rafmacalaba/datause-encoder-data huggingface dataset:
# **DIFFERENTIAL ADAPTATION STRATEGIES TO CLIMATE CHANGE IN AFRICAN CROPLAND BY AGRO-ECOLOGICAL ZONES**<sup>1</sup>

Niggol Seo<sup>2</sup> , Robert Mendelsohn<sup>3</sup> , Pradeep Kurukulasuriya<sup>4</sup> , Ariel Dinar<sup>5</sup> , and Rashid Hassan<sup>6</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.. 2 School of Forestry and Environmental Studies, Yale University and consultant to the World Bank; 230 Prospect St. (Room 203), New Haven, CT 06511, USA, and a Consultant to the World Bank,; email- Niggol.seo@yale.edu

> 3 School of Forestry and Environmental Studies, Yale University and consultant to the World Bank; 230 Prospect St. (Room 207), New Haven, CT 06511, USA, and a Consultant to the World Bank,; email- <u>Robert.Mendelsohn@yale.edu</u>

> 4 Energy and Environment Group, Bureau of Development Policy, United Nations Development Programme, New York; phone 212-217 2512; email: <u>pradeep.kurukulasuriya@undp.org</u>

> 5 Development Research Group, World Bank, 1818 H St. NW, Washington DC 20433; phone 202-473-0434; email <u>adinar@worldbank.org.</u>

> 6 Department of Agricultural Economics, University of Pretoria, and Center for Environmental Economics for Africa; email Rashid.hassan@up.ac.za.
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## _2.2 Sroie Enterprise Adjustment_

The adjustment evidence presented here is based on a direct survey of 75 large SOEs picked from the Lisma 500. Poland's version of the Fortune 500. Our reason for restricting attention to large state companies is that these companies were expected to be the most resistant to reform. The enterprises were visited twice, once in mid-1991 and once in mid-1992. The data obtained cover the period June 1989June 1992, six months before the big bang of January 1. 1990, and 30 months into the reforms. The 64 enterprises responding to the repeat survey were classified into three groups depending upon their financial performance in 1992: AAA connoting positive retained earnings after paying all taxes; AA connoting positive pre-income tax profit but negative retained earnings; and A connoting negative preincome tax profit, i.e., loss-making enterprises.<sup>5</sup> There tumed out to be 31 AAA firms. 8 AA firms and 25 A firms. We focus on the AAA and A firms in our attempts to discover differences in response and the impact of governance. Annex A contains details on sample selection and the data collected.

# _Labor Shedding and Unit Labor Costs_

Table 1 shows that in the sample as a whole, labor was reduced by a phenomenal 27 percent : between December 1989 and June 1992. What is even more interesting is that the least profitable firns shed the most labor. This makes economic sense, but clearly contradicts the standard view on the modus operandi of state enterprises in post-commumist Europe.

Table 1: Index of Employment (September 1989= 100)

|_____|_____|<br>Dec-89|June-90|Dec-90|June-91|Dec-91|XJtue-92|
|---|---|---|---|---|---|---|
|AAA|[L01.7|95.1|93.4|87.4|-84.4|-79.4|
|AA|99.7|98.7|95.2|87.3|78.5|76.5|
|A|100 1|95.9|88.5|81.9|74.0|67.1|
|TOTAL|10.8|-95.8|91.3|84.7|78.8|73-2|

Real sales also fell after the big bang in 1990. There was an immediate and abrupt contraction in output and thereafter some recovery after mid-year. but 1990 ended with a 12 percent decline in GDP.

s The standard way of classiiring enterprises is by industrial sector. i.e.. chemicals, engineering, food processing, etc.. but this is not verV illuminating when studying adjustnent.
# School Participation & Progression - (Post-preprimary)

Study countrycode Type: of intervention ES (95% Cl) Outcome_varable High Income - Basic (Felfe et al 2014) ESP Subsidized Childcare & TT & Curriculum 0.11 (-0.23, 0.007, Falling behind a grade in primary* (12 y.0.) (Fetfe et al 2014) ESP Subsidized Childcare & TT & Curriculum -0.08 (-0.25, 0.087, Falling behind a grade at secondary* (15 y.0.) (Drange et al 2076) NOR Subsidized Kindergarten & Play Curriculum ~0.04 (-0.04, 0.027, School performance (test score) (15-16 y.o.) (Brotman et al. 2018) USA TT & Curmiculum & Parental Engagement 0.21 (-0.08, 0.5171, Academie performance (7 y.0.) (Cameiro and Ginja 201454 (H5} - Subsidized Preschool 0.97 (-0.13, 2.0777, In high school (16-17 yo.) Male (Gray-Lobe et al 2027) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.12 (-0.01, 0.2477, Enrollment in college (18 y.0.) (Gray-Lobe et al 2021) USA Subsidized Pre-K & TT & Coaching & Curriculum 0.15 (0.00, 0.30) T. Graduated high school (13 yo.) (Lipsey et al. 2078) USA Subsidized Pre-K, 70.03 (-0.11, 0.057, School attendance (8 yo.) (Lipsey et al. 2078) USA Subsidized Pre-K ~0.04 (-0.08, 0.077, School attendance (6 y.0.) (Lipsey et al. 2078) USA Subsidized Pre-K ~0.04 (-0.12, 0.047, School attendance (7 y.o.) (Watis et al 2013) USA (H5)}-TT & Coaching & Mental Health Counselling 0.19 (-0.01, 0.40)1, Self-reported GPA (10-11 y.0.) Low & Middle Income - Basic (Spier at al 2020) BED Subsidized Preschool (Additional year) (0.14 (-0.31, 0.58)7, Primary school enrollment (6 y.0.) (Brinkman et al. 2017) IDN Preschool Construction & TT & Community Outreach > (0.68 (0.60, 0.78) T, Enrolled in playgroup (8-0 yo.) (Brinkman et al. 2017) IDN Preschool Construction & TT & Community Outreach > 0.86 (0.74, 0.97) T, Enrollment duration in playgroup (months) (2-9 y.o. (Martinez etal. 2017) Moz Preschool Consiruction & TT & Parental Engagement > 0.21 (0.09, 0.32) T. Enrolled at primary school (5-8 y.o.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement ~> 0.19 (0.07, 0.30) T, Ever goneto primary school (5-9 yo.) (Martinez etal. 2017) Moz Preschool Construction & TT & Parental Engagement > 0.12 (0.04, 0.21) T, Appropriate grade for age (5-9 y.o.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement 0.41 (-0.17_ 0.38)T, Dropout from school* (5-8 yo.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement > 0.14 (0.05, 0.22) T, Schooling index (5-2 y.o0.) (Berlinski et al. 2008) URY Preschool Construction 0.04 (-0.03, 0.02), Years of schooling at age 7 (7 y.0.) (Berlinski et al. 2008) URY Preschool Construction -0.00 (-0.03, 0.027, Years of schooling at age & (8 yo.) (Berlinski et al. 2008) URY Preschool Construction 0.04 (0.02, 0.03)7, Years of schooling at age 9 (8 y.o.) NOTE: Weights are from random effects analysis -2.07 Lt) 2.07
3 banks to the exposure of all US banks that are not in the largest 24 (largeness is defined by asset size). Quarterly data over the 1986-1988 period for 43 countries is employed. In addition to a measure of concentration, two other types of variables are considered as possible determinants of secondary market prices: A first set of variables include borrower country characteristics that may function as indicators of a rountrvls repaymont prospects; suih AS deht ratios and GNP _rer_ capita.

Second, bank exposure to countries and bank capital aggregates--variables that may reflect regulatory features of the creditor's country--are slso considered.

Employing a Tobit model we find that the degree to which a country's debt is concentrated is an important and statistically significant detarminant of the secondary market price of that debt. Specifically, a higher debt concentration is found to imply a higher secondary market price. Our estimations suggest that as concentration is doubled from its sample mean of 5 to 10, secondary market prices show an increase of 7 cents to 30 cents on the dollar from their sample mean of 48.

Our finding of a positive effect of debt concentration on secondary market prices has a number of important implications for policy making and theoretical debates. The first lesson is that secondary market prices are not influenced solely by the "good" or "bad" behavior of the debtor countries. The degree of debt concentration is a feature that characterizes creditors: the structure of the banking system, the regulatory systems in the lender countries, and optimal portfolio considerations are among the factors that might lead to a particular distrioution of the debt among banks in the initial syndication of a loan. The contribution of each of these factors to debt
**Table 10: Regulatory Objectives and Instruments**

|**Objective**|**Instrument**|
|---|---|
|Financial Sustainability|Tariff levels and, where needed, subsidies|
|Allocative efficiency|Tariff structure|
|Social Equity||
|Productive efficiency|Tariff level reflects current efficiency, and tariff<br>regime incentivizes future efficiency gains.|

Source: Own elaboration The economic sustainability of the operator is driven mainly by the tariff level (or in other words the average tariff), which should allow an efficient firm enough revenue to cover its economic cost including a reasonable rate of return on its capital. In addition to this reasonable rate of return, it accounts for the invested capital assessed at reasonable values and for reasonable expected efficiency gains all of which are the responsibility of the regulator. If the tariff level does not cover costs, subsidies will be a complementary option. Their design may however have an impact on allocative efficiency.

Allocative efficiency and equity are influenced essentially by the tariff structure. To achieve allocative efficiency, charges should reflect the long-run marginal cost of supplying each consumer. However, to meet the objective of financial sustainability, charges will need to be higher than long-run marginal cost. To minimize the behavioral response associated with tariffs above long-run marginal cost, and so to maximize the utility’s revenues, the necessary mark-up should be inversely proportional to demand elasticity.<sup>11</sup> However, this may often result in relatively higher charges to residential consumers who tend to have lower price elasticities of demand, raising concerns about social equity, which is the other objective that needs to be met through the design of the tariff structure. The trade-off becomes evident, as does the level of challenge involved in designing a good tariff structure.

In practice, the structure may be differentiated in many dimensions: across clients, regions, between fixed and variable charges, according to the consumption level, etc. An optimal structure will depend on both: demand and supply considerations. From the demand side price and income elasticities for different types of users, willingness and ability to pay, costs of substitutes, consumptions patterns over time (day, season), etc., are all elements that need to be taken into account. From the supply side costs of serving different types of users, cost patterns over time (day, season), fixed vs. variable costs, and the allocation of common and joint costs are among the main elements affecting the tariff structure.

In general, the service provider will have better information than the regulator on these variables, and this is why in many cases decisions on the tariff structure are left with the operator. But, with social concerns in mind, the regulator often issues some guidelines because an operator concerned only with maximizing revenues may end up charging tariffs that are socially inequitable.

> 11 This is known as the Ramsey principle.

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change in the poorest decile’s welfare equal to 14 percent and negligible proportions for intermediate deciles. Of course, it represents a loss for the top deciles approximately equal to the share of ODA in the GNI of donor countries, that is, approximately 0.25 percent in 2002.<sup>16</sup> As a consequence, only international inequality indices which are very sensitive to the relative income of the poor are significantly affected by the international redistribution through aid. The Gini coefficient increases by .44 percentage point, from its base case value of 0.538, when aid is netted out. By contrast, the mean logarithmic deviation increases by almost 2 percentage point, from the base case value of 0.5316.

Interestingly, nearly half the .44 percentage point reduction in the Gini coefficient is removed when technical cooperation and debt relief are deducted. This is not surprising given that nearly half of all grant aid in 2002 was in the form of either technical cooperation or debt relief. In figure 2, we see that nearly half the impact on mean GNI per capita in the first decile is also eliminated. Nevertheless, the increase of nearly 8 percent for the bottom decile is still fairly substantial.

The preceding results should not be over-emphasized, however. Even when focusing on the change in the welfare of the poorest decile or considering poverty sensitive inequality measures, estimates of the effect of redistribution through aid are dwarfed when compared to the extent of redistribution that takes place within countries equipped with effective redistribution schemes.<sup>17</sup> Thus, the international redistribution that is taking place in the

> 16 Note that there is little difference between the three scenarios for the rich countries.

> 17 By comparison, tax and transfer programs in the United Kingdom lower the Gini coefficient from 0.53 to 0.40 – a full fifteen percent reduction—during the 1990s (Lakin 2001).  In France, the system of tax and transfers raises the final income (per adult equivalent) of the poorest 20 per cent by nearly 70 percent,  several other European countries having an even higher degree of overall redistribution (Bourguignon 1998).  In the developing country context, Mexico’s public expenditures (including in-kind services) decrease the Gini from 0.48 to 0.39, an 18% reduction in inequality  (World Bank 2004a). Even though these various studies adopt different definitions of redistribution and are not strictly comparable, they show how important the scope for redistribution may be at the national level..

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Mancini (2019).

Finally, the segmentation of production across countries does not only impact the synchronization of GDP but also inflation (Auer et al. (2017), Auer et al. (Fort)). GVCs also impact the trade elasticities (Amiti et al. (2014), Gunnella et al. (2019), de Soyres et al. (2018)) as well as the extent to which regional trade agreement divert trade flows with the rest of the world (de Soyres et al. (2019)).

# **2 Data Sources**

**GDP.** We use GDP data from the Word Development Indicators of the World Bank. It is measured using constant 2010 prices in US dollars.<sup>8</sup> For our analysis, GDP series need to be filtered in order to extract the business cycle component from the trend. We use three different filtering methods that capture different but related information. Our main filter is the standard Hodrick-Prescott filter (HP) with a smoothing parameter of 100 which is consistent with the yearly frequency of our data. Such a transformation allows us to capture the standard business cycle fluctuations.<sup>9</sup> Second, we also use a Baxter and King filter (BK) to isolate medium-term fluctuations in the spirit of Comin and Gertler (2006a).<sup>10</sup> Finally, we also present results with a more “agnostic” transformation which is a simple log-first difference. Such a measure of growth rate accounts for both the cyclical and the trend components embodied in any year-toyear fluctuation, but it is sometimes considered as less sensitive to researcher’s assumptions and preferences regarding the parameters of the filtering method.

**Trade Proximity.** We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014. The data are classified according the 4-digit Standard International Trade Classification (SITC), Revision 2. Only products and commodities are considered. Then, we aggregate these bilateral trade flows at the country-level.

To classify trade flows into final and intermediate goods, we use a concordance table from SITC Rev. 2 to Broad Economic Categories (BEC).<sup>11</sup> We then classify goods into five categories: primary, semi-finished goods, parts and components, capital goods, consumption goods, and a residual category called goods non-specified. We group these categories into intermediate

> 8We used the data serie called “NY.GDP.MKTP.KD”

> 9With this setting, we mostly keep fluctuations that have a frequency between 8 and 32 quarters.

> 10We keep fluctuations between 32 and 200 quarters, following Comin and Gertler (2006a)

> 11The concordance table from SITC Rev2 to BEC can be found on the UN Trade Statistics webpage: `https: //unstats.un.org/unsd/trade/classifications/correspondence-tables.asp` .

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# IV. The Structure of Asian Trade Barriers

The analysis to this point has demonstrated that Asian commodity imports reflect a strong bias against processed goods (Table 1), and that the bias against Asian exports of processed commodities is greater in intra-Asian trade than in non-regional markets (Box 2). Furthermore, between 1970 and 1990, Asian markets' import bias showed no evidence of narreiwing relative to other markets (Table 2). These facts raise the question of whether, and to what extent, this sub-par performance can be accounted for by escalation in Asian trade barriers.<sup>9</sup> Table 5 draws on GATT tariff data for 10 Asian countnies in order to provide relevant information." The table shows the average import duty each country applies to the primary stage item in the 48 commodity chains, as well as the tariff on the final stage product. Tariff averages have been computed for all 48 commodity chains and their differences are used as an overall measure of escalation for each market.

It is clear from Table 5 that ther are major differences in Asian tariff levels, and that tariff escalation occurs in most processing chains. Concerning the first point, GATT records indicate that Hong Kong has no tariffs on any primary or processed commodity (for this reason Hong Kong is not included in Table 5), while Singapore has minor duties of under 4 percent on seven processed commodities (refined petroleum is an exception with a 10 percent tariff). In contrast, tariffs of over 30 percent occur in Australia, Indonesia, Japan, Korea and 'It mw: te acknowledged that studies have identified numerous other problems besides external trade barriers that developing countries face in trying to increase processing of domestically produced commodities. These include: inappropriate economic policies (import substitution) pursued by the country itself; insufficient access to intemational capital or technical markets; tansnational corporation policies; monopoly pricing practices of liner conferences that may increase freight costs; and anticompetitive practices of established firms in major OECD markets. For a survey of these problems see UNCTAD (1979). It should also be acknowledged that some countries' comparative advantage may be in exporting unprocessed commodities and not in processing these commodities.

'The underlying tariff data were compiled by GATT and then recorded in the UNCTAD-World Bank SMART system. No data on China's tariffs were collected since this country currently is not a member of the General Agreement. Due to the magnitude of the effort required to compile matched trade and tariff statistics for a single country - more than 12,500 tariff line level products may be involved - GATIT did not attempt to compile statistics for several of the smaller Asian countries that are GATT members. UNCTAD's Inventory of Trade Control Measures was the source of information used in this study on nontariff barriers - see Table 6.
costs have clearly outweighed the possible benefits. The drug money provides funds for corruption, **and it** also causes troubles for making of monetary policy and for managing of the exchange rate. (2) The earnings from both oil and coffee exports are subject to world oil market shocks which have happened several times since the 1970s. The ambitious spending plan for development could become a burden once the oil revenue is negatively affected.

## Reference

1. World Bank. _Country Brief: Colombia._ March 5, 1996.

2. Economic Intelligence Unit. _Country Profile: Colombia._ London, United Kingdom. 1992/93, 1993/94, 1995/96, & 1996/97.

# **India**

# **Chronology of Development Strategy and Recent Economic Reforms**

### **Development Strategy before 1980s**

India's approach to economic development had until recently been highly interventionist and inward-oriented. In the mid-1950s, India initiated the trend among newly independent nations in selecting the economic strategy of state-led industrialization or SLI, characterized by economic planning, high protectionism, and extensive state regulation of the economy. In most of the four decades following independence, India had relied heavily on a complex system of industrial licensing, high protection against imports, and extensive government intervention in financial intermediation.

The choice of SLI in India and elsewhere is understandable given the choices facing the leaders of developing countries at the end of World War 11. By 1949, only a handful of countries had convertible currencies, and global trade was managed through national governments, and accounted for a small fraction of national income. At the same time the industrialization of the Soviet Union also point to a model of development based on state-ownership and extensive barriers to trade. (Bajpai and Sachs, 1996)

## **Economic development before the June 1991 Crisis**

During the 1980s, the government started to liberalize trade, industrial and financial policies. Export incentives were improved by subsidies, tax concessions and the depreciation of the currency. These measures helped GDP growth to accelerate to over 5 percent annually during the 1980s and reduced poverty more rapidly. However, tariffs continued to be extremely high and quantitative restrictions remained pervasive. State owned banks continued to dominate the banking system and public enterprises (which generate 17 percent of GDP) continued to operate very inefficiently.

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not accompanied by an increased intensity. This may be seen as a sign that the attention to the SCST priorities is met only in form and does not a¤ect their predominance in the deliberations.

In Table 9 we decompose the discussion by the gender of the speaker. The ...rst notable result is that within women’s talk, the preferences of women take up more time (column (2)). This e¤ect is particularly important in the light of the measures, such as political reservations, taken by the Indian government to promote the political participation of women. In a related paper, using the same transcript data, we have found that in villages where the position of Gram Panchayat president is reserved for women, women to tend to talk more during the village meetings (Ban and Rao 2008b). This ...nding implies that a¤ording voice to the women has real bene...ts for the women’s community. A similar result was found by Chattopadhyay and Du‡o(2004b): in constituencies reserved for women the public goods investments re‡ect the preferences of women. The second notable (non)result is that within women’s talk, the e¤ect of landowership disappears. This may be interpreted as women’s talk being insulated from the traditional power of the landed class. The e¤ect of landownership is present within men’s talk, but only in the indicator equation. Another interesting result is the age e¤ect within men’s talk. Older individuals are less likely to have their preferences mentioned when men are speaking.

In Table 10 we examine the e¤ect of individual characteristics on the likelihood of a decision being reached with regards to one’s preferred topic. We ...nd that again, owning more land increases the likelihood of having one’s preference decided upon. When we distinguish between for and against decision, we ...nd that the land e¤ect is driven by the for decisions. Speci...cally, owning 10 more acres of land increases the likelihood by 2.5 percent (2.7 percent within for decisions). This ...nding further emphasizes the power of the landed class in the 17