rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Descriptive Statistics of IFPRI and HES Samples

|Variables||||IF<br>|PRI<br>||||HES<br>|
|---|---|---|---|---|---|---|---|---|---|
||Rou|nd 1|Roun|d 2|Rou|nd 3|Roun|d 4|Rural|
||Mean|Std|Mean|Std|Mean|Std|Mean|Std|Mean|
|HH size|6.57|2.98|6.54|2.94|6.46|2.79|6.40|2.79|5.25|
|Share of<br>||||||||||
|Boy|0.11|0.13|0.11|0.13|0.11|0.13|0.11|0.13||
|Girl|0.10|0.13|0.10|0.13|0.10|0.13|0.10|0.13||
|Madol|0.12|0.13|0.12|0.13|0.12|0.14|0.12|0.14||
|Fadol|0.10|0.12|0.10|0.12|0.10|0.12|0.10|0.12||
|Male adult|0.30|0.13|0.29|0.13|0.29|0.13|0.30|0.13||
|Female adult<br>|0.27<br>|0.11|0.27<br>|0.11|0.27<br>|0.11|0.27<br>|0.11||
|Preg|71||64||55||67|||
|Lact<br>|286||303||299||251|||
|W/H (kg/cm)||||||||||
|Boy|0.139|0.026|0.141|0.025|0.141|0.026|0.142|0.045||
|Girl|0.135|0.026|0.135|0.026|0.137|0.025|0.136|0.025||
|Madol|0.221|0.044|0.225|0.046|0.228|0.045|0.227|0.044||
|Fadol|0.231|0.048|0.233|0.047|0.233|0.045|0.233|0.047||
|Male adult|0.299|0.036|0.298|0.036|0.298|0.035|0.300|0.053||
|Female adult|0.280|0.038|0.277|0.038|0.279|0.038|0.282|0.040||
|Mcapx|676.35|465.01|607.46|454.36|716.17|471.34|697.14|642.33|662.00|
|Lpc (acre)|0.22|0.25|0.23|0.26|0.23|0.26|0.23|0.27||
|VUV of||||||||||
|Rice|10.87|0.83|9.79|0.70|11.41|1.10|10.00|0.79||
|Pulses|31.52|2.92|34.23|3.78|32.52|2.46|29.84|3.91||
|Big fsh|77.07|13.05|60.40|10.40|68.34|10.20|85.42|17.51||
|Small fsh|65.67|8.98|46.69|6.63|64.72|13.73|68.77|12.64||
|Egg|64.95|7.29|78.03|10.31|65.32|8.22|69.24|9.48||
|Pov rate (DCI)|||Sh|are of Ind|ividual Be|low the Po|verty Line|||
|Abs poor|67.57||66.48||71.81||59.48||47.1|
|Hcore poor|42.53||45.69||48.82||37.46||24.6|
|Pov rate (CBN)||||Sh|are of Indi|vidual belo|w|||
||UPL|LPL|UPL|LPL|UPL|LPL|UPL|LPL|UPL|
|Site 1|0.54|0.36|66.81|53.25|50.24|32.02|54.65|38.86|55.2|
|Site 2|0.43|0.37|59.52|51.01|38.68|32.39|46.18|39.34|LPL|
|Site 3|0.53|0.33|62.29|47.58|51.72|37.98|54.37|35.74|38.5|
||||T|ime Invar|iant House|hold Chara|cteristics|||
|Landless|4.75||||||||5.5|
|_<_ 1 acre|48.75||||||||61.3|
|1 - _<_ 2.5 acres|25.27||||||||19|
|2.5 - _<_ 7.5 acres|19.13||||||||12|
|_≥_7.5 acres|2.1||||||||2.2|
|Spouse charc.||Mean|Std||Mean|Std||||
|Age|Head|46.48|12.16|Wife|37.63|11.04||||
|Education|Head|3.50|4.16|Wife|1.74|2.79||||
|Assets|Head|32,266|149,113|Wife|2,683|10,704||||
|of which,||||||||||
|housing|Head|2,364|6,624|Wife|184.29|2,044.36||||
|cattle|Head|7,178|29,758|Wife|479.74|3,322.83||||
|durables|Head|2,874|8,956|Wife|1,205.12|4,388.96||||
|food|Head|0|0|Wife|382.29|930.86||||
|land|Head|20,284|139,639|Wife|604.35|7,972.25||||

Note: HH, household; Madol, male adolescent; Fadol, female adolescent; preg, pregnant; lact, lactating; W/H, weight for height; Mcapx, monthly per capita expenditure; lpc, land, per capita; vuv, village unit values; pov, poverty; abs, absolute; hcore, hardcore; CBN, cost of basic needs approach; DCI, direct calorie intake method; UPL, upper pov line; LPL, lower pov line; charc, characteristics; STD, standard deviation.

36
Policy ReseaRch WoRking PaPeR 4662

# **Abstract**

Infrastructure is the engine for economic growth. The international donor community has spent about 70–100 billion U.S. dollars on infrastructure development in developing countries every year. However, it is arguable whether these financial resources are used efficiently, particularly whether the current infrastructure procurement prices are appropriate. Without doubt a key is competition to curb public procurement costs. This paper analyzes procurement data from multi and bilateral official development projects in three infrastructure sectors: roads, electricity, and water and sanitation. The findings show that the competition effect is underutilized. To take full advantage of competition, at least seven bidders are needed in the road and water sectors, while three may be enough in the power sector. The paper also shows that not only competition, but also auction design, especially lot division, is crucial for reducing unit costs of infrastructure. Based on the estimated efficient unit costs, the annual financial needs are estimated at approximately 360 billion U.S. dollars. By promoting competition, the developing world might be able to save at most 8.2 percent of total infrastructure development costs.

This paper—a product of the Economics Unit, Finance, Economics and Urban Development Department—is part of a larger effort in the department to understand and investigate efficiency and effectiveness in public infrastructure procurement. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aiimi@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
Building Trust in the State with Information: Evidence from Urban Punjab<sup>∗</sup> Adnan Khan Sanval Nasim Mahvish Shaukat LSE LUMS World Bank Andreas Stegmann University of Warwick

**JEL:** D02, D83, C93, O12, O17, P16.

**Keywords:** Trust, state capacity, information, COVID-19, experimenter demand.

> ∗We would like to thank Sam Bazzi, Eli Berman, Migara De Silva, Ben Marx, Chris Roth and Jacob Shapiro for valuable suggestions. We would also like to thank seminar participants in the ESOC Lab at Princeton and the LUMS brownbag seminar for comments. We would like to thank Ahmad Mustafa, Jazib Parvez, Sardar Abdullah Mahmood, Omer Qasim, Haaris Mahmood and CERP Survey Unit for outstanding research assistance in Lahore, and Anna Lane, Iana Gerina and Youpeng Zhang for excellent support in Bonn. We would also like to thank Asim Khwaja and Ben Olken for generously sharing data. We gratefully acknowledge financial support from the briq Institute and the World Bank Development Research Group. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Adnan Khan; email: A.Q.Khan@lse.ac.uk. Sanval Nasim; email: sanval.nasim@lums.edu.pk. Mahvish Shaukat; email: mshaukat@worldbank.org. Andreas Stegmann; email: Andreas.Stegmann@warwick.ac.uk.
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_36_

# **Percentage of Final Pay Replaced at Retirement under the Jamsostek Program**

|Assu|mptions|Contrib|utions Starting|at Age|
|---|---|---|---|---|
|Real Return on|Real Wage||||
|Investment (%)|<br>Growth (%)|20|30|40|
|0|0|9.2|6.6|3.9|
||2|6.7|5.2|3.4|
||4|5.0|4.2|3.0|
|2|0|16.6|10.7|5.8|
||2|11.6|8.3|5.0|
||4|8.4|6.6|4.3|
|4|0|29.8|17.0|8.3|
||2|20.1|13.0|7.0|
||4|14.2|10.1|6.1|

Sources: Staff calculations.

# **Jamsostek Operations**

**_Compliance_** An issue of concern today is the degree of compliance under the mandatory Jamsostek program. Started in the 1970s and substantially revised in 1992, the social security program has a coverage of 24 percent of the target group. The general public shows little confidence in the program design and administration. Even those who are making contributions to the program express skepticism about the benefits promised and regard their contributions as a tax. With the current design and administration, the prospect for improving public confidence and broadening participation is limited.

The concern is not without foundations. The returns credited to members' accounts have been below what they can get from bank deposits. Members complain that disclosure and services have been unsatisfactory. Furthermore, information system has been inadequate, with about 40 percent of the assets currently not being posted to individual accounts. Moreover, Astek's financial management imposes undue financial risks on members, as no segregation is made of the assets and eamings under different program components. Losses under the workmen compensation (accident) and health insurance (medical) schemes could siphon off the assets of the retirement plan. (Management of Pt. Astek believes, however, that for the time being adequate reserve is maintained for each program component.)

**_Collection Lapses_** The low rate of Jamsostek participation is well known. But another dimension of the compliance problem is more difficult to detect. Among those participating in the program, the contributions made are much lower than what is expected. Based on total contributions and the number of participants in 1994 as reported by Pt. Astek, the average monthly salary is some Rph. 125,000, which is almost 60 percent below the average salary of Rph. 300,000 for workers in the formal sector. The contributions collected are therefore lower than what is to be expected.
Policy Research Working Paper 6445

# **Abstract**

As countries develop, they restructure away from agriculture and urbanize. But structural transformation and urbanization patterns differ substantially, with some countries fostering migration out of agriculture into rural off farm activities and secondary towns, and others undergoing rapid agglomeration in mega cities. Using cross-country panel data for developing countries spanning 1980–2004, the analysis in this paper finds that migration out of agriculture into the missing middle (the rural nonfarm economy and secondary towns) yields more inclusive growth patterns and faster poverty reduction than agglomeration in mega cities. This suggests that patterns of urbanization deserve much more attention when striving for faster poverty reduction.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 lchristiaensen@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
while the average margin for cases that have been the basis for WTO zeroing complaints is 36.2%.<sup>32</sup> This does not mean that the practice of zeroing has not affected the margins in the other cases, but it does suggest that the margins for most cases are not entirely driven by zeroing. It also suggests that countries choose to file WTO appeal on cases where it is more likely that the elimination of zeroing could mean _de minimis_ margins and the removal of antidumping duties altogether.

The more robust finding is that the impact of zeroing is to increase the dumping margin. In Table 6 we use the WTO disputes and calculate the impact on the margin due to zeroing. On average, dumping margins would have been 12.3 percentage points lower. While this is smaller than the Lindsey and Ikenson’s study estimates we note that it is greater than the average margin (10.5%) for these cases. This is again compelling evidence that zeroing has a large and significant impact on margins.

**Table 6 – WTO Cases, Change in Margin Due to Zeroing** **<u>(percentage point change)</u>**

||**Median**|**Mean**|
|---|---|---|
|Cases where dumping margin was<br>lowered but not eliminated|3.9%|3.3%|
|Cases where dumping margin was eliminated|7.2%|13.3%|
|Allcases|4.8%|12.3%|

Source: Compiled from the information in Table 4.

> 32 The difference is statistically significant at the 1% level.

– 41 –
• Quality of regulation is a significant determinant of the divergence between the overall profitability of the concession and its corresponding hurdle rate, explaining around 20 percent of the variation. However, regulatory efforts seem to be more closely associated with keeping tariffs as low as possible for current consumers, than keeping profitability well aligned with hurdle rates of return.

• Price caps lead a significant increase of the probability of renegotiation. This phenomenon brings the convergence of rate of return regulation and price cap regulation even closer, because the outcomes of the renegotiation process often include increasing the number of cost components with an automatic pass-through to tariffs, toward a hybrid system.

• Existence of a regulator at the signing of contract reduces renegotiations. Comparing three specific contracts out of the initial sample, and using the probabilities predicted by the empirical model, we show that had a regulator been in place at the time of awarding the contract, the respective probabilities of renegotiation in the last year of existence of the contract would have been reduced from 29.7, 9.9, and 3.1 percent, to 5.3, 0.3, and 0.2 percent respectively. Also autonomy/independence of regulator matters. When the regulator does not belong to a ministry, there is a significantly lower probability of government-led renegotiation. In that regard, these firm-level results confirm some cross-country studies results that show the importance of experienced and independent regulators in the telecommunication and electricity sectors • The regulators filter and dissuade opportunistic private operator led renegotiation and in the case of government-led renegotiation, the regulator acts as barrier against political opportunism. Regulation attempts to protect investors and ultimately consumers from the opportunistic behavior of the government. Ideally, the regulator’s objective should be to maintain 47
**Figure 8 MENA has lower 'influence'**

<!-- Start of picture text --> 'Influence' by Region<br>mean of influence median of influence<br>AFR EAP ECA LCR MNA SAR<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text -->

**Figure 9  'Influence' by exit year**

<!-- Start of picture text --> Influence as a function of exit year<br>2000 2005 2010 2015<br>ExitFY<br>bandwidth = .8<br>80<br>60<br>40<br>Influence<br>20<br>0<br><!-- End of picture text -->
36 Appendix Table 1. Effect of Remittances on Poverty for Non-Remittance and Remittance-Receiving Households, Ghana, 1998/99

||**Receive no**<br>**remittances**|**Receive**<br>**internal**<br>**remittances**<br>**(from**<br>**Ghana)**|**Receive**<br>**international**<br>**remittances**<br>**(from**<br>**African or**<br>**other**<br>**countries)**|**Receive**<br>**internal and**<br>**international**<br>**remittances**|**Percent**<br>**change**<br>**(Internal**<br>**remittances**<br>**vs. no**<br>**remittances)**|**Percent**<br>**change**<br>**(International**<br>**remittances**<br>**vs. no**<br>**remittances)**|**Percent**<br>**change**<br>**(Internal**<br>**and**<br>**international**<br>**remittances**<br>**vs. no**<br>**remittances)**|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Poverty<br>headcount|39.49|38.01|39.16|37.61|(-3.75)|(-0.84)|(-4.77)|
|(percent)||||||||
|Poverty gap<br>(percent)|13.97|13.30|13.82|13.11|(-4.80)|(-1.08)|(-6.16)|
|Squared<br>poverty gap<br>(percent)|6.71|6.32|6.64|6.21|(-5.82)|(-11.05)|(-7.46)|
|Gini<br>coefficient|0.412|0.419|0.420|0.423|+1.70|+1.94|+2.67|
|Observed<br>mean per<br>capita||||||||
|household<br>expenditure<br>(including<br>remittances)<br>(cedis)|1,099,913|1,142,750|1,125,581|1,160,293|+3.89|+2.33|+5.49|
|N|5,998|5,998|5,998|5,998||||

Notes: All values based on observed – not predicted – per capita household expenditure. Poverty calculations made using poverty line of 684,401 Ghanaian cedis per person per year, which is equivalent to poverty headcount index of 39.5 percent that is cited as Ghana poverty line in Ghana Statistical Service (2000: Table 2). All values are weighted.

In 1999, US $1.00 = 2,394 Ghanaian cedis.

Source: Calculated from 1998/99 Ghana Living Standards Survey (GLSS 4).
6 _Figure 1 :_ **_Subnational governments revenue sources (1999)_** _10 European transition countries_ <!-- Start of picture text --> 0,80<br>0,60<br>0,40<br>0,20<br>0,00<br>(0,20)<br>(0,40)<br>(0,60)<br>(0,80)<br>(1,00)<br>BGR CZE EST HUN LVA LTU POL ROM SVK SVN<br>Grants w ith autonomy Non-tax autonomy Tax autonomy<br>Fiscal dependency Tax Sharing<br><!-- End of picture text --> Source : OECD, _Fiscal Design Across Levels of Government_ , 2001, 2002. Note : Every vertical bar represents 100 % of subnational governments revenue. Parts that appear above the horizontal axis represent the own source revenues (grants with autonomy, non-tax autonomy and tax autonomy). Parts that appear under, with scale in parentheses, represent dependent revenues (fiscal dependency and tax sharing).

Figure 1 illustrates the different sources of subnational governments revenues for the ten countries while Table 1 provides more details. Like GFS data, OECD identifies three main sources of subnational revenues: tax revenues, non-tax revenues and intergovernmental grants. However the OECD data provide additional information on tax revenues that allows further subdivisions into “own tax revenue” and “tax sharing”. The first represents the portion of subnational tax revenue on which subnational governments have significant control (over rates and/or bases). The other, tax sharing, represents the portion on which subnational governments have no significant control. The main source of own tax revenue for the sample used here is taxes on property, while the main source of tax sharing is taxes on income, profits and capital gains. non-tax revenues from OECD surveys include income from business operations, property, administrative fees, duties, and fines. Usually, non-tax revenues are considered as fully controlled by subnational governments. However central government can set some prices for local services or administrative fees. In the liberal interpretation given here to the revenue autonomy, non-
a range of poverty estimates that reflect uncertainty in the estimated pass-through rate and the underlying national accounts growth rates, as well as allow for changes in inequality.

Under our preferred specification, using a pass-through rate of 0.67 applied to growth in Household Final Consumption Expenditure in national accounts between 2015 and 2017, we estimate a national extreme poverty rate (those living below the $1.90 poverty line) for 2017 of 10.4 percent.<sup>5</sup> Using a survey-to-survey estimation, the national poverty rate would be slightly smaller (9.9 percent), but its confidence interval, between [8.1, 11.3] percent, includes the estimates from the pass-through method.<sup>6</sup> Our estimates indicate a considerable decline in poverty since 2011/12, when poverty was estimated at 22.5 percent. Important caveats in the methodologies adopted, as well as some robustness checks to control for different assumptions, indicate that poverty rates could be higher than our preferred estimate. But we find no evidence that poverty has actually increased, or the mean declined, between 2011/12 and 2017/18, thus contradicting estimates that have been circulated in the press based on a leaked report on the 2017/18 survey (see Appendix for further details).

The paper discusses three sources of evidence about the evolution of poverty in India. Section 2 uses alternative survey data, from both public and private organizations, to provide descriptive statistics on household mean consumption. Section 3 describes the survey-to-survey imputation method, whiles section 4 describes the results from the pass-through method. Section 5 summarizes and concludes. The Appendix includes additional robustness checks and further details on the methods.

# **2. Available survey data for India**

The Consumption Expenditure Surveys (CES) by the National Statistics Office are the main source of poverty and inequality statistics in India. These surveys have also informed the World Bank’s poverty monitoring and are used to track progress towards the Sustainable Development Goal (SDG) number 1, which is focused on ending poverty. The release of the 2017/18 round of the consumption expenditure survey was eagerly anticipated, given that the last available expenditure survey dates to 2011/12. As indicated above, the government decided to withhold these data and hence we explore alternative data sources to provide updated estimates of poverty in India.

Table 1 lists several recent household surveys, all of which are nationally representative and include estimates of household consumption. As indicated above, the CES is the official source for poverty estimation. It includes around 400 questions covering expenditures on a comprehensive

> 5 This estimate underpins the World Bank’s estimate of global poverty in 2017, as reported in World Bank (2020). Also see Castañeda Aguilar et al. (2020).

> 6 The interval for the pass-through method [10.0, 10.8], calculated utilizing the confidence interval of the 0.67 passthrough rate, is also within the confidence band of the survey-to-survey method.

3
Measurement errors may be another source of inconsistency in our estimates. In particular the pervasiveness of quantitative restrictions and other import barriers in developing countries implies the need for a distinction between domestic and border prices of imports. Only the domestic price represents an accurate measure of import costs to domestic agents, but national account sourc.s usually rely on border prices information. This discrepancy in prices introduces an extra term into the error in equation 3 which, under a quota system, would be correlated with all the explanatory variables Y(t) and relative (border) prices, resulting again in inconsistent estimates.5 To assess the importance of this factor, two misspecification tests for dynamic simultaneous equation models, the Sargan (1964) and the Godfrey (1976) tests, were used. The Sargan test provides a statistical check on whether errors and instruments are independent, while the Godfrey test is for serial correlation of the error term.

# III. The Results

The model presented in the previous section was applied to a set of developing countries, using annual data for GDP, imports of goods and nonfactor services, and implicit prices for the period 1967-1987. The results are presented in table 1. The cyclical elasticity of income a2 was separately estimated using the two-step least-squares procedure. All the other coefflcients come from the two-stage least-squares (TSLS) estimation of equation 3.

- 5/ This can be easily seen as follows. Suppose that the supply of foreign exchange is not infinitely elastic. Then an increase in Yt will result in a higher domestic price of imports even if border prices remain unchanged.