rafmacalaba/data-use-mentions-extended huggingface dataset:
e.g. office stationery versus defense procurement (National Audit Office, 2010).

A high quality quasi-experimental study aiming to estimate the amount of passive and active waste (that is low administrative capacity versus corruption) in Italian standardized goods purchases also touches on the effect of central framework agreements. It finds that centralized purchasing of standard goods such as office stationery achieved a 34%-78% price reduction according to the invoices paid by procuring bodies reaping the benefits of demand aggregation as the central agency can save on transaction costs and has more buyer power that can be exploited to obtain lower prices (Bandiera et al., 2009). Considerably dampening the positive expectations, another Italian study looking at the implementation of the centralized framework agreements by receiving public bodies found that roughly one-third of procuring bodies (30.75%) received products in substantial breech of contractual terms (Albano & Zampino, 2012). More alarmingly, a negligible portion of these procuring bodies have actually issued a penalty to the supplier according to the contract (2.49%). Taken together, such findings highlight the potential for savings through centralized procurement with the risks carried by dissociating tendering and contract management.

Examining the case of CONSIP in Italy, Decarolis (2018), using procurement data on all public contracts awarded between 2015 and 2017, finds that administrations expecting to lose their ability to contract independently game the centralization requirements in three ways. In the short run, they anticipate their purchases to avoid delegating to a central body. In the longer run, they both manipulate contract values, breaking down purchases into smaller lots of amounts below the thresholds driving centralization requirements, and, when given the option, aggregate into the smallest types of centralized purchasing bodies. These three distortions partially offset the potential benefits of the centralization reforms (Decarolis, 2018).

Another study using a unique dataset on tender prices of selected drugs for hospital usage provided by a sample of 52 Italian local health service providers between 2009 and 2012 tests which procurement system (
0.08|=-0.07_|0.07|~—--0.10|
|Household dependencyratio|0.04|0.04|0.04|0.05|0.05|Household dependencyratio|0.09|0.10|0.10|0.11|O11|
|Population density|-0.01**|-0.01**<br>=|-0.01**|-0.01**|-0.01**|Population density|-0.05**|-0.05**|-0.05**|-0.05**|— -0.05**|
|R-squared||||||R-squared||||||
|Within|0.15|0.14|0.16|0.13|0.13|Within|0.23|0.23|0.23|0.22|0.22|
|Between|0.07|0.07|0.07|0.06|0.06|Between|0.27|0.27|0.26|0.26|0.26|
|Overal<br>|0.04<br>|0.04<br>|0.04<br>|0.03<br>|0.04<br>|Overal<br>|0.13<br>|0.13<br>|0.13<br>|0.13<br>|0.13<br>|
|Observations|959|959|959|959-|959|Observations|959|959|959|959|«959|
Policy Research Working Paper 6433

# **Abstract**

The agricultural and food sector is an ideal case for investigating the political economy of public policies. Many of the policy developments in this sector since the 1950s have been sudden and transformational, while others have been gradual but persistent. This paper reviews and synthesizes the literature on trends and fluctuations in market distortions and the politicaleconomy explanations that have been advanced. Based on a rich global data set covering a half-century of evidence on commodities, countries, and policy instruments, the paper identifies hypotheses that have been explored in the literature on the extent of market distortions and the conditions under which reform may be feasible.

This paper is a product of the Agriculture and Rural Development 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 Wmartin1@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
Here, the question of whether commodity price volatility has increased over time is addressed with analytical rigor and at a disaggregated level. We use a unique, newly compiled dataset of 45 individual commodity prices and five commodity price indices at a monthly frequency. Two econometric tests are applied for identifying structural breaks in GARCH-type processes in order to provide a robustness check and these are described below.

# **3. Data**

For this paper we use a newly compiled dataset which covers 45 individual commodities and 5 commodity price indexes. We use a monthly unbalanced panel of observations from Global Financial Data (GFD) covering the period 1784 – 2009. We take a pragmatic approach as to what constitutes a “commodity” and take all “commodities” included in the GFD dataset as our subject of study. We use data from GFD as it is the most comprehensive source, verifying that the price data is consistent with that from alternative sources (World Bank, IMF, and UNCTAD) for any overlapping commodity and time period. The details of the dataset can be found in Table 1.

7
# **Bibliography: General**

Arestoff, Florence & Hurlin, Christophe, 2006. "Estimates of government net capital stocks for 26 developing countries, 1970-2002, _Policy Research Working Paper Series_ , No 3858, The World Bank.

- Chao, Kang (1974), _Capital Formation in Mainland China_ , University of California Press, Berkeley.

Chen, Nai- _Ruenn_ (1966), _Chinese Economic Statistics_ , Edinburgh University Press.

- Chow, Gregory C. (1993), “Capital Formation and Economic Growth in China”, _Quarterly Journal of Economics_ , Vol. 108, No 3, pp 809-842.

- Denison E.F (1961), “Measurement of Labor Input: Some Questions of Definition and the Adequacy of Data”, in _Output, Input, and Productivity Measurement,_ Conference on Research in Income and Wealth, Princeton University Press.

- Eckstein O., W. Galenson and Ta-chung Liu (1968), _Economic Trends in Communist China,_ Social Science Research council, Aldine Publishing.

- Feenstra, Robert C., Robert Inklaar and Marcel P.  Timmer (2015a), "The Next Generation of the Penn World Table", _American Economic Review_ , 105(10), 3150-3182.

- Feenstra, Robert C., Robert Inklaar and Marcel P.  Timmer (2015b), “User Guide to PWT 9.0 data files”, www.rug.nl/ggdc/docs/user_guide_to_pwt90_data_files.pdf

- Hamilton J.D. (2017), “Why You Should Never Use the Hodrick-Prescott Filter”, _Review of Economics and Statistics,_ doi: _10.1162/REST_a_00706_

- Herd, Richard, Vincent Koen, Xiao Wang and Thomas Chalaux, (2013). “Policies for Inclusive Urbanisation in China”, OECD Working Paper, Paris.

- Jorgenson D.W. (1991), “Productivity and Economic Growth”, in (eds) Berndt E. and J. E. Triplett, _Fifty Years of Economic Measurement:
|(0.928)|(1.109)|(1.237)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.067|0.078|0.048|0.066|0.075|0.056|0.029|0.029|0.069|
|# of EAs|433|290|143|433|290|143|433|290|143|
|N|12937|9337|3600|12937|9337|3600|12937|9337|3600|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on logarithm (first three columns) or “started log” (last six columns) of the components of total household consumption expenditure per capita. Household sample weights from the ESS2 applied. The first, middle, and last three columns report results for food, education, and non-food consumption expenditure per capita, respectively. Columns labeled (1), (2), and (3) report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

43
**Figure 1: Sales decline gap (mean values)**

<!-- Start of picture text --> 20.0<br>17.0<br>16.3 16.4<br>14.7<br>15.0<br>13.0<br>12.2<br>11.3 11.4 11.4<br>10.0<br>9.7<br>10.0<br>7.9<br>6.5<br>6.2<br>5.8<br>5.3<br>5.0 3.9<br>0.0<br>-2.0<br>-5.0 -4.1<br>-10.0<br>sales of large firms (percentage points)<br>Percentage decline in sales of SMEs minus percentage decline in<br><!-- End of picture text --> Note: Sales decline gap is the decline in sales in the last month (post COVID-19 outbreak) compared to the same month last year (pre-COVID-19 outbreak) and expressed as a percentage of the latter.

Source: Authors’ own calculations based on data from World Bank Enterprise Surveys.

46
be explained by logistic constraints. This necessitates the development of measures of data reliability that prioritize identifying discrepancies that go beyond structural factors.

Numerous studies have attempted to measure and reconcile the discrepancies observed in UNSD’s Comtrade database. Often, they rely on measures of bilateral asymmetry to capture discrepancy between recorded value and its mirror (Ferrantino & Wang 2008; Chen et al., 2022; Farhad et al., 2023). However, some suggested measures are often linear to the observed discrepancy (Carrère & Grigoriou, 2015), i.e., they change linearly in relation to the unaccounted trade value irrespective of whether the unaccounted trade value is due to the addition of insurance and freight costs for imports or an underreporting of imports in the equivalent amount.

This is inconvenient because the first type of discrepancy is expected and structurally guaranteed by IMTS2010 guidelines, while the latter indicates missing imports, which is not easily explained by logistic factors alone. In addition, some measures are constructed using absolute differences in recorded values (Chen et al., 2022), resulting in indicators that cannot explicitly account for the direction of misreporting, i.e., whether an import was over- or underreported. This is similarly inconvenient because import overreporting is measured as equally severe as import underreporting. While indices of bilateral asymmetry that are non-linear to unaccounted trade value and can account for the direction of discrepancy have been proposed (Ferrantino & Wang 2008), their application in the literature is usually limited to specific countries.

# 3. Methods for measuring discrepancies in trade reporting

The quality of reported trade is usually investigated in relation to its mirror, i.e., the data reported by the trading partner. A comparison between the recorded value and its mirror is usually achieved through a measure of bilateral asymmetry that quantifies the magnitude (and direction) of the observed discrepancy. Many such measures have been proposed to assess the quality of the UN Comtrade database (Carrère & Grigoriou, 2015, Chen et al., 2022
of level variables is replaced by an error correction term ( _EC_ ) is estimated, using the ARDL approach to cointegration. The maximum lag order is set to four for this estimation.<sup>18</sup> Following Narayan (2004) and Okada and Samreth (2013), Schwarz Bayesian information criterion (SBIC) is used for selecting the optimal lag order. Based on SBIC, ARDL(1,0,0) is obtained. Table A1 presents the estimation results of the error correction model. From the table, the coefficient of the error correction term ( _ECt_ -1) is statistically significant and negative. Its

> 18 Setting maximum lag order to four is common in an estimation using quarterly data.

22
Policy Research Working Paper 8800

# **Abstract**

This study analyzes the impacts of the recent rural road development in Ethiopia on welfare and economic outcomes. The identification of the impacts relies on a difference-in-differences matching approach, taking advantage of the nationally representative household survey and the original road database, both of which are panel data spanning between 2012 and 2016. The results of the econometric analysis overall suggest that Ethiopia’s recent rural road development has substantially increased household welfare and supported households in coping with the recent severe droughts. This study estimates that rural roads increased, on average, household consumption by 16.1 percent between 2012 and 2016 (or 3.8 percent per year). The effects of rural road development were largest in the most remote communities, as it increased household consumption by 27.9 percent. Furthermore, in the communities most affected by the El Niño drought, the likelihood of falling into poverty was 14.4 percent lower between 2012 and 2016 if the community was connected by a rural road. Taken together, the results suggest that, by connecting remote communities to markets, rural roads have substantially increased the welfare and resilience of rural households in shock-prone environments.

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/research. The authors may be contacted at snakamura2@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
, or who had a<br>job or enterprise, or who worked at least one hour for earning<br>money, or who worked without a wage at family farm, business<br>(ALL PIHS); or who worked at least one hour for earning money<br>(ALL PSLM/HIES/ HIICS).|Regular: paid employee.<br>Casual: contract cultivator.<br>Self-employed: owner cultivator, share cropper,<br>livestock (only), and employers.<br>Unpaid: family worker.|-|
|**Sri Lanka**|<br>Those who were employed as usual status (ALL HIES), or who<br>were involved in an economic activity, or who worked for<br>pay/profit/family gain, or who had a job or enterprise at which<br>she/he did not work when interviewed, or who were engaged,<br>even for a few hours in paid employment, own account profitable<br>work or contributing family work as an economic activity<br>(including employer), or who already got a job and ready to<br>go/already plan for self-employment activity (ALL LFS).|<br>Regular: employees that didn't receive a daily<br>payment.<br>Casual: employees that did receive a daily<br>payment.<br>Self-employed: own account worker, and<br>employers.<br>Unpaid: unpaid family worker.<br>(No employment type for HIES 2002)|Public: government employee and semi-government<br>employee.<br>Private: other type of ownership.<br>(No public/private for HIES 2002)|

7
As with all policy and programmatic interventions, the moral and commonly aspirational arguments for justice interventions must be enhanced by collection of data representing how different intervention designs enhance or undermine the social groups’ equality before and after access to justice. Sucha a granular measure of the equality of social groups under specific justice policy settings could enhance measurement of this component of process legitimacy. Further, the legitimacy of justice interventions can be enhanced by better quantifying and comparing (and communicating) their economic and societal benefits, particularly where this can be done in such a way to demonstrate disproportionate impact for excluded social groups. Justice data collection and analysis, which employs broader societal data, as well as data generated from existing justice sector analytical methods, is a prerequisite for a ‘business case for justice’. An enhanced CBA APP, as described above, serves these objectives by improving the accuracy of justice sector resource allocation for maximum economic and societal outcomes while targeting society’s most vulnerable and excluded social groups.

Traditional CBAs commonly seek to weigh anticipated policy or intervention benefits against a policy reform or an intervention’s cost to determine overall societal benefit. CBAs demand resources and time due to the many challenges of quantifying anticipated costs and benefits, idiosyncratic to the nature of policies and interventions in the diverse complexity of contexts in which they occur (Prescott, 2010). Drawing on multiple sources of societal and justice process data in a systematic way, allows governments and stakeholders more exhaustive identification of interventions’ cost-benefits. It also enables systematic intervention comparison across a range of metrics beyond monetary benefits (such as crimes avoided and level of safety improvement) and to identify which interventions have greatest benefit for the most vulnerable social groups. Future utilization of an enhanced CBA APP, which includes the tested heterogenous impact component and future machine learning capacity, will enable significant contextual factors identified in previous CBAs to be tested in future intervention CBAs.

The enhanced CBA APP confronts the human and material resource problem of repeated individual CBAs across different justice processes. Broader societal data
plots_

Our analysis of differences in drought impact within the same enumeration areas first determines whether some but not all plots belonging to the same enumeration area recorded a drought loss and then estimates the simple, unweighted proportion of enumeration areas for which this is the case. Our analysis of within-household differences in shock impacts first limits the sample to households with multiple maize plots and where at least one of the household’s maize plots recorded a drought shock. We then calculate the share of remaining maize plots belonging to the same household that also record a drought loss and report a simple, unweighted average of this share across households for each country and year.

# v. _Comparison of survey micro data with aggregate sources_

Our estimates of the number of people affected by disasters and aggregate economic losses are totals at the national level and employ the household-level sampling weights.

14
the household level in Table 6. To be consistent with previous literature, following the analysis on district-level household expenditure in India in Fan et al. (2021), we use state fixed effects instead of district fixed effects. Column (1) shows that a 10% increase in tradable service employment leads to a 3.6% increase of household expenditures on education, which is significant at the 1% level. This is consistent with the argument that spillovers from tradable service growth on education are due to increases in local final demand from consumers. We find similar effects for other important non-tradable services, although differences in sector classifications do not allow us to test each of the sectors driving our results separately. Column (2) analyzes medical services, but these are insignificant. Column (3) shows a highly significant coefficient of 0.53 for entertainment. Similarly, the effects on consumer services and the total of these services are also large (at 0.26 and 0.29 respectively) and statistically significant. Lastly, column (6) shows positive and significant effects on the overall monthly per capita expenditure (MPCE) of households.

In sum, we find suggestive evidence that the consumer demand channel rather than the sectoral linkages channel, plays a larger role in explaining the relationship between the district-level growth in non-tradable and tradable services employment.

16
|||Kindergarten|Dummy variable, equals one if schools have kindergartens|
|||High school|Dummy variable, equals one if schools have high school|
|||State schools|Dummy variable, equals one for state schools|
|||Duration of school day|Average duration of school day in hours|
|||% of classes with full-time education|Percentage of classes with at least 7 hours duration of school day|
|||Students' enrollments|Number of students enrolled in the school in agivenyear|
||INEP|Portuguese grades|Schools' average grades in Portuguese|
||INEP|∆Portuguese grades|Difference between schools' average grades in Portuguese in t and t-1|
|_Prova Brasil_|INEP|Mathematics grades|Schools' average grades in mathematics|
||INEP|∆ Mathematics grades|Difference between schools' average grades in mathematics in t and t-1|
|IDEB|INEP|IDEB Index|Educationalqualityindex,aggregatingstudents' learningand school flow measures|
|||_Mais Educação_|Dummy variable, equals one if schools adhered to_Mais Educação_|
|||Fields of knowledge|Dummy variable, indicating which fields of knowledge schools had chosen|
|_Mais Educação_|MEC/FNDE|Activities|Dummy variable, indicating which_Mais Educação_activities are being offered|
|||Program's costs|Total program's costs by school, divided into fixed and variable costs|
|||Students enrolled in_Mais Educação_|Number of students from the school that enrolled in_Mais Educação_|
|IPEA||GDPper capita|Gross Domestic Production divided by population|
|National Treasury|Secretariat|Spendingon education and culture|Municipal spendingon education and culture|
|Demographic Census|IBGE|Population|Population estimates for 2007, 2009, and 2011|
||UNDP/UN|IDHM|Municipal human development index(2010)|
_Table 16: Share of the national population affected by floods according to EM-DAT and LSMS-ISA data_

|Country|Year|Percent of the<br>population affected<br>by floods according<br>to LSMS-ISA data|Percent of the<br>population affected<br>by floods according<br>to EM-DAT data|
|---|---|---|---|
||2012 - 2013|8.26 %<br>[5.73 %;10.78 %]|0.95 %|
|Malawi|2015 - 2016|1.18 %<br>[ 0.49 % ; 1.87 % ]|3.74 %|
||2018 - 2019|22.75 %<br>[ 16.37 % ; 29.12 % ]|5.38 %|
|Niger|2011|1.72 %<br>[0.30 %;3.13 %]|0.24 %|
||2014|3.76 %<br>[ 1.58 % ; 5.95 % ]|0.85 %|
|Nigeria|2019|4.62 %<br>[3.17 %;6.08 %]|0.03 %|

_Note: missing entries correspond to cases where either no event was reported or information on the population affected was missing in the EM-DAT. 95% confidence intervals for estimated totals are calculated with LSMS-ISA microdata_ 37