rafmacalaba/datause-encoder-data huggingface dataset:
Table A.2.2 Studies on EMDEs (some including advanced economies) (continued)

|Authors|Questions|Economies (sampl<br>period and<br>frequency)|e<br>Methodology|Results|
|---|---|---|---|---|
|Baskaya,<br>Gulsen, and<br>Kara (2012)|<br> <br>Are inflation expectations<br>well anchored in Turkey?|<br> <br>Turkey (2006-12,<br>monthly)|Regression of inflation expectations on the<br>lagged inflation rate|Inflation expectations are more sensitive to inflation<br>realization at higher levels, but this sensitivity has<br>decreased over time.|
|Capistrán<br>and Ramos-<br>Francia<br>(2010)|Does inflation targeting<br>affect inflation<br>expectations?|12 advanced<br>economies and 13<br>EMDEs (1989-<br>2006, monthly)|Regression of dispersion of inflation<br>expectations on a dummy for inflation<br>targeting, the actual inflation rate, and world<br>average inflation|Inflation targeting affects inflation expectations only in<br>EMDEs. There is no effect of inflation targeting on the<br>dispersion of inflation expectations in advanced<br>economies.|
|Carrasco and<br>Ferreiro<br>(2013)|<br>Does inflation targeting<br>anchor inflation<br>expectations?|Mexico (2004-11,<br>monthly)|Tests whether inflation expectations follow a<br>normal distribution under inflation targeting<br>using the Shapiro-Wilk test, Jarque-Bera test,<br>and Doornik-Hansen test. If the inflation<br>expectations are anchored, they are assumed to<br>follow a normal distribution where the mean is<br>the inflation target and the variance is<br>constant.|<br> <br>Inflation expectations are anchored to the inflation<br>target.|
||Does inflation targeting<br>anchor inflation||||
|Cerisola and<br>Gelos (2009)|<br> <br>expectations? Is the<br>inflation targeting<br>framework supported by<br>the perceived<br>sustainability of public<br>finances?|Brazil (2000-04,<br>monthly)|Recursive OLS regression of inflation<br>expectations on inflation target, lagged inflation<br>rate, primary balance as a percent of GDP, and<br>other control variables|<br> <br>The adoption of inflation targeting helped anchor<br>expectations; the stance of fiscal<br>policy was important in shaping inflation expectations.|
|Cortes and<br>Paiva (2017)|<br>How does the loss of<br>credibility affect inflation<br>expectations?|<br>Brazil (2004-16,<br>monthly)|Using VARs, estimation of impulse responses of<br>inflation expectation to actual inflation,<br>exchange rate movements, and output gap with<br>two subsamples: 2004-10 and 2011-16|<br> <br>During 2011-16, inflation expectations reacted more<br>strongly to actual inflation, exchange rate movements,<br>and output shocks than during 2004-10.|

31
> - **75** -

# **Table A.19:** Seam-rput-Output Parameters for **India,** 1979/80

|Input-Output Coefficients|Irrigated<br>Agriculture|Rainfed<br>Agriculture|Nontradable<br>Agriculture|Manufacturlng|Tertlary|
|---|---|---|---|---|---|
|Manufacturinga/|0.141|0.042|0.067|0.326|0.145|
|Tertiary|0.081|0.033|0.015|0.188|0.157|
|Nontradable<br>agriculture|0.025.|_0r_45|0.056|||
|Valtie added to<br>gross output ratio|0.669|0.8U7|C.761|0.300|0.640|

|Household Coofficlerts|Urban Households|Rural Households|
|---|---|---|
|Marginal Budget Shares<br>Manufacturing<br>|0.181<br>|0.195<br>|
|Tertiary<br>|0.401<br>|0.338<br>|
|Nontradable agr!cuiture<br>|0.215<br>|0.204<br>|
|Leakage ratios|0.3|0.3|
|Value Added Shares<br>|||
|Agri, ulture:<br>|||
|-<br>Irrigated<br>|0.008<br>|0.661|
|-<br>Rainfed<br>|0.005<br>|0.802<br>|
|-<br>Nontradable<br>|0.005<br>|0.756<br>|
|Manufacturing<br>|0.108<br>|0.192<br>|
|Tertiary|0.230|0.410|

Source:lnput-output coefficients and value added shares adapted from 1979/80 input.outp%;: tables for India and Punjab reponed in Bhalla et al. (1989). Consumption parameters based on Sarveksha (1988) and Hazell and Ramasamy (1986). We took averagd budget shares from SavieksniFa and multiplied by expenditure elasticties from Hazell and 1amasamy.

a/ Does not include direct imports from abroad.
# **Figures and Tables**

Figure 1: Revenue Collection Efficiency and Number of Utility Customers <!-- Start of picture text --> 2015 2016 2017 2018 2019<br>Date<br>Proportion of bills paid Number of customers<br>.75<br>10000<br>.7 8000<br>.65<br>6000<br>.6<br>Number of connections<br>Proportion of total bills paid .55 4000<br>.5<br>2000<br><!-- End of picture text --> _Notes._ Blue line shows the proportion of payments received from total bills (including consumption and loans) over time; Red line shows total number of utility customers in informal settlements having either a piped water connection or both a water and sewer connection.

Figure 2: Self-reported Reasons for Nonpayment <!-- Start of picture text --> Didn't receive<br>water<br>Don't have<br>money<br>Don't know how<br>to make payment<br>Didn't know that<br>I needed to pay<br>Account is<br>not yet set up<br>Don't<br>have time<br>I'm not responsible<br>for making payments<br>Meter is<br>broken<br>Payment system<br>was not working<br>0% 20% 40% 60%<br>No payment for more than 2 months Never paid<br><!-- End of picture text --> _Notes._ Based on property owner self-reported reasons for not paying, captured during the August/September 2018 listing exercise. Multiple choices were allowed. N is 2,488 for not making in more than 2 months and N is 1,387 for never paying.

27
Policy Research Working Paper 5701

# **Abstract**

Country export quality (measured by unit values) is correlated with income level suggesting that studying quality dynamics potentially offers insights into the development process. This paper uses highly disaggregated trade data to explore the export quality (unit value) dynamics of goods exported to the United States over the 1990–2000 period. In addition to finding considerable heterogeneity in the relative quality of exports across countries and across goods within countries, the authors find that the rate of quality growth varies substantially across countries, as well. Specifically, the fastest growth is seen in exports from the richer (OECD) countries, implying an evolving divergence in product quality across regions. This divergence obtains despite evidence of conditional convergence in quality over time- goods with lower initial relative quality levels experience faster growth in quality. The data suggest that part of this divergence is driven by the product mix itself—OECD exported products experience intrinsically higher growth rates. This is consistent with the argument of Hausmann, Hwang and Rodrik (2007) that what countries export does matter for growth. However, it is partly driven by a higher growth rate of quality in the richer countries independent of convergence effects, suggesting that other country-specific factors impeding overall convergence are at work. Finally, there is very limited technological “leap-frogging” by countries across product lines as the relative quality of new exports, on average, is roughly the same as incumbent exports, both in richer countries and elsewhere.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the Western part. For instance 25% of households in Chittagong division and 16% in Sylhet division receive remittances, while less than 5% did in Khulna, Rajshahi and Barisal division (World Bank 2008b). If the poorest groups are excluded from such networks due to their lack of social capital, or by virtue of the fact they live in regions which are not connected to these migration corridors, they are also likely to be excluded from taking advantage of global labor market _despite_ the skill match.

Even when international employment has been secured, there remain several potential problems regarding transfer of remittance from the migrant to his/her household back home. Factors such as access and transaction costs related to receiving remittances through the banking system have led to the use of informal transfer channels known locally as the ‘hundi’ system.

Despite the significance of remittances in the Bangladeshi economy, there is little empirical work on the challenges that the poor face in taking advantage of globalizing labor markets. Very little information is collected about how households actually go about interacting with labor market operators in securing overseas employment or the level of transaction costs involved in processing remittance receipts. Further, typically, data in household surveys is collected only on current migrants, those who are currently overseas. When a significant part of international migration is temporary and strictly under time bound contracts, there is a great amount of flux, and ignoring returnee migrants introduces a significant bias in assessing the effects of migration.

The study aims to make a modest contribution to filling this knowledge gap by using household survey data to (i) identify the characteristics of households who choose to migrate, (ii) assess migration related costs as well as remittance transfer channels and (iii) assess the impact of migration on household welfare. The rest of the paper is organized as follows. Section 2.0 3
Policy Research Working Paper 6719

# **Abstract**

The paper presents a newly compiled and improved database of national household surveys between 1988 and 2008. In 2008, the global Gini index is around 70.5 percent having declined by approximately 2 Gini points over this twenty year period. When it is adjusted for the likely under-reporting of top incomes in surveys by using the gap between national accounts consumption and survey means in combination with a Pareto-type imputation of the upper tail, the estimate is a much higher global Gini of almost 76 percent. With such an adjustment the downward trend in the Gini almost disappears. Tracking the evolution of individual countrydeciles shows the underlying elements that drive the changes in the global distribution: China has graduated from the bottom ranks, modifying the overall shape of the global income distribution in the process and creating an important global “median” class that has transformed a twin-peaked 1988 global distribution into an almost single-peaked one now. The “winners” were country-deciles that in 1988 were around the median of the global income distribution, 90 percent of whom in terms of population are from Asia. The “losers” were the country-deciles that in 1988 were around the 85th percentile of the global income distribution, almost 90 percent of whom in terms of population are from mature economies.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at clakner@worldbank.org and bmilanovic@worldbank.org (or bmilanovic@gc.cuny.edu).

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
In tropical products, developing countries’ exports to industrial countries have declined significantly as a proportion of world exports, and Sub-Saharan Africa has lost relatively more from this change.

Temperate product exports have increased and essentially because of the increasing exports to developing countries. Again, ECA and Latin America are the major beneficiaries of this increase.

The dynamic products of seafood, fruits and vegetables, etc have not increased as much over the last decades.<sup>17</sup> The reason for it is the decline in the share of exports going to industrial countries, which are not fully made up by the increases in exports to developing countries. The same situation applies to Other Processed foods.

# **_Degree of Processing_**

Despite significant tariff escalation in processed products, trade has moved toward processed (final) agricultural products and away from raw material and intermediate products.<sup>18</sup> In 1990/91 final products made up about 33 percent of world exports, and raw and intermediate products made up two-thirds. By 2006/07 the share of final products had increased to 41 percent of total exports (table 12). The share of final products in exports increased for both developing and industrial countries, but in 2006/07 final products still constituted only 20 percent of the exports from low-income countries, compared with 46 percent for industrial countries.

**Table 12: Share of Agricultural Final Products in Exports, 1990/91-2006/07**

|||Ex<br>|port Share (%)<br>|||
|---|---|---|---|---|---|
|Period Avg.|World|Developing<br>Countries|Low Income<br>Countries|Middle Income<br>Countries|Industrial<br>Countries|
|1990-91|32.6|24.0|13.6|25.4|35.9|
|2000-01|37.3|30.8|17.3|32.8|41.3|
|2006-07|40.8|34.3|19.9|36.2|45.5|

Note: Includes China and India.

_Source_ : Based on mirror data from UN COMTRADE Statistics.

On the other hand, there are significant gains made by developing countries in increasing their share of world trade in processed products. This increases their value added and industrial capabilities. Low income countries have also increased the share of processed products in their exports.

More detailed disaggregation of export flows by degree of processing does not yield much more information than the aggregate flows. The export share of final products increased for tropical and temperate product groups. For seafood and fruits and vegetables the shares of final product stayed the same because of the higher value of fresh produce. In tropical products trade among industrial countries is now mostly trade in final products.

> 17 These product groups grew very rapidly during the 1980s and early 1990. Part of the slower growth could also caused be the lower price increases for seafood and fruits and vegetables as compared to temperate foods. Our data is in nominal US dollars.

> 18 To have consistent data going back, this analysis uses Standard International Trade Classification (SITC 3), which is not as precise as the Harmonized System (HS) in separating the products by degree of processing. Thus the results are not as precise.

19
TABLE A22: Impact of leaders’ exposure on ECD outcomes, by closest leader’s predicted commitment

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|ITT Leader|-0.05|-0.04|-0.05|
||(0.09)|(0.18)|(0.09)|
|ITT leader X low commitment|-0.13|-0.06|-0.19|
||(0.11)|(0.18)|(0.12)|
|ITT leader X medium commitment|-0.15|-0.12|-0.19|
||(0.12)|(0.24)|(0.12)|
|ITT leader X high commitment|0.08|0.30|-0.01|
||(0.11)|(0.20)|(0.13)|
|ITT leader X very high commitment|-0.05|-0.02|-0.06|
||(0.12)|(0.28)|(0.12)|
|Observations|2051|624|1427|
|ITT leader + ITT leader X low commitment = 0|0,01|0.33|0,01|
|ITT leader + ITT leader X medium commitment = 0|0.01|0.20|0.01|
|ITT leader + ITT leader X high commitment = 0|0.74|0,02|0.54|
|ITT leader + ITT leader X veryhigh commitment = 0|0.24|0.74|0.19|

_Note:_ Low, medium, high and very high commitment indicates whether leader is predicted to be in the 2, 3, 4, or top quintile of leaders with highest participation in the quizzes. All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 and 35 month. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, memory and self-control) of children aged 36 to 83 months. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 70
2

# **1. Introduction**

In a recent paper (Wagstaff et al. 2018), we provided an overview of an international database on Health Equity and Financial Protection Indicators (HEFPI). The data set provides data on the delivery of health service interventions, health outcomes, and ‘financial protection’ in health, at both the population level, and for subpopulations (defined by household living standards) along with a summary measure of inequality known as the concentration index (Wagstaff et al. 1991; Kakwani et al. 1997). The data are computed from well-known household surveys that have been conducted by, or in partnership with, national governments, such as the Demographic and Health Survey (DHS) and the Living Standards Measurement Study (LSMS).

This paper outlines changes that have been made in the 2019 version of the HEFPI database. On the financial protection side, the number of indicators has been expanded in the 2019 database from five to 14. Moreover, several estimates have changed from the 2018 database, reflecting in part our analysis of new surveys (or adaptations thereof) since releasing the 2018 database, but also refinements we have made to out-of-pocket expenditure estimates for some surveys included in the 2018 database. On the health equity side, the 2019 HEFPI database includes 197 more datapoints than the 9,733 in the 2018 database. This increase is the result of two major counteracting enhancements of the database: the addition of 535 new datapoints, of which 493 come from recent Multiple Indicator Cluster Surveys (MICS) and several new DHS; and the removal of 338 previously included datapoints for eight health equity indicators which, after a thorough quality review, were concluded to be substandard. These changes are detailed in the sections below.

# **2. Financial protection indicators**

On the financial protection side, for the 2019 HEFPI database, 1,846 surveys were analyzed and 650 were retained covering 149 countries. The number of indicators has also been expanded from 5 to 14. In addition, in many cases, estimates have changed from the 2018 database, reflecting
42 Table 19. Emissions from different transport modes (per vehicle-km for road vehicles and per car-km for rail vehicles)

|Mode<br>F<br>fuel<br>elec|uel type or<br>source for<br>tical energy|co|Emissi<br>HC|on in**gmrns**<br>NOx|SOx|Aldehydes|PM|
|---|---|---|---|---|---|---|---|
|Motorcycle||||||||
|2-Stroke Engine'|Gasoline|17.00|9.9|0.075|0.024|0.068|0.21|
|4-Stroke Engine'|Gasoline|20.00|2.39|0.150|0.014|0.029|0.029|
|Light Duty Vehicleb|Gasoline|||||||
|Passenger Car||||||||
|Low Speed (30 kmi/hr)||33.66|2.63|1.05|0.21|..|0.33|
|High Speed (60 km/hr)||18.75|1.11|0.75|0.11|||
|Van/Light Duty Truck||||||||
|Low Speed||84.19|7.61|1.48|0.37|||
|High Speed||40.15|3.41|5.87|0.22|||
|Small Bus/Combi||||||||
|Low Speed||47.12|_5.81_|1.88|0.26|||
|High Speed||12.53|0.S2|1.61|0.12|||
|Light Duty Vehicle'|Gasoline|||||||
|Low Altitude||22.00|2.20|3.20|0.08|..|0.33|
|High Altitude||45.00|3.80|1.70||||
|Light Duty Truck'|Diesel|1.10|0.28|0.99|0.45|||
|Large BuSb|Diesel|||||||
|Low Speed||7.66|5.50|12.37|15.27|..|0.75|
|High Speed||6.77|4.76|11.61|11.55|||
|Heavy Duty Vehicle'|Gasoline|||||||
|High Altitude||120.00|11.00|3.00||||
|Low Altitude||81.00|9.90|5.70|0.16|..|052|
|Heavy Duty Vehicle'|Diesel|12.70|2.10|21.00|1.50|0.20|0.75|
|Commuter Rail|Diesel|||||||
|Locomotive'||18.44|13.17|19.76|17.13|1.08|6.58|
|Light Rail Transit||||||||
|(LRT)c|Coal|0.211|0.086|8.63|32.36|0.002|67.86|
||Natural Gas|neg.|neg.|4.42|0.004|0.011|0.168|
||Fuel Oil|<br>0.003|<br>0.253|8.21|6.32|0.05|0.8|
|Rapid Rail Transit||||||||
|(RRT)'|Coal|0.272|0.111|11.14|41.83|0.002|87.73|
||Natural Gas|neg.|neg.|5.706|0.004|0.014|0.217|
||Fuel Oil|0.004|0.326|10.59|8.15|0.062|1.032|

a) Source: [EPA 1973] b) Source: [Walsh 1989b]; representative of the vehicle fleet in Mexico City.

c) Source: [Reno **and** Bixby 1985]
**Table 32: Major constraints to seeking innovative product covered by IP law**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|_None_|-|30|-|12|
|_Cost constraints_|||||
|Cost involved in getting IPR|7|5|13|8|
|Difficulty to achieve successful|-|-|-|-|
|commercialization of IPR|||||
|_Legal issues_|||||
|Enforcing IP law|7|5|13|8|
|High cost in lawsuit|-|15|-|6|
|Limited laws for innovation in process and|13|25|53|30|
|business systems|||||
|_Resource shortage_|||||
|Lack of knowledge of IP|7|5|-|4|
|Limited manpower|7|5|-|4|
|_Shanghai_|||||
|_None_|14|-|-|6|
|_Cost constraints_|||||
|Cost involved in getting IPR|_-_|_-_|_5_|_2_|
|Difficulty to achieve successful|_5_||_10_|_6_|
|commercialization of IPR|||||
|_Legal issues_|||||
|Enforcing IP law|_-_|_11_|_-_|2|
|High cost in lawsuit|_-_|_-_|_5_|2|
|Limited laws for innovation in process and|_19_|_11_|_10_|14|
|business systems|||||
|_Resource shortage_|||||
|Lack of knowledge of IP|_12_|_-_|_10_|_10_|
|Limited manpower|_24_|_-_|_-_|_10_|

**Table 33: Elements most needed for ensuring an effective IP law regime**

||High-tech|KIBS|Media/Content|Overall|
|---|---|---|---|---|
|_Beijing_|||||
|Improving the IP law system|13|15|27|18|
|Strengthen the enforcement|67|20|60|46|
|Improving the business ethics|33|40|13|30|
|_Shanghai_|||||
|Improving the IP law system|43|22|30|34|
|Strengthen the enforcement|62|11|55|50|
|Improvingthe business ethics|19|-|15|14|

_Note_ : percent may not add to 100% as multiple responses and no opinions are included.

Table 33 shows the suggestions for the elements that are needed most at present for ensuring an effective IP law regime in Beijing and Shanghai. Overall, firms in both cities most frequently selected to strengthen the enforcement of the IP law (cited by 46% firms in Beijing, 43