rafmacalaba/data-use-mentions-extended huggingface dataset:
0.08*|
|<br>attendingschool++|[0.75]|[0.78]|[0.70]|[0.63]|[0.71]|[0.64]|[0.60]||
|N_(all households)_|5840|1824|638|516|1987|541|334||
|N_(households with children)_|4205|962|286|459|1735|441|322||

_Note: “Lost” or “Gained” columns refers to groups of households who had at least one more/one fewer child aged 0-17 at the 6 months follow-up; columns 2-4 are limited to the Control group and columns 5-7 the Treatment group; the value displayed in the "Differences (6-4)" column are the differences in the means across the groups in columns 6 and 4, t-test significance level p< 0.01 - ***, 0.05 - **, 0.1 - *; dependency ratio defined as the ratio of dependents (children under 18 and adults 60+) to working age adults (aged 18-59 years old);_<sup>_++_</sup> _sample limited to household with children; all expenditure and debt values are deflated across regions and months into average 2017 Turkish Lira and are winsorized at the 99th percentile; The food consumption score (FCS), reduced coping strategies index (rCSI), and livelihoods coping strategies index (LCSI) are calculated as per the WFP's methodology described in section 3, for the FCS a higher score indicates better food consumption outcomes, for the rCSI and LCSI a lower score indicates a better coping strategies outcome (i.e. fewer coping strategies employed)._ 47
<!-- Start of picture text --> 1991 1996 2001 2006 2011 2015 1991 1996 2001 2006 2011 2015<br>Year Year<br>All situations No AF VN−CN SY All situations No AF VN−CN SY<br>(a) Average duration of exile (b) Median duration of exile<br>15 15<br>10 10<br>5 5<br>Average duration (years) Median duration (years)<br>0 0<br><!-- End of picture text --> Figure 5: Exile duration statistics In the absence of any refugee flow (inflow or outflow), the averages should increase by one year for each additional year, i.e. the slope of the line should be equal to 1. Any downward deviation from that “45-degree line” can be caused by an outflow of refugees who have been in exile for longer than the average (due to either returns, integration in host countries, or any other reason associated with discontinuation of registration). It may however also be caused by a net inflow of new refugees who would reduce the average duration of exile. Conversely, when the line is steeper than the 45-degree line, it indicates that outflow mainly consisted of refugees that had not been exiled for long, i.e. less than average.

Since the late 1990s, the mean duration of exile has consistently remained between 10 and 15 years. The relatively low average duration in the 1990s corresponds to a peak flow of refugees leaving their country, as a result of the dislocation of the Soviet Union, of the conflicts in Former Yugoslavia, and of violence in Somalia and the genocide in Rwanda. In this case, a low average duration is the symptom of a deterioration of the global situation. From 1996 on, the mean duration increases along the “45-degree line”, which reflects the relative moderation of in- or outmovements until 1998-1999 and a large return of Rwandans which
risk, _capital-grant_ contracts alleviate the financing risk. _Annuity-based_ contracts insulate the project company from the demand risk, but the company is still exposed to the full financing risk.

In 2012, an unusually high number of PPP contracts were awarded based on _premium payments_ and about half of them were canceled (figure 13). The data shows that this increase occurred partly because of a decrease in the development of annuity-based projects by the NHAI after 2011 and the bidding behavior of private sponsors. The decrease in the capital-subsidy contracts indicates that private sponsors were more optimistic about the projects offered in 2012 compared to the earlier toll-based projects.

# **Figure 13 Number of national highway public-private partnership projects in India by cancellation status, contract type and financial closure year**

<!-- Start of picture text --> 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014<br>Premium Capital grant Annuity<br> Not canceled Canceled<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source:_ PPI Database and National Highways Authority of India.

_Note:_ Concession agreement year is used if financial closure had not been achieved by the time of cancellation.

To assess which contract features could be associated with cancellations, the following equation is estimated using the maximum likelihood method: 0 p 1 � (7) ii ii,p ii where the dependent variable is the event of cancellation, CCPPaacceePPeeaa = ff�ββ + XX ββ + PP is the logistic function, and p 0 p 1 � is the logistic function, and p are project-specific variables. ii,p ff(. ) XX The contract characteristics are introduced in the regression in three different ways. First, they are introduced as indicator variables for each type of contract in columns (1) through (3) in table 4. Second, the net present value (NPV
Thus recent research on corruption has shifted from expert opinion on the overall level of corruption to firm-level surveys on individual experiences with corruption. This allows for directly measuring corruption and leaves room for variation across firms. Unfortunately, this firm level analysis requires us to rely on self-assessments of firms. If these questions are politically sensitive, personally embarrassing, or could lead to criminal sanctions, we should be dubious about the incentives of firms to provide truthful answers.

One strategy used within surveys is to ask the enumerator to evaluate the level of truthfulness of the respondent. This strategy has the strength of allowing enumerators, often during face to face interviews, evaluate the truthfulness of respondents. Yet these are based on perception that can be biased, and they are based on overall evaluations of truthfulness, and not linked to specific questions. We directly illustrate the limits of this approach within our survey.

To evaluate the extent of truthfulness we utilize a randomized response technique (RRT). This strategy is simply to have respondents flip a coin for each question we ask. If the coin turns up heads, the respondent automatically responds as “yes”, and is instructed to answer truthfully if the coin turns up tails. If respondents are answering questions truthfully, at least half of the response should be “yes” (since 50% of the coin flips should be heads). If we find that responses to corruption questions answer “no” more than half of the time, there is systematic false responses on the survey. Our survey results do indeed show systematic false responses on politically sensitive questions such as levels of corruption and the extent of tax evasion. We find that the same types of firms 3
21 shares of NTRI2, which includes NTRI as well as household services and personal services (using I2D2’s classification). Finally, the results indicate significantly higher self/unpaid employment in DEINDU countries compared to MFGSERV countries. While I2D2 allows us to consider more countries, the database consists of surveys, not censuses, and classical measurement error in the dependent variable should affect precision.

Notwithstanding, relying on various measures, specifications, and databases, the evidence presented here suggests that cities in NRXGDP and DEINDU countries may be disproportionately consumption cities relative to the cities in MFGSERV countries. **5. Mechanisms: Urban Construction and Consumption Cities** We now explore some of the mechanisms behind the rise of consumption cities. In particular, cross-country differences in urban construction may stem from “white elephant” projects financed by resource rents. In the absence of a global historical data base on “white elephants”, we examine whether exports of natural resources correlate with the over-construction of tall whose economic rationale be very buildings, might questionable as discussed below. As shown by Ahlfeldt and Barr (2020), construction costs increase with tall are also more as non-linearly height. Very buildings inefficient they require more occupiable square feet to be dedicated to essential building functions (e.g., due to elevators).

We use a novel data set that inventories all the world’s “tall buildings”, with information on their of construction and The database is maintained year height.<sup>26</sup> by the _Council on Tall Buildings and Urban Habitat_ (CTBUH) and is publicly available online.<sup>27</sup> The database mostly captures buildings above 80 meters (Jedwab et al., 2020b). Since some countries have no such buildings, to avoid having their stock of heights equal to 0 when using logs, we consider for each country buildings above 80 meters as well as their 10 tallest buildings, even if some of them are below 80 meters. In the end,
, respectively), with Lower Egypt and Frontier Governorates in between. Moreover, the gaps between the best and worst region have been substantially reduced between 2000 and 2009, particularly in housing services, but much less in nutrition and early childhood development. However, based on the same aggregate HOIs, Lower Egypt offered in 2009 better opportunities than Frontier Governorates for children 0-4 years of age, but not for aggregate opportunities of housing services of children 9-15 years of age (79 points in Frontier Governorates compared to 75 in Upper Egypt). In contrast, a count of the number of opportunities for which each region offered the first best _or_ second best opportunities (see Table A-4) shows the lead of the Metropolitan region with 14 HOIs (of 16 HOIs in total), followed by Lower Egypt with 11, Upper Egypt with four, and Frontier Governorates with only three. This would suggest considerable lack of opportunities in the Frontier Governorates, compared to Upper Egypt and Lower Egypt, contradicting the regional ranking based on aggregate HOIs. However, we should follow the interregional ranking based on the more rigorous aggregate HOIs that shows that Frontier Governorates offers better 15
With regard to fertilizer usage, we employ three different estimations: a probit model for fertilizer usage, a Tobit model for the amount of fertilizer application in kilograms, and an OLS model on non-zero values of fertilizer purchases (Tables 4, 5, and 6, respectively). Rwanda is excluded from these regressions since the EICV survey did not collect this information. For fertilizer use (Table 4), the coefficients on the recall variables are not significant, suggesting the lack of recall bias, which is consistent with fertilizer application being a decision of high salience for farmers. Even after introducing the interaction terms, again, we find little evidence of recall bias overall. A few exceptions are by gender of the head in Malawi. There are recall effects for fertilizer use on maize plots for female-headed households and on tobacco plots for male-headed households, with underestimation of use when the household is interviewed further from the harvest (whereas in Table 3 both male and female-headed households under-report hired labor for maize plots when interviewed further from the harvest date).

The level of fertilizer use is presented in Table 5. For maize in Kenya, although not significant, the results suggest that smaller landholders are more likely to under-report with recall duration. The result is the opposite for Malawi tobacco, where under-reporting is statistically significant for larger landholders; there is also more under-reporting among male-headed households. For maize in Malawi, overall fertilizer use levels decline with recall duration, although the results are not statistically significant. This recall decay is more concentrated among female farmers and is offset with the education of the head and smaller landholdings.

With respect to the value of fertilizer and the OLS results (Table 6), Kenyan farmers report larger values when interviewed further from harvest and the value is associated with smaller landholdings. Combined with the kilo quantity results in the previous table, this suggests that smaller landholders are over-estimating the price paid for fertilizer when interviewed further from the purchase itself. This is also the case for female heads growing maize in Malawi; they
**Limitations associated with the use of microdata and the reconciliation with macroeconomic statistics should be considered** . Nationally representative household surveys are incorporated in the CGE modeling framework to provide information related to the contribution of labor to value added, disaggregated by sector and type of worker. To incorporate this information, one must reconcile macro and microdata sources. This reconciliation must deal with the fact that (1) the aggregates obtained from microeconomic data do not add up to the aggregate statistics in national statistics; and (2) microeconomic data may not provide accurate information about some very small sectors.

# 6. Concluding remarks

**This paper discussed a simulation framework used for the ex-ante assessment of the distributional aspects of trade, at the sectoral and sub-national district levels.** In this regard, the assessment of the distributional aspects needs to operate within a systematic framework. The CGE-GIDD is a macro and microeconomic model linked through a set of linked aggregate variables, including labor supply, skill formation, and real earnings. The model operates in a top-down fashion. The CGE model relies heavily on economic theory and provides aggregate and complex interactions within consistent market-clearing mechanisms. The microsimulation can project the behavior of consumers and workers at a very disaggregated level. The CGE-GIDD allows us to track patterns from policy shocks, to macro and sectoral outputs, and finally to disaggregated effects on labor at a sub-national sectoral level. On a technical level, the labor market specification is a key element that drives the distributional results. In such circumstances, the assumptions used to model and calibrate the labor market and the complementarities between workers and capital have a great influence on the results.

**A set of relevant trade policy simulations were assessed for the case of Sri Lanka** . An ex-ante evaluation requires the creation of a counterfactual simulation that serves as a comparison. The baseline follows historical trends and is key in understanding the net effect of the alternative policy scenarios. The alternative scenarios contemplate i. a unilateral para-tariff liberalization, ii. FTA with
Policy Research Working Paper 10925

# **Abstract**

This paper exploits gaps between observed and recently forecasted Gross Domestic Product growth in export destinations to estimate the effects of unexpected demand shocks on worker compensation. Using employer-employee panel data, the paper finds that the revenues from these demand shocks are partly transmitted to workers in the form of higher average wages, especially close to the top of the within-firm wage distribution. These wage responses occur both in the form of higher overtime payment and base wage increases. The findings also show that there are significant increases in bonus-related pay in firms managed by highskilled managers, and the unequal average distribution of unexpected revenues is also mainly driven by wage effects in the same subset of firms. This suggests that the way in which revenues from unexpected demand shocks are transmitted to workers is significantly related to managerial capabilities.

This paper is a product of the Development Research Group, Development Economics. 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 pbastos@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 use of the Fischer Z-transformation in this context.

> 26The standardization in the units of measurement is automated and works by parsing the text in the WFP data base that describes the food items and inferring multipliers that standardize the prices to comparable units. In particular, each text string is parsed as _amount×unit_ and a multiplier for food item _d_ is calculated as _wd_ = _amount_ <u>1</u><sup>_c_,where</sup><sup>_c_isaconversionfactorfrom</sup><sup>_unit_toeitherKilogramsorLiters.</sup> For example, the text string 10 _Pounds of Rice_ would result in a multiplier of _wrice_ = 101<sup>_×_</sup> 0 _._ 4535921<sup>=</sup> 0 _._ 2204624, while 1 _kg of wheat_ would simply result in a weight of _wwheat_ = 1. This strategy is usually
1 percent per annum. However, if one assumes no expenditure to have occurred under the older program, this underestimates NREGA’s impact on agricultural wages by 43%, making it insignificantly different from zero. This null result without SGRY is consistent with the findings reported by Berg et al. (2018) who also use AWI data.<sup>4</sup> This supports the hypothesis that ignoring the older program leads to an overestimation of the “dosage” of treatment by public workfares, which downward biases the results.

Second, I use data from National Sample Surveys (NSS) to estimate a baseline specification that evaluates the effect of NREGA on daily casual earnings without controlling for expenditure under SGRY. The results indicate

> 2 Similarly, Imbert and Papp (2015) find that increases in private sector wages represent “a third of the total welfare gain for the poor.”

> 3 I acknowledge the possibility of some residual omitted variable bias even in the preferred specification with all possible controls. However, using statistics on the selection of observables and unobservables (Altonji et al. 2005; Oster 2019), I show that the degree of omitted variable bias is inconsequential and unlikely to overturn the main results.

> 4 Berg et al. (2018) do find NREGA to increase wages by 4.3% in a different specification where they use “exposure” to NREGA by adding the number of months a district is under NREGA. I abstain from discussing this specification as the focus of this paper is to highlight how the contemporaneous impact of public works policy may be under-reported if we ignore SGRY. See Bahal (2016) for a theoretical and empirical exposition on the spot vs. stock effects of public works.
the adoption of e-filing leads to the reduction of the number of tax payments and the time to prepare and pay taxes.

# _4.3.2. Firm-level evidence_

Table 3, columns III and VI, and Table B.4 in the Online Appendix present the results of estimating specification (3) using the firm-level data for e-filing. We find a negative and significant effect on the e-filing and income interaction term only for the number of tax visits. With respect to other development measures (Table B.4), we find that higher gross secondary school enrollment and the number of Internet users in a country significantly reduces the number of visits by tax officials, perception of tax administration as an obstacle, and the incidence to bribe tax officials when e-filing is adopted. In the case of the business freedom index, we also find negative and significant coefficients on the interaction terms when estimating the impact on the perception of tax administration as an obstacle and the incidence of bribery. Only for the rule of law indicator as a proxy for the level of development do we find a negative and significant coefficient on the interaction term for the probability of being visited by a tax official. These results, in general, are in line with the country-level evidence. However, they also show that for a sample of developing countries proxies for development and institutions are more heterogeneous.

12
# 1. Introduction

India’s growth performance over the past three decades has been nothing short of spectacular. Between 1985 and 2017, gross domestic product (GDP) per capita grew on average 4.5 percent a year (World Bank 2018). As a result, India is catching up with more advanced economies, defying the claim that the world suffers from “divergence, big time” (Pritchett 1997). Growth patterns at the subnational level are less encouraging, however. Income disparities within India are wide, to the point of justifying a distinction between high‐ and low‐income states. And a key question is thus whether poorer locations are catching up with the richer ones as the country experiences rapid growth.

The consensus among economists is that within itself India is characterized by “divergence, big time.” Datt and Ravallion (2002) found that the states that were richer in the 1980s grew faster in the 1990s. Kumar and Subramanian (2012) documented not only a continuation of state‐level divergence in the 2000s, but also an increase in its magnitude. In the most positive assessment, Ghate and Wright (2013) concluded that there was no clear evidence in favor of absolute convergence or absolute divergence across states from the 1980s to the 2000s. A recent Economic Survey by the government of India featured this debate, showing the sharp contrast between growth divergence in India and growth convergence within China and other large federations (Ministry of Finance 2017).

However, the same Economic Survey also pointed to new evidence that nonmonetary measures of well‐ being are improving across the board and especially in low‐income states. The Survey also reported an increase in cross‐state transactions and cross‐state migration, two trends that could support convergence (Ministry of Finance 2017). Unlike previous research, a recent study using the intensity of nighttime light to proxy for income found evidence of convergence across Indian states, districts, and cities (Tewari and Godfrey 2016).

In this paper, we