rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5-b: Stock Market Regression Results (2000-2006)**

||Model (11)|Model (12)|Model (13)|Model (14)|Model (15)|Model (16)|Model (17)|Model (18)|
|---|---|---|---|---|---|---|---|---|
||Stock market<br>capitalization|<br>Log value<br>traded|Number of<br>listed<br>companies|Stock market<br>cap. of 10<br>largestfirms|<br>Percentage<br>of closely<br>held shares|Value public<br>offerings to<br>GDP|Stock price<br>synchronicity|Stock market<br>turnover|
|Log GDP per capita|0.565<br>[0.027]***|1.311<br>[0.045]***|0.548<br>[0.029]***|0.02<br>[0.011]*|-0.099<br>[0.028]***|0.36<br>[0.117]***|0.047<br>[0.024]**|0.689<br>[0.034]***|
|Offshore|0.694|0.272|0.572|-0.468|-0.481|0.613|0.389|-0.208|
||[0.206]***|[0.338]|[0.185]***|[0.081]***|[0.148]***|[0.550]|[0.134]***|[0.226]|
|Log fuel|0.003|-0.097|-0.072|-0.003|0.075|0.169|-0.027|-0.075|
||[0.022]|[0.038]**|[0.024]***|[0.012]|[0.031]**|[0.088]*|[0.029]|[0.028]***|
|Log poverty gap|0.165<br>[0.053]***|-0.507<br>[0.088]***|-0.4<br>[0.056]***|0.077<br>[0.023]***|-0.159<br>[0.072]**|0.271<br>[0.305]|-0.193<br>[0.053]***|-0.52<br>[0.066]***|
|Log population density|0.121<br>[0.022]***|0.155<br>[0.037]***|0.105<br>[0.024]***|-0.016<br>[0.009]*|0.049<br>[0.015]***|0.158<br>[0.049]***|0.084<br>[0.016]***|0.062<br>[0.028]**|
|Log population size|0.138|0.696|-0.327|-0.016|0.071|0.038|0.15|0.535|
||[0.023]***|[0.039]***|[0.024]***|[0.010]|[0.024]***|[0.075]|[0.021]***|[0.029]***|
|GDP per capita *|-0.004|-0.017|0.007|-0.017|-0.015|-0.007|-0.009|-0.012|
|population size|[0.004]|[0.006]***|[0.004]*|[0.001]***|[0.002]***|[0.007]|[0.002]***|[0.005]***|
|Year-fixed effects?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|665|670|704|362|264|282|292|676|
|Adjusted R-squared|0.5|0.64|0.6|0.49|0.47|0.15|0.34|0.53|
|Standard errors in bracket<br>* significant at 10%; ** sig|s<br>nificant at 5%; **|* significant a|t 1%||||||

20
The general financial conditions affecting global growth are represented using cross-country term spreads and the S&P 500 index prices. High-interest rate term spreads are assumed positively linked with output growth. This is in line with the following authors: Harvey (1989), Estrella and Hardouvelis (1991), Haubrich and Dombrosky (1996), Stock and Watson (2003), Wheelock and Wohar (2009), Aretz and Peel (2010), and Kao, Kuan, and Chen (2013), among others. The stock market performance measured by the S&P 500 index prices—and other equity indexes—is assumed to be positively associated with anticipated output growth; as per Mitchell and Burns (1938), Fama (1981), Fischer and Merton (1984), Harvey (1989), Jay Choi, Hauser, and Kopecky (1999), Hassapis and Kalyvitis (2002), and Tsouma (2009), among others.

Energy sector performance—measured via crude oil and gas prices—is assumed to be negatively associated with output growth. This association is in line with Hamilton (2005), Jimenez-Rodriguez and Sánchez (2005), Blanchard and Gali (2007), Blanchard and Riggi (2013), among others. Absolute deviations of CPI inflation targets are negatively associated with GDP growth performance. In this empirical application, the assumed benchmark inflation target is fixed at two percent across the study countries.

Based on data origination, the four selected risk factors affecting world GDP growth are divided into two categories: i) survey-based; and ii) market-based. The survey-based risk factors are a) the term spread, and b) the CPI inflation. The survey-based observations are collected by Consensus Economics. The market-based risk factor information is collected using Bloomberg. implied volatilities of option prices. This information is put together for the S&P 500 index, and the West Texas Intermediate (WTI) and Brent crude oils. As previously mentioned, elicited most likely forecasts of world GDP growth are obtained from World Bank (2016).

19
# **Table 1: Countries exempted from the prohibition of export subsidies in the WTO Agreement on Subsidies and Countervailing Measures Least developed country WTO Members Members with a per capita GNP<$1,000**** **<u>(SCM Art. 27.2(a) and Annex VII(a)) (SCM Art. 27.2(a) and Annex VII(b))</u>**

|**eveloped country WTO Members**<br>**Art. 27.2(a) and Annex VII(a))**<br>Angola|**Members with a per**<br>**(SCM Art. 27.2(a) a**<br>Bolivia|
|---|---|
|Bangladesh|Cameroon|
|Benin|Congo|
|Burkina Faso|Cote d’Ivoire|
|Burundi|Egypt|
|Cambodia|Ghana|
|Cape Verde|Guyana|
|<br>Central African Republic|<br>Honduras|
|Chad|India|
|Democratic Republic of the Congo|Indonesia|
|Djibouti|Kenya|
|Gambia|Nicaragua|
|Guinea|Nigeria|
|Guinea-Bissau|<br>Pakistan|
|Haiti|Philippines|
|Lesotho|Senegal|
|Madagascar|Sri Lanka|
|<br>Malawi|Zimbabwe|
|Maldives||
|Mali||
|Mauritania||
|Mozambique||
|Myanmar||
|Nepal||
|Niger||
|Rwanda||
|Senegal||
|Sierra Leone||
|Solomon Islands||
|Togo||
|Uganda||
|United Republic of Tanzania||
|Zambia||

**Members with a per capita GNP<$1,000**** **<u>(SCM Art. 27.2(a) and Annex VII(b))</u>**

*) As of 28 July 2008.  In addition, 12 LDCs are in the process of WTO accession: Afghanistan, Bhutan, Comoros, Equatorial Guinea, Ethiopia, Lao People’s Democratic Republic, Liberia, Samoa, Sao Tome and Principe, Sudan, Vanuatu, Yemen. **) In 1990-US Dollars.

_Source_ : World Trade Organization website, World Trade Organization, Document G/SCM/110/Add.4 (21 December 2007).

20
# BIBLTOGRAPHY

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61
8. Calculate simulated total per capita consumption in 2016 that includes non-collected items and express it in 2022 prices, as in equation (9).

9. Calculate poverty and inequality using the new consumption aggregate.

10. Repeat 1,000 times all steps 1 to 9.

This process allows for estimating confidence intervals for poverty and inequality. A new LASSO model is fit for every bootstrap sample of the data, and the whole consumption vector is constructed via the improved LASSO PMM process described above. In this exercise, a thousand bootstrap repetitions were implemented. As a result, both point and interval estimates are calculated for every welfare indicator.

# **III. Limitations**

Despite the strengths of the matching approach implemented in this analysis, several limitations must be considered. First, the imputation process accounts for missing data, as well as for data collection and fieldwork improvements between the survey rounds. These last enhancements can introduce additional sources of variation in the imputed data, which may affect the accuracy of the imputation results.

Another limitation is that certain items collected in both surveys have changed. For example, rice, categorized as fine, medium, and coarse in previous rounds, was divided into six categories in 2022, primarily for capturing quality. This change in categorization introduces complexities in the imputation process and may impact the accuracy of the imputed consumption values for these items. The categorization in 2022 may align differently from the previous categorization, which could introduce discrepancies in the imputed data.

Furthermore, the 2022 poverty lines were defined at the domain level, whereas the 2016 (and 2010) lines were defined at the stratum level. This difference in the level of aggregation poses a challenge in determining the poverty status of each household during the imputation procedure. The imputation process involves identifying donor households in the 2022 survey and assigning their shares and domains to the corresponding 2016 (2010) households. This additional step introduces an element of uncertainty in the imputation and may affect the accuracy of poverty estimates.

Additionally, the imputed consumption data for 2016 (2010) are based on predictions and statistical matching techniques. While efforts have been made to improve the accuracy of the imputed values, there is inherent uncertainty associated with imputation methods. The imputed data may not perfectly capture the actual consumption patterns in 2016 (2010), and errors or biases in the imputed values may be introduced. This could be especially concerning if the assumption that the share of additional consumption relative to common items remained stable over time for comparable households does not hold. However, analysis of consumption patterns suggests they have not drastically changed, particularly for the last 6 years, as indicated in Table A.1 in Annex 1.

In summary, although the imputation approach provides a valuable tool for reconstructing poverty and inequality trends, it is essential to consider the limitations of the methodology. The inclusion of data 8
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23
practice of interviewing the most knowledgeable household member (Arm 1), interviewing multiple adult members in the same household with a questionnaire that asks the respondents to identify the assets owned by each member of the household, exclusively or jointly with others within or outside the household (Arm 4) drives both female and male respondents to be more inclusive in their reporting of which household members of the opposite sex own the priority asset classes, namely dwelling, agricultural land, livestock and financial assets.

While the effects are large in magnitude, they are underlined by distortionary proxy respondent effects. These effects are most evident in the comparison of respondent versus proxy-respondent reporting regarding the respondent’s reported ownership, economic ownership and right to bequeath in Arm 4 households with 2 or more individual interviews. This comparison, in the context of dwelling, agricultural land and financial accounts, reveals that even when respondents, both men and women, do not claim to be owners or rights holders, they may be tagged as reported owners, economic owners and rights holders by other respondents within their household. For instance, 9 percent of the female respondents that do not consider themselves to be dwelling owners are classified as joint reported owners by others. Similarly, 11 percent of the female respondents that do not report owning a financial account are reported to be exclusive financial account owners by others.

These types of distortionary proxy respondent effects are also observed in Arm 5 households with 2 or more interviewers, but they are markedly smaller in magnitude since the Arm 5 individual questionnaire focuses only on respondents’ personal ownership of, and rights to assets, and is set up to identify alternative household members as owners and/or right holders in a given interview only through joint arrangements with the respondent.

In view of these findings, together with the Arm 5 questionnaire design effects reviewed in the previous section, Kilic and Moylan (2016) recommend, for those intending to collect intra-household information on individual ownership of, and rights to physical and financial assets as part of household surveys, 1) reducing the reliance on a single respondent, 2) expanding the practice of interviewing multiple age-eligible individuals per household, with a focus on the members of the principal couple if a couple is present, and 3) probing directly regarding respondents’ personal ownership of and rights to assets, whether exclusively or jointly with someone else, as in Arm 5.

These recommendations are buttressed by previous calls for collecting data on ownership of, and rights to assets at the individual level as well (Grown et al., 2005; Doss et al., 2011), and have already informed the design of the Malawi Fourth Integrated Household Survey 23
The price variables continue to have the expected signs. Inflation has a significantly negative effect. Looking at the country-specific environmental variables, water availability increases agricultural productivity. Potential dry matter is significantly negative.<sup>31</sup>

# **Estimates utilizing FAO data on tractors**

As discussed, in the absence of cross-country data sets on agricultural fixed capital, researchers often used FAO data on number of tractors as a proxy. We showed earlier that the data series on tractors are not a convincing proxy for agricultural fixed capital data. To demonstrate the implications for econometric analysis, we estimate agricultural productivity once again, with the FAO data on number of tractors in use replacing the data on agricultural fixed capital stocks. The results are presented in Table 6. The coefficient on tractors is not significant in the within estimations. Due to the omission of the fixed capital input, the other input elasticities change as well; the elasticities of capital of agricultural origin and land increase, while the elasticitiy of fertilizer falls slightly, and the labor elasticity becomes negative. Omitting this critical input for agricultural production from the econometric analysis leads to unrealistic results.

Interestingly, the input elasticities from the between-country estimations using the tractor data are similar to those using the agricultural fixed capital data (compare the last two columns of Tables 5 and 6). The coefficient on tractors is 0.11, while the coefficient on fixed capital is 0.13. The choice of capital series (tractors vs. fixed capital) has little effect on the between-country elasticities of capital of agricultural origin and labor. The reason for the similar results from the two regressions lies in the correlations of the transformed capital data. Examining the transformations of the capital data series, the correlations of the between-country transformations are much higher than those of the within transformations (0.79 vs. 0.19). This is because the differences in the composition of capital and the declining importance of tractors are lost as the time-dimension of the data is collapsed into country averages. Thus if the capital data series were compared on a cross-country basis, one may erroneously conclude that tractors are a valid proxy for agricultural fixed capital. It is critical to view both the between and within transformations of the data series to fully capture its evolution.

> 31 In a previous study where agricultural capital entered the estimation as a composite variable, potential dry matter had a significantly positive effect. Once agricultural capital was disaggregated into its various components, the coefficient on potential dry matter lost significance and even turned negative (Mundlak, Larson, and Butzer,1999).

17
but has not progressed very far since then. During 1990-92, 12 sizable companies were sold for a total $3.9 billion in revenue. The focus has been on infrastructural projects in energy, telecommunications, and transport. The largest projects were the sale of the Tenaga Nasional Berhad power plant for $1.2 billion and the National Airline for $689 million, both privatized in 1992. Two telecommunications companies (Syarikat and Telekom) were also sold since 1990, generating a combined $1.1 billion. Again, the participation by foreign investors was difficult to determine because of data constraints. Butt while the importance of foreign investment is generally recognized, foreign participation is .estricted to 25 percent and any acquisition exceeding M$5 million also requires a cumbersome approval process by the Foreign Investment Committee.<sup>45</sup>

# Philippines

Under President Marcos, the public sector expanded rapidly frem 71 SOEs in 1972 to 301 in 1986, absorbing almost a quarter of the public budget.<sup>46</sup> With the ad,lition of transferred assets of government financial institutions, government-owned entities swelled to 700 companies in 1987. The Aquino Administration's privatization program intended to rationalize this large and diverse group of government assets, through sales to the private sector and through abolishing, consolidating, and merging with existing line agencies. The program has been quite successful in accomplishing the sale of transferred assets: by end-1992, 293 out of 399 transferred assets had been sold, yielding almost US$1.5 billion with foreign participation providing slightly over one quarter of total revenues. Steady progress is also being made on the privatization program's targets on public enterprises. As of mid-1993, 96 government corporations, representing 70 percent of government-owned assets, have been offered for sale. Of these, 78 had resulted in actual sales yielding another US$1 billion.

# China

In line with a rapidly expanding economy and growing inflows of foreign investment, the Chinese government began a process of incorporating major enterprises. While the word "privatization" is not officially used, shares of a small number of companies were offered in the stock markets of Shanghai and Shenzhen. Foreigners participated in this process through the purchase of non-voting "B"-shares in 12 companies since 1992. Given that China is expected to be the next growth pole in East Asia, foreign interest is strong and likely to increase rapidly. In June 1993, for example, the U.S. beer producer AnheuserBusch bought a 5 percent share in the well-known Tsingtao Brewery for $115 million.

## D **South Asia**

## **Sri Lanka**

Among South Asian countries, the nation with the strongest privatization efforts in recent years is Sfi Lanka. Starting in the 1950s, and accelerating in the 1970s, SOEs increased

> 45 International Financing Law Review, Special Supplement (September 1992), p.34. 46 Forbes (1993), p.<sup>1</sup> .

40
economic status, input taxes based on production inputs such as land, trees and animals, and enterprise taxes. These taxes are thought to be less risky due to absence of factors that would cause variation and they also have lower transaction costs. Variable taxes category includes trade taxes from market exchange of goods in towns and output taxes such as taxes on grains, legumes and fibers. These taxes are riskier due to high variability and also carry greater transaction costs for the same reason. Using data from Ottoman tax registers (tahrir defterleri) the authors conduct an ordered probit analysis for the influences on tax assignments and find that the proportion of variable taxes are negatively associated with the higher levels of government lending support to the transaction cost hypothesis which asserts that variable taxes can be more efficiently collected at the local level due to lower transaction costs. The key underlying assumption here is that transaction costs are higher for higher levels of government. The authors conclude that transaction costs were more important in tax assignment in the Ottoman Empire. However, they do not model fixed taxes specifically.

In summary, we see that the taxation system was not as centralized in the early period (until the end of the sixteenth century) of the Ottoman Empire as it was in the later period. Centralization gained momentum particularly during the Tanzimat (Reorganization) period in the nineteenth century when the empire was declining rapidly and consolidation of power at the center was seen as a solution to prevent total collapse. Centralization that started in the sixteenth century is, by no means, unique to the Ottoman Empire. In fact, Europe started its transformation from small, decentralized city-states to large and centralized territorial states at the end of the fifteenth century.<sup>4</sup> It was also

> 4 Alesina and Spolaore (2003) note that French invasion of Italy in 1494 marks the beginning of the period of absolutism in Europe. They argue that main reasons behind the centralization trend were the need for

6