rafmacalaba/datause-encoder-data huggingface dataset:
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has been calibrated using the initial period’s data, it can then be used to gauge the country’s multidimensional inequality through time and, with additional assumptions, through space.

We illustrate the methodology using simulated data to allow specific inequalities, distributional means, and correlation levels to vary freely. Examples show the impact of each factor on multidimensional inequality for measures based on the Lorenz curve, the Gini coefficient, and other Lorenz-consistent measures. A second illustration using data from Azerbaijan examines the evolution of multidimensional inequality during a period of rapid income growth.

Previous studies have considered linear aggregation as one among several options, but to our knowledge this is the first paper that selects this structure based on the axiomatic properties of its associated measures. Likewise, a number of authors have linked multidimensional inequality to specific inequalities or to positive association, but none has the elegant simplicity of our decomposition, which highlights the fundamental role of Shorrocks mobility in representing a positive association in multidimensional inequality. In addition, the approach is unique in its use of normative weights and Lorenz curves to calibrate the measure in a base year, and then to judge subsequent changes accordingly. Finally, the paper is unusual in its focus on identifying multidimensional inequality measures that are especially useful in conducting policy.

Section II introduces the definitions and notation used in the paper. Section III presents different approaches to multidimensional inequality measurement and then establishes our main characterization result, identifying the subset of measures from Maasoumi’s (1986) two-stage approach that are axiomatically sound. Section IV provides expressions linking members of this class of multidimensional inequality measures to specific inequalities and Shorrocks mobility. Our calibration method is described in Section V, selecting initial parameters of the measure based on normative weights and Lorenz curves. Section VI illustrates our methods, while the final section concludes.

**II. Notation, Axioms, and Other Fundamentals** The data for measuring inequality are given in an array with rows and columns. The row lists data for person ; the column lists data for dimension ; xx nn dd ii<sup>th</sup> and each entry ii ≥0 is person ’s achievement in dimension ∙jj . The population size can vary, xx ii= 1, ... , nn jj<sup>th</sup> xx jj= 1, ... , dd but is fixed in any given application; hence, the set iijj of possible arrays is a subset of xx ii jj nn dd XX

5
designed to collect the main variables of interest that a close relative would know about (such as educational attainment, employment status, marital status, and the number of children). Table A1 summarizes the number of respondents for each survey type and the corresponding type of information we have.

# **Appendix B. Attrition and Sample Selection**

To account for attrition, we apply inverse-probability weights (IPW) following Wooldridge (2010). We predict the probability that the respondent has skills measures rather than the probability of having any data (see Appendix A and Table A1 for more details). We follow this approach as our main results relate skills to labor market outcomes, effectively treating individuals with missing skills measures as attritors in these regressions.

We implement two alternative approaches to modeling this probability. We start with a basic model that uses only correlates of attrition from the first round of data collection of the LEAPS project in 2003. We then add variables indicating if the respondent had already attrited in 2011 and 2016 (when we started the tracking process). We describe these two approaches in turn.

<u>Model 1. Probability of having full data (including skills measures) as a function of variables</u> collected in 2003 We start with a list of 35 variables from 2003 and select variables that are predictive of having skills measures using Lasso. This procedure leads us to keep 31 variables, including the respondent’s sex, age, housing characteristics (toilet on-premises, electricity access, etc.), parental education and occupation, whether the household head could read and write, and the language of the interview. We then use a probit model to predict the probability that we have full data for the respondent.

<u>Model 2. Probability of having full data (including skills measures) as a function of variables</u> collected in 2003 and indicators of attrition in 2011 and 2016 We use the same specification as in Model 1 but add two variables: a dummy indicating that the individual was already an attritor in 2011 and one indicating that the individual was an attritor in 2016, at the early stage of the tagging and tracking process.

Then, we used two additional methods to assess the robustness of our main results for sample selection:

- First, we applied the Heckman two-step selection model. For the selection equation, we used as instruments an indicator for whether the mother was born in the village, the number of boys in the family, whether the father was living in the household in 2003, whether the male interview was conducted in another language than Punjabi in 2003, and the number of schools in the village in 2003. We also control for the household size and wealth in 2003,

52
# **Enforcing Competition and Firm Productivity: Evidence from 1,800 Peruvian Municipalities**

## **Marc Schiffbauer**<sup>**a**</sup> **James Sampi**<sup>**b**</sup>

**JEL Classification codes** : O33, O47, D72, D24 **Keywords:** competition, TFP, productivity, structural reforms, subnational, Peru.

> a Marc Schiffbauer. The World Bank. Email: mschiffbauer@worldbank.org.

> b James Sampi Bravo. The World Bank. Email: jsampibravo@worldbank.org.

> * The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or its member countries.
# **Table A7.1 International Reform Sentiment and FDI Inflows into the GCC**

|Dependent Variable:|FDI In|flows (% of lagged|GDP)|
|---|---|---|---|
|Int'l Chatter (t)|-0.0463||-0.704|
||(0.423)||(0.488)|
|Int'l Sentiment (t)|0.965<sup>**</sup>||0.989<sup>***</sup>|
||(0.322)||(0.227)|
|Int'l Chatter * Int'l Sentiment (t)|0.0740||-0.153|
||(0.287)||(0.251)|
|Int'l Chatter (t-1)||0.978|1.381<sup>*</sup>|
|||(0.719)|(0.675)|
|Int'l Sentiment (t-1)||0.785|0.390|
|||(0.508)|(0.378)|
|Int'l Chatter * Int'l Sentiment (t-1)||0.505|0.484|
|||(0.358)|(0.362)|
|GDP Growth (t)|0.115|0.141<sup>***</sup>|0.109<sup>**</sup>|
||(0.0581)|(0.0197)|(0.0348)|
|Inflation Rate (t)|0.0226|-0.0174|-0.00993|
||(0.175)|(0.127)|(0.123)|
|Trade Openness (t)|0.107<sup>**</sup>|0.106<sup>**</sup>|0.106<sup>*</sup>|
||(0.0413)|(0.0331)|(0.0443)|
|Institutional Quality (t)|0.724|-1.534|-0.407|
||(3.495)|(2.454)|(2.291)|
|Country FE||||
|Year FE||||
|R2|0.605|0.622|0.655|
|Mean of Dependent Variable|2.858|2.899|2.899|
|Number of Countries|6|6|6|
|Observations|103|100|100|

Note: This table presents the relationship between GCC reform chatter and reform sentiment in non-GCC international media and FDI net inflows to the GCC. Coverage includes the six GCC countries between 2000 to 2020. Reform discussion and reform sentiment are in both English and Arabic and normalized to have unit variance. Trade openness is measured as the value of exports plus imports as a percentage of GDP. Institutional quality is the average of six measures from the World Governance Indicators database. Standard errors are clustered at the country level and papered in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 47
**Table 3.1: Descriptive statistics**

|Share of employed<br>||2002||||2004|||
|---|---|---|---|---|---|---|---|---|
|household members per<br>|||||||||
|household|mean|std|min|max|mean|std|min|max|
|All sectors|0.903|0.192|0|1|0.891|0.203|0|1|
|Agriculture|0.545|0.416|0|1|0.506|0.413|0|1|
|Manufacturing|0.089|0.220|0|1|0.101|0.230|0|1|
|Services|0.221|0.333|0|1|0.230|0.334|0|1|
|Commerce|0.116|0.251|0|1|0.121|0.253|0|1|
|Transport|0.027|0.116|0|1|0.027|0.113|0|1|
|Finance|0.003|0.040|0|1|0.003|0.041|0|1|
|Other|0.075|0.203|0|1|0.079|0.205|0|1|
|Mining|0.006|0.061|0|1|0.006|0.061|0|1|
|Utility and construction|0.042|0.140|0|1|0.048|0.150|0|1|

Table 3.1 provides descriptive statistics on the shares of employed household members across sectors between 2002 and 2004.<sup>3</sup> Most people worked in agricultural sectors (54.5 percent on average in 2002). The share of people working in services (22.1 percent) was larger than that in the manufacturing sector (8.9 percent). While the share of workers in the agricultural sector declined between 2002 and 2004, the share of workers in manufacturing increased from about 8.9 percent in 2002 to 10.1 percent in 2004. Looking at the sub-sectors of services, the share of household members working in commerce was the largest and tended to rise during the period of 2002-2004.

# **4. Identification strategy and conceptual framework**

# **4.1 Provincial tariff reduction**

Local labor markets are considered at the province level. Each province is treated as a subeconomy subject to differential trade shocks, per initial pattern of industry specialization. Vietnam

> 3 We also report the descriptive statistics for the whole sample of VHLSSs 2002 and 2004 in Appendix A2. The results are quantitatively similar. This suggests the panel data set is similar to the whole sample in terms of employment shares.

10
# **The limits to competition in urban bus services in developing countries**

## **Abstract**

During the past three decades urban public transport policy has gone through several phases. From public ownership and monopoly provision, the eighties and nineties were characterized by a strong liberalization of the sector. This experience showed the limits of liberalization of the sector in terms of safety, prices and accountability. This paper discusses the market failures that justify this claim and the regulatory options available in this emerging new role of government, illustrating how they are being used in practice in some countries.

Keywords: transport, efficiency, safety, regulation, developing economy, reform JEL Classification: D43, L51, L92

## **1. Introduction**

Any review of the perception of best practice in terms of the market structure and organization of urban public transport over the last 30 years or so would reveal the popularity of a hybrid model, that is a model in which the public and the private sector share responsibilities for the delivery of the service.<sup>1</sup> A more careful review would reveal an evolving consensus with respect to the optimal degree and form of government intervention in this hybrid model.<sup>2</sup> This evolving consensus is particularly obvious in bus transport which represents the largest mode of urban transport in the developing world since, according the latest data published by the UN (2001), over 40% of all trips to work are done by buses in low and middle income countries.

The new consensus on the organization of the bus services market has roughly emerged as follows.<sup>3</sup> During most of the 1970s, public provision and self-regulation were the norm but ended with major fiscal difficulties and the consequences of the two major oil price shocks. Starting in the 1980s, liberalization and privatization of services became the new norm but ended with major safety and environmental problems in addition to some social issues resulting from tariff rebalancing in the sector. The result, toward the end of the 1
The current situation raises many important questions about the future of the sector and prospects for various players. How will multilateral groundnut trade liberalization affect the competitive positions of the various players? Which countries are likely to gain and capture larger market shares and which ones will lose? How will small SSA producers be affected? We address these questions in this paper.

The next section examines the dynamics of global production, trade and prices of groundnut products. The third section reviews the groundnut policies of the major players in the groundnut market, with an emphasis on the most distortive policies. In the fourth section, we use a multi-market, partial-equilibrium model to assess the impact of removing major policy distortions on world prices, trade flows, and welfare for major producing and trading countries involved in the groundnut market. We analyze multiple scenarios. First, we consider full multilateral trade liberalization for groundnut products with and without the removal of the U.S. groundnut program. Then we consider multilateral groundnut trade liberalization, again with and without the removal of the U.S. farm groundnut program. Then we consider full trade liberalization in the two largest and most distorted groundnut markets, India and China. The last section derives the major policy implications of the study and concludes.

# **II. GROUNDNUT PRODUCTION AND TRADE**

# **Groundnut Production, Cost, and Utilization**

Groundnuts belong to the oilseeds category and account for 16 percent of world oilseed harvested area. The major producers are China, with 40 percent of world’s production, followed by India (23 percent), Sub-Saharan Africa (8.4 percent), the United States (5.6 percent) and, to a lesser degree, Japan (Table 1).

**Table 1. Groundnut production and utilization, average 1996-01**

|Country|Area<br>harvested|Yield|Production|Dom estic<br>Utilization|Food use|Crushed|Feed/seed|Net<br>Export|
|---|---|---|---|---|---|---|---|---|
||(1000 ha)|(m t/ha)|(1000 m t)|(1000 m t)|(1000 m t)|(1000 m t)|(1000 m t)|(1000 m t)|
|W orld|21452|1.4|29997|29896|12416|14590|2891|169|
|**M ain producers and exporters**|||||||||
|China|4234|2.9|12204|11777|4753|6140|884|427|
|India|7902|0.9|7176|7082|534|5581|967|94|
|USA|569|3.0|1701|1428|978|280|170|220|
|Argentina|280|1.5|403|191|21|155|15|213|
|**M ain producers in Africa**|||||||||
|Nigeria|1187|1.1|1340|1340|636|427|278|0|
|Senegal|690|1.1|722|730|317|304|109|-6|
|South Africa|98|1.7|161|123|72|32|19|33|
|Malawi|117|0.9|103|101|78|18|5|2|
|Gam bia|89|1.0|95|80|26|54|0|15|
|**M ain im porters**|||||||||
|EU|1|1.0|1|454|433|17|5|-449|
|Canada|0|0.0|0|115|115|0|0|-115|
|Japan|12|2.3|28|129|121|2|6|-103|
|south Korea|7|2.2|15|30|30|0|0|-15|

**Source** : USDA **Note** : The difference between production + net exports and dom estic utilization reflects stock variation, not shown here Ending stocks are negligible for all country except the US fo which it stood at 28 percent of total production during 1996-01

Groundnut production conditions vary considerably across countries, reflecting 5
|**Table (B.3)**<br>Heterogeneous Effects by Social Desirability<br>Dep. Variable<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>**Altruism**<br>**Policy Altruism**<br>**Opinion on Effort**<br>**Opinion on Economy**<br>**Opinion on Taxes**<br>Priming=1_×_STD (Social Desirability Index)<br>-0.028<br>-0.028<br>-0.067*<br>-0.105***<br>-0.038<br>(0.037)<br>(0.035)<br>(0.035)<br>(0.035)<br>(0.034)<br>Treatment (Priming = 1)<br>-0.043<br>-0.008<br>-0.055<br>-0.055<br>-0.188***<br>(0.036)<br>(0.035)<br>(0.035)<br>(0.034)<br>(0.034)<br>STD (Social Desirability Index)<br>0.093***<br>-0.037<br>-0.063***<br>0.007<br>-0.008<br>(0.025)<br>(0.024)<br>(0.024)<br>(0.024)<br>(0.023)<br>R-squared<br>0.007<br>0.003<br>0.011<br>0.005<br>0.010<br>Observations<br>3100<br>3347<br>3311<br>3324<br>3376<br>_Notes:_ The dependent variable_Yi_ represents the outcome for individual_i_as measured in the survey and standardized using the mean and standard deviation of the<br>control group. The variable_Treatment (Priming=1)_is the assigned treatment status to COVID priming of the individual. The variable Social Desirability Index is<br>constructed using 4 of the 33 questions of theCrowne and Marlowe(1960) scale. Each question’s answer was assigned a score of 1 or 0 depending on whether the<br>scale identifed the answer with someone who wanted to be socially desirable as explained inCrowne and Marlowe(1960). We add the answer of the 4 questions<br>having a scale from 1 (no social desirability) to 4 (maximum social desirability) and standardize it for ease of interpretation. We interact the variable on_Treatment_<br>_(Priming=1)_with the standardized variable of Social Desirability Index. _∗∗∗_signifcant at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%. Standard errors<br>are reported in parentheses.|**Table (B.4)**<br>Exploring Secondary Outcomes: Priming Effects on Beliefs on the Impacts of Migrants<br>Dep. Variable<br>(1)<br>(2)<br>(3)<br>**Opinion on Jobs**<br>**Opinion on Culture**<br>**Opinion on Crime**<br>Treatment (Priming=1)<br>0.002<br>-0.021<br>0.031<br>(0.026)<br>(0.026)<br>(0.025)<br>R-squared<br>0.000<br>0.000<br>0.000<br>Observations<br>6,174<br>6,180<br>6,181<br>_Notes:_ The dependent variable_Yi_ represents the outcome for individual_i_as measured in the survey and standardized using the mean and standard deviation of the<br>control group. The variable_Treatment (Priming=1)_is the assigned treatment status to COVID priming of the individual. _∗∗∗_signifcant at the 1%, _∗∗_signifcant at<br>the 5%, _∗_signifcant at the 10%. Standard errors are reported in parentheses.|
|---|---|

27
# **Figure 9a. Changes in Log Employment for Tradable Industries Grouped by the Magnitude of the Direct Export Shock, 2007-2009**

<!-- Start of picture text --> Average Change in Log Employment<br>(industries grouped by size of shock to exports)<br>time<br>Ave change in log empl in sectors with small shocks<br>Ave change in log empl in sectors with medium shocks<br>Ave change in log empl in sectors with large shocks<br>Note: groups defined by % drop in ln(exports) from Oct - Nov 2008<br>.1<br>.05<br>0<br>-.05<br>-.1<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text -->

**Figure 9b. Changes in Log Employment for Tradable Industries Grouped by the Magnitude of the Related Export Shock, 2007-2009**

<!-- Start of picture text --> Average Change in Log Employment<br>(industries grouped by size of shock to exports in related industries)<br>time<br>Ave change in log empl in sectors with small related shocks<br>Ave change in log empl in sectors with medium related shocks<br>Ave change in log empl in sectors with large related shocks<br>Note: groups defined by % drop in ln(exports) in related inds from Oct - Nov 2008<br>.05<br>0<br>-.05<br>-.1<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text --> 42
13

# **Table** **<u>3. Gasoline Taxes in Selected Countries in 2005</u>**

|**Country**|**Cents/liter **|**Country**|**Cents/liter **|
|---|---|---|---|
|**Western**|**Europe**|**Developing**|**Countries**|
|Portugal|103|Kenya|47|
|Netherlands|100|Malawi|50|
|United Kingdom|97|Mali|71|
|Belgium|94|Mauritius|29|
|Italy|90|Morocco|65|
|Germany|90|Niger|57|
|France|89|Nicaragua|24|
|Finland|85|Pakistan|17|
|Sweden|80|Panama|09|
|Norway|74|Senegal|65|
|Spain|72|Sri Lanka|27|
|Denmark|70|Turkey|99|
|Austria|68|India|42|
|Ireland|62|Ghana|04|
|Luxembourg|60|China|03|
|Switzerland|50|Brazil|39|
|**Other OECD **|**Countries**|Albania|78|
|Japan|46|Colombia|27|
|New Zealand|42|Costa Rica|33|
|Australia|35|Bolivia|09|
|Canada|26|Chile|40|
|Mexico|21|||
|USA|10|||

a expressed in purchasing power parity at 2000 constant price. Source: IEA (2006) for OECD countries and Metschies (2005) for developing countries
|**my**<br>009, where the dependent variable is a<br>total amount repaid by the borrower for<br>`up Liability Loan Dummy` is a dummy<br> `Amount` refers to the amount the client<br>sample. `Number of Previous loans` is<br>he diference between the month of the<br> clients who received both an individual<br>arentheses beneath each point estimate.<br>(4)<br>-0.064**<br>(0.031)<br>-0.000<br>(0.000)<br>0.055***<br>(0.020)<br>0.007***<br>(0.002)<br>0.281***<br>(0.087)<br>Yes<br>Yes<br>0.251<br>6055<br>0.105|
|---|
|**ment Dum**<br> to March 2<br>month if the<br>variable `Gro`<br>y loan. `Loan`<br>ches in the <br> `Months` is t<br>des loans of <br>are given in p<br>(3)<br>-0.148***<br>(0.014)<br>-0.000***<br>(0.000)<br>0.068***<br>(0.016)<br>-0.003<br>(0.007)<br>0.007***<br>(0.002)<br>0.118***<br>(0.017)<br>No<br>No<br>0.094<br>6055<br>0.105|
|**issed Pay**<br> April 2005 <br>particular<br>level. The <br>dual liabilit<br>e MFI bran<br>`an Age in`<br>ample inclu<br>dual level,<br>(2)<br>-0.160***<br>(0.014)<br>-0.000***<br>(0.000)<br>0.068***<br>(0.015)<br>0.151***<br>(0.016)<br>No<br>No<br>0.086<br>6055<br>0.105|
|**Variable: M**<br> data from <br>ue of 1 for a<br> loan-month <br> is an indivi<br>or one of th<br>ent loan. `Lo`<br>us 1. The s<br>at the indivi<br>(1)<br>-0.169***<br>(0.014)<br>0.170***<br>(0.013)<br>No<br>No<br>0.072<br>6055<br>0.105<br>|
|**Table 3: Dependent **<br>This table reports results from OLS regressions with panel<br>dummy for missing a payment. This dummy takes on the val<br>that month is nil, and 0 otherwise. Observations are at the <br>variable equal to 1 if the loan is a group liability loan, 0 if it <br>received for that particular loan. `Behrampura` is a dummy f<br>the number of loans that the client received before the curr<br>observation and the month when the loan was disbursed, pl<br>and a group liability loan. Robust standard errors, clustered<br>Group Liability Loan Dummy<br>Loan Amount<br>Behrampura<br>Number of Previous Loans<br>Loan Age in Months<br>Constant<br>Control for Calendar Month<br>Individual FEs<br>R-squared<br>N<br>Mean of Dep Var<br>* _p <_0_._10, ** _p <_0_._05, *** _p <_0_._01|

24