rafmacalaba/datause-encoder-data huggingface dataset:
# **A STRUCTURAL RICARDIAN ANALYSIS OF CLIMATE CHANGE IMPACTS AND ADAPTATIONS IN AFRICAN AGRICULTURE**<sup>1</sup>

S. Niggol Seo<sup>2</sup> and Robert Mendelsohn<sup>3</sup>

> 1 This paper is one of the product of a study “Measuring the Impact of and Adaptation to Climate Change Using Agroecological Zones in Africa” funded by the KCP Trust Fund and conducted in DECRG at the World Bank.  We benefited from comments on an earlier draft by David Zilberman.

> 2 School of Forestry and Environmental Studies, Yale University, 230 Prospect St. , New Haven, CT06511, and a consultant to the World Bank;  phone 203-432-9771; email <u>Niggol.seo@yale.edu.</u>

> 3  School of Forestry and Environmental Studies, Yale University; 230 Prospect St. , New Haven, CT06511, and a consultant to the World Bank; ; phone 203-432-5128; email <u>Robert.mendelsohn@yale.edu.</u>
**<u>Sector of main product</u>** In order to classify establishments by industry, BPS records the five-digit International Standard Industrial Classification (ISIC) for firms based on the product with the largest production value in any given year. In 2001, BPS changed the classification of plants from the second revision of the ISIC to the third. A consistent bridge between these different coding systems was constructed based upon the inclusion of both codings in the dataset for the 2000 survey database. This bridge was corroborated using a bridge provided by BPS. In many cases, the industry code provided in the dataset was truncated to four or fewer digits. Where possible, if an adjacent year’s reported industry for the same firm was available that was used to fill in the truncated digits.  Where plants produce multiple products or the production processes allow changing from one product to another, we may expect to see the coded industry of production change from year to year. In such cases, the mode sector code is used (with ties going to the initial sector code reported).

**<u>Total labor</u>** for an establishment was defined as the sum of all paid and non-paid workers, whether production or non-production workers.<sup>xvii</sup> Production workers were defined by BPS as all workers who work directly in the production process or activities connected with production process, and non-production workers were defined to be all other workers. These definitions roughly correspond to traditional definitions of blue- and white-collar workers, respectively (see below).

**<u>Unskilled ratio</u>** The unskilled ratio was constructed as the share of blue collar workers (including unpaid production workers) as a share of the total establishment employment.

**-** **<u>% No Primary Men(/Women)</u>** Proportion of production works who were men (women) that did not complete primary school

**-** **<u>% Primary Men(/Women)</u>** Proportion of production workers who were men (women) and for whom primary school was their highest level of schooling.

41
# **Table 2. Evolution of Bank-Level Indicators after the Shock**

Regressions at the bank*month level using bank balance sheet data. Dependent variables are listed in the columns. _ExposureEnergyb,m_ represents lending to the energy sector as a share of its tier 1 capital of bank _b_ in month _m_ . _Total Lendingb,m_ are total monthly loans of bank _b_ in logs. _CDS Spreadsb,m_ is the log of CDS spreads of five-year maturity bonds of bank _b_ in month _m. Stock Priceb,m_ is the log of the stock price of bank _b_ in month _m. Delinquencyb,m_ is the share of delinquent loans of bank _b_ in month _m_ . The regressor _ExposureEnergyb,Aug14_ represents the exposure to the energy sector of bank _b_ in August 2014. _Postm_ is an indicator for month _m_ after the energy price shock in August 2014. All regressions include bank and month fixed effects. The results show that there was relocation of lending toward the energy sector by banks that were more exposed to it. However, other margins were unchanged, suggesting that there was a reallocation across sectors. Robust standard errors are double clustered at the bank and month levels. Detailed variable definitions are provided in Table A1. Observations are at the bank-month level <u>for January 2013 to June 2016. *** p<0.01, ** p<0.05, * p<0.1.</u>

||ExposureEnergyb,m<br>(1)|Total Lendingb,m<br>(2)|CDS Spreadsb,m<br>(3)|Stock Priceb,m<br>(4)|Delinquencyb,m<br>(5)|
|---|---|---|---|---|---|
|ExposureEnergyb,Aug14*Postm|0.192***|-0.003|0.016*|-0.005***|0.024***|
||(0.022)|(0.002)|(0.009)|(0.002)|(0.007)|
|Observations|612|612|272|350|612|
|R-squared|0.884|0.992|0.706|0.996|0.896|
|Bank FE|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|
|SD(ExposureEnergyb,Aug14)|6.5|6.5|6.3|6.2|6.5|

28
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# **V. Discussion**

42. One may wonder if too many instruments are created in our system GMM (i.e., the problem of instrument proliferation). Especially in the specification including both time- and cost-based transport variables (i.e., the last column model in **Table 4** ), the number of constructed instruments reaches 312. When a large number of instruments are created as in the system GMM setting, the Hansen test statistics tend to be weak because the endogenous variables are almost perfectly fit at the first stage (e.g., Roodman, 2009). The robustness of the results is examined through reducing the number of instruments. With different sets of lags, the system GMM estimation is performed again ( **Table 5** ). It is confirmed that the estimation results are robust when the instrumenting variables are limited to the second to at least fifth lags. If the number of instruments is reduced further (e.g., the number of lags is equal to 3), the statistical validity of instruments would be lost. The Hansen overidentifying restriction test statistic is estimated at 224.59 and rejects the null hypothesis. The _p_ -value on the Hansen test is 0.02.

**<u>Table 5. System GMM estimation with different lags of instruments</u>**

||lags of instr<br> <br>|uments =<br>|3<br>lags of inst<br> <br>|ruments = 5<br>|lags of ins<br>|truments = 7<br>|
|---|---|---|---|---|---|---|
||Coef.<br>|Std.Err.|Coef.<br>|Std.Err.|Coef.|Std.Err.|
|ln_N_t-1|0.667|(0.064)|***<br>0.667|(0.064) ***|0.666|(0.064) ***|
|ln_N_t-2|0.174|(0.051)|***<br>0.174|(0.051) ***|0.174|(0.050) ***|
|ln_N_t-3|0.146|(0.049)|***<br>0.145|(0.048) ***|0.146|(0.048) ***|
|ln_HRREGION_|0.038|(0.064)|<br>0.040|(0.060)|0.032|(0.057)|
|ln_HRLOCAL_|-0.184|(0.059)|***<br>-0.172|(0.057) ***|-0.165|(0.056) ***|
|ln_TCREGION_|-0.031|(0.063)|<br>-0.029|(0.058)|-0.027|(0.056)|
|ln_TCLOCAL_|0.111|(0.063)|*<br>0.098|(0.060)|0.092|(0.058)|
|ln_POPD_|-0.076|(0.012)|***<br>-0.077|(0.012) ***|-0.078|(0.012) ***|
|_t_=2014|-0.060|(0.030)|**<br>-0.060|(0.030) **|-0.061|(0.030) **|
|_t_=2015|-0.116|(0.020)|***<br>-0.116|(0.020) ***|-0.116|(0.020) ***|
|_t_=2016|-0.110|(0.021)|***<br>-0.110|(0.021) ***|-0.110|(0.021) ***|
|_t_=2017|0.015|(0.020)|<br>0.016|(0.020)|0.016|(0.020)|
|_t_=2018|0.296|(0.028)|***<br>0.298|(0.029) ***|0.298|(0.029) ***|
|_t_=2019|0.170|(0.040)|***<br>0.172|(0.040) ***|0.173|(0.039) ***|
|_t_=2020|0.160|(0.032)|***<br>0.161|(0.032) ***|0.162|(0.031) ***|
|constant|0.344|(0.080)|***<br>0.350|(0.074)***|0.369|(0.073)***|
|Obs.|1,374||1,374||1,374||
|Wald stat|549658||552955||552778||
|No. of groups|255||255||255||
# **I. Introduction**

Over the past decade, Latin American and Caribbean countries achieved strong growth and shared prosperity. Yet, as commodity tailwinds have receded, South American countries in particular face the challenge of securing and expanding these social achievements in a sustainable manner, namely based on productivity growth. Spurring firm-level productivity upgrading in ways that ensure shared prosperity has become a top priority for the region.

The growth implications of technology adoption are well understood. Productivity upgrading through creative destruction is the main driver of long-run per-capita income growth. This dynamic involves the commercialization of frontier technologies, the adoption of existing improvements in managerial and production technologies by firms, and the reallocation of resources across industries and across firms within industries (Aghion and Howitt, 2009, Comin and Ferrer, 2013).

The distributional impact of technology adoption is more complex. The existing literature suggests that the impact is ambiguous. On the one hand, technology adoption can lead to income inequality if the benefits are disproportionately appropriated by owners of physical capital or managers and high-skill workers able to implement new technologies, without sufficient benefit to low-income or less skilled workers. At the limit, specific types of technology adoption that substitute machines for labor can make certain categories of workers redundant and unemployable (see Acemoglu and Autor (2011) and the related large literature on automation and skill-biased technological change). For example, technology adoption has recently been associated with a decline in mid-skill occupations relative to low- and high-skill ones in the United States (Autor and Dorn, 2013).

On the other hand, automation and other forms of technology adoption may complement labor, decreasing variable costs and increasing productivity, thereby raising output in ways that lead to higher demand for labor and increased earnings (Autor 2015). Such types of technology adoption and catch-up innovation that generate higher productivity, employment and sales, when spurred by a competitive business environment, have been recently shown to be associated with a larger employment share of low-skilled and female workers by an empirical study of over 26,000 manufacturing establishments across 15 OECD and 56 developing countries (see Dutz, Kessides, O’Connell and Willig, 2012). This finding suggests more inclusive firm growth from innovation and technology adoption. The use of the internet and the presence of job training programs are shown in these data to make significant contributions at every stage of the flow from ideas to inclusive employment growth.<sup>2</sup> And in a recent theoretical task-based model where labor has

> 2 The finding by Dutz, Kessides, O’Connell and Willig (2012) that new technology-adopting innovating firms have higher employment growth rates and employ a higher share of unskilled and female workers than non-innovating

2
**<u>Table 33: Cruise Lines and their Destinations in the Caribbean</u>**

|**Cruise Company**|**Capacity**|**Origin and**<br>**Destination **|**Caribbean Ports of Call included per Route**|**Duration **|
|---|---|---|---|---|
|AIDA Cruises|2500|Jamaica|Jamaica - Dominican Republic - St. Kitts and<br>Nevis - Dominican Republic – Belize - Guyana|14 nights|
|Carnival Cruise|3002|Puerto Rico -<br>Miami|Grenada - Dominica|8 days|
|Celebrity Cruise Lines|2449|Puerto Rico|Puerto Rico – Barbados -St. Lucia-|7 nights|
|Compagnie du Ponant|264|Curaçao,<br>Martinique|Grenadines - St. Lucia - Dominica|7 days|
|Costa Crociere|2828||Antigua|23 nights|
|Cruise & Maritime Voyages|848|United Kingdom|St. Vincent and Grenadines - St. Lucia -<br>Barbados|42 nights|
|Fred Olson Cruise Line|1350|Savannah, USA –<br>United Kingdom|Bahamas, The|35 nights|
|Holland America Line|1258|FT. Lauderdale|Bahamas, The|7 day|
|MSC Cruises|2550|Miami|Bahamas, The|7 nights|
|Norwegian Cruise Line|2376|Houston|Belize|7 days|
|Oceania Cruises|1250|Miami|Belize - Guyana|10 days|
|P & O Cruises|3100|Barbados|Barbados - Grenada - St. Lucia - Dominica - St.<br>Kitts - Barbados|14 nights|
|Princess Cruises|3100|Ft. Lauderdale|Ft. Lauderdale- Cayman Islands -> Mexico -><br>Ft. Lauderdale|5 days|
|Regent Seven Seas|490|Miami|Belize|7 nights|
|Royal Caribbean Int.|6296|Ft. Lauderdale|Haiti - Jamaica|7 nights|
|Saga Cruises|706|United Kingdom|Trinidad and Tobago - Jamaica - St. Lucia -<br>Antigua – The Bahamas|34 nights|
|Sea Cloud Cruises|96|Antigua|Antigua - St. Kitts and Nevis - Antigua|7 days|
|Sea Dream Yacht Club|110|Barbados|Barbados-> St. Lucia - St. Vincent and the<br>Grenadines - Barbados|7 days|
|Silversea Cruises|540|Ft. Lauderdale|St. Vincent and the Grenadines- Jamaica – The<br>Bahamas|8 days|
|Star Clippers|228|Barbados|Barbados - St. Vincent and the Grenadines -> St.<br>Lucia - Barbados|7 nights|
|Thomson Cruises|1506|Jamaica|Jamaica - St. Vincent and the Grenadines|7 nights|
|Crystal Cruises|1070|Miami – Costa<br>Rica|Belize|11 days|
|Cunard Line|2620|United Kingdom-<br>St Lucia|Dominica - St. Lucia|19 days|
|Windstar Cruises|148|Barbados|Barbados - St. Lucia - Grenada - St. Vincent and<br>Grenadines - Barbados|7 days|
|Seabourn Cruise Line|208|St. Maarten|Antigua|7 days|
|TUI Cruises|1807|Barbados - Spain|Barbados - Dominica -St. Kitts - Grenada - St.<br>Lucia - Barbados|22 nights|

_Source: Authors' compilations through the CLIA (n.d.), and cruise lines' web sites._ 78
-2-

can be addressed directly owing to th. lack of detailed data on this trade.<sup>4</sup> We concentrate on five EE countries, Bulgaria, Czech and Slovak -- Federal Republic (for brevity, Czechoslovakia), Hungary, Poland and Romania "East European Five" -- and as markets, the EC and the US, which account for about 80 percent of all OECD imports of textiles and clothing from non-OECD (plus Turkey) sources.<sup>5</sup> As comparators we use the "East Asian Four" composed of China plus Hong Kong and Taiwan (China), and the Republic of Korea (for brevity, Korea), representing the most dynamic exporters, and "all MFA suppliers".<sup>6</sup> For data, we mainly rely on the World Bank computer files on the MFA, and the COMTRADE Data Base from the United Nations Statistical Office (UNSO).7 In Section II of the paper, we analyze the impact of the MFA on the East European Five, relative to our comparator groups, using the indicators of restrictiveness developed in Erzan, Goto and Holmes (1990). In Section III, we briefly outline the factor proportions argument for the pattern of international specialization in trade. Against this background, we analyze the composition of the EE countries' textile and clothing exports to the industrial markets, in a comparison with other MFA suppliers, as a guide to future EE export patterns. A concluding section sums up our findings, considers some predictions, and discusses avenues for further research.

4 Similarly < cannot compute Balassa (1965) type indices of revealed comparative adva ge.

I During the period under study, 1985 to 1989, all the EE countries except Bulgaria were subject to MFA quotas in both the EC and the US. Although Bulgaria was never formally a party to the MFA, it faced similar quotas in both markets. As the bilateral MFA agreemeats expire, most will likely be renewed under the extended MFA IV. There may be exceptions, however. For instance, the US MFA agreement with Romania has not been renewed since it expired in December 1989.

6 In the US MFA, Japan appears as an exporter in addition to the developing countries. Excluding Japan, as well as Guam and the Northern Marianas due to data limitations, there are 44 developing country suppliers which are either subject to the MFA quotas or similar restrictions. We call this group "All MFA Suppliers" which includes the East European Four and the East Asian Four. In the EC market, this group previously consisted of 27 countries, but declined to 22 under MFA IV. See Raffaelli (1990) for the developed and developing country signatories of the MFA, and the bilateral restraint agreements concluded.

7 See Erzan, Goto, and Holmes (1990), Appendix A on data for a description of the World Bank computer files on the MFA.
5 (excluding autos); consumer goods (except autos); and automotive vehicles and parts.<sup>8</sup> While this typology makes possible to assess change in exports and imports in terms of increased or decreased processing, it does not give a more direct information on production sharing. The fall in the combined share of foods, feeds and beverages, industrial supplies and materials merely indicates that processed goods play a growing role in country's trade. But it remains unclear which portion of this trade can be directly attributable to the shift in stages of production (or value chains) across borders.

For instance, developments in CEEC-10 trade with the EU in terms of 'end-use' categories seem to suggest the growing importance of processed goods. The combined share of foods, feeds and beverages, industrial supplies and materials in both exports and imports dramatically declined between 1989 and 1997, and that of capital goods and automotive vehicle and parts significantly expanded (see Appendix Table 1). The picture is clear on the export side and less so on the import side; but the overall trend is that of convergence towards the EU composition of trade in terms of end-use categories. CEECs continue exporting relatively more of food, beverages and industrial supplies than the EU does, but the difference between their respective compositions has been on the decline. It fell from 11 percentage points in 1989 to 8 in 1993 and 4 in 1997. But as was pointed out earlier, it is not clear whether these changes can be attributed to production sharing.

There is a more direct way of estimating this trade. Yeats (1998) brought to attention a simple but overlooked fact-data required to estimate trade in parts have been available in foreign trade statistics based on SITC (Standard International Trade Classification) Revision 2 for the last two decades. Many countries shifted to the SITC Rev. 2 system in the late 1970s. The most complete coverage is within machinery and transport equipment group (SITC 7). It distinguishes among around 60 individual three, four, and five digit product groups consisting solely of other manufactured equipment. As far as other sectors are concerned, the SITC does not do a good job in distinguishing between assembled goods and parts. His empirical findings suggest that trade in parts was the fastest growing component in OECD trade over 1976-96; that it accounts now for around 30% of their trade; and that it is dominated by a very small number of product groups (motor vehicle parts, office equipment and telecommunication equipment).

> 8 The industrial supplies and materials include mainly raw materials but also some basic manufactured goods such as steel, newsprin **t** , extile yarns, etc. The capital goods are used for both investment and as intermediate products (all electrical parts and components except finished consumer goods are regarded as capital goods). The consumer goods are finished household products.
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