rafmacalaba/datause-encoder-data huggingface dataset:
Knowledge is at the heart of the World Bank’s comparative advantage as a development institution. Research and data collection are an essential component of the Bank’s core countrybased programs and the leading edge of the Bank’s contribution to the global public goods agenda. To maintain its usefulness, however, knowledge must grow as new challenges arise and as new findings confirm or call into questions previous ideas. And knowledge must be used well to be useful for development.

Development economics, both within and outside the Bank, faces many challenges in its quest to inform development policy making. Much has been learned about development from research and operational experience. Development data are also more available and accessible than in the past. However, some observers are asking whether development economics in its present form is well positioned to tackle the most urgent issues of the day. Even before the current global crisis, we had a sense that the course of development economics needed rethinking. The crisis has only made this more compelling.

- It is now clear that the global economic and financial architecture is itself a development issue.  How this evolves after the crisis will influence the progress of developing countries as well as growth in the emerging multi-polar world economy.  Looking forward, developing countries will be more active participants in the new financial system.

- There has been enormous overall progress against extreme poverty in the world, helped by faster economic growth in developing countries.  The number of people living below $1.25 a day (at 2005 purchasing power parity) is estimated to have fallen from 1.9 billion in 1981 to 1.4 billion in 2005. There has also been great progress overall in improving health and education. However, progress has been uneven across countries and over time. For example, excluding China, the number of people living below $1.25 a day has stayed around one billion or more since the early 1980s (though of course the proportion of people who are poor has fallen).

- The wave of trade reform that swept the developing world—with the move away from exchange rate overvaluation, export taxation of agriculture, use of nontariff barriers, and with a two-thirds reduction in average tariffs—has been associated with a dramatic

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gravest shape. A survey conducted during the 1990/91 season had found that, depending upon location, some one-third to one-half of farmers with cashew shambas were not harvesting nuts (see Table 9). Even those who did harvest faced severe sales problems. By July 1991 (some two to three months after the expected end of the buying season), still 30% of the crop had not been purchased due to cash shortages. The government needed to intervene with the banks to clear the crop. Especially in the southern zone, where, depending upon location, cashew nut sales account for between 36% and 80% of total farmer crop incomes, the decline in production and the inefficiencies in marketing were contributing to 'ignificant decline in living standards. At the same time, the government's huge investment in processing facilities was bringing minimum return, with most factories lying idle.

Table 9: Importance of Cashews and Cashew Sales in Tanzania's Southern Zone

|Area<br>l__________|%of<br>Households<br>with<br>Cashews|Of Those<br>with<br>Cashews, %<br>Harvesting in<br>90/91 Season<br>___________|Of Those<br>With<br>Cashew<br>% Sold<br>Cashews<br>________|Of Those<br>With<br>Cashes<br>% Use<br>Cashews<br>for Food|Mean<br>Cashew<br>Income<br>(Tsh.)|Mean<br>Total<br>Income<br>from Crop<br>Sales<br>(Tsh)|Cashew<br>Sales as<br>% of<br>Total<br>Crop<br>Sales|
|---|---|---|---|---|---|---|---|
|E.Makonde|70|64.|59|40|7436|12630|_59_|
|W.Makonde|96|83|75|80|24319|28988|84|
|S.Masasi|73|77|73|67|13531|21804|62|
|Tinduru|81|44|40|35|6880|19007|36|
|Lindi/Nach./<br>NEMasasi|73|60|_53_|45|6218|10244|61<br>l|
|Coastal Plain|75|65|61|29|7473|10937|68|

Source: ODA Cashew Research Project, Ai culturM **a** rketin l Suwvey X1991 The government's original intension was that the TCMB would retain a monopoly on export marketing, yet primary marketing would be liberalized. The Ministry of Agriculture issued its guidelines in July 1991, leaving only three months for preparation before the beginning of the marketing season. These guidelines were vague and were announced only through the media (e.g. radio and newspapers), with no official documentation being sent to the regional and district authorities who would be responsible for implementing and monitoring the structural changes. Local authorities thus were given no explanation of the rationale behind the policy changes and were largely unaware of the structural changes envisioned within the marketing system. Implementation of the guidelines was seemingly left at the discretion of local authorities.

This weak communication, combined with the conservative attitude of some local authorities and the vested interests of the cooperatives, caused a delay in the registration of private cashew traders and resulted in ad hoc procedures for doing so. In some regions, local officials viewed would-be private traders not as competitors with the cooperatives, but as supplementary actors in the procurement of the 23
La Porta, Lopez-de-Silanes, and Shleifer (1998) also examines the means through which control is enhanced. The study shows that owners extend their resources through the use of pyramiding and management appointments, as well as through frequent crossownership and the use (less frequently) of shares that have more votes. Another interesting pattern is also documented: control of East Asian corporations can be achieved with significantly less than an absolute majority share of the stock, as the probability of being a single controlling owner through holding only 20% (or more) of the stock is very high-above 80% across the four East Asian countries.

The previous research leaves unanswered several questions. First, are there any differences in the patterns and distribution of control across the East Asian countries, including less developed ones? Second, are there within-country differences in the concentration and distribution of control? Third, do within-country differences depend on size or age of the corporation? Fourth, to what extent is corporate control concentrated in the hands of particular families and is the relation between business and government very strong? Finally, if such differences in ultimate ownership exist across countries, what are their determinants? The answers to some of these questions have strong implications for the level of transparency, openness and market-based transactions in East Asian economies.

# **III. Construction of the data**

The analysis in the following sections is based on newly-assembled data for 2,980 publicly-traded corporations (including both financial institutions and non-financial institutions) in Hong Kong, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore, Taiwan, and Thailand. As our starting point in the data collection, we use the Worldscope database which generally provides the names and holdings of large owners. Worldscope has over 8,000 publicly-traded firms in the nine East Asian countries, but only 2,300 companies provide detailed ownership information. We supplement the data with information from the Asian Company Handbook 1999 (1998), the Japan Company Handbook 1999 (1998), the 1997 Annual Reports of the Hong Kong, Jakarta, Seoul, Kuala Lumpur, and Manila Stock Exchanges, as well as with ownership data from the Korean Fair Trade Commission, the Securities Exchange of Thailand Companies 6
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length by two years--thus increasing the average yields of cane. In terms of the earlier discussion, such changes shift the supply curve rightwards. As discussed in the Appendix 2, and in line with earlier discussion, government ownership of mills and ratooning length are important factors affecting cane supply. Changes in these factors with a move to free trade are representative of the types of supply shifts likely to arise from easing of regulations and controls which presently underpin interventionist policies.

42. The results of the free trade scenario are given in Table 6. Compared to Table 5, production, trade and stocks are more variable and consumption is curtailed. Production is much higher with the result that import demand is eliminated and replaced by significant exports. By adding to world supplies, the average world price declines. The effect of these changes on the welfare of producers, consumers, the government and the Mexican economy in general are given in Table 7. In the case of welfare changes in producer rents, account is taken of the increase in growing cost likely to arise from reducing the number of ratoons and increasing the proportion of plant cane in the composition of harvested cane. Costs of growing plant cane are greater than for ratoon cane. From industry data it was estimated that reducing the number of ratoons by two would increase the production costs by about USCO.5/lb-which must be offset against the gains from the increase in yield.

43. The welfare measures in Table 7 were calculated in the manner discussed in Section III. For computational ease, the welfare effects (producer rents) of the supply shifts were calculated by solving for the equivalent cane price increase needed to induce a supply response equal to
percentile of the mode price of soybean across _mandis_ in May 1998 is Rs.775/Qtl. and Rs.1002/Qtl. respectively. I examine the amount of price dispersion by computing the standard deviation and the coefficient of variation of mode price across _mandis_ located in a district within a given month.<sup>39</sup> These two complementary measures are commonly used in the literature (Alan Sorensen 2000). The coefficient of variation, defined as (sd/mean), is a unit free measure of relative price dispersion. With an average of six _mandis_ located in each district, the sample is collapsed to observations at the district-month level. Table 5, shows that the mean coefficient of variation of price across _mandis_ in a district declines on average after internet kiosks are introduced. The coefficients in columns (1)-(2) are negative and significant but very small. Similarly, the standard deviation in the price of soy across _mandis_ is also decreasing post intervention, suggesting a reduction in spatial price variability.

[Insert Table 5 about here]

# _C. Output Response_

If the average price of soybean in the _mandis_ increased after the introduction of the kiosks, it is important to examine the output response on the part of farmers. The presence of kiosks and hubs may create entry and exit in response to an increase in the price. To the extent that farmers have an incentive to expand area and production of soy, we might expect a change in the mix of crops grown by farmers. This can be directly tested by using annual district level production data to estimate the following equation: where _yjt_ is the outcome of interest for district _j_ in year _t_ . _δj_ ’s are a full set of district fixed effects that control for time invariant district characteristics. _νt_ ’s represent a full set of year fixed effects controlling for climate shocks and other secular changes in outcome variables that are common across districts of the state. District-specific linear time trends ( _tωj_ )

> 39It is important to point out that inter-district spillovers, would result in underestimation of the impact. Nevertheless, within district comparison remains a useful starting point to estimate the baseline effect of the intervention on price dispersion.

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<!-- Start of picture text --> Costa Rica<br>"precios"<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20 Series Average<br>10 A,N,N<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>80<br>60 Unsystematic Part<br>40<br>20<br>0<br>20<br>40<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Figure 3: Smoothed results for the average of the “precios” keyword in Costa Rica. The top pane contains the original series and the smoothed, in-sample forecasted series. The forecasted series is labeled _A, N, N_ indicating additive errors, no trend, and no seasonality according to the Hyndman et al. [2002] taxonomy. The bottom pane contains the unsystematic or “surprise” component of the series.

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|Exlanator variables||Depe|ndent variable i|s thegrowth of|per capita incom|e(LogYt– Log|Yt-1)||
|---|---|---|---|---|---|---|---|---|
|py|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|Model 8|
||(0.010)|(0.009)|(0.112)|(0.012)|(0.011)|(0.010)|(0.012)|(0.009)|
|Lagged share of urban population|0.002***|0.002***|0.002***|0.034***|0.002***|0.002***|0.003***|0.002***|
||(0.001)|(0.001)|(0.001)|(0.009)|(0.001)|(0.001)|(0.001)|(0.001)|
|Lagged share of population with high-school<br>diloma|0.111|0.100|0.105|0.067|3.171|0.108|0.071|0.152|
|p|(0.192)|(0.176)|(0.188)|(0.177)|(3.373)|(0.205)|(0.166)|(0.213)|
|Lagged share of ethnic minority population|-0.001**|-0.000|-0.001*|-0.000|-0.001**|0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.004)|(0.000)|(0.000)|
|Lagged Gini index|-0.409**|-0.390**|-0.419**|-0.574***|-0.460**|-0.432**|-0.192|-0.421**|
||(0.188)|(0.171)|(0.201)|(0.122)|(0.183)|(0.202)|(0.208)|(0.208)|
|Lagged poverty rate|-0.074|-0.056|-0.047|0.091|-0.032|-0.117|-0.098|0.148|
||(0.096)|(0.121)|(0.106)|(0.083)|(0.115)|(0.111)|(0.091)|(0.149)|
|Lagged share of wage income|0.091|0.116|0.095|0.034|0.079|0.088|0.209**|0.037|
||(0.108)|(0.100)|(0.105)|(0.110)|(0.106)|(0.108)|(0.099)|(0.099)|
|Lagged share of non-farm income|-0.223*|-0.221|-0.258**|-0.260**|-0.202|-0.227*|-0.224|-0.254**|
||(0.122)|(0.139)|(0.131)|(0.131)|(0.147)|(0.137)|(0.142)|(0.129)|
|Constant|3.750***|1.794|3.011***|2.487***|3.224***|3.544***|3.529***|3.273***|
||(0.904)|(2.396)|(0.536)|(0.434)|(0.784)|(0.578)|(0.551)|(0.456)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|567|567|567|567|567|567|567|567|
|R2/ Chi2|3446|3460|3666|3055|3255|3335|3043|2985|
|Number of provinces|63|63|63|63|63|63|63|63|
|Arellano-Bond test for AR(1)|-4.913|-4.915|-4.998|-5.048|-5.176|-5.363|-4.898|-5.006|
|Arellano-Bond test for AR(2)|1.172|1.243|1.234|1.131|1.193|1.031|1.402|1.503|
|Sargan test|173.5|175.8|195.9|151.5|161.6|147.5|174.1|194.9|

Note: i) Authors’ estimation from VHLSSs using province GMM models, ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01, ** p<0.05, * p<0.1.

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The HHI considers the portion of a household’s income generated from each income source. In calculating the HHI, we include all revenue generated by households but do not net out costs of production. The HHI is calculated as: where _M_ represents each household’s total number of income sources. Each _pm_ represents the percentage of the household’s income generated from income source _m_ . A highly specialized household with only one income source would receive the highest possible score of 1 (1<sup>2</sup> ). Similarly, a household with two income sources each accounting for 50 percent of household’s total income would receive a score of .5 ( _._ 5<sup>2</sup> + _._ 5<sup>2</sup> ). As with the simple fractional index, higher scores indicate more income specialization and less diversification. The HHI includes only pre-COVID data and so there are no time sub-scripts. To generate the index, we use 12 income categories standardized across countries, and the respective amount of income each household earns from each source. These categories are: (1) remittances, (2) in-kind assistance from family and friends, (3) investments and savings, (4) income from properties, (5) pension, (6) non-farm enterprises, (7) crop sales and consumption, (8) livestock sales, (9) livestock product sales and consumption, (10) wages, (11) government and NGO assistance, and (12) other.

Before proceeding, it is necessary to comment on the limitations of our measure. First, when it comes to survey-based measures of household finances, asking questions about consumption is generally preferred to asking questions about earned income. Wealthier households tend to underreport earned income (Carletto et al., 2022). In this study, however, we examine specifically the sources of income, and so a consumption approach would provide no information on the sources from which a household earns its livelihood. Second, diversification as a coping strategy is not about how many different sources of income one has but about spreading the risk by diversifying to income sources with different risk profiles. To that end, one would want to have information on crop choice and the sectoral composition of wages. Unfortunately, the data, particularly the HFPS data, does not capture this information. Third, the way in which we have measured diversification cannot distinguish between voluntary and involuntary changes in diversification. For example, a person may be separated from a job, which would appear to be a decrease in diversification, though an involuntary one, not a deliberate coping strategy. Ideally, we could differentiate between involuntary and voluntary diversification actions, but we can only observe the level of diversification and the subsequently related associated food insecurity. Finally, as job searches, crop production, and starting a new business typically extend over several months, one would want to have data over a long enough time frame to adequately allow for inter-household adjustments to livelihood sources. While our data span a time period of more than two years, we acknowledge that this time relates to food insecurity.

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_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 52 predictor is not based on any optimality criteria, so good properties are not guaranteed. As discussed in Chapter 5, Pfeffermann and Sverchkov (2007) proposed the sample likelihood approach that adjusts the likelihood for the selection process and obtained adjusted EBLUPs of area means. Cho et al. (2024) extended the approach of Pfeffermann and Sverchkov (2007) to the case of general area indicators, obtaining EB predictors under informative sampling. In simulation experiments, Cho et al. (2024) show how the adjusted EB predictors reduce design bias under informative selection of units within areas, but also showcased a surprisingly good behavior of the Pseudo EB predictor, which does not make model assumptions about the survey weights. Cho et al. (2024) also propose a parametric bootstrap procedure to estimate the MSE of adjusted Pseudo EB predictors, which performs well in simulations for sampled areas.

In the case of very complex non-linear indicators, where the EB/CEB predictor has no closed form, the complete MC simulation procedure for EB/CEB, along with the bootstrap procedure for MSE estimation described above, are computationally demanding. Hierarchical Bayesian procedures avoid the application of resampling procedures for MSE estimation, since they provide samples from the posterior distribution of the target indicators, from which posterior variance or credible intervals can be obtained directly. Hence, Bayesian SAE procedures can be computationally faster for large populations. However, when estimating living conditions of people, Bayesian procedures need to be applied with caution. To minimize the influence of priors on the final results, only noninformative priors are recommended for this kind of application. Molina, Nandram and Rao (2014) proposed a hierarchical Bayes (HB) version of the EB procedure based on non-informative priors. This HB procedure yields practically the same point estimates as EB and avoids the convergence issues of Markov Chain MC (MCMC) procedures. A Census HB counterpart is straightforward.
from the baseline in 2030. Since domestic production would be more expensive due to carbon tax, Annex I countries’ exports would drop by 5 percents in 2030.

Since the emerging developing countries, such as India and China, are planning to reduce their CO2 emissions per capita GDP, they are expected to scale up deployment of clean energy technologies and substitute towards cleaner fuels. This could lead to a decrease in emission intensity unless the income effect (i.e., the rate of increase in GDP per capita) is greater than intensity effect (i.e., the rate of decrease in emission per capita GDP). In such case, the gap in emission intensities between the developed and developing countries may not converge unless developed countries reduce their emissions at levels much higher than estimated in this study.

# **References**

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**den Elzen, Michel and Malte Meinshausen** . 2006. ―Meeting the EU 2 Degrees Celsius Climate Target: Global and Regional Emission Implications.‖ _Climate Policy_ 6 (5): 545-64.

**Dimitrov,  Radoslav S.** 2010. ―Inside Copenhagen: The State of Climate Governance.‖ _Global Environmental Politics_ 10 (2): 18-24.

**Gaisford, James** . 2010. ―From Kyoto to Copenhagen: Meeting the Climate Change Challenge.‖ _Estey Centre Journal of International Law and Trade Policy_ 11 (1): 227-41.

**Intergovernmental Panel on Climate Change (IPCC)** (2007), Climate Change 2007:

Mitigation of Climate Change, Working Group III contribution to the Intergovernmental Panel on Climate Change Fourth Assessment Report, Summary for Policy Makers, www.ipcc.ch

**International Energy Agency (IEA)** . 2011a. CO2 emissions from fuel combustion (2011 edition), IEA, Paris.

**International Energy Agency (IEA)** . 2011b. World Energy Outlook 2011, IEA, Paris.

**Lecocq, Franck and· Jean-Charles Hourcade** . 2012. ―Unspoken ethical issues in the climate affair: Insights from a theoretical analysis of negotiation mandates.‖ _Economic Theory_ 49:445– 471.

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