rafmacalaba/datause-encoder-data huggingface dataset:
39

# Table 11: Brazil - Average Per Capita Income and Income Gap by Household Type and Metropolitan Area, 1984

|Area and<br>Household Type|Average Per<br>Capita Income<br>(Minimum Salaries)|Average<br>Income Gap<br>(percent)|
|---|---|---|
|Recife|||
|All households|1.27||
|FHH*s|1.04|0.22|
|FHHs w/children|0.58|0.57|
|S3o Paulo|||
|All households|2.52||
|FHHs|2.39|0.06|
|FHHs w/ children|1.14|0.56|
|Porto Alegre|||
|All households|_2.54_||
|FHHs|2.56|0.01|
|FHHs w/ children|1.26|0.52|

Source: author's tabulations Note: Income Gap is equal to the average income of the household type (Yp) subtracted from the average income of all the other houses (Yc) divided by Yc or (Yc - YF / Yc). A negative value indicates the average income of household type is above that of other households.
Figure 3.1. Growth Incidence Curves for Indonesia: 1993-1999 <!-- Start of picture text --> 60<br>40<br>20<br>1993-199 6<br>0<br>1996-1999<br>-20<br>-40<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative Percentage of Population<br><!-- End of picture text --> Our analysis is based on the international poverty line of $65.48 per person and per month (or $2.16 per day).

Figures 3.1 and 3.2 present sets of growth incidence curves for the three subperiods: 1993-1996, 1996-1999 and 1999-2002. The fact that the GICs for 1993-1996 and 1999-2002 lie entirely above the zero mark indicates that poverty unambiguously fell between 1993 and 1996, and again between 1999 and 2002. Table 3.2 shows that the proportion of people living below the international poverty line of about 2 dollars a day fell from 61.5 percent in 1993 to 50.5 percent in 1996.

16
distributions displayed in Figure 3. Top outliers (extremely large observations) are more frequently flagged than bottom outliers (extremely small observations) – the latter are, however, present, despite the fact that small values typically do not attract as much attention in welfare analysis.

Besides counting how many observations are flagged, it is important to figure out how influential these observations are. A simple way to assess the sensitivity of any statistics of interest to extreme values is to track the value of the statistic – say, the Gini index – when we progressively drop the largest and smallest observations in the distribution of per capita consumption, a process we can describe as _incremental trimming_ of the distribution. The resulting graph, the Incremental Trimming Curve (ITC), is displayed in Figure 5 for selected statistics.<sup>13</sup> The figure uses data from Mexico’s Encuesta Nacional de Ingresos y Gastos de los Hogares, conducted in 2014, and reports ITCs for eight different statistics of interest: 1) average per capita consumption, 2) Gini index, 3) mean logarithmic deviation, 4) Theil index, 5) Atkinson index (with inequality aversion parameter epsilon equal to 2), 6) poverty headcount ratio, 7) poverty gap, and 8) poverty gap squared.

The ITCs in Figure 5 show that inequality statistics can be extremely sensitive to the presence of extreme values, although to different extents. The Theil index, for instance, experiences a vertical drop – 45.5% to 36% – when as few as 0.5% of large values, or about 100 observations, are excluded from calculations. The Atkinson index with a relatively high value for the inequality aversion parameter (epsilon is set equal to 2 in the figure), on the other hand, is more sensitive to the exclusion of small values. Poverty indices experience smaller and more linear changes in general when extreme values are dropped. The case of Mexico is not peculiar: in fact, it is representative of patterns that emerge in most of the countries examined in this section.

> 13 The ITC is defined in Section 2.3.

26
Table <u>6. Determinants of TFP</u>

|VARIABLES|(1)<br>TFP|(2)<br>TFP|(3)<br>TFP|(4)<br>TFP|(5)<br>TFP-2SLS|
|---|---|---|---|---|---|
|**Labor**||||||
|Proportion of|0.400|0.391|0.766|0.731|0.768**|
|temporary worker|(0.307)|(0.292)|(0.395)|(0.369)|(0.306)|
|Avg. Education of|0.0440|0.0365|0.0890**|0.0906**|0.132**|
|Workers|(0.0282)|(0.0300)|(0.0312)|(0.0291)|(0.0553)|
|**Size and Age**||||||
|Market share|6.899**|6.553**|4.706*|4.393*|12.71***|
||(2.636)|(2.432)|(2.010)|(1.720)|(2.258)|
|Young|-0.0117|-0.0265|0.0918|0.108|0.149|
||(0.0376)|(0.0302)|(0.116)|(0.122)|(0.133)|
|Mature|0.0744|0.0734|0.0838|0.0887|0.0258|
||(0.0728)|(0.0698)|(0.0692)|(0.0719)|(0.0390)|
|**Access to finance**||||||
|Credit line or overdraft||0.266***|0.267**|0.200**|0.165***|
|Facility||(0.0649)|(0.0812)|(0.0703)|(0.0517)|
|**Investment Climate (%)**||||||
|Cost of insecurity|||0.0198|0.0142|0.0157*|
||||(0.0107)|(0.00949)|(0.009)|
|Cost power outage|||-0.00646***|-0.00529**|-0.00490***|
||||(0.00142)|(0.00184)|(0.00235)|
|Bribe|||0.0239***|0.0232**|0.0405*|
||||(0.00525)|(0.00593)|(0.0217)|
|Time Tax|||0.00168|-0.00113|-0.00187|
||||(0.00547)|(0.00723)|(0.00748)|
|**Outward-orientation**||||||
|Exporter||||0.434*|0.379|
|||||(0.180)|(0.487)|
|**Sector**||||||
|Manufacturing|-0.599|-0.541|-0.374|-0.206|0.420|
||(0.667)|(0.633)|(0.447)|(0.558)|(0.504)|
|Observations|2,708|2,705|1,402|1,402|1,275|
|R-squared|0.073|0.086|0.351|0.365|0.397|
|Weak identification test|||||15.24|
|Region FE|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|

Source: Authors based on the data from the Enterprise Surveys 2007/2009 and 2014.

Note: TFP are obtained using Levinshon and Petrin (2003)’s approach. Robust standard errors clustered at the States level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All the estimates use the weights from the survey.

22
# 4.2. Availability of Vaccines

Vaccines are pivotal and cost-effective solutions for individual and collective well-being (Owais, Hanif, Siddiqui, Agha, & Zaidi, 2011; Ramalingaswami, 1989; Schuchat, 2011). In Kenya, more than one out of seven children die before the age of five, largely from causes related to preventable diseases for which the best response is to promote immunization (CDC Kenya, 2017). From our sample, 78.45% of the facilities provide vaccination services, and only these are asked about the availability of vaccines. The survey also gathered information about whether the vaccines were stored at the facility and whether the center used a fridge for storing vaccines.<sup>18</sup> All the vaccines mentioned in the survey questions require refrigeration (i.e., Rubella, Polio (OPV/IPV), Rotavirus, Pentavalent, Pneumonia, Bacille Calmette-Guerin (BCG), and Vitamin A). The relationship between the percentage of available vaccines and access to electricity and fridge is expected to be positive and significant, and it is indeed the case.

_Table 3. Linear regression for vaccines availability_

||(1)<br>% Vaccines|(2)<br>% Vaccines|(3)<br>% Vaccines|
|---|---|---|---|
|Stable Electricity vs No Access|10.47***|1.18|1.07|
||(2.63)|(1.32)|(1.25)|
|Unstable Electricity vs No Access|11.21***|0.98|0.90|
||(2.66)|(1.30)|(1.23)|
|Having a fridge||5.46**<br>(2.70)||
|Having a working fridge|||10.93***<br>(2.71)|
|Uses ICT for supply chain||0.01|0.01|
|||(1.21)|(1.22)|
|Second quantile road distance|5.52***|1.56|1.61|
||(1.75)|(1.17)|(1.18)|
|Third quantile road distance|4.80**|1.57|1.55|
||(1.88)|(1.15)|(1.14)|
|Fourth quantile road distance|3.09|1.32|1.35|
||(1.96)|(1.14)|(1.13)|
|# Outpatient last 3 months|0.00**|0.00**|0.00**|
||(0.00)|(0.00)|(0.00)|
|Catchment pop.|0.09|0.03|0.03|
||(0.06)|(0.04)|(0.04)|
|Constant|71.60***|88.67***|84.53***|
||(7.05)|(4.19)|(4.54)|
|Observations|1,994|1,794|1,794|
|R-squared|0.11|0.09|0.12|
|Controls|Yes|Yes|Yes|
|Fixed Effects|County|County|County|
|Mean|89.64|95.67|95.67|
|Robust standard errors in paren|theses *** p_<_0|.01, ** p_<_0.05,|* p_<_0.1|

Column 1 in Table 3 shows the importance of access to electricity for the availability of vaccines. Having access to electricity is associated with an increase of 10 to more than 11 percentage points in the percentage

> 18 A description of the scheme of questions is in the appendix, Figure A1 .

14
Policy Research Working Paper 10272

# **Abstract**

This paper studies the effect of the 2018 drought on household consumption and poverty in Afghanistan, a semi-arid and conflict-affected country. The paper combines geolocated household data with remote-sensing weather data on precipitation, vegetation, and temperature. The findings show that drought-like conditions decreased monthly per capita consumption expenditures and hence increased poverty, with a highly nonlinear relationship between consumption and weather shocks. When forced to cut back, households reduced nonfood consumption to maintain their food consumption; only under severe stress did they reduce food consumption. Households that owned agricultural land were more resilient to the 2018 drought. Based on the historical distribution of weather shocks, estimates of vulnerability to poverty suggest that 62.5 percent of people have a one in four probability of falling into poverty due to weather shocks. Given that climate change will exacerbate the frequency and severity of future droughts, these findings highlight the importance of investments in resilience and shock-responsive social protection to supplement urgent humanitarian assistance.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at nkochhar@worldbank.org and eknippenberg@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
# APPENDIX B: ROBUSTNESS OF PROJECT RANKINGS (NOT FOR PUBLICATION)

Table B1: Spearman (rank) correlation of changes in (log) market access under different assumptions, when all BRI projects are built in complement

|||||No cost<br>to switch|||Halfway||No|Using|Using<br>pop.<br>instead of||||Add<br>external|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||θ=8.1,|between||Add retail|reduction|θ=15.7,|terminal|simple|GDP for|Oxford|No tariffs|ESCAP-W|B<br>markets|
|||Labor share|<br>for agri-|road and|Labor share|<br>distribution|in trade|for|handling|average|market|Economic|s<br>or border|tariffs and|(Rest of|
||Baseline|β=0.8|culture|rail|β=0|costs|costs|mining|charges|tariff|size|GDP|costs|border costs|<br>World)|
|Baseline|1|1|0.99|1|0.98|0.98|0.97|0.97|0.96|0.94|0.91|0.91|0.85|0.76|0.71|
|Labor share||||||||||||||||
|β=0.8|1|1|1|0.99|0.98|0.98|0.98|0.97|0.96|0.95|0.92|0.91|0.85|0.76|0.71|
|θ=8.11, for<br>agriculture|0.99|1|1|0.99|0.98|0.98|0.98|0.98|0.96|0.94|0.92|0.92|0.85|0.75|0.72|
|No cost to switch||||||||||||||||
|between road and||||||||||||||||
|rail|1|0.99|0.99|1|0.98|0.98|0.97|0.97|0.96|0.94|0.92|0.91|0.85|0.76|0.72|
|Labor share<br>β=0|0.98|0.98|0.98|0.98|1|0.97|0.96|0.97|0.94|0.93|0.93|0.93|0.87|0.77|0.71|
|Add retail<br>distribution costs|0.98|0.98|0.98|0.98|0.97|1|0.96|0.96|0.95|0.96|0.9|0.89|0.86|0.77|0.71|
|Halfway reduction<br>in trade costs|0.97|0.98|0.98|0.97|0.96|0.96|1|0.96|0.95|0.92|0.89|0.89|0.83|0.72|0.69|
|θ=15.72, for<br>mining|0.97|0.97|0.98|0.97|0.97|0.96|0.96|1|0.93|0.92|0.92|0.92|0.83|0.74|0.72|
|No terminal<br>handling charges|0.96|0.96|0.96|0.96|0.94|0.95|0.95|0.93|1|0.92|0.86|0.86|0.81|0.73|0.7|
|Using simple<br>average tariff|0.94|0.95|0.94|0.94|0.93|0.96|0.92|0.92|0.92|1|0.88|0.86|0.83|0.78|0.7|
|Using population||||||||||||||||
|instead of GDP<br>for market size|0.91|0.92|0.92|0.92|0.93|0.9|0.89|0.92|0.86|0.88|1|0.97|0.85|0.81|0.77|
|Oxford<br>Economics GDP|0.91|0.91|0.92|0.91|0.93|0.89|0.89|0.92|0.86|0.86|0.97|1|0.85|0.81|0.8|
|No tariffs or||||||||||||||||
|border costs|0.85|0.85|0.85|0.85|0.87|0.86|0.83|0.83|0.81|0.83|0.85|0.85|1|0.83|0.7|

78
<u>Table 2. Sample Coverage by Country</u>

||Number of<br>contracts|Number of<br>bids|
|---|---|---|
|China|20|90|
|Croatia|4|20|
|India|5|11|
|Iran|6|38|
|Kazakhstan|1|2|
|Mexico|9|31|
|Morocco|1|6|
|Peru|4|16|
|Tanzania|4|12|
|Thailand|8|52|
|Viet Nam|3|7|
|Yemen|4|16|
|Total|69|301|

Source: Author’s calculation.

<u>Table 3. Summary Statistics</u>

|Variable|Abbr.|Obs.|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Bid amount 1/|_b_|301|14.95|23.64|0.33|276.66|
|Number of bidders|_n_|301|6.64|3.67|2.00|16.<br>00|
|Dummy for technical evaluation|_D _<sup>_TE_</sup>|301|0.72|0.45|0|1|
|Dummy for water supply projects|_D _<sup>_WATER_</sup>|301|0.41|0.49|0|1|
|Treatment capacity (million m3 per day)|_x_1|301|0.09|0.17|0|0.<br>60|
|Total length of distribution pipes (km)|_x_2|301|35.81|61.58|0|375.58|
|Engineering working months|_MONTH_|301|26.06|13.90|3.00|72.00|
|Past transport ODA in each country 1/|_CAID_|301|285|354|3|889|
|Past transport ODA in the world 1/|_WAID_|301|6,192|1,111|4,418|9,143|

1/ In millions of constant 2005 U.S. dollars.

Source: Author’s calculation.

# **III. ESTIMATION RESULTS AND IMPLICATIONS**

The truncated negative binomial regression model indicates that the bidder entry, hence the number of bidders, is endogenously determined, particularly by the size of contract (Table 4). The null hypothesis that _n_ is not affected by the explanatory variables in _x_ ’s, _Z_ and _W_ can be strongly rejected by the chi-squared test; the conventional Wald test statistics are estimated at 14.80 and 36.27. Especially, the variables for technical specifications in _x_ ’s are statistically significant. Based on the model with their quadratic terms, fewer bidders would participate in the competitive bidding for technically larger specifications. The elasticities are evaluated at mean values. Not surprisingly, this is supportive of the auctioneer’s unbundling tendency toward smaller lot contracts to promote more competition in auctions.
9 are observed if we widen the scope by replacing the United States with all developed countries and Bangladesh with all developing countries (developed countries are highincome countries in 2017 according to the World Bank classification). To obtain these profiles, we obtain the average wage differential for each country (1990-2016) and construct the mean wage differentials for each group of countries weighted by the population of each country in 2017. The “median” developed (developing) country is Belgium (Jordan).

**Returns.** Measures of the returns to experience should take the integral below the profiles.<sup>12</sup> For each bin (5, 10, etc.) one by one, we estimate an annualized return and then take the mean of the annualized returns across the seven bins to obtain the _mean annualized return_ throughout the experience distribution. More precisely, for individuals belonging to bin _e_ , we obtain the _bin-specific annualized return_ as (( _βe_ + 1)<sup>(1</sup><sup>_/e_)</sup> _−_ 1) _∗_ 100, with _βe_ being the estimated coefficient for bin _e_ in equation (1). For this subgroup of individuals in the society, it tells us by how many percentage points wages increased on average for each extra year of experience. We then take the average of these seven bin-specific annualized returns so that each bin is represented.<sup>13</sup>

We find average population-weighted returns of 2.0% for the world. In contrast, returns to education, measured by the coefficient of schooling, are 4 times higher, at 8.2%. Our specification has several advantages over the Mincerian specification that includes a quadratic function of experience: (i) With seven coefficients, it is more flexible and informative, especially given that 34 countries have non-monotonic wageexperience profiles; (ii) We construct returns that are independent of the distribution of experience in the country. Since we keep samples with at least 10 observations in each bin, and then give each bin the same weight, the constructed returns are less affected by the facts that developed countries and developing countries have more experienced workers and more inexperienced workers, respectively;<sup>14</sup> and (iii) We can construct measures using only specific parts of the experience distribution (e.g., 0-25 or 0-15),

> 12Lagakos et al. (2018b) calls this the “sum of heights”.

> 13In our view, it is important to use information from the seven experience coefficients. However, we acknowledge that there is no right or wrong way to use this information to construct the returns. 14A related issue with the Mincerian specification is how to calculate returns from the coefficients of experience ( _β_ 1) and its square ( _β_ 2). It is common to calculate the returns as _β_ 1 + _β_ 2 _×_ the median number of years of experience. However, this number varies across countries. This method then captures the _local_ return at the median experience level, not for the full distribution of experience in the country.
R9 mu Altshuler, Rosanne, **and** Alan J. Auerbach, 1989, 'The<sup>Importance of Tax Law</sup> Asymmetries: An Empirical Investigation," Ouarterly Journal of Economics, forthcoming.

Andersson, Krister, Kenji Aramaki, A. Lans Bovenberg and<sup>Sheetal K. Chand,</sup> 1990, "Tax Incentives and Irnternational Capital Flows: The Case of the United States and Japan," in Assaf Razin and Joel Slemrod, eds.,<sup>International</sup> AsLects of Taxation, Chicago: University of Chicago<sup>Press, forthwoming.</sup> Auerbach, Alan J., 1983a, "Corporate Taxation in<sup>the United States," Brookinus</sup> PaRers on Economic Activity, 2, pp. 451-505.

----------, 1983b, "Taxation, Corporate Financial Policy and the<sup>Cost of</sup> Capital," Journal of Economic Literature, September,<sup>pp. 905-940.</sup>

----------, 198v, 'Tax Reform and Adjustment Costs: The Impact<sup>on Investment</sup> and Market Value," International Economic Review, December, forthcoming.

.-------- and Dale W. Jorgenson, 1980, "Inflation-Proof Depreciation of Assets," Harvard Business Review, September/October, pp. 113-18.

---------- and James R. Hines, 1988, "Investment Tax Incentives and Frequent Tax Reforms," American Economic Review, May, pp. 211-16.

--------- and James M. Poterba, 1987, "Tax Loss Carryforwards<sup>and Corporate</sup> Tax Incentives," in M. Feldstein, ed., The Effects of Taxation on Capital Accumulatin, pp. 305-38.

Doyle, Chris and Sweder van Wijnbergen, 1984, "Taxation<sup>of Foreign</sup> Mu?'inationals: a Sequential Bargaining Approach to Tax Holidays,"<sup>Institute</sup> fcL International Economic Studies seminar paper no. 284.

Fazzari, Steven M., R. Glenn Hubbard and Bruce Petersen, 1988<sup>"Financing</sup> Constraints and Corporate Investment," Brookings Papers on Economic Activity, 1; pp. 141-195.

Gordon, Roger, 1986, "Taxation and Investment in a World<sup>Economy," American</sup> Economic Review, December, pp. 1086-1102.

Guisinger, Stephen, 1988, "Total Protection: A<sup>New Measure of the Impact of</sup> Government Interventions on Investment Profitability, mimeo.

Hansson, Ingemar and Charles Stuart, 1989, "Why<sup>is Investment Subsidized?,"</sup> International Economic Review, August, pp. 549-59.

Hartman, David, 1985, "Tax Policy and Foreign<sup>Direct Investment,"</sup> Journal of Public Economics, February, pp. 107-21.

Hayashi, Fumio, 1982, 'Tobin's Marginal q and<sup>Average q: A Neoclassical</sup> Interpretation," Econometrica, January, pp. 213-24.

39
refinement", and as a consequence we have left "...replication and peer review as impossibilities." With one important caveat we think this line of criticism is entirely unfounded. First, the caveat. Several years ago we decided to include the Country Policy and Institutional Assessment (CPIA) ratings from the World Bank, African Development Bank, and Asian Development Bank among the sources for the WGI. We included these data sources based on our judgement that they provided valuable information on the dimensions of governance we wanted to measure. Unfortunately, however, only as of 2005 have these institutions fully publicly disclosed the CPIA data, and then only for the set of low-income countries eligible for concessional lending from the International Development Association (IDA). For a few years earlier the CPIA data was disclosed, but at a more aggregated level and only by quintiles, for the same set of IDA-eligible countries. And prior to this the CPIAs have been confidential, which we have had to respect.

We have however fully disclosed all of the remaining data used to construct the WGI. Much of this data is already in the public domain, and we have simply reproduced the data as we have used it in the WGI simply for the convenience of users. The data from our proprietary data sources is commercially available to anyone, and moreover these data sources have kindly agreed to allow us to post their proprietary data, again exactly in the way that we use it in the WGI. This means that users of the WGI can visit our website (www.govindicators.org) and immediately retrieve the data from our individual sources exactly as it enters into the WGI.

T's critique of the availability of our data for replication and peer review can thus only be based on the fact that, in some cases where we average individual questions from a single data source before including it in our aggregate indicators, we have thus far not posted on our website the data at this lowest level of aggregation.<sup>22</sup> We think this concern is marginal. This critique applies only to six of our commercial data sources that

> 22 Our primary reason for posting the data at this level of aggregation is because we thought it would be most useful for helping users understand how levels and changes in our aggregate indicators depend on changes in the underlying data sources.  If there is sufficient demand (which so far has been negligible) we may subsequently explore the possibility of posting the fully disaggregated data.

27