rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 8 Developing country exports will be increasingly concentrated in manufactured goods**

<!-- Start of picture text --> Contribution to total merchandise exports, percent<br>25<br>20<br>15<br>10<br>5<br>0<br>2005 2030 2005 2030 2005 2030 2005 2030<br>China India Developing High income<br>Agriculture Processed foods Fossil fuels<br><!-- End of picture text --> The result that agricultural products become a more important component of high income countries’ exports may seem counter-intuitive at first glance. There are two main reasons for this development. First, our baseline does not include any significant removal of domestic support in agriculture, which allows high income farm producers to sell a significant portion of their output on the world markets. Second, as developing countries, led by China and India, gain competitiveness in the manufacturing sector, the relative price of agricultural products imported from high income countries declines. This is illustrated in Table 1, which shows global market shares for China and India, as well as the standard set of World Bank-defined regions. While China and India’s global market shares in exports of manufactured goods double and triple, respectively, their significance as exporters in world agricultural markets diminishes even more dramatically. Furthermore, while India’s share of global food imports does not change appreciably, by 2030 China could account for a large share of total imports of agriculture and processed food. It is important to note that the numbers in Table 1 are not forecasts, and are predicated on a number of assumptions, including the same rate of productivity improvement in farm products across high income and developing countries. If, on the other hand, China’s productivity growth in agriculture is able to follow the path of its manufacturing productivity more closely, the relative price of imported agriculture would rise and limit the country’s willingness to purchase agricultural products from abroad.<sup>11</sup>

> 11 An additional factor influencing the results is the relative land scarcity in China. Over the last decades, about 0.7 percent of arable farm land has been converted to non-agricultural use annually—including for roads, factories and residential and business construction.

14
Table S.9: Flexible length specification: Corruption

||TI|WGI|WGImed|
|---|---|---|---|
||(1)|(2)|(3)|
|TI Corruption|0.071<sup>_∗∗∗_</sup><br>(0.025)|||
|WGI Corruption||0.11<sup>_∗∗_</sup><br>(0.044)||
|WGI Corruption>Median|||0.156<sup>_∗∗∗_</sup><br>(0.052)|
|Log Ruggedness|0.112<sup>_∗∗∗_</sup>|0.124<sup>_∗∗∗_</sup>|0.126<sup>_∗∗∗_</sup>|
||(0.035)|(0.034)|(0.033)|
|Log of Rainfall|-.094|-.082|-.063|
||(0.071)|(0.069)|(0.071)|
|Log dist to coast|-.058|-.053|-.052|
||(0.046)|(0.045)|(0.043)|
|Population Density|0.081<sup>_∗∗∗_</sup>|0.088<sup>_∗∗∗_</sup>|0.079<sup>_∗∗∗_</sup>|
||(0.016)|(0.016)|(0.017)|
|Log of Surface Area|0.08<sup>_∗∗∗_</sup>|0.085<sup>_∗∗∗_</sup>|0.093<sup>_∗∗∗_</sup>|
||(0.021)|(0.021)|(0.021)|
|20km<length<50km|-.101<sup>_∗_</sup>|-.098<sup>_∗_</sup>|-.103<sup>_∗_</sup>|
||(0.058)|(0.058)|(0.058)|
|50km<length<100km|-.137<sup>_∗∗_</sup>|-.135<sup>_∗∗_</sup>|-.143<sup>_∗∗_</sup>|
||(0.066)|(0.067)|(0.067)|
|Length>100km|-.188<sup>_∗∗_</sup>|-.194<sup>_∗∗∗_</sup>|-.199<sup>_∗∗∗_</sup>|
||(0.074)|(0.075)|(0.074)|
|Estimate|-.021|-.021|-.009|
||(0.059)|(0.06)|(0.059)|
|Contract|-.064|-.062|-.058|
||(0.076)|(0.076)|(0.075)|
|Log of GDP pc (1985)|-.028|-.030|-.011|
||(0.045)|(0.046)|(0.047)|
|Foreign frm or JV|0.269<sup>_∗∗_</sup>|0.303<sup>_∗∗∗_</sup>|0.303<sup>_∗∗_</sup>|
||(0.13)|(0.118)|(0.119)|
|World Bank|-.0006|0.01|-.013|
||(0.088)|(0.09)|(0.091)|
|Bilateral Donor|0.166|0.187|0.177|
||(0.132)|(0.133)|(0.13)|
|Obs.|3322|3322|3322|
|_R_<sup>2</sup>|0.898|0.898|0.898|

_Notes_ : Dependent variable is the log of cost per km; all models control for work activity fixed effects, year fixed effects, an interaction between work type and five-year period fixed effects and region fixed effects; base categories are actual costs; robust standard errors in parentheses, clustered at the country;<sup>_∗_</sup> ,<sup>_∗∗_</sup> ,<sup>_∗∗∗_</sup> denote significance at 10%, 5% and 1% levels. _Source_ : Authors’ analysis based on data described in the text.

12
-31-

<!-- Start of picture text --> 8STEP 4:<br><!-- End of picture text --> Net of tax income

- (NW1 - NWo') - 210

<!-- Start of picture text --> After-Tax<br>BALANCE SHEET, End of Period 1<br>ASSETS A1 LIABILITIES L1<br>MAl'-Tax - MA1 10 1000 ML1<br>NMA1 1200 210 NW1 - Al -ML1<br>Al 1210 1210 L1<br><!-- End of picture text --> In this simple example, the adjustment of the balance sheet yields: i. a balance sheet where all real assets and liabilities including net worth are carried at inflation-adjusted cost.

ii. a measure of ticome (420) that is greater than income without inflation (350) by the same percentage as the rate of inflation (20%). This shows that income measured is inflation-neutral. Notice that the problem of income measurement in this example was due to nominal interest deduction under conditions of inflation. The net worth adjustment ensured that the accrued income from the decline in value of ML under inflation was captured in the measure of income.
- 37 -

data, a translog model falsm coneavLty for 6 observations whlle **the** logistic does not fail once. More importantly the own price **elastioities** from **the** tranalog vary greatly from observeLion to observation, whle for the logiLtLc they are nearly constant. in a search for other functlonal forms Mountaln and _esLao_ [19891 have made omo further suggestions. For a three input **model** they **firstly** look at **a** Quadratic quasi Cobb-Douglas (QQCD) form in which **the relative** inputs **are** related to prices bys ln(-X/X,) - a2 + an ln(P2/P) + an ln(P,/Pg)

**+** s am=ln(PJ/P,) **+** a2 ln(P2P,)ln(P 3<sup>/Pi)</sup> + I a2(ln(P,/P)12 (28)

ln(X 3 /Xl) - a3 + a2 ln(P2Pl) + e% ln(P3/Pl)

**+** 4 aj(ln(P/P,32

**+** am3 ln(P2/P) ln (P1/P,) **+** h am (lln(P/P)31 (29) As an alternative to thLs form they propose a Fournler based approximation for the expenditure shares:

**3** 8- a, + Z a1 ln(Pj/Pt) - 2{do sLn(1n(PJ/Pj)j

+ db sin(ln(P2PJ)j +e cos(ln(PJPj)J + **e,3** cosln(P2P,)I)

# (30)

For this to be consistent with a cost functlon the following restrictions must hold: du - -d4, ean -e",, an - **an** (31) Both models are applied to fuel choice for indLvidual industries in Canada. Both produce good results in **some cases.** An Lmportant **aspeat** of their
is employed by Eurostat to identify minimum wage earners in the Structure of Earnings Survey. To account for individuals earning less than the minimum wage, we also incorporate a lower threshold, symmetrically set at 95%.

- For part-time workers, the minimum wage value adjusts proportionally based on the number of working hours they engage in.

We may overestimate the number of minimum wage earners because some taxpayers in the first range are entitled only to the basic minimum wage. Unfortunately, our data source does not provide information on the education level.

# Results

**Despite the challenges posed by the pandemic crisis, Romania's labor market exhibited a relatively resilient performance during the period spanning from 2020 to 2021** . Despite the economic activity distortion caused by the COVID-19 crisis, the impact on the labor market was moderate, thanks to support measures adopted by authorities. Labor market adjustments were predominantly made through reduced working hours rather than personnel layoffs. Absences from the workplace were generally temporary amid the moderation of economic activity. In Romania, both direct (technical unemployment) and indirect (liquidity support measures, SME investments, tax deferrals) facilities from authorities limited adverse effects on the labor market, preventing a sharp increase in the unemployment rate. Technical unemployment support allowed job retention, providing employees with financial assistance comparable to that of other EU member states in terms of duration and amount. Notably, the unemployment rate steadily rose from 4.9% in 2019 to 6.1% in 2020, but a positive trajectory was evident in 2021, with unemployment receding to 5.6%. Consequently, with support measures, the workforce experienced a complete recovery in the last quarter of 2021 compared to the pre-crisis situation.

**Signs of stability were also observable in the wage distribution.** The mean gross wage demonstrated an upward trend, progressing from 4,458 RON in January 2020 to a noteworthy 5,295 RON by the end of December 2020 in nominal terms. Regarding the minimum wage policy, given the high uncertainty triggered by the COVID-19 pandemic and the concentration of layoffs among low-paid workers, the government opted to index the minimum wage by the average annual inflation rate registered over the last 12 months – the latest available data by the end of 2020 (3.1%). The main goal was to maintain the purchasing power of employees. However, due to upward inflationary pressures in 2021, in real terms, minimum wage earners experienced a decline of 2% (the CPI inflation rate reached 5.1% in 2021). The general minimum wage, while escalating from 2,230 RON to 2,300 RON, experienced no change in the minimum wage designated for employees with higher education and those employed within the construction sector. This alignment suggests a prudent attempt to

24
In addition to the basic antidumping data described in section 2, additional data for Turkey is as described in the following table

**Table 3.12.1 : Additional Turkish Data Included in AD-TUR-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-TU**|**R-Master :**||
|**U.**|**P_AD_DUTY**|Description of the preliminary antidumping duty imposed|
|**V.**|**F_AD_DUTY**|Description of the final antidumping duty imposed|
|**W.**|**F_AD_GP**|_Official Gazette_government publication number documenting the final<br>antidumping determination|

# **3.13 United States (USA)**

The United States’ antidumping process is a two-track procedure handled by two distinct government agencies – the dumping investigations are administered by the International Trade Administration within the Department of Commerce, and the injury investigations are handled by the quasi-judicial United States International Trade Commission. Information on United States’ antidumping investigations is reported by these agencies to the public through the _Federal Register_ . The US antidumping process has been studied extensively, see for example, Boltuck and Litan (1991), Staiger and Wolak (1994), Blonigen and Prusa (2004), Horlick (2005), and Nelson (forthcoming).

In addition to the standard antidumping information described in section 2, the AD-USA workbook contains much additional information<sup>13</sup> that may be of use to researchers.

> 13 Much of the information in the workbook on US domestic and foreign firms is graciously provided by Bruce A. Blonigen as posted at his website, http://darkwing.uoregon.edu/~bruceb/adpage.html. The data can also be compiled from notifications to the _Federal Register_ (foreign firms and outcomes) and the ITA’s website, <u>http://ia.ita.doc.gov/stats/pet-init.htm</u> (domestic petitioners), last accessed on 22 July 2005.

32
We assume, as is common in the literature (Masciandaro, Pansini, and Quintyn 2011), that the mandate for financial stability and macroprudential supervision is with the central bank. However, we acknowledge that, while the central bank typically had a mandate for the oversight of macroeconomic and financial stability before 2008, explicit mandates for macroprudential policy, together with broader macroprudential tools, were given to central banks only as a result of the global financial crisis. There is also an on-going debate on whether the central bank should be tasked with macroprudential policy or, rather, how to separate implementation of monetary and macroprudential policy functionally (Galati and Moessner 2011). In spite of the absent macroprudential policy mandates before the 2008 crisis and the ongoing theoretical debates, central banks take on the role of the de facto macroprudential supervisor implicitly in association with their mandate for price stability or explicitly by setting financial stability as one of the goals in the central bank law (Borio and Shim 2007; Claessens, Ghosh, and Mihet 2013).

Our paper, therefore, attempts to shed light on whether placing bank supervision inside or outside the central bank (the macroprudential supervisor) could help achieve better outcomes —that is, more proactive and accurate policy on financial stability and greater resilience of the financial system. In particular, we try to shed light on questions such as: Can placing bank supervision in the central bank help because of the possible synergy effects? Or can it hurt because it may lack checks and balances? Or could the two theoretically opposite effects simply cancel each other out so that the data reveal no significant effect in general? And could the positive or negatives effects of this arrangement work only in a countryspecific context (for example, at high levels of financial development)?

To identify whether microprudential supervision of banks was in the central bank prior to 2007, we rely on the data from Melecky and Podpiera (2013) and the <mark>2003, 2007, and 2012 Bank Regulation and Supervision Surveys of the World Bank.</mark> For banking crisis classification, we rely on Laeven and Valencia’s (2013) database and cross-check our results against the crisis classification by Reinhart and crisis and find that higher regulatory restrictions on bank activities and private monitoring decreased the likelihood of crises. Barth, Caprio, and Levine (2004) provide empirical evidence that enforcing accurate information disclosure to empower private sector monitoring of banks and creating incentives for private agents to exert corporate control improve bank performance and stability.

5
effect associated with the transition. The experience of these countries is clearly not typical of the developing world.<sup>19</sup> The longest available spell between two surveys is used for each country. Both surveys use the same welfare indicator, either consumption or income per person, following standard measurement practices. When both are available, consumption is preferred, in the expectation that it is both a better measure of current economic welfare and that it is likely to be measured with less error than incomes.<sup>20</sup> Three-quarters of the spells use consumption.

For about two-thirds of the countries there are three or more surveys. This sub-sample will be used in testing robustness to relaxing various specification assumptions.

Naturally the time periods between surveys are not uniform. The median year of the first survey is 1991 while the median for the second is 2004. The median interval between surveys is 13 years and it varies from three to 27 years. All changes between the surveys are annualized. Given the most recent survey for date _ti_ in country _i_ and the earliest available survey for date<sup>_t_</sup> _i_  _i_ , the growth rate for the variable _x_ is _gi_ ( _xit_ )  ln( _xit_ / _xit_ <sup>/)</sup>  (dropping the _i_ subscript on _t_ and  for brevity). National accounts and social indicators are also used, matched as closely as possible to survey dates. All monetary measures are in constant 2005 prices (using countryspecific Consumer Price Indices) and are at Purchasing Power Parity (PPP) using the individual consumption PPPs from the 2005 International Comparison Program (World Bank, 2008).

<u>Poverty</u> is mainly measured by the headcount index ( _H it_ ), given by the proportion of the population living in households with consumption per capita (or income when consumption is not available) below the poverty line. For the bulk of the analysis the poverty line is set at $2.00 per person per day at 2005 PPP, which is the median poverty line amongst developing countries based on the compilation of national poverty lines in Ravallion et al. (2009). $2 a day is also very close to the median consumption per person in the developing world for 2005; see Chen and Ravallion (2010) which also describes the methods used here in measuring poverty and inequality. The (unweighted) mean poverty rate for the $2 line fell from 43.6% in the earliest round of surveys to 38.3% in the latest rounds. This line is clearly somewhat arbitrary; for example, there is no good reason to suppose that $2 a day corresponds to the point where credit

> 19 Nor are they typical of developed countries; for this reason, Voitchovsky (2005) chose to drop these countries from her sample for industrialized countries.

> 20 The only exception was Peru, for which incomes allowed a much longer time period.

10
# **Table S1.1: Countries and years in sample**

|_Physical Violence Sample_|_Sexual Abuse Sample_|
|---|---|
|Azerbaijan; 2006|Azerbaijan; 2006|
|Bangladesh, 2007|Burkina Faso; 2010|
|Burkina Faso; 2010|Cambodia; 2005, 2006, 2014|
|Cambodia; 2005, 2006, 2014|Democratic Republic of Congo; 2007, 2013, 2014|
|Cameroon, 2011|Dominican Republic; 2007 , 2013|
|Colombia 2004, 2005, 2009, 2010|Gabon; 2012|
|Democratic Republic of Congo; 2007, 2013, 2014|Ghana; 2008|
|Dominican Republic; 2007 , 2013|Haiti; 2005, 2006, 2012|
|Gabon; 2012|Honduras; 2011, 2012|
|Ghana; 2008|India; 2005, 2006|
|Haiti; 2005, 2006, 2012|Kenya; 2008, 2009, 2014|
|Honduras; 2011, 2012|Kyrgyz Republic; 2012|
|India; 2005, 2006|Liberia; 2006, 2007|
|Jordan, 2007, 2012|Malawi; 2010, 2015, 2016|
|Kenya; 2008, 2009, 2014|Mali; 2012, 2013|
|Kyrgyz Republic; 2012|Mozambique; 2011|
|Liberia; 2006, 2007|Peru; 2010, 2011, 2012|
|Malawi; 2010, 2015, 2016|Philippines; 2008 , 2013|
|Mali; 2012, 2013|Rwanda; 2014, 2015|
|Mozambique; 2011|Sao Tome and Principe; 2008, 2009|
|Pakistan, 2012, 2013|Tajikistan; 2012|
|Peru; 2010, 2011, 2012|Tanzania; 2009, 2010, 2015, 2016|
|Philippines; 2008 , 2013|Timor Leste; 2009, 2010|
|Rwanda; 2014, 2015|Uganda; 2006, 2011|
|Sao Tome and Principe; 2008, 2009|Ukraine; 2007|
|Tajikistan; 2012|Zambia; 2007 , 2013, 2014|
|Tanzania; 2009, 2010, 2015, 2016||
|Timor Leste; 2009, 2010||
|Uganda; 2006, 2011||
|Ukraine; 2007||
|Zambia; 2007 , 2013, 2014||

_Source:_ Demographic and Health Surveys (DHS).
market share of global manufactured exports and imports from about 20 and 35 percent in 1991/92 to 44 and 46 percent in 2007/08 respectively.

Section B explains the methodology and the data. In Section C, the shares of total imports from the rest of the world and their decomposition are analyzed separately for the selected industrial and developing countries. Section D focuses on exports from developing countries to industrial and other developing countries, and extends the analysis to 3-digit ISIC sectors. Very rapid growth of China, especially during the 2000s distorts most of the conclusions and analyses in the previous sections. The impact of China is discussed throughout the text in but given its importance, Section E summarizes its impact on world trade. Conclusions are presented in Section F.

Our results show the following: World demand growth was very different during the 1990s and the 2000s. Demand growth has accelerated during the 2000s. While market share changes explain most of the growth during the 1990s, its contribution is relatively smaller during the 2000s. Even without demand growth, continued liberal trade regimes could generate 3-5 percent p.a. manufacturing trade growth caused by market share changes.

Imports from developing countries have grown much faster both in industrial and developing country markets. Despite the rapid growth, their share in the absorption of the sample countries is still very low and can expand substantially even if demand growth is much lower in the near future. However, most of this share increase is driven by China. Furthermore, China is among the few countries that have reduced their share of imports from other developing countries. Future manufacturing trade growth will depend on the evolution of trade patterns of China as much as other economic developments.

# **B. Methodology and Data**

The total gross absorption (demand) in each country is estimated as gross production in manufacturing, minus exports plus imports, for the beginning and end years. Gross production is taken from UNIDO database and checked against other sources for consistency.<sup>4</sup> All gross

> 4 UNIDO is the only agency that publishes manufacturing gross output series. In many cases, these numbers were compared to other national sources to ascertain the similarity of movements.

4
range from 1,282 to 1,315, depending on the outcome of interest). The coefficients instrumented with the IV approach remain strongly significant and barely change in magnitude.

Second, we check if the results are robust to using a less disaggregated sectoral classification than 5-digit KBLI to compute the female share of workers at baseline. Table 6 shows that the results are robust to using the 3-digit KBLI sector specification, for both OLS and IV estimations.

Third, we also check the robustness of our results to using two alternative methods of calculating preschool density during the years that are missing in PODES. The first consists of restricting our constructed panel to PODES years. The second, is to predict preschool density for the missing years using linear projection with the closest two data points available. For example, to estimate the preschool density in 2007, we fit a linear projection using preschool density data in PODES 2005 and 2008. Table 7 presents the results. The coefficients on TFPR and TFPQ remain strongly significant. They are 5 and 8 percentage points lower in magnitude, respectively, when we use reduced sample (columns 1-3), and 6 and 2 percentage points higher in magnitude, when we use linear projection (columns 4-6). Notably, the coefficient on markup becomes significant at the 10% level when linear projection is used to impute missing years. However, overall, the results are similar in direction of effect and in magnitude.

Finally, we address a further concern to the robustness of our estimates, i.e. that some district-level time-variant variables, potentially correlated with preschool expansion, may affect plants differently depending on the share of female workers. For example, if investment in education or overall changes in district-level infrastructure, such as road improvement, affect plants differently depending on the share of women, our estimates in (1) may be biased, even though we control for district level time-variant heterogeneity with district-time fixed effect γdt . Consider the following scenario: preschool expansion may be correlated with changes in local public sector efficiency more generally. Such district-level change may differentially affect firms with different shares of female workers, if, for instance, higher ability managers employ more women and are more able to take advantage of greater efficiency of the local public sector.

14
# **Can Government Intervention Make Firms More Investment-Ready?**

# **A Randomized Experiment in the Western Balkans**<sup>**#**</sup>

Ana Paula Cusolito, _World Bank_ Ernest Dautovic, _European Central Bank_ and _University of Lausanne_ David McKenzie, _World Bank_ _Keywords:_ Investment readiness; start-ups; innovation; equity investment; entrepreneurship; randomized controlled trial.

_JEL codes_ : L26, M2, M13, O1

> # Funding for this project was received from the European Commission. Funding to support the impact evaluation from the World Bank i2i Trust Fund is gratefully acknowledged. We gratefully acknowledge comments from seminars at Duke, the IGL/Nesta conference, the IPA SME conference, Lausanne, Toronto and from Marius Starke, Janko Milunovic and Peter Trapp. This experiment and a pre-analysis plan were registered in the AEA RCT registry on October 2, 2015: https://www.socialscienceregistry.org/trials/895.