rafmacalaba/datause-encoder-data huggingface dataset:
_Source: Reform Committee data Note: Figure based on 107 economies with a functioning business reform committee._ In Azerbaijan, the Commission on Business Environment and International Rankings relies on different strategies to monitor progress, including reform strategy action plans, progress reports, closed meetings with supervisors, firm surveys, user feedback, and progression towards international benchmarks. For example, progress reports to monitor progress are provided by working groups to the Commission every three months and the final evaluation report is prepared by the Commission every 9-12 months. Similarly, in Peru, the website of the National Council of Competitiveness and Formalization (CNCF)<sup>5</sup> provides information about the fulfilment rate of the policy measures of the National Plan of Competitiveness and Productivity.

# **4.3. Stakeholder Engagement and Communication**

International organizations such as the World Bank and other development banks provide relevant technical and advisory support to clients regarding their business regulatory environment, including on setting up adequate institutional structures to implement reform agendas. These organizations can provide funding, technical advice, or training to the reform committee.

According to the data on reform committees, as expected, these international organizations have supported the creation of these reform committees mostly in low and lower middle-income setups. None of the reform committees in high-income economies have engaged international organizations in their development. The data seems to suggest that economies seem to rely on international development organizations in instances where they lack the requisite technical skills and financial capacity domestically. An exception to this is the case of Uzbekistan, a lower-middle income economy, where no international organizations were involved in the creation of the Republican Council for working with International Ratings and Indices.

Notably, more than a third of all reform committees have engaged the World Bank Group to support their creation (Figure 10). For example, the International Finance Corporation extended technical assistance towards the design and operationalization of the Ethiopia National Doing Business Reform Steering Committee. In other cases, the interventions from international organizations have been limited to providing technical assistance to support their reform agendas. For example, the World Bank has supported the actions of the reform committee in Brazil through technical support and the indication of reforms based on international best practices.

Figure 10: _Involvement of international organizations in the creation of Reform Committees_

> 5 Website of the National Council of Competitiveness and Formalization (CNCF): https://www.mef.gob.pe/ayni/

18
**Table 4:** Income and Trust in Other Institutions

||National Legislature<br>(1)|**Trust in**<br>Judicial System<br>(2)|Media<br>(3)|Church<br>(4)|
|---|---|---|---|---|
|NTL Density at Municipality (in logs)|0.042*|0.137**|0.094***|0.000|
||[0.022]|[0.060]|[0.020]|[0.027]|
|Homicide Rates|-0.032**|-0.095***|-0.042***|-0.008|
||[0.013]|[0.028]|[0.015]|[0.013]|
|FODES transfer per inhab. (previous year)|0.000|-0.002|0.002***|0.002***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Observations|11,826|11,864|10,471|11,952|
|Number of Municipalities|114|114|114|114|
|Number of Waves|8|8|7|8|
|R-squared|0.068|0.092|0.068|0.057|

Notes: All regressions include municipality and year fixed effects as well as individual controls (e.g., gender, age, age squared, and rural dummy). Robust standard errors clustered at the municipality-year level in brackets. FODES refers to Development Municipal Fund Economic and Social transfers. Statistical significance: *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

In Table 4 I look at trust towards other institutions. Specifically, I study the empirical link between income (proxied by NTL density) and attitudes towards the national legislature, judicial system, mas media, and the Catholic Church. Income appears to be a strong predictor of confidence in the judicial system (column 2), trust in mass media (column 3), and to a lesser extent of trust in national legislature (column 1). These results are consistent with the one in Stevenson and Wolfers (2011) showing that higher unemployment rates in the US predict lower levels of trust in public institutions such as the congress and journalists. Interestingly, income does not predict trust in the Catholic Church (column 4). Another interesting pattern emerges from Table 4: homicide rates are negatively associated to trust in all the institutions but the Catholic Church. As an example, moving an individual from a municipality in the 10% percentile to another in the 90% percentile distribution of crime rates would be associated with an approximately 10 percentage point drop in the confidence that the judicial system would punish the guilty in case respondent were victim of a crime. Further, the size of central government transfers is positively and statistically associated to higher levels of trust in mass media and the Catholic Church wheres it is negatively associated to confidence in the judicial system. Finally, Table A.2 shows that all previous results hold when using the original ordered dependent variables and ordered probit models are estimated instead.

Next, I study potential heterogeneous effects underlying the aforementioned empirical associations. Results in Table 5 shows that most of the positive association between income and trust in the Salvadoran president is explained by individuals living in rural areas (column 1 in Panel A) and municipalities with low levels of state capacity (column 1 in Panel C) -as proxied by the share of households with access to electricity and water services. A similar pattern emerges when looking 17
The paper is organized in the following way. The next section discusses the contributions on global income distribution, identifying the sources behind the debate about its recent trends. Section 3 describes the model and dataset used behind the Global Income Distribution Dynamics (GIDD) module which is then used to undertake ex-ante simulations of global inequality. The simulation results are presented in Section 4. In this section we use the GIDD to project the global income distribution for 2030; we decompose the total change in inequality into the effects due to changes in average income between-countries and changes explained by shifts in within-country inequality. The ex-ante changes in the global middle class in each of the 120 countries included in the GIDD are also presented in Section 4. Finally, Section 5 draws policy implications and concludes.

# **2 Are incomes around the world becoming more unequal?**

Assessing what has happened to global income distribution in the last two decades, and predicting what will happen in the next 25 years, presents a number of challenges. Part of the difficulty lies with choosing an appropriate measure of inequality to capture income disparities around the world. The literature identifies three main approaches to measuring income inequality around the world, all of which have strengths, but each of which measures a slightly different thing.<sup>3</sup> _1. Intercountry inequality_ is a concept favored by macroeconomists. It measures relative movements across countries and gives each country an equal weight in the world distribution (that is, population size does not matter). This literature tends to conclude that in the last two decades, income distribution has become more unequal.

_2. International inequality_ takes into account the relative sizes of countries (that is, results are population-weighted). Its proponents (such as Theil and Seale 1994) point out that failing to use population weights will cause, for example, the fast

> 3   In this discussion the authors have adopted the naming conventions of World Bank (2005). Milanovic (2005) refers to the following different measurements as inequality concepts 1, 2, and 3.

4
The implementation of policy directed lending in China has been heavily influenced by the underdevelopment of the private financial institutions, the immaturity of the financial markets, the rapid growth of market-oriented economic activities, and the considerable decentralization of decision-making power from the central government to local governments and local financial institutions. Central government found it very difficult and costly to monitor closely the waste and diversion of financial resources to priority sectors.

Policy directed lending was closely associated with excessive credit expansion during the reform period. The priority loans became one of the major channels for expansion of central bank credit, which then generated excessive credit expansion in the economy. In recent years, about one third of total bank credit was allocated for policy-based lending and most of it was financed by central bank credit. Most priority borrowers benefitted from negative real interest rates. Moreover, banks often operated with negative spreads. Sometimes, the banks were compensated by transfers from the budget, but in other cases, they absorbed the negative spreads. This approach created inflationary pressures and price instability similar to those experienced by Korea in the 1960s and 1970s.

Policy directed lending in China had a very significant indirect impact on non-priority sectors through excessive credit expansion and distorted implementation of policy loans. For example, the rural Township and Village Enterprises (TVEs) were not a priority sector and received very few subsidized loans. Nevertheless, TVEs have expanded very rapidly through both self-financing and heavy borrowing from the banking system. The ratio of the loans to TVEs over the total state bank loans increased from 2.2% in 1979 to 6.1% in 1984 and 8.5% in 1991. The rapid growth of non-priority sectors such as TVEs increased the supply of goods and services and reduced the inflationary pressure rooted in credit expansion.

Policy directed lending has now become a crucial issue for China's macroeconomic stability, financial sector reform as well as economic transition in general. The issue of policy lending is closely related to a number of important economic problems: the future of the loss-making large state-owned enterprises; the growth of non-state enterprises; the stability of farm production and marketing; the stability of exchange rates; and the general price level.

# E. CREDIT POLICY IN INDIA **15**

During the last few decades, India has experienced rapid financial deepening. Owing to its stable macroeconomic environmnent, it now has an impressive financial system, which ranks in the top quarter among the financial sectors of most developing countries. Until the liberalization of the financial system in 1991, one of the key policy instruments used to steer the financial sector was the directed credit policy _15_ This section draws on Madhur (1993).

23
1

# A. Introduction

The increase in trade, the increasing internationalization of production and the improvements in communications, together with the legalization of foreign currency instruments in a growing number of countries, have led to a _defacto_ liberalization of the capital account. In line with the greater reliance on open goods markets and a _defacto_ opening of the capital account, developing country governments naturally are raising questions aJout fully opening the capital account. As a background to answering such questions, this paper surveys the existing literature on opening up domestic capital markets, much of which was written prior to the debt crisis, ae it applies to the current situation.

In this context, it should be noted that this survey defines a liberalized or open capital market as one in which individuals and firms can access international financial markets freely, not just one in which the government intermediates international capital flows to balance differences in private saving and investment. In fact, private agents in many developing countries cutrently have greater access to international capital markets than their governments, particularly given the possibilities that private agents have for collateralizing their debt. Ibis situation is a dramatic reversal of the situation of the late 1970s, when developing country governments intermediated large volumes of foreign funds at low real interest rates while private agents lacl.ed comparable access to international capital.

This survey begins with a brief summary of the costs and benefits of capital account liberalization. paying particular attention to the issue of the loss of policy effectiveness and noting the new theories of capital flows based on international portfolio diversification of risky assets, which raise the possibility of benefits from capital account liberalization that are not linked solely to higher investment rates. The survey then reexamines dte evidence on the results of open capital account. Unforunately, much of the evidence relates to industrial countries and much of the evidence relating to developing countries relates to the turbulent period in Mexico and the Southern Cone at the end of the 1970s and beginning of the 1980s. Finally, the survey reexamines the question of sequencing the liberalization of the current and capital accounts, to provide a background for programs to liberalize the capital account.
# TAX REFORM IN HALAWI

|Execut|ive S|ummary ..................................................|<br>i-vi|
|---|---|---|---|
|I.|INT|RODUCTION.<br>..........................................|1|
|II.|AN<br>198|OVERVIEW OF MALAWI'S ECONOMY AND TAX SYSTEM PRIOR TO<br>5............<br>*....................*..**.<br>.|1|
||1.|Salient Features of Malawi's Economy ............. .|1|
||2.|Tax Structure up to 1978...................<br>...|2|
||3.|Revenue Crisis in the Period 1978-84.........|3|
||4.|Ad Hoc Revenue Measures in response to the revenue||
|||crisis .............................................|5|
|III.|198|5 TAX STUDY AND TAX REFORM PROPOSALS ....<br>...........|<br>10|
||1.|Potential Problems Arising from the Ad Hoc Revenue||
|||Measures ........<br>..................................|<br>11|
||2.|Framework of the Tax Reform<br>.<br>.|14|
||3.|Taxes on Goods and Services<br>..|18|
||4.|Company Income Taxes ..............................|26|
||5.|Personal Income Taxes ............... ..<br>............|30|
||6.|Tax Administration....<br>..................|33|
||7.|Revenue Impact .....................<br>...............|34|
|IV.|IMP|LEMENTATION OF THE TAX REFORM PROPOSALS<br>............|<br>35|
||1.|Handover from Study Team to Technical Assistance||
|||Team..........<br>...................................|.<br>35|
||2.|Reform of Taxes on Goods and Services<br>.............|<br>37|
||3.|Reform of Taxes on Income .........................|38|
||4.|Reform of Tax Administration .......................|39|
||5.|New Issues<br>............................|40|
|V.|SOM|E LESSONS FROM TAX REFORM INMALAWI.................|41|
|Biblio|graph|y .<br>....<br>..<br>..................<br>.|45|
1981, see Cingranelli and Richards 2005). The Empowerment Rights indicator is constructed from several sub-indicators such as freedom of speech, freedom to participate in politics and freedom of religion. We also include a ranking that rates countries by their democratic institutions (on a scale from 0 to 10), which comes from the Freedom House Imputed Polity measure (available since 1972).

Apart from the measures for country governance, we add several macroeconomic variables including the population of the country, the log of GDP per Capita and the openness of the country<sup>2</sup> .

Governance and macroeconomic variables are included in our data set in five-year steps from 1965-2010, when available, in order to align with our adjusted test score database.

# _C) Results_

Next we provide results on the association between our explanatory governance variables discussed above, and adjusted test scores. Results are reported in Table 2.0. Our dependent variable is the overall Average Score, the Average Primary Score and the Average Secondary Score. We control for macroeconomic factors such as GDP per Capita, Population and Trade Openness. Columns (1)-(3) provide cross-sectional evidence pooling data over time, without country or time fixed effects.

We find positive associations between our indicators for Globalization, Economic Freedom and Democracy and test scores in columns (1), (2) and (3), respectively. As discussed, this association could suffer from unobserved omitted variables at the country-level and which vary over time. To address these biases, we include fixed effects in columns (4), (5), (6), and (7). Specifically, in column (4) we include country fixed effects and control for macroeconomic factors. Column (5) includes country and time fixed effects as well as macroeconomic controls. Column (6) and (7) include lagged governance indicators in order to explore whether changes in governance in period t-1 affect contemporaneous test scores.

> 2 Population measures stem from the United Nations National accounts, GDP data and Openness from the PENN World Tables.

28"
|1–2007_†_|**Geographic**|Partners<br>Herfndahl<br>Pen|49<br>0.21<br>0.00|49<br>0.15<br>0.00|132<br>0.15<br>0.04|104<br>0.12<br>0.01|128<br>0.14<br>0.02|136<br>0.09<br>0.06|127<br>0.10<br>0.02|104<br>0.14<br>0.02|159<br>-53<br>489|applied at the 4-digit HS level after|malized assuming full quorum of 250|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|, Syria, 200||Theil (W)|0.19|0.29|1.95|0.46|1.29|2.01|0.75|0.99|296|hin calculations|exports and nor|||
|sectors|**hin**_‡_|Theil|0.13|0.18|2.22|0.62|1.24|2.64|1.17|1.17|817|ines. Wit|to total|.|n).|
|and within|**Wit**|Herfndahl|0.51|0.32|0.24|0.32|0.26|0.29|0.31|0.32|-40|orum of 1,213 l|culations applied|4-digit HS level|of Theil (withi|
|between||Products|6|9|30|12|23|34|18|19|200|ming full qu|graphic cal|plied at the|e exemption|
|n,||)||||||||||su|Geo|ap|th|
|sifcatio||Theil (B|0.26|0.33|1.49|0.65|0.98|0.80|0.43|0.71|62|rmalized as|(within).|which was|year, with|
|ble 4: Export diver|**Between**|Herfndahl<br>Theil|0.62<br>0.45|0.46<br>0.62|0.40<br>3.45|0.33<br>1.11|0.38<br>2.27|0.15<br>2.81|0.14<br>1.18|0.35<br>1.70|-78<br>160|e 4-digit HS level and no|ith the exception of Theil|on of market penetration,|ns across sections for each|
|Ta||Products|142|206|881|363|651|974|509|532|258|applied at th|1 sections, w|h the excepti|ues are media|
||||||||||||ge (%)|ulations|ing into 2|ntries, wit|orted val|
|||ar|01|02|03|04|05|06|07|an|an|Calc|ort|ou|Rep|
|||Ye|20|20|20|20|20|20|20|Me|Ch|_†_|s|c|_‡_|

12
# **Appendix: Table A.1 Variables Description**

|**Variable**|**Description and unit**|**Source**|
|---|---|---|
|Liquid liabilities/GDP|Ratio of liquid liabilities to GDP|Financial Structure Dataset (Beck and Demirgüç-Kunt, 2009)|
|Private credit/GDP|Private credit by deposit money banks to GDP|Financial Structure Dataset (Beck and Demirgüç-Kunt, 2009)|
|Account at a formal financial institution|Account at a formal financial institution in the past year (% of people older than 15 year old)|Global Findex Database (Demirguc-Kunt and Klapper, 2012)|
|Loan from a financial institution|Loan from a financial institution in the past year (% of people older than 15 year old)|Global Findex Database (Demirguc-Kunt and Klapper, 2012)|
|Mobile phone used to pay bills|Mobile phone used to pay bills (% of people older than 15 year old)|Global Findex Database (Demirguc-Kunt and Klapper, 2012)|
|Mobile phone used to receive money|Mobile phone used to receive money (% of people older than 15 year old)|Global Findex Database (Demirguc-Kunt and Klapper, 2012)|
|Mobile phone used to send money|Mobile phone used to send money (% of people older than 15 year old)|Global Findex Database (Demirguc-Kunt and Klapper, 2012)|
|Population|Total population / 1,000,000|World Development Indicators, World Bank|
|Population density|People per square km of land area /1,000|World Development Indicators, World Bank|
|Natural resources|Net exports in resource intensive industries as described in the text.|Lederman and Maloney (2008)|
|Offshore center|Dummy variable that takes the value 1 if the country is a Offshore Center and 0 otherwise.|IMF (2007)|
|GDP per capita|GDP per capita (constant 2000 US$)|World Development Indicators, World Bank|
|Growth GDP per capita|GDP per capita growth (annual %)|World Development Indicators, World Bank|
|Consumer price inflation|Inflation, consumer prices (annual %)|World Development Indicators, World Bank|
|Road density|Km of road per 100 sq. km of land area|World Development Indicators, World Bank|
|KKM Index|Measure of broad institutional development|Kaufmann, Kraay, and Maztruzzi (2007)|
|Manufacturing/GDP|Manufacturing (% of GDP)|World Development Indicators, World Bank|
|Secondary/Primary school enrollment|Secondary school enrollment over primary school enrollment|World Development Indicators, World Bank|

18
|Table 10<br>Estimated parametric adjustmen<br>Export shareinsales effect esti<br>|t functions<br>mation<br>White<br>|Blue<br>|Capital<br>|
|---|---|---|---|
|Constant|0.089|0.169|0.042|
||[0.026]***|[0.026]***|[0.024]*|
|Pos Shortage|0.095|0.105|0.073|
||[0.043]**|[0.041]**|[0.040]*|
|(ShortageW)^2|0.179|0.040|0.003|
||[0.012]***|[0.013]***|[0.013]|
|(ShortageW)^2*Pos|-0.071|||
||[0.019]***|||
|(ShortageB)^2|-0.031|0.096|-0.028|
||[0.014]**|[0.014]***|[0.014]**|
|(ShortageB)^2*Pos||-0.047||
|||[0.021]**||
|(ShortageK)^2|-0.013|0.014|0.187|
||[0.011]|[0.010]|[0.008]***|
|(ShortageK)^2*Pos|||-0.160761|
||||[0.015]***|
|(ShortageW)*(ShortageB)|-0.024|-0.023|0.018|
||[0.015]|[0.013]*|[0.017]|
|(ShortageW)*(ShortageK)|-0.016|0.004|0.004|
||[0.013]|[0.015]|[0.013]|
|(ShortageB)*(ShortageK)|0.023|-0.048|-0.063|
||[0.017]|[0.013]***|[0.011]***|
|Constant*ExportS|-0.090|-0.005|0.073|
||[0.060]|[0.068]|[0.063]|
|Pos Shortage*ExportS|0.330|0.187|<br>-0.210|
||[0.107]***|[0.111]*|[0.100]**|
|(ShortageW)^2*ExportS|0.018|||
||[0.028]|||
|(ShortageW)^2*Pos*ExportS|-0.190<br>[0.052]***|||
|(ShortageB)^2*ExportS||0.072||
|||[0.044]*||
|(ShortageB)^2*Pos*ExportS||-0.127<br>*||
|||[0.065]||
|(ShortageK)^2*ExportS|||-0.033|
||||[0.022]|
|(ShortageK)^2*Pos*ExportS|||<br>0.121|
||||[0.039]***|
|Observations|4862|4862|4862|
|Number of id|618|618|618|
|<br>R-squared<br>Note: Pos Shortage: Dummy<br>ShortageB. ShortageK are the<br>capital. Exports is the share of<br>inbrackets*significant at10%;|0.3<br>=1 if Shortage<br>shortages for w<br>firms sales that i<br>**significant at 5|0.3<br>is positive.<br>hite collar. blu<br>s exported. St<br>%;***significa|<br>0.38<br>ShortageW.<br>e collar and<br>andard errors<br>nt at1%|

27
# **Reconstructing 2010–2022 Poverty and Inequality Trends in Bangladesh: A Statistical Matching Approach**<sup>**1**</sup>

Jaime Fernandez, Sergio Olivieri and Ayago Wambile<sup>2</sup> JEL code: C53, D31, O15, I32 _Keywords: poverty trends, inequality, poverty measurement, LASSO; statistical matching_

> 1 This paper has benefited from discussions and technical support from Faizuddin Ahmed, Agustin Arakaki, Mohiuddin Ahmed, and the Bangladesh Bureau of Statistics 2021-22 HIES project team. The paper has also been enriched by feedback from reviewers Paul Corral, Nobuo Yoshida, and Maria Eugenia Genoni, additional comments from David Newhouse, seminar participants at the World Bank workshops, and the guidance of Ximena del Carpio.

> 2 All authors are with the World Bank.