rafmacalaba/datause-encoder-data huggingface dataset:
changes in RCA.<sup>13</sup> One implication of these results is that cross-sectional export baskets change as revealed comparative advantage changes and countries start exporting goods that were previously absent in their export baskets. Moreover, to the extent that the loss in comparative advantage in the goods in the “old” set of exported goods is not too large (i.e. these are goods for which the country could regain its comparative advantage in the future), these goods would still be part of the latent export basket. In such case, this would imply N growing faster than N’.

Our empirical approximation of latent export lines takes the total number of goods exported by a country in at least one year over a long period of time (in the case of this paper the 1962-2012, period). Formally, the empirical approximation is where X<sup>c,t</sup> is the set of goods exported by country _c_ in year _t_ , t0 is the initial year, T is the final year, and |.| is the operator measuring the cardinality of the set.

As mentioned earlier, there are two components of latent export lines which are relevant for analyzing the relationship between our proposed proxy of and the concept of latent diversification. The first comprises export lines already open but not necessarily active (i.e. with positive exports in a given period). The second component comprises export lines that are not open in period _t_ but that could be opened in period _t+1_ . The latter component is not observed ex-ante, and hence cannot be incorporated in our simple proxy of latent diversification. Moreover, the first component is also difficult to measure accurately, partly because of the finite time dimension of available data, and also because there may be export lines that are obsolete. In other words we have that

> 13 More precisely, the authors study an econometric model of transition probabilities in the RCA index. They find that France, the US, the UK and Germany have high transition probabilities. Proudman and Redding (2000) also argue that the evidence they find is inconsistent with standard models of international trade with endogenous growth as this model tends to reinforce RCA patterns. To address this problem, Redding (1999) proposes a modified version of a Ricardian model that incorporates learning-by-doing across goods. His model allows for endogenous changes in RCA over time.

12
# **3. Data description**

The analysis in this paper relies on data from the Life in Transition Survey (LiTS), administered by the World Bank and the European Bank for Reconstruction and Development (EBRD). LiTS provides data from a unified survey for the entire set of Transition Economies,<sup>2</sup> and five Western European countries (France, Germany, Italy, Sweden, and the United Kingdom). For each of the countries, the LiTS survey provides a nationally-representative sample of households; in each household the responses are based on a face-to-face interview with a randomly-selected member of the household.<sup>3</sup> Data from the second round of the survey, collected in 2010, are used in this paper. A newer round of data was collected in 2016, but the questionnaire from the third round omits one of the two key variables used in the analysis, namely the question whether respondents have access to connections (see the empirical strategy section below for details).

The notion of expected socio-economic mobility is defined here based on the respondents’ assessment of their position on the society’s 10-step welfare ladder today and four years hence. These are derived from the respondent’s answers to the following questions: “Please imagine a ten-step ladder where on the bottom, the first step, stand the poorest 10% people in our country, and on the highest step, the tenth, stand the richest 10% of people in our country. On which step of the ten is your household today? And where on the ladder do you believe your household will be 4 years from now?” The distribution of responses to the current and future welfare ladder questions for the pooled sample is shown in Table 1. One can observe clustering of responses in the middle of the scale for both questions. About 60 percent of respondents in the sample place themselves on the 4th, 5th, or 6th step of the welfare ladder today, and more than 50 percent place themselves on one of these three steps on the future welfare ladder. This higher density in the middle of the welfare ladder

> 2No data are available for Turkmenistan. Mongolia and Turkey are excluded from the analysis given the focus on Transition Economies.

> 3The details of the sampling methodology can be found on the EBRD website at http://www.ebrd.com/downloads/research/surveys/LiTS2eh.pdf

8
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# **3. Mechanisms of adaptation**

This section categorizes and describes the main mechanisms of adaptation identified in the reviewed studies. It also discusses some key observations about adaptation mechanisms by agent type.

# **3.1. Types of adaptation mechanisms in the reviewed studies**

Economic agents—firms, households, and farmers—have a variety of strategies available to adapt to the negative effects of climate shocks. These adaptation mechanisms are grouped into nine categories, as shown in Table 3.<sup>5</sup> _Reallocation_ is the largest adaptation mechanism, comprising 17 percent of the top journal studies and 27 percent of the sample overall. Reallocation is a broad category, referring to any behavioral response that involves changing the composition of a portfolio in response to a shock. For example, farmers may reallocate cropland to less heat-sensitive crops, or households may reallocate labor supply in response to extreme heat. In the full sample, 15 percent of the studies on reallocation study firms, 35 percent study households, and 64 percent study farmers. Table 4 shows that most reallocation studies investigate responses to temperature rise, with other climate shocks less prominent.

_Technology_ adoption can help agents adapt to climate change—16 percent of all studies involve adoption of some sort of technology to adjust to climate change, and the rate is similar for both top journals and lower-ranking ones. Some examples of technological adaptation include installation of air-conditioning by households or firms (Davis and Gertler 2015), or the use of improved seed varieties to enhance the climate resilience of agriculture. In top journals, 39 percent of the article on technology adoption study firms, 39 percent study households, and 33 percent study farmers (Table 3, left panel).

_Migration_ is a common coping strategy for rural households, and a large literature has emerged identifying migration responses to climate shocks that reduce agricultural productivity or otherwise negatively affect rural economies. Migration studies comprise 15 percent of the literature on climate adaptation in top journals and 11 percent in the full sample. Table 3 shows that more than 90 percent of the journal articles on migration study households in both top and non-top journals. Table 4 shows that the migration studies in the top journals are also dominated by temperature, although a significant minority study flooding and drought.

_Financial markets_ can provide insurance against climate-related losses or credit to businesses as part of disaster relief. As such, finance represents an important margin of adjustment to climate shocks. Studies on financial markets represent 15 percent of the adaptation literature and are similarly represented in the top journals and all journals. Financial markets studies are dominated

> 5 Neither adaptation mechanisms nor agents are mutually exclusive categories, and, as a result, their shares add up to more than 100 percent.

7
# **Appendix Table I. 10: Household Unorganized Sector Log (Employment) & Log (Real Output)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Emplo|yment|Real O|utput|
|Horizontal Employment Spillovers from|_Other-s_|_ector Employm_|_ent/Output Spi_|_llovers_|
|Organized Sector|0.1771***|0.0459**|0.1105***|0.0257+|
||(0.0262)|(0.0211)|(0.0180)|(0.0161)|
|Forward Employment Spillovers from|||||
|Organized Sector|-0.0734|0.0066|1.2810*|-5.9704***|
||(0.2776)|(0.6825)|(0.6636)|(1.6519)|
|Backward Employment Spillovers from|||||
|Organized Sector|1.0791***|-0.0036|3.1033***|7.4222***|
||(0.1661)|(0.2474)|(0.4280)|(1.3464)|
||_Oth_|_er-sector Prod_|_uctivity Spillov_|_ers_|
|Average TFP of Horizontal-linked|||||
|Organized Plants|0.1456**|0.1678***|0.2619+|0.3532**|
||(0.0641)|(0.0582)|(0.1603)|(0.1502)|
|Average TFP of Forward-linked|||||
|Organized Plants|0.1996++|2.3229***|0.2721|3.2706***|
||(0.1547)|(0.4539)|(0.3466)|(0.8430)|
|Average TFP of Backward-linked|||||
|Organized Plants|0.7042***|0.3905|0.9785***|0.3618|
||(0.1382)|(0.3380)|(0.3130)|(0.8386)|
||_Own-se_|_ctor Employm_|_ent/Output Spil_|_lovers_|
|Forward Employment Spillovers from|||||
|Unorganized Sector|0.1087|1.6486**|-0.6660*|0.1358|
||(0.3109)|(0.7999)|(0.3632)|(0.3935)|
|Backward Employment Spillovers from|||||
|Unorganized Sector|0.0701|0.7582*|0.6306*|-0.7973|
||(0.1740)|(0.4470)|(0.3664)|(1.1374)|
|Constant|-13.5806**|-33.5410***|-101.4007***|-25.7192|
||(5.3486)|(9.1676)|(17.2523)|(34.3912)|
|Observations|5888|5888|5888|5888|
|R-squared|0.0635|0.2893|0.0507|0.2270|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2 and 4 include state-year and industry-year fixed effects while Columns 1 and 3 do not. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 63
|P158333|2017|2020|10/31/2019|1 Closed|Africa|
|---|---|---|---|---|---|
|P158346|2018|2018|3/23/2018|1 Active|Morocco|
|P158346|2018|2019|10/5/2018|2 Active|Morocco|
|P158346|2018|2019|4/11/2019|3 Active|Morocco|
|P158346|2018|2020|11/15/2019|4 Active|Morocco|
|P158346|2018|2020|5/13/2020|5 Active|Morocco|
|P158346|2018|2021|7/22/2020|6 Active|Morocco|
|P158347|2017|2017|2/14/2017|1 Closed|Kosovo|
|P158347|2017|2019|11/7/2018|2 Closed|Kosovo|
|P158347|2017|2021|7/6/2020|3 Closed|Kosovo|
|P158359|2017|2017|6/9/2017|1 Active|Armenia|
|P158359|2017|2020|5/4/2020|2 Active|Armenia|
|P158363|2017|2018|9/1/2017|1 Closed|Western Africa|
|P158363|2017|2019|9/28/2018|2 Closed|Western Africa|
|P158363|2017|2020|11/18/2019|3 Closed|Western Africa|
|P158363|2017|2021|7/21/2020|4 Closed|Western Africa|
|P158364|2018|2018|5/7/2018|1 Active|Nepal|
|P158364|2018|2019|11/6/2018|2 Active|Nepal|
|P158364|2018|2019|5/21/2019|3 Active|Nepal|
|P158364|2018|2020|11/8/2019|4 Active|Nepal|
|P158364|2018|2020|5/14/2020|5 Active|Nepal|
|P158372|2020|2020|6/16/2020|1 Active|Uzbekistan|
|P158379|2016|2016|6/30/2016|1 Closed|Ukraine|
|P158379|2016|2017|6/13/2017|2 Closed|Ukraine|
|P158379|2016|2019|10/11/2018|3 Closed|Ukraine|
|P158379|2016|2020|8/1/2019|4 Closed|Ukraine|
|P158380|2016|2016|6/29/2016|1 Active|Albania|
|P158380|2016|2017|6/14/2017|2 Active|Albania|
|P158380|2016|2019|11/19/2018|3 Active|Albania|
|P158380|2016|2020|8/2/2019|4 Active|Albania|
|P158380|2016|2021|7/16/2020|5 Active|Albania|
|P158387|2017|2018|9/26/2017|1 Active|Bosnia and Herzegovin|
|P158387|2017|2018|4/4/2018|2 Active|Bosnia and Herzegovin|
|P158387|2017|2019|11/13/2018|3 Active|Bosnia and Herzegovin|
|P158387|2017|2019|4/25/2019|4 Active|Bosnia and Herzegovin|
|P158387|2017|2020|10/23/2019|5 Active|Bosnia and Herzegovin|
|P158387|2017|2020|5/6/2020|6 Active|Bosnia and Herzegovin|
|P158411|2017|2019|8/23/2018|1 Active|Rwanda|
|P158411|2017|2020|10/3/2019|2 Active|Rwanda|
|P158411|2017|2021|7/14/2020|3 Active|Rwanda|
|P158418|2019|2019|6/4/2019|1 Active|Turkey|
|P158418|2019|2020|11/18/2019|2 Active|Turkey|
|P158418|2019|2020|6/16/2020|3 Active|Turkey|
|P158434|2017|2018|9/28/2017|1 Active|Malawi|
|P158434|2017|2018|4/24/2018|2 Active|Malawi|
|P158434|2017|2019|12/7/2018|3 Active|Malawi|
|P158434|2017|2020|7/29/2019|4 Active|Malawi|
5 and the key features of the neoclassical growth literature. The model is then briefly described, highlighting in particular the importance of the underlying policy framework in determining 2 economic growth. The following section discusses our empirical results . Section V presents some convergence and growth exercises, with special emphasis on the implications of different policy and non-policy scenarios. We conclude by discussing the main policy implications that can be derived for CEE countries and their accession quest. This paper will build on the findings of the recent econometric studies of the growth literature (e.g., Barro, 1991) and on the convergence discussions for CEE countries initiated by Sachs and Warner (1996).

# **II. Convergence of Incomes across Countries**

First, some definitions. In neoclassical growth models a country's per capita growth rate is inversely related to its starting level of per capita income. This is known in the growth literature as the _absolute convergence_ hypothesis, which hinges on the existence of diminishing returns to reproducible capital: _given sufficient time all countries should converge to similar per capita_ 3 _incomes ._ The hypothesis of absolute convergence implies that rich countries should grow at a slower rate than poor countries. However, this is not consistent with cross-country data. As shown in Figure 1, the growth rates per capita (defined in this paper in terms of persons in the labor force) show little correlation with the starting levels of GDP per capita in a sample of over 80 countries for the period 1965-89. Other authors, working with different data sets, have also identified similar patterns (see, for example, Barro, 1991). This contrast between the empirical evidence and the 2 The more formal presentation of the model is found in Appendix I and is followed in Appendix 2 by a thorough discussion of the estimation results. 3 Some growth models treat savings as exogenous (e.g., Solow, 1956) while others have aimed at endogeneizing the savings decision process by introducing an optimizing representative consumer. The latter group is best represented by Ramsey (1928), Caas (1965), and Koopmans (1965). As Erhlich (1990) has highlighted, the growth literature entered in the late 1960s into a dormant phase which was interrupted by the papers of Romer (1986) and Lucas (1988). The socalled "new growth literature" is mostly directed to providing an interpretation for long-run economic growth which is not based on the exogenous technological progress explanation.
case for precipitation which, as we will show in the following section, is closely related to losses from floods and droughts. To provide a sense of what is possible from the current scientific perspective, our projections use two GCMs that are the wettest and driest of approximately 20 available GCMs at the global level.<sup>5</sup> The driest overall is CSIRO’s Mk 3.0 model, which was transmitted to the IPCC’s data collection center in 2005.<sup>6</sup> The driest is NCAR’s Community Climate System Model (CCSM), version 3.0, which was released to the public in 2004.<sup>7</sup> To illustrate the implications of the forecasts, Figure 3 provides historical and projected data for India for income per capita, population, life expectancy, fertility, mean annual rainfall, and maximum (monthly) annual temperature. We include both the CSIRO and NCAR weather projections. It is immediately clear that the India of 2050 bears little resemblance to present-day India, even in our relatively moderate scenario. GDP grows at an annual rate of 4.4%, increasing GDP per capita (constant $US 2000) from $450 in 2000 to $4,300 in 2050. Life expectancy increases from 63 to 76 years and the total fertility rate declines from 3.25 to 1.85 (below replacement). In 2050, India is an upper middle income country by current World Bank standards, closely resembling the Chile of 2000 in income per capita, life expectancy and fertility. But not, of course, in population: By 2050, India has 1.66 billion people in this scenario.

The climate projections provide a sense of the disparities in GCM predictions associated with the IPCC A2 scenario. From a monthly average of 88 mm in 2000, precipitation increases 8% by 2050 to 95 mm in the NCAR scenario, and decreases by 8% to 81 mm in the CSIRO scenario. Thus, the total difference attributable to GCM variation within the same IPCC scenario is 16% -- a very large number in this context. The two scenarios are

> 5  In general, the IPCC’s SRES scenarios project higher variance in precipitation events with increases in the atmospheric concentration of greenhouse gases.  Although the SRES A2 scenario anticipates a rapid increase in atmospheric concentration, the A1 scenario anticipates even faster growth and, by implication, higher variance in precipitation (i.e., more heavy rainfall events and serious droughts).  The A2-based results reported in this paper are therefore likely to be more “moderate” than A1-based results would be. 6  CSIRO is Australia’s Commonwealth Scientific and Industrial Research Organization (www.csiro.au/) 7  NCAR is the US National Center for Atmospheric Research.  For a detailed description of the CCSM program, see http://www.ccsm.ucar.edu/about/.

7
Pack, Howard and Janet Rothenberg Pack (1993), “Foreign Aid and the Question of Fungibility,” _The Review of Economics and Statistics_ , 75(2), 258-265.

Rao, Govinda, Richard Bird and Jennie Litvack (1999), “The Changing Requirements of Fiscal Relations: Fiscal Decentralization in a Unified State,” in Jennie Litvack and Dennis Rondinelli (eds.) _Market Reform in Vietnam: Building Institutions for Development_ , Westport, Connecticut: Quorum Books.

- Ravallion, Martin (2006), “Evaluating Anti-Poverty Programs,” in Robert Evenson and T. Paul Schultz (eds.) _Handbook of Development Economics_ , Vol. 4, North-Holland, forthcoming.

- Roemer, John and Joaquim Silvestre (2002), “The ‘Flypaper Effect’ is Not an Anomaly,” _Journal of Public Economic Theory_ ,  4(1), 1-17.

- Rosenbaum, Paul and Donald Rubin (1983), “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” _Biometrika_ , 70, 41-55.

- Smith, A. Jeffrey and Petra E. Todd (2005), “Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?” _Journal of Econometrics_ , 125, 305-353.

- van de Walle, Dominique (2007).  “Evaluating the impacts of rural road projects,” mimeo, DECRG, World Bank, Washington D.C.

- World Bank (1996), “Rural Transport Project Staff Appraisal Report,” No. 15537-VN, East Asia and Pacific Region, World Bank, Washington, D.C.

- World Bank (1998), _Assessing Aid: What Works, What Doesn’t, and Why?_ Oxford University Press: New York.

22
1991-1996, since their calculation requires a longer time series.<sup>10</sup> We exclude 8 countries that have less than 10 firms with non-missing data for both years (Egypt, Jordan, Liechtenstein, Luxembourg, Morocco, Russian Federation, Slovakia and Zimbabwe). We are left with 11,033 firms in 46 countries.

Table 1 presents the sample countries and shows the number of firms per country. The mean number of firms per country is 240 and the median 94. The lowest number of firms per country is 11 for Venezuela, and the maximum number is 2715 for the US. The data cover mainly large firms. This selection pattern arises since firms have to be listed on a stock exchange in order to enter the database, and listed companies tend to be among the largest firms in each country. Previous work for nine East Asian countries (Claessens et al., 2000) suggests that the Worldscope sample covers between 64% and 96% of the total market capitalization of firms listed on the stock market. We expect that this to be the case for this larger sample of countries as well, especially for the developed countries where reporting is generally better.

The table also provides the classification of countries along different dimensions (for detailed definitions, see Table 2). We use information from La Porta et al. (1998) on legal origin to classify countries as common or civil law origin countries, with the latter further classified as French, German, or Scandinavian. Using the same primary sources, we expand on their sample of legal origin by classifying China, the Czech Republic, Hungary, and Poland as Germanic, civil law countries. We end up with 14 common law courntries and 32 civil law counties, of which 18 French, 10 Germanic, and 4 Scandinavian civil law countries.

13
- 22 -

production are announced by the Fed at 9:15 a.m. They are reported in the Federal Resurve G.12.3 Statistical Release. The unemployment rate figures are announced by the BLS at 8:30 a.m. They are reported in the SLS Press Release.

35. Great care **was** taken to match the dates of the announcements with the price changes. Since money announcements are made on Thursday afternoons after the comodity markets are closed, the unanticipated component of money announcements were matched with the difference between the Friday close and the Thursday close prices to measure the immediate impact of shocks in the money supply on coimodity prices. Also, since all the other announcements are made while the markets are open, the unanticipated components of these announcements were matched with the differences between the close of the announcement day and the close of the previous day to measure the immediate impact.

# Expectations and Market Data, and Economic News

36. For those variables (MS, PPI, CPI, IP, UR) for which regular announcements are made, market expectations data were used. These expectations are from surveys conducted by MMS International, Redwood City, California, USA. They consist of median responses from surveys of approximately 40 to 60 market participants. These market expectations are good proxies for market expectations since they have been shown to be unbiased and efficient (Pearce and Roley, 1985).

37. The unanticipated component of the money supply is defined in percentage terms as UMS(t) = ([MS(t)-(MS(t-l) + EIS(t))I/M(t-l))*l00, where
# Appendix I.A.1 Historical Demographic Data, Argentina 1895-1970

|Year|1895|1914|1947|1960|1970|
|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|28.71|25.78|16.63|11.25|9.14|
|Population aged 20 to 59/<br>population over 60 years old|12.77|11.68|8.02|5.80|4.73|
|Percentage<br>of population<br>over 60 years old|3.61|4.01|6.54|8.92|10.78|
|Percentage<br>of population<br>under 15 years old|40.33|38.43|30.73|30.71|29.30|
|Women over 60 years old/<br>Men over60 years old|1.03|0.93|0.97|1.06|1.15|

Ratios calculated using data found in Mitchell, B. R. 1983 "Intemational Historical Statistics: The Americas and Australasia'.

# **Appendix I.A.2 Historical Demographic Data, Australia 1891-1971**

|Year|1891|**1901**|1911|1921|1933|1947|**1954**|1961|1971|
|---|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|21.45|15.24|15.00|14.40|10.21|8.31|7.62|7.19|7.53|
|Population aged 20 to 59/<br>population over60 years old|9.56|7.84|8.07|6.93|5.37|4.45|4.19|4.03|4.09|
|Percentage<br>of population<br>over60 years old|5.05|6.17|6.38|7.51|9.92|12.22|12.50|12.33|12.28|
|Percentage<br>of population<br>under 15 years old|36.97|35.09|31.59|31.75|27.48|25.05|28.54|30.23|28.77|
|Women over 60 years old/<br>Menovcr60yearsold|0.70|0.78|0.88|0.92|0.99|1.12|1.18|1.25|1.27|

Ratios calculated using data found in Mitchell, B. R. 1983 "International Historical Statistics: The Americas and Australasia".