11|2.2|97.8|-|-|-|
|111|3.8|96.2|-|-|-|
|IV|2.8|97.9|-|-|-|
|V|7.1|92.9|-|-|-|
|QV/QI|1.4|1.0||||

Source: See table **III.** 1

# **Appendix II.D Income Sources of the Elderly, Germany 1984**

Percentage of households with income from the following sources:

|Age|Earnings<br>only|Public pension<br>only|Private pensio<br>only|Eamings &<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|78.2|21.8|-|-|-|
|65-74 years|3.9|96.1|-|-|-|
|75+ years|0.3|99.7|-|-|-|
|65+ years|2.3|97.7|-|-|-|
|uintile (65+)||||||
|I<br>|0.5|99.5|-|-|-|
|II|0.7|99.3|-|-|-|
|III|-|100.0|-|-|-|
|IV|1.3|98.7|-|-|-|
|V|15.9|84.1|-|-|-|
|QV/QI|31.8|0.8|-|-|-|

Source: See table III.A
age for every person in pension age in the EU11. By the year 2060, this is expected to fall to less than 1.5 persons of working age for every person.

**Since labor participation and savings rates both tend to be lower for older people, such a demographic development has direct effects on macroeconomic factors of production.** If age-specific behavior with respect to participation in the labor market and saving is held constant, without remarkable increases in productivity, EU11 income per capita is likely to grow more slowly.

**The expected decline in labor force participation is larger and much more persistent in EU11 than in EU15.** Taking into account the sizable differences in participation rates by educational attainment, methods put forward by Loichinger (2012) were used to obtain projections of the labor force for EU11 and the rest of the EU for 2015 through 2055 (Figure 19).<sup>18</sup> The resultant benchmark labor force growth projections for EU11 and the rest of the EU suggest that the fall in the labor force in EU11 will be more dramatic than in the rest of Europe. The loss in labor force for the forthcoming decades is expected to be of approximately 19% between 2010 and 2050 in EU11, compared to 3% in the rest of the EU. The policy challenges posed by such changes in age structure in EU11 are thus even more urgent than in EU15.

<!-- Start of picture text --> Figure 18. Median Age in EU Countries, Figure 19. Labor Force Growth<br>2010 Projections, EU and EU11, 2015–2055<br><!-- End of picture text --> <!-- Start of picture text --> 0.04<br>44 EU11<br>42 0.03 Rest of EU<br>40 0.02<br>38<br>0.01<br>36<br>0<br>34 2015 2020 2025 2030 2035 2040 2045 2050 2055<br>-0.01<br>32<br>-0.02<br>30<br>-0.03<br>-0.04<br>Median age<br>Growth in labor force (%)<br>Cyprus Ireland Luxembourg Malta United Kingdom France Spain Denmark Netherlands Sweden Portugal Belgium Greece Austria Finland Italy Germany Slovakia Poland Romania Lithuania Czech Republic Estonia Hungary Latvia Croatia Bulgaria Slovenia<br><!-- End of picture text --> _Source:_ UN World Population Prospects (2010) _Source:_ Loichinger (2012) 18 The method combines age-, sex-, and educational attainment-specific participation rates obtained from the European Labor Force Survey with population projections by age, sex and education calculated using the probabilistic population projection methods described by KC et at (2010) 20
contract labor. However, there is no compelling economic rationale for measuring firm size or labor input in the formal sector in this way, and clear reasons not to do so.

# **IV. Conclusion**

The role of size and age in influencing plant dynamics is crucial to understanding productivity differences across firms and countries. In this paper, we use unique survey data across 120 developing countries to examine the relationship between establishment size and age in the formal sector. We find that the average 40-year-old plant in developing countries is about five times larger than the average plant less than 5 years of age. Our results are robust to a number of checks in smaller samples, including looking at just the manufacturing sector, dropping former Socialist economies, dropping firms with any government ownership and dropping firms that started operations before the countries’ year of independence.

We then undertake an in-depth analysis of plant life cycles in one developing country, India. Based on five repeated cross-sections of census data in India spanning 23 years, this study shows that there are significant age effects in the determination of firm size. While the average 20-year-old plant in India is not that much larger than plants younger than five years, by age 40, the average plant in India in the formal sector is two to four times larger than the average plant five years or younger. We find evidence of a flat life cycle only in the informal sector in India.

We also find that the differences in financial development across Indian states, while substantial, only modestly affect the life cycles of established firms in most industries. The one exception is firms in large-firm-dominated labor intensive industries, which are larger in financially developed states versus underdeveloped states. We also find that plants in financially developed states that have rigid labor market regulations are larger than plants in financially 38
developed and developing countries and in order to present the information in a more organized way, in Table 3 we present a summary of the weighted (by population) average marginal effects of different groupings of countries on the intensive margin.<sup>28</sup>

**Table 3: Tobit estimation – Average Marginal Effect on the intensive margin (by groupings)**

# Dependent variable: log (exports)

||log (GDPd)|log (dist)|North<br>Pa|rts & Comp|FTA|
|---|---|---|---|---|---|
|LAC|0.252|-0.665|-0.009|0.392|0.046|
|Developed|0.527|-0.412|0.134|0.587|0.759|
|SSA|0.107|-0.646|0.516|0.146|0.171|
|ASIA|0.210|-0.296|0.458|0.200|0.326|
|ECA - MENA|0.116|-0.419|0.323|0.236|0.245|

Authors calculations using COMTRADE and WDI data. The estimation also includes sector and time dummies. Groupings: LAC: Argentina, Brazil, Chile, Peru and Mexico. Developed: Australia, Canada, European Union, Japan and the United States. SSA: Ghana, Kenya, Mauritius, South Africa and Uganda. ASIA: Bangladesh, China, Indonesia, India, Malaysia and Thailand. ECA-MENA: Morocco, Tunisia and Turkey.

The results obtained for the gravity variables reflect that the larger the size of the market at destination and the closer the markets (lower trade costs), the larger the increase in the volume of exports. This finding supports the evidence presented in Appendix 2. Distance seems to be, on average, more important for the sample of developing countries; and within our group of developing countries, we observe that the coefficients are larger for Latin American and African countries. On the other hand, the size of the destination market is more important than distance for Developed, Latin American and Asian countries.

As for the North dummy, as we would expect, trading with the North is more important for developing countries, in particular for countries in Africa and Asia. This is in line with the stylized facts presented in section 2.

Signing an FTA and therefore reducing trade costs has a positive effect on the intensive margin as we would expect and significant in the majority of the cases. As for the groupings, we can observe that developed countries seem to be the ones benefiting more from this, followed by the Asian countries (that trade more with the North) and the countries surrounding Europe (ECA-MENA).

Given that trade in parts and components is becoming more important in recent years for some developing countries, we report the coefficient obtained for the dummy for this group of products. As we can see, we obtain positive coefficients confirming the premium that trade in parts and components has for developing countries. Although the average coefficient is higher for developed countries, the countries in Latin America, Asia and ECA-MENA regions (groups highly integrated to large markets like U.S. and Europe) show also significant premiums.

As for the marginal effects on the change of the probability of observing more positive values (exporting more goods), the results are presented in Table 4.

> 28 The source of the data on population is the World Development Indicators.

18
cluster and inter-area correlations are large and are not explicitly accounted for. Central to the ELL approach is the fact that it is not generally possible to separate the overall location effect into the area level effect ‘η’ and the cluster level effect ‘ _e_ ’ and, in general, just a single location effect can be calculated. Thus, in the simulation phase, the ELL method requires that one either assumes that *2 2 2 the estimated location effect measured by ση = (ση + σ _e_ ) is entirely a cluster level effect - an optimistic assumption that rules out any correlation at a higher level - or that it occurs entirely at the area level, a conservative assumption that will likely lead to an overstatement of the variance of the estimate.<sup>9</sup> How large are inter-cluster correlations in practice? Given the availability of income data in the Census, we can analyze in this study the presence of inter-cluster correlation at multiple levels. Within the state, we discern five possible locational levels above the household at which intercluster correlations might apply: the meso-region, micro-region, municipality, district and enumeration level (see Table 3). We use the full census data to estimate a single model for the state as a whole, including a set of locational controls (aggregated from unit record census data and included as regressors) at the enumeration area, district and municipality level. We apply mixedeffects maximum likelihood estimation and decompose the overall error term into a household component and separate sub-components for each of the five respective locational levels described above. Tables 4a and 4b indicate that irrespective of the number of areas one allows for, the bulk of the overall location-effect arises at the enumeration area level. While some contribution does derive from correlations at a higher level, they account for less than 0.5% of the total variance. Moreover, after having controlled for locational characteristics in our specification, the entire intercluster correlation contribution (including the EA-level effect) remains below 3% of the total

> 9 See details in Elbers, Lanjouw and Lanjouw (2002) or Demombynes et al (2006).  Tarozzi and Deaton (2007) claim that application of the conservative assumption, while feasible, is not appealing as it would inevitably result in estimates of poverty that are so imprecise as to be unusable.  For this reason they conclude that the “optimistic” assumption is essentially unavoidable for the ELL methodology.  We shall empirically assess this assertion in greater detail below.

17
**_5_**

capital and borrow more.' This also suggests that a firm that can more easily mark-up price over costs and pass through increased costs to the buyer is less likely to reduce output. Box I tests this view by reporting regression results based on **55** firms in the sample for which a complete data set was available.

# **Box I:** Januara 1990 Outyu; Declin

# Model:

Jan. real **soals** _=_ f Jan. mark up, interest/cost., **Jan.),** Dec. real sales ( Dec. mark up interest/costs, **DM..)** or in shorthand: RSRATIO **=** f(MRATIO, INTRATIO) (+) **_(-)_**

**where** the expected signs **are** shown below the explanatory variables: ability to mark up **and** ?ass through costs (inelastic demand, low demand resistance) would encourage firms to produce **more;** while an increase in **the** share of interest in costs (low dollar deposits, greater dependence on bank loans) would discourage fims from borrowing and producing'. Based on a log-log specification, the results were (t-statistics in parentheaes):

RSRATIO = -0.0177 + 0.2089 MRATIO **-** 0.1465 INTRATIO (-0.18) (+4.10) (-3.71) Rbar sqd.=0.28, S.E. of regression=0.32, F-stat=11.31.

**Mm* up ws cakuhW as (p-tax profit - ml cxzuordna gas)Icom of asks, to lim natc** **_d6e_ effd of ifltioasly gais** a **dol de,osx in Dec. 1989, dftchy capturing mai-up over eods** o **the** basic **_bus_ of Xe** **_finn._**

As Box 1 shows, both elasticity of demand and interest burden were important in explaining January output decline. Since on average the interest share in costs grew by 23% compared to an average reduction in mark up of 90%, demand constraints were much more significant in explaining the drop in output.

What about monopoly power as an explanation of the initial contraction in sales? Metallurgical firms, commonly regarded as monopolistic, maintained sales and production in Jan. 1990 despite falling margins. Since there was no contraction in output, monopoly power as an explanation is irrelevant for this set of firms. Electromachinery firms suffered a big decline in sales; but were heavily burdened by cost increases and rising interest costs. **The** light sector (textiles and leather), widely regarded as non-monopolistic, suffered reduced sales and rapidly rising finished goods stocks (Amnex V). Since the opportunity cost of holding inventories (the interest rate on working capital) was extraordinarily high, one would have to conclude that the stock accumulation was involuntary and that there was insufficient demand for what was being produced.

The regression results for January 1990 indicate the predominance of deficient demand in explaining shrinking sales, rather than supply-side factors such as a credit squeeze. The behavior of finished goods inventories (Fig. 2 and Annex V), which rose sharply and then declined in the

**2Annex 11 presnts** this fonnally. In a steady state, with all prices growing at the same rate, it may not be **bad to** comparm the nominal interest **mte** with inflation; but **in a transition from a non-market to a price-basod economy involving** drastic elative price realignments, such comparisons could **be** misleading.