rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6:  Simple Dynamic LDV Models for per Capita Generation Capacity**

|**Dependent Variable**|Log(Electricity<br>Generation<br>capacity per<br>capita)|Log(Electricity<br>Generation<br>capacity per<br>capita)|
|---|---|---|
|**Explanatory variables**|||
|Lagged dependent variable|0.885|0.879|
||(66.186)|(66.094)|
|Real GDPper capita(log)|0.119|0.121|
||(4.558)|(4.610)|
|Index of regulatory governance 0-4|0.007||
||(1.835)||
|Regulator aged 1-3years||0.010|
|||(0.893)|
|Regulator aged over 3years||0.032|
|||(2.247)|
|Implied long run multiplier 1/(1-λ)|8.732|8.249|
|Estimation method|Fixed effects|Fixed effects|
|Adjusted R-squared|0.996|0.996|
|S.E. of regression|0.083|0.083|
|F-statistic|4366.527|4399.462|
|Durbin-Watson|1.850|1.834|
|No of observations|576|584|
|_Note: t statistics in parentheses_|||

In all the main generation capacity equations reported above, the R<sup>2</sup> statistics are high – around 0.95 in the static fixed effects models and over 0.99 in the dynamic model. The latter in particular raises questions as to whether, given the fixed effects, the empirical results are dominated by the purely statistical relationship of one highly trended variable (per capita generation capacity) with another (real per capita GDP). In fact, neither of these series is dominated by an obvious trend<sup>28</sup> but the issue requires more formal investigation. See 5.2.2 below.

# **5.2.2 Error Correction Models and Lags**

# _5.2.2.1 Time Trends and Lags_

Our first test was to establish whether the coefficients on the regulatory governance variables remained positive and significant when we (a) included a time-trend and (b) lagged the index by 3 years.

The results were as follows:

> 28 See Data Annex in Cubbin and Stern (2004).

30
In the interests of greater transparency, these organizations have kindly agreed to allow us to report their proprietary data in the form in which it enters our governance indicators. As mentioned above, and as documented in detail in Appendix A and B, we in some cases use a simple average of multiple questions from the same source as an ingredient in our governance indicators. We do this when we find more than one question from a single data source that is relevant to one of the dimensions of governance that we measure. On the interactive part of our website we report either the individual question, or the average of individual questions, from each source that enters into our governance indicators. All the individual variables have been rescaled to run from zero to one, with higher values indicating better outcomes.

These individual indicators can be used to make comparisons of countries over time, as all of our underlying sources use reasonably comparable methodologies from one year to the next. They also can be used to compare different countries' scores on each of the individual indicators, recognizing however that these types of comparisons too are subject to margins of error. We caution users however not to compare directly the scores from different sources for a single country, as they are not comparable. To take a specific example, it does not make sense to compare a question rated on a 1-10 scale from a data source covering only developing countries with a similar question rated on the same 1-10 scale, but covering developed countries, as the distribution of true governance is likely different in the two groups. For example, the same score of 7 out of 10 on the two sources might correspond to quite different levels of governance quality. As discussed in detail in Kaufmann, Kraay, and Mastruzzi (2004) and also in Appendix D, our aggregation procedure provides a way of placing such different sources in common units that allows for meaningful aggregation across sources.

The only data sources we have not been able to obtain permission to publicize fully are the World Bank's Country Policy and Institutional Assessment, and the corresponding internal assessments produced by the African Development Bank and the Asian Development Bank. We do note however that starting in 2002 the World Bank began publishing limited information on its CPIA assessments on its external website. For the years 2002-2004 the overall CPIA ratings are reported by quintile for countries eligible to borrow from the International Development Association (IDA), the concessional lending window of the World Bank. Starting in 2005, the individual country 9
12

# **_3.3. Deregulation across the U.S. States_**

Next, we examine whether a policy reform that improved the quality of banking services increased, decreased, or had no effect on the distribution of income. Thorsten Beck, Alexey Levkov, and Ross Levine (2007) (henceforth BLL) examine the impact of the same policy reform in different states of the United States on the distribution of income. The policy was implemented during different years over a twenty year period ranging from the mid-1970s to the mid-1990s. In particular, by end of this period, all states had removed regulatory prohibitions on banks opening branches within state boundaries. Past work shows that liberalizing restrictions on intra-state branching (i) increased the average size of banks through consolidation, (ii) improved bank efficiency by reducing the difference between interest income received and interest income paid by banks, and (iii) accelerated average per capita income growth. BLL examine the impact of bank deregulation on the distribution of income.

Thus, they examine the effects of a single, exogenous policy event on 50 economies within the United States. Methodologically, the deregulation of intra-state branching provides a natural setting for identifying and assessing the impact of regulatory reform on the distribution of income. Kroszner and Strahan (1999) show that national technological innovations triggered deregulation, which was exogenous to income distributional changes within individual states. The invention of automatic teller machines (ATMs), in conjunction with court rulings that ATMs are not bank branches, weakened the geographical bond between customers and banks. Checkable money market mutual funds facilitated banking by mail and telephone, which weakened local bank monopolies. Improvements in communications technology lowered the costs of using distant banks. These innovations reduced the monopoly power of local banks, and therefore weakened their ability and desire to fight deregulation. Kroszner and Strahan (1999) further show that cross-state variation in
31

# **Figure 19. Export changes by sectors and destinations under the CPTPP compared to the baseline in 2030, in billion $ and %**

<!-- Start of picture text --> Agriculture Agriculture<br>Natural resources/mining Natural resources/mining<br>Food, beverages, tobacco Food, beverages, tobacco<br>Textiles Textiles<br>Wearing apparel and leather Wearing apparel and leather<br>Chemical, rubber, plastic products Chemical, rubber, plastic products<br>Metals Metals<br>Transport equipment Transport equipment<br>Electronics Electronics<br>Machinery and equipment Machinery and equipment<br>Other manufacturing Other manufacturing<br>Utilities Utilities<br>Construction Construction<br>Trade and transport Trade and transport<br>Finance and other business services Finance and other business services<br>Communication and business services Communication and business services<br>Social services Social services<br>-20 0 20 40 60 80 0 1 2 3 4<br>China EU China EU<br>Other RCEP Other TPP Other RCEP Other TPP<br>RCEP/TPP join members Rest of the world RCEP/TPP join members Rest of the world<br>Russia USA Russia USA<br><!-- End of picture text --> Source: Authors’ estimates. *Other TPP – Canada, Chile, Mexico, Peru; RCEP/TPP joint members – Australia, Brunei, Japan, Malaysia, New Zealand, Singapore, Vietnam; Other RCEP – Indonesia, South Korea, Philippines, Thailand, Cambodia, India, Lao PDF, Myanmar.

**Figure 20. Export changes by sectors and destinations under the TPP12 compared to the baseline in 2030, in billion $ and %**

<!-- Start of picture text --> Agriculture Agriculture<br>Natural resources/mining Natural resources/mining<br>Food, beverages, tobacco Food, beverages, tobacco<br>Textiles Textiles<br>Wearing apparel and leather Wearing apparel and leather<br>Chemical, rubber, plastic products Chemical, rubber, plastic products<br>Metals Metals<br>Transport equipment Transport equipment<br>Electronics Electronics<br>Machinery and equipment Machinery and equipment<br>Other manufacturing Other manufacturing<br>Utilities Utilities<br>Construction Construction<br>Trade and transport Trade and transport<br>Finance and other business services Finance and other business services<br>Communication and business services Communication and business services<br>Social services Social services<br>-50 0 50 100 150 0 1 2 3 4<br>China EU China EU<br>Other RCEP Other TPP Other RCEP Other TPP<br>RCEP/TPP join members Rest of the world RCEP/TPP join members Rest of the world<br>Russia USA Russia USA<br><!-- End of picture text --> Source: Authors’ estimates. *Other TPP – Canada, Chile, Mexico, Peru; RCEP/TPP joint members – Australia, Brunei, Japan, Malaysia, New Zealand, Singapore, Vietnam; Other RCEP – Indonesia, South Korea, Philippines, Thailand, Cambodia, India, Lao PDF, Myanmar.
The experiment was implemented in the República Bolivariana de Venezuela 2006 Enterprise Survey (ES). The República Bolivariana de Venezuela ES was conducted between February and June 2006 covering 500 private sector firms.<sup>3</sup> All firms are formal (registered with a government entity) and include 5 or more employees. The sampling methodology was stratified random sampling. Levels of stratification were size, sector, and location. The República Bolivariana de Venezuela ES covered three regions: Caracas, Maracay, and Valencia. The sample is representative of the non-agricultural, non-mining private sector of the economy.<sup>4</sup> The respondents are typically business owners and managers; interviews are administered faceto-face. There were 33 enumerators and 5 supervisors that carried out the fieldwork.

The survey experiment was conducted as follows. The sample was randomly split into half – 250 firms in each group. The first group received the typical Enterprise Surveys where at the end of each section, a question is posed to the respondent to rate an element of the business environment related to the section. The question is structured as follows: “ _To what degree is XX an obstacle to the current operations of this establishment?_ ” where “XX” refers to the specific business environment element referenced at the end of the section. The respondent has 5 options: “no obstacle,” “minor obstacle,” “moderate obstacle,” “major obstacle,” and “very severe obstacle.” The business environment elements covered in the survey include: (1) Tax administration, (2) Access to land, (3) Access to finance (availability and cost), (4) Corruption, (5) Crime, theft and disorder, (6) Electricity, (7) Inadequately educated workforce, (8) Functioning of the courts, (9) Political instability, (10) Labor regulations, (11) Business licensing and permits, (12) Practices of competitors in the informal sector, (13) Customs and trade regulations, (14) Tax rates, and (15) Transportation. The second group of 250 firms received the business environment elements at the very

> 3 Note that in 2006 the Venezuelan economy had a GDP per capita growth rate of 8 percent.

> 4 Note that certain sectors are not covered by the Enterprise Surveys including agriculture, mining, and some service sectors such as education, health, and financial intermediaries. More information on the Enterprise Surveys global methodology is available on the website http://www.enterprisesurveys.org/.

10
-15-

> 6 percent of GDP is represented by the curve r. One can verify that it fits rather well the data for the countries with high inflation. This fit is not a strong proof that high inflation does not bring more revenues. But it is an indication that the increase of revenue is not large. In the range of 30 to 60 percent, an increase of the inflation rate does not generate on average, much higher levels of revenues. It is however, quite possible that very high inflation rates generate large revenues for short periods. This evidence will be compared with the time series data.

# III.3.b Time Series: High Inflation in Sub-Sahara

I now turn to the four countries that have experienced the higher levels of taxation on financial assets in sub-sahara Africa, which are by increasing order, Somalia, Ghana, Zaire (see Figure la)<sup>17</sup> . These are the countries where one can expect the most significant impact of taxation on financial intermediation and on financial deepening. The evolution of inflation and the monetary aggregates in represented in Figures 2a to 2c.

The response of the level of assets is measured through the estimation of an error correction model<sup>18</sup> .

# Demands for Financial Assets

The error correction model is composed of two equations of the form 17The data for Uganda was too poor to be usable at this stage.

180rdinary least squares equations with lagged dependent variables generate estimates of the long-term interest elasticity that are much overstated.
Government statistics for African countries are very poor and data on government revenues and expenditures are especially scarce. Relying on the International Financial Statistics (IMP), the African Economic and Financial Data (World Bank) and World Bank country reports, government expenditure and revenue"4 data were retrieved for the following countries and for the following years: Burkina Faso (1973-87); Cameroon (1975-87); COte d'Ivoire (196987) ; Gabon (1973-76 and 1979-87) ; Senegal (1977-1987) ; ancd, Togo (1977-87). Variables for which we had several data source were systematically checked for consistency. Given the lags in the Euler equation (t-2,t-1 and t+l), an observation qualifies for inclusion in the data set if data for the preceding two years and for the following year is also available. The final data set available to test the model includes 61 observations. Finally, the capital K variable had to be constructed. Since no data on capital stocks were available, a time series for K was constructed (using available investment data) starting in 196015 using an initial capital output ratio of 5 and a depreciation rate of 5 percent. Taylor (1979) suggests as a rule of thumb a depreciation rate of 4% and a capital output ratio of 3. Since he is referring to developing countries as a whole, *lightly higher figureL are

> 14In some **cases,** data on government **revenue** was unavailable. However, **a** figure could be constructeo using data on government's expenditures and primary deficit.

**15 K is** built recursively **as**

K **K .1** (1-6) + **I,** with **KN** * 5 **YO.** Although all **series,** except C6te d'lvoire which starts **in** 1969, **start In** 1973 or after, the **capital *erLe** ..as inltiallzed in 1960 **In** order to reduce sensitivity to **the** choice of the initial capital output ratio.
# Contents

|1.|Introduction|3|
|---|---|---|
|2.|Armenia’s National Poverty Measurement Methodology|4|
|3.|Multidimensional Poverty: A New Indicator|5|
|4.|Data and Summary Statistics|13|
|5.|Results|18|
|6.|Conclusion|28|
|Bibliogr|aphy|30|
|Annex 2|. Summary Statistics|32|
|Annex 3|. Construction of Deprivations|36|
|Annex 4|. Program Codes for the Measure of Multidimensional Poverty (sample 2015)|39|

## **Acronyms**

CBN Cost of Basic Needs

- FAO Food and Agriculture Organization

ECA Europe and Central Asia region ILCS Integrated Living Conditions Survey

- MPI Multidimensional Poverty Index NSSRA National Statistical Service of the Republic of Armenia UNDP United Nations Development Program WB World Bank

2
two sample groups could explain – at least in part – the differences between the full sample and PSM sample results. This is plausible because smaller firms, which tend to be more financially constrained, could react more to equity internationalization events than larger corporations.

To verify this formally, we disaggregate the connected firms in the PSM sample by size (defined by total assets in 2010-12). We then re-estimate the difference-in-differences equation for each subgroup (Table 5 and Figure 5). We find that the smallest firms (in the lowest quartile) in the PSM sample raised the most equity post-2012 (as a fraction of total assets in 2012), and the magnitude of the impact decreases monotonically in firm size (Figure 5). Consistent with this pattern, we also show that the smaller connected firms (below the median) increased their equity issuances relative to the larger connected firms (above the median) during 2014-20 (Table 6). The correlation between equity issuance reactions and firm size survives even when excluding stateowned enterprises (SOEs) from the sample. This is important because SOEs are relatively large corporations whose investment reacted less to the Stock Connect than privately owned firms (Ma et al., 2021).<sup>27</sup>

# _Robustness and Extensions_

We perform and report robustness tests for the full and PSM samples (Table 7). We (1) control for lagged assets and sales growth, (2) control for the size of bond issuances, (3) exclude firms with margin trading stocks, and (4) exclude firms with stocks purchased by the government during 2015-19.<sup>28</sup> Controlling for lagged total assets and sales growth allows us to ensure that changes in

> 27 We define as SOEs firms for which the main (top 1) shareholder is a government-related entity (following Ma et al., 2021). We merge our main dataset with 2007-20 firm-level data on firm ownership structure downloaded from Wind. We consider SOEs to be all firms with a government-related principal shareholder any year between 2007 and 2020. Around 37 percent of firms in our domestic listed sample are SOEs. They are about twice as large as the rest of the (private) firms.

> 28 As two additional robustness tests, we use the log of equity raised as dependent variable (instead of the amount raised over assets) and exclude financial firms (right columns in Table 7). The log of equity raised as dependent variable provides an alternative equity issuance measure that is not scaled by firms’ assets. Financial firms only constitute around 3.4 percent of our sample and excluding them barely changes the results.

17
# **2. Data**

To analyze issuance activity around the world, we use comprehensive, transaction-level data on corporate bonds and syndicated loans issued in domestic and international markets between 1991 and 2014. The data come from Refinitiv’s (previously Thomson Reuters’) Security Data Corporation (SDC) Platinum, which provides information on the issuance characteristics of publicly and privately placed bonds and syndicated loans.<sup>3</sup> We focus the analysis on the non-financial corporate sector and exclude financial sector issuances (those conducted by firms with a Standard Industrial Classification or SIC code between 6,000 and 6,800).<sup>4</sup> We also exclude asset-backed issuances and public-sector issuances, comprising issuances by national, local, and regional governments, government agencies, regional agencies, and multilateral organizations. Moreover, we exclude economies considered to be offshore financial centers.<sup>5</sup> To estimate the amount raised per issuance in each market, we aggregate transactions per firm at the daily level, such that issuances with different tranches within the same market are considered single issuances. We use information on issuances of common and preferred equity to compare the size of debt markets. All reported values are in constant 2011 U.S. dollars.

The data we use for the paper consist of 56,826 listed and unlisted non-financial firms and 184,496 daily debt issuances: 84,761 bond issuances and 99,735 syndicated loan issuances (Table 1). We organize the data as an unbalanced firm-level panel of daily observations with positive issuances during 1991-2014. Days with no issuance for any given firm—that is, with no demand for credit—are not included in the sample.

> 3 SDC Platinum is one of the most widely used databases on research exploring transaction-level issuance data. Some prominent studies such as Henderson et al. (2006), Kim and Weisbach (2008), and Bruno and Shin (2017) use the same database.

> 4 We divide non-financial industries across eight main categories by using the first digit of the Standard Industrial Classification (SIC) codes: agriculture, forestry, and finishing; mining; construction; manufacturing; transportation and utilities; wholesale trade; retail trade; and services.

> 5 We use the list of offshore financial centers as defined by the IMF.

9
factors influence agricultural productivity and market access, thereby affecting food security (Jawid & Khadjavi, 2019).

Seasonality affects agricultural productivity and food security (Aweke et al., 2022; Tay et al., 2023). Harsh winter weather often decreases agricultural productivity, leading to food shortages. While food availability generally increases during the summer, extreme heatwaves and droughts can reduce crop yields. Spring is important for planting, with favorable weather boosting productivity, whereas fall marks the harvest season, but unpredictable weather can impact crop quality and yield. To account for seasonal variations, we included three seasonal dummy variables as additional controls in our analysis.

## 3.3 Data Preparation

The rainfall and NDVI data were initially divided into 401 administrative codes and aggregated at the administrative division level. Each month included three data points corresponding to the 1st, 11th, and 21st days. For rainfall, we aggregated these data points using sum and mean operations to calculate average and total rainfall per month. We then standardized the monthly rainfall values using Z-scores within each market to create a rainfall anomaly variable, representing long-term rainfall variability. This approach aligns with methodologies used by Ngoma et al. (2023) and Penson et al. (2024) to capture deviations from normal rainfall patterns. A similar procedure was applied to the NDVI data. We aggregated NDVI data to obtain average NDVI per month and calculated NDVI anomalies by standardizing the average NDVI values as Z-scores within each administrative division.

To account for extreme weather events such as droughts and floods, we modeled anomalies asymmetrically. Excessive rainfall increases flood risk, while rainfall shortages increase drought risk; both conditions can exacerbate food insecurity (Coumou & Rahmstorf, 2012). We created two dummy variables for anomaly types—one indicating positive anomalies (flood anomalies) and the other indicating negative anomalies (drought anomalies). We then generated interaction terms by multiplying these dummies with the anomaly variable, allowing us to separately assess the impacts of floods and droughts.

Data on agricultural wages, exchange rates, domestic food prices, and fuel prices were organized according to administrative levels and market names. These datasets included monthly open, close, high, and low values, which we aggregated using mean operations to obtain typical monthly averages. To capture the dynamic interrelations among these economic factors, we used their percentage changes in our analysis.

Time-invariant variables such as population density, terrain ruggedness, cropland, and pastureland were available up to 2020. Due to their static nature, we extended these variables to 2023 using forward-filling techniques and applied logarithmic transformations to normalize their distributions.

# 4. Methods

## 4.1 Conceptual Model

To develop a model to investigate the influence of climate and weather shocks on domestic food prices and local agricultural wages, we start with a general form of the K-variable VAR: 10