rafmacalaba/data-use-mentions-extended huggingface dataset:
accounting for time-specific effects (with the inclusion of period-specific dummies), equation (7) could be rewritten as<sup>11</sup> : or This differencing, however, introduces a new bias since the error term (ε _it -_ ε _it-1)_ is correlated with the lagged dependent variable ( _git-1 -git-2)_ . Under the assumptions that the error term ε is not serially correlated, Arellano and Bond (1991) propose a two-step generalized methods of moments (GMM) estimator using as moment conditions:

> and if the explanatory variables _x_ are predetermined but not strictly exogenous:

In the first step, the error terms are assumed to be independent and homoskedastic across countries and time. In the second step, the residuals obtained in the first step are used to construct a consistent estimate of the covariance matrix, and thus relax the assumptions of independence and homoskedasticity. Using the moment conditions in (16) and (17) and denoting θ=[αβ’]’, the GMM estimator of θand corresponding covariance matrix Σθ are given by: where _~~x~~_ =[g _it-1 x_ ], _Z_ is the matrix of instruments, and Ω<sup>ˆ</sup> is a consistent estimate of the covariance matrix of the moment conditions constructed with the residuals of the first step regression.

The consistency of the GMM estimator above depends on the validity of the assumption that the error terms do not exhibit serial correlation as well as on the validity of the instruments. We present two specification tests to address these issues. The first is the Sargan test of overidentifying restrictions. The second test examines whether the error term ε is not serially correlated, which in turn would imply that the difference error in (14) does not present second-order serial correlation.

We rely on two main sources of data. Inequality data come from Dollar and Kraay’s database on inequality. This database expands the inequality data used in their 2002 paper and contains 953 observations of
include according to the IPCC classification. This information builds the vector of emissions multipliers. Emissions from Land-use changes and International Bunkers are not considered since they do not clearly align with economic activities in the other classifications.

**Table 4 – CAIT Sector Data**

|**Sector/ Subsector**|**Content**|**IPCC Category**|**Gas**|
|---|---|---|---|
|Energy||1|C<br>2|
|-<br>Electricity & Heat|Electricity & Heat plants (fossil fuels)<br>-<br>Public plants (electricity, heat, CHP)<br>-<br>Autoproducers (electricity, heat, CHP)<br>Other Energy industries (fossil fuels)|1 A 1 a<br>1 A<br>1 A 1 b,c|C<br>2<br>C<br>2<br>C<br>2|
|-<br>Manufacturing<br> <br>Construction|&<br>Manufacturing & Construction (fossil fuels)|1 A 2|C<br>2|
|-<br>Transportation|Transportation(fossil fuels)|1 A 3|C<br>2<br>|
|-<br>Other Fuel Combustion|Other sectors (fossil fuels)<br>Biomass combustion<br>Stationaryand Mobile Sources|1 A 4<br>1 A 5<br>1 A 5|C<br>2<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>C<br>4<sup>,N</sup>2<sup>C</sup>|
|-<br>Fugitive Emissions|Gas Venting/Flaring<br
an increasing function of income. The rich can offer larger campaign contributions or bribes, and may have lower costs of political activity due to personal connections with politicians. Several papers show that inequality reduces the influence of the poor. The effect of higher median income in less clear.<sup>28</sup> However, a _key_ implication of papers in this literature is that the distribution of income _affects_ policy, by affecting the distribution of political power.

It seems reasonable that both channels matter, but their effects have not been separated by the existing literature. In the next section, I show how median income and inequality affect investment. I then attempt to apportion the estimated effects between the two channels.

# **5 Empirical Strategy**

Theory suggests that higher median income and higher inequality lead to: (i) lower enrollment in public school, (ii) higher public education quality, (iii) lower public education expenditure, and (iv) higher public expenditure on goods without private substitutes, like infrastructure. Here, I present an empirical model that captures these theoretical predictions. I test the model using panel data on Brazil’s over 5,000 municipalities during 1995-2008. Shortly, I take up the issues of identification and measurement of income, inequality, enrollment, and school quality. For now, assume that these variables are accurately measured and all variation in revenue is exogenous.

The availability of revenue affects investment. Ideally, one would randomly assign per capita revenue, _rit_ to municipalities with varying distributions of income, and see how the distribution of income moderates how revenue is spent. This experiment implies the following empirical specifications:

> 28A variance-preserving, mean-increasing shift in the distribution of income would have different effects, for example, if the function converting income into influence is linear vs. exponential.

16
in the household sector, McAndrew et al. (2021) report a wide variation of reduction in electricity use ranging from a low of 0.5% to a high of 80% of total electricity use, with a median value of 7.9%. Some selected studies are summarized in Table 5.

With an econometric analysis using data from a field survey of residential energy consumption in Amman and Zarqa regions of Jordan, Abd Alfattah et al. (2017) find the net saving by implementing EE measures (refrigerators and freezers) from 2011 to 2020 ranges from 4,451 GWh to 17,807 GWh depending upon different scenarios, which is equivalent to saving of electricity bills by Jordanian Dinar 320 million to 1,282 million, respectively, roughly US$450 million to 1,808 million in the current exchange rate. Using a multivariate statistical approach on data collected from 1,400 dwellings in France, Belaid et al. (2021) find that low-temperature and condensing boilers, as well as floor insulation, are the most cost-effective EE measures. The

> 12 EE investments in buildings sector include building envelopes, heating, ventilation and air conditioning (HVAC), appliances, and lighting.

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326|326|325|325|324|
|**Panel B: Baseline Schoolgirls**|||||||
|=1 if Conditional Schoolgirl|0.069|0.013|0.125|0.123|-0.048|0.050|
||(0.108)|(0.095)|(0.117)|(0.103)|(0.167)|(0.118)|
|=1 if Unconditional Schoolgirl|0.254|-0.315*|0.462|0.175|0.171|-0.066|
||(0.199)|(0.183)|(0.389)|(0.109)|(0.123)|(0.201)|
|p-value UCT vs. CCT|0.374|0.078|0.392|0.683|0.252|0.586|
|p-value Treatment|0.414|0.196|0.325|0.208|0.336|0.837|
|Mean in Control Group|0.000|0.000|0.000|0.000|0.000|3.194|
|Sample Size|543|543|543|543|542|541|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. All variables are constructed so that higher values are better. The husband gender empowerment super index is a standardized index of the other variables in this table. Additional detail on the construction of the indices can be found in in Appendix A and Appendix B. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended
(like in the case of the EU) and hence do not permit to investigate the effect of TAs and their provisions. We overcome this limitation by using instead inter-country input-output tables to measure government procurement flows. The drawback of this data source is that we do not observe procurement purchases directly, but we employ constructed data based on national accounts. The use of the empirically successful gravity model aims at filtering out noise and identify central tendency in the data.<sup>6</sup> Our analysis draws extensively from the large literature on the gravity model of trade (Head and Mayer, 2015; Anderson, 2011) to assess the policy determinants of trade flows. In doing so, we do not attempt to develop a fully-fledged theoretical model that explains, for instance, the allocation of public and private expenditures across sectors. Our focus is on the incidence of trade policy, taking aggregate sales and expenditure as given. Owing to the separability between allocation of resources within and across countries that is common to many models of trade, we infer trade costs in a “conditional general equilibrium” setting (Anderson and van Wincoop, 2004), i.e. taking as given the allocations of resources across type of goods and services in the public and private sectors. As Fronk (2014) shows, a gravity-style empirical model can be derived also from the auction framework of McAfee and McMillan (1989) combined with a standard comparative advantage model à la Eaton and Kortum (2002). We rely on this validity of the gravity framework for analyzing bilateral flows in government procurement and estimating measures of home bias. Further, our work expands the literature on the partial equilibrium effects of trade agreements (Baier and Bergstrand, 2007; Bergstrand et al., 2015) and its provisions (Dür et al., 2014; Kohl et al., 2016; Mattoo et al., 2017) by focusing on trade where the public sector is the buyer.<sup>7</sup> When we
the conditional independence assumption of PSM was not valid. Given the sequence of the two evaluations, the key omitted variables in the later study were known — they mainly related to local level connections (as evident in memberships of various neighborhood associations and length of time living in the same barrio). However, the lighter survey instrument used by Ravallion et al. (2005) had the advantage that the same households were followed up over time to form a panel data set. It would appear that Ravallion et al. were able to satisfactorily address the problem of bias in the lighter survey instrument by tracking households over time, which allowed them to difference-out the miss-matching errors arising from incomplete data.

> 42 This is my interpretation; Pitt and Khandker (1998) do not mention the _DD_ interpretation of their design. However, it is readily verified that the impact estimator implied by solving equations (4a-d) in their paper is the _DD_ estimator described here. (Note that the resulting _DD_ must be normalized by the proportion of landless households in eligible villages to obtain the impact parameter for GB.)

> 43 Equivalently, they measure impact by the mean gain amongst households who are landless from living in a village that is eligible for GB, less the corresponding gain amongst those with land.

35
fishery workers in central Vietnam using two main data sources. We collect labor-market information of fishery workers from the Labor Force Surveys of Vietnam (“LFS”) in 2015 and 2016. The second set of data comes from the Visible Infrared Imaging Radiometer Suite (“VIIRS”), which is a remote-sensing data source storing satellite-imaged night-light luminosity and administered by the National Oceanic and Atmospheric Administration (“NOAA”). We specifically utilize VIIRS’ Boat Detection Module (“VBD”), which processes worldwide night-time light emitted from fishing boats to attract catch. Importantly, VBD also implements an automatic boat detection identification system

> 5 See the detailed time-line pertaining to the Formosa environmental pollution incident in the Appendix.

> 6 As mentioned earlier, this region also defines the _deepwater_ fishing-ban zone which was effective until May 2018.

5
EU has done since 2010. As discussed earlier, e-invoicing in the EU, which now relies on liberal ETIs, has been increasing since 2013 when new rules on e-invoicing allowed unstructured e- invoicing such as a PDF. However, structured and full-fledged XML-type e- invoicing in the EU, which enables substantial cost cutting with integration of tax, accounting, and other operation systems, remains in a relatively low range of 7 to 8 percent, pointing to a need for further action.

Countries that put more emphasis on combating tax frauds tend to opt for compulsory e-invoice and centralize e-invoice information into tax authority databases in order to better tackle tax evasions. Such compulsory enforcement would eliminate information asymmetry and resolve the problem of “prisoner’s dilemma” with a single stroke. It is notable that several EU countries including Demark, Finland, Italy and approximately ten others adopted, or planned to adopt, a partial compulsory e-invoice only for the government and public sectors (Gartner, 2009). Although compulsory e-invoicing has the potential to accelerate the adoption of e- invoicing, it may well also increase the burden of compliance in the short run. In addition, compulsory e-invoicing can lead to taxpayer resistance when implemented too quickly. SME, micro businesses do not have the capability or cannot bear the 27
– major region of the world more so in Latin America or the Middle East than in Asia. The largest declines occurred in the first three pandemic months and the subsequent recovery has been only partial. Using local area poverty estimates, the paper finds high lockdown stringency significantly reduced mobility even in high-poverty areas, although to a lesser degree than in low-poverty areas. Google Mobility Trends data also show substantial reductions in workplace visits during the first months of the pandemic in all sub-Saharan African countries where data are available. These mobility reductions happened during periods of stringent lockdowns. On April 15th, for example, the number of visitors to workplaces were 50-75% lower than the pre-pandemic level in Uganda, South Africa, Botswana, Namibia and Zimbabwe, and 25-50% lower in in Kenya, Tanzania, Angola, Gabon, Nigeria, Ghana, Guinea-Bissau, and Senegal.<sup>9</sup> Even if there were pronounced economic downturns related to the pandemic, it may still be that low-income country lockdown policies were comparatively ineffective, and the reductions in economic activity came purely from private behavioral responses. Emerging evidence suggests, however, that lockdowns did have some effect. Beyer et al. (2021) exploit differential timing and severity of lockdown measures across 600+ Indian districts and a difference-in-differences estimator to show that more severe lockdown restrictions curtailed economic activity substantially at the district level, relative to localities with less severe restrictions. Thus, it appears that lockdown policy interventions did have a direct effect on economic activity in India.<sup>10</sup> Finally, we note that throughout our analysis, there continues to be community transmission that is not entirely eliminated by the lockdown, and we calibrate community transmission to be higher in developing countries. This feature of our quantification could be thought of as one means to capture lower state capacity in poorer countries.

> 9The data are here: https://ourworldindata.org/covid-google-mobility-trends.

> 10Ultimately it may never be possible in the data to cleanly separate the impact
Finally, the average number of sessions attended by compliers was 5.68 sessions in NEP-B and 7.89 sessions in NEP-I. Therefore, the estimated impact on participants reported in tables 5 and 6 can be interpreted as the average impact of these number of sessions in each treatment arm.

5.2.2 Impact on program participants Table 5 shows the IV estimates corresponding to equation (4). Participation in NEP-I improves receptive language performance by 0.432 SD and socio-emotional development by 0.540 SD, and NEP-B improves receptive language by 0.418 SD and socio-emotional development by 0.315 SD. However, only the former estimates are robust to multiple hypotheses testing. There are no statistically significant impacts on the remaining variables in the table, and the point estimates are also smaller in magnitude than those for language and socio-emotional. These results consistently mirror the ITT analysis that shows no significant changes in other child outcomes.<sup>11</sup> As we discussed above, our estimates indicate that the program was remarkably effective in improving language and socio-emotional outcomes of children in the medium term, given its low cost and low intensity. The estimates are striking both because of their size, and because the endline was collected so long after participants stopped participating in the program. As in the case of ITT estimates, when we add more controls in the 2SLS estimation, the coefficients for child outcomes remain fairly stable, and standard errors hardly change either (Table A7 in Appendix 2).

11 These impacts are subject to the accuracy of the administrative data of participation rates. When a second source of participation based on survey records (from the endline) is considered, participation is significantly higher: 44% in NEP-B and 50% in NEP-I. Administrative records are usually judged to be superior to survey answers, but they might still have some degree of under-reporting if for example the facilitators at the health center fail to register some sessions electronically. Household surveys might overreport participation because they are subject to recall
prevented _in situ_ data collection during the spring, a ground survey for the 2022 crop could eventually be organized in June 2022 and is still ongoing at the time of writing.

All optical data from Sentinel-2 and SAR data from Sentinel-1 during the vegetation period was then used to generate crop classification map using a convoluted neural network on the Amazon Web Services cloud computing platform (Kussul _et al._ 2017; Shelestov _et al._ 2020) as well as a random forests classifier on the Google Earth Engine (GEE) platform (Shelestov _et al._ 2017).<sup>13</sup> For classifier training, half of the data was randomly assigned to training and independent validation samples and accuracies calculated based on independent validation dataset for each of the crops received (Kussul et al. 2018). For 2022, Sentinel-2 data was used to create a winter crop mask for the 2022 cropping season by computing the maximum value of vegetation index NDVI for the entire territory of Ukraine from Feb. 1 to May 31 on GEE and applying a threshold segmentation for winter crop mask creation.<sup>14</sup> Table 2 shows the number of training samples for winter and summer crops collected each year as well as the F1 scores for winter crop classification maps which exceeds 95% in each of the years. Maps of the estimated winter crop area by VC generated on this basis as displayed in figure 2 illustrate a concentration of winter crops in the country’s South and East and suggest a much lower level of winter crop cover in 2022 and to some extent in 2020 than in 2021 and 2019.

Panel A of table 3 supports this by showing that, with 8.38 mn ha, area cultivated with winter crops in 2022 is indeed 11% below the 2019-21 average but well above the 7.5 mn. ha attained in 2020 when adverse weather conditions led to widespread winter crop failure, especially in the southern and central part of the country. Figures at national level are in line
In a parallel experiment on the provision of an extra teacher to randomly-selected schools in Andhra Pradesh, Muralidharan and Sundararaman (2010) find that the impact of the extra teacher was identical in both the first and second year of the project – suggesting that teacher inputs were less likely to be substituted away. Similarly, inputs like school infrastructure that retain some aspects of public-goods and would thus be under-provided by non-coordinating households are a good candidate for government provision.

The approach followed here of treating test scores as a household maximization problem with the production function acting as a constraint explicitly recognizes the centrality of households in the domain of child learning. This has important implications for both estimation and policy and further research could potentially separate inputs with high/low degrees of substitutability with regard to private expenditures. One hurdle for such studies is the lack of matched school and household data and the identification of "surprises" in the provision of inputs; long-term data on schooling inputs and panel data on student learning would allow for a deeper understanding in varied contexts based on deviations from means, as is standard in the consumption literature (following Hall 1978). Investments in such data collection will provide the necessary infrastructure for evaluation of short, medium, and long-run impacts of education policy innovations and should be a high priority for education policy makers and funders of education research.

29
9

- The allocation process allocates the gas injected and withdrawn at specific sites among the shippers. This task is performed by an allocation agent at entry points and by' BGT at daily metered and non-daily metered exit sites and storage. Shippers receive allocation data for review.

- Each shipper's balance is calculated on the basis of the allocation data.<sup>4</sup> Shippers with imbalances above a specified tolerance level are charged an imbalance penalty. In addition, they are charged or credited for gas that is above the tolerance level, at a price equal to the last accepted flexibility bid in the flexibility market.

- BGT uses the allocation data to calculate the commodity charges. It adds to these clharges capacity overrun penalties, if applicable, and credits or charges for the flexibility bids accepted.

- Finally, BGT produces a detailed monthly invoice for each shipper that includes all applicable charges and credits and sends it to the shipper through the U.K. Link.

The final rule of the Network Code is that BGT cannot earn positive or negative profits from maintaining system balance. So at the end of each month BGT sums up all receipts and payments for the flexibility bids accepted and the imbalance and scheduling charges and then credits or charges a share of the net sum to each shipper in proportion to the amount of gas it shipped during that month.

> 4 Allocation data are finalized within seven days after the gas day. If changes occur, the balance is recalculated and adjustment is made in the next invoice.
operations. A specific threshold for the standard deviation needs to be defined to determine when a deviation is significant enough to indicate stress. Global demand for container shipping has seasonal patterns, with a marked low in February. While disruptions typically have no seasonal patterns, the industry may have more capacity to absorb them in the first quarter of the year.

In this conceptual framework, global container shipping is schematized as a directed network, where the nodes represent ports, and the edges represent the shipping connections between ports used by the shipping lines' loop, or inter-range, services (Figure 1). Under normal circumstances, these loop services are highly predictable, with consistency in:

- (i) Lead times: The transit time between the departure port (d) and the subsequent arrival port (a).

- (ii) Turnaround times: The time spent at each port, especially for loading and unloading operations.

Network data at a given time is therefore characterized by the moving trade capacity (expressed in TEUs) between ports, as well as the consistent transit times, which tend to be relatively stable within the same ship class. Therefore, the mobilized trade capacity (flowing capacity) on a given connection can be calculated as: Mobilized trade capacity = Flowing capacity per unit of time × Transit time However, operational disruptions such as congestion, around the destination port can lead to:

> 1 <u>https://www.flexport.com/research/ocean-timeliness-indicator/</u>

> 2 <u>https://kpmg.com/us/en/articles/2023/supply-chain-stability-index.html</u>

3