rafmacalaba/data-use-mentions-extended huggingface dataset:
8|-132.8|86.7|
|**_Industry_**|_Manufacture_|131|18.0|46.2|-121.2|91.3|
||_Construction_|66|12.4|47.1|-154.3|91.3|
||_Service_|45|19.9|36.5|-98.0|90.8|
||_Tourism_|13|-2.1*|16.7|-22.4|64.5|
|**_Size_**|_Small_|69|25.7**|42.3|-121.2|90.6|
||_Medium_|156|13.3|46.8|-154.3|91.3|
||_Large_|29|4.4|28.3|-80.9|71.5|
|**Total**||255|14.8|45.3|-154.3|91.3|

Note: the top/bottom 5% of the observations are not reported; all figures are weighted by sampling weights; *, ** denote significance at 10% and 5% respectively; _Source: WB PICS Mongolia (2004) & Tax Office Data 2003_ The MIMIC approach has three main advantages over the direct and indirect approaches. First, the MIMIC approach uses more information by incorporating firm-level incentives to comply with taxes and a production function model linking true sales with observable firmlevel production characteristics. Second, the MIMIC model allows for measurement errors in sales reported in the survey and to the tax office. And third, the MIMIC model allows for underreporting not only to the tax office but also in the survey.

> 25 37 out of the 97 negative indicators are below minus 10%.

23
# **Annex 4. Detailed Transport Questions in LSMS Surveys (18 Countries in LSMS-tagged Collection)**

|**Survey Question**|**Afghanistan**|**Armenia**|**Benin**|**Bhutan**|**Burundi**|**Cameroon**|**Colombia**|**Gambia**|**Ghana**|
|---|---|---|---|---|---|---|---|---|---|
|**_I. Education_**||||||||||
|1. Main mode of transport to and from education<br>facility|||X|X||||X|X|
|2. Time taken to get to school by this means of<br>transport||||X||||X<br>(and<br>distance)||
|3. Education cost on transportation||X|X|X|X|||X|X|
|4. Transportation difficulty as main reason for non-<br>attendance in education/missingthe classes||X||||||||
|**_II. Health_**||||||||||
|1. Main mode of transport to and from health facility||||||||X|X|
|2. Distance to the nearest health facility||||||||||
|3. Time taken to travel from the household to the<br>nearest health facility|X<br>(with different<br>transport<br>|||||||||
||modes)|||||||||
|4. Health
, and upper castes. The wealth categories are based on the national household wealth distribution. Standard errors in parentheses are clustered by state. Baseline mean refers to under-5 mortality for children born in the pre-ultrasound period, by mothers with the specific socioeconomic status. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
is mandated, reports point towards limited compliance with such rules, lack of transparency and limited enforceability of rules (Ng'ombe et al. 2014). As it would irrevocably remove a piece of land from the chief’s to the state’s control and entail the loss of the flexibility (e.g., in terms of re-assigning land if an investor’s plans turn sour), conversion of customary to state land is resisted by chiefs unless facilitated by significant payments (German _et al._ 2013). A draft land policy, first adopted by the Cabinet in 2002, that among others was expected to clarify the role of chiefs and regulate state land conversion has not yet been finalized.

As a result, while farmers with title were found to have higher levels of fixed and long-term investment and productivity (Smith 2004), many of those who could have benefited from formal documentation were unable to afford it (Larson 2014). Titled state land seems to have often been acquired by individuals with non-farm jobs and the knowledge, resources, and connections to navigate the conversion process but did not affect productivity (Sitko _et al._ 2014).<sup>9</sup> This led scholars to conclude that, instead of furthering productivity and investment, Zambia’s land titling transfers land to ‘speculators’ who are unable to make productive use of the land, potentially jeopardizing the sector’s long-term potential (Sitko and Jayne 2014).<sup>10</sup> Case studies reinforce this notion and suggest that insecure property rights to land may trigger exclusionary processes favoring rich in-migrants over poorer locals whose access to natural resources and welfare (including children’s nutritional indicators) is compromised in the process (Merten and Haller 2008). Without countrywide data on extents of land under customary tenure, coverage with formal or informal documents, perceived tenure security and ability to transfer, assessing representativeness of case studies, defining the objectives to be achieved by the new land policy
is in question. This study therefore examines whether integrating input subsides with other interventions, such as irrigation schemes and social transfers, can result in improved food and nutrition security indicators (dietary diversity, food consumption scores, coping strategy index).

The combined effects of such interventions are only occasionally evaluated by studies (Matita & Chirwa, 2014; Pace et al., 2017; Shigute, Strupat, Burchi, Alemu, & Bedi, 2019; Thome, Filipski, Kagin, Taylor, & Davis, 2013; Thome, Taylor, & Filipski, 2014). For example, Pace et al. (2018) taking advantage of random assignment of participants to a social cash transfer program (SCTP) in Malawi, assessed the joint effects of the program with input subsidies. They found that participating in both programs can improve household expenditures, livestock, and agricultural productivity. Specifically, among households without labor constraints, the number of meals per day was higher for those who received input subsidies and cash transfers. Similarly, synergies between a community health insurance program and Ethiopia's productivity safety net program were reported by Shigute et al. (2019). The effects on labor supply, the number of livestock holdings, and the utilization of health care facilities were all favorable. According to an impact 2
profile is a sequence _s_ = ( _si_ ) _i_ ∈ _N_ of vote strategies for all individuals. The truthful vote for an individual _i_ ∈ _N_ , denoted by _s_<sup>∗</sup> _i_<sup>(</sup><sup>_R_) = (</sup><sup>_s_∗</sup> _i_<sup>_pq_</sup> ( _R_ ))1≤ _p_ < _q_ ≤ _m_ ), is defined for all 1 ≤ _p_ < _q_ ≤ _m_ by: Let _s_ = ( _si_ ) _i_ ∈ _N_ be a voting strategy. For all _p,q_ with _p_ < _q_ , we denote by _N_<sup>1</sup> ( _s_<sup>_pq_</sup> ) = { _i_ ∈ _N_ ∶ _s_<sup>_pq_</sup> _i_<sup>= 1}thesetofindividualswhohaveranked</sup><sup>_xp_above</sup><sup>_xq_inthevotingstrategy</sup> _s_ , by _N_<sup>−1</sup> ( _s_<sup>_pq_</sup> ) = { _i_ ∈ _N_ ∶ _s_<sup>_pq_</sup> _i_<sup>= −1}thesetofindividualswhohaveranked</sup><sup>_xq_above</sup><sup>_xp_,</sup> and by _N_<sup>0</sup> ( _s_<sup>_pq_</sup> ) = { _i_ ∈ _N_ ∶ _s_<sup>_pq_</sup> _i_<sup>= 0}thesetofvoterswhoareindifferentbetween</sup><sup>_xp_and</sup><sup>_xq_.</sup> The social ranking under the voting rule _W_ induced by a voting strategy _s_ , which we denote by _F_ ( _s_ ), is given by: where _xpxq_ ∈ _F_ ( _s_ ) means that _xp_ is socially ranked above _xq_ , so he or she is ranked above _xq_ in the social
the location of an industry in a region (for example, the tanneries in Calcutta), which are difficult to measure but are important for analytic purposes. In addition, economic diversity of large metropolitan regions encourages inter industry knowledge transfers, which tend to enhance average levels of efficiency in the region. In principle, introduction of the fixed effects terms captures some of these city specific characteristics. There are some pitfalls of taking administratively defined entities (Indian districts in this case) as the unit of analysis for capturing the efficiency gains from location in urban areas. Often times, either the extent of metropolitan areas/agglomerations extends beyond administratively defined boundaries, or there are several specialized sub-centers within a large agglomeration, which cannot be identified using information at the district level.

Figure 3 provides a schematic of these ideas, where L is the metropolitan area or agglomeration and S is the specialized sub center. We consider these cases (A, B, and C) using the Bombay metropolitan areas for reference. In case A, the specialized sub center and the agglomeration are part of the same jurisdiction. Take for example, the Indian motion picture industry which is highly concentrated, and localized in the Bandra suburb of Bombay. In principle, while urbanization benefits can be adequately represented for the motion picture industry as the same benefits accrue to all firms from locating in an urban area (such as Bombay), the estimation of localization benefits is likely to be biased. This is because the industry is not spread throughout the metropolitan area, but clustered in one sub center. If spatially disaggregated data were available (for both firm location and regional attributes), it would be possible to estimate more realistic measures of industry concentration, which take into account localization in tightly clustered geographic space.

In case B, the sub center and the agglomeration are in adjacent jurisdictions. An example is the Bombay metropolitan area and its adjacent district, Thane. Even though these two entities belong to different jurisdictions, they are part of the same agglomeration.
<mark>Zimmermann, K.F., 1995. Flexibility in the face of demand fluctuations: Employment, capacity utilization, and industry structure.</mark> _<mark>International Journal of Industrial Organization</mark>_ <mark>,</mark> _<mark>13</mark>_ <mark>(2), pp.179193.</mark>

**Figure 1: Linear relationship between SBM Efficiency and Volatility in Occupancy**

<!-- Start of picture text -->
-1 0 1 2 3<br>Volatility in Occupancy (residuals)<br>coef = .03710704, (robust) se = .06862381, t = .54<br>.5<br>0<br>SBM Efficiency (residuals)<br>-.5<br><!-- End of picture text -->

Note: The figure is a partial scatter plot of residuals obtained after controlling for region dummies (city fixed effects), dummy variables for the star-rating of the hotel (2-star, 3-star, and 4- or 5-star rating), hotel size (log of number of workers). The line of fit shown is not statistically significant at the 10 percent level or less.

Source: Authors’ own calculations based on ES data for hotels in Malaysia in 2019.

34
Table 15 also shows that, in June, 44% of the households in affected areas had received assistance and that 32% had received food assistance. 37% of the respondents’ houses were partially or totally destroyed, making it impossible to live in them. 44% of the households slept in their homes, while 44% slept in a camp. 12% of the households had agricultural production as their main source of income, 37% had trade, 26% unqualified work, 17% professional work and 3% lived mostly out of transfers. Aid was received from both abroad (12%) and from within Haiti (18%).

**Table 15. Descriptive** **<u>statistics</u>**

||Mean|Std|
|---|---|---|
|Food consumption score|55.79|19.75|
|Assistance|0.44|0.50|
|Food assistance|0.32|0.47|
|Housing not damaged|0.17|0.37|
|Housing damaged but still usable|0.46|0.50|
|Housing partially destroyed|0.11|0.32|
|Housing totally destroyed|0.26|0.44|
|Sleeping in the house|0.44|0.50|
|Sleeping beside the house|0.09|0.29|
|Sleeping in the neighborhood|0.27|0.44|
|Sleeping in the commune|0.18|0.38|
|Sleeping outside the commune|0.02|0.14|
|Sleeping in a camp|0.44|0.50|
|Main income source before the earthquake|||
|Agricultural production|0.12|0.32|
|Trade|0.37|0.48|
|Unskilled work|0.26|0.44|
|
and φ<sup>�</sup> k set high enough so that the absolute value of the score for βk must be large relative to its standard error in order for regressor xijt,k to be selected.

Intuitively, the value of the score reflects the impact that a small change in βk has on the fit of the model. When evaluated at 0, it tells us how much the fit of the model improves when we make βk non-zero. The standard logic of the lasso is that this improvement in fit must be large relative to the penalty in order for β<sup>�</sup> k to be non-zero. One of the main innovations of the plug-in lasso is to allow the regressor-specific penalty� φk to adjust to reflect the standard error of the score. This way, we counteract the possibility that regressors could be mistakenly selected due to estimation noise rather than because of their true impact on the model. These regressor-specific penalties play an important role in the presence of heteroskedasticity, which of course is an important feature of trade data. Because the provision sets in xijt vary by agreement, and because we expect errors to be serially correlated over time, we use the cluster lasso approach to constructing these weights as in Belloni, Chernozhukov, Hansen, and Kozbur (2016). Specifically, we cluster all observations belonging to pairs that form agreements by the 11
|12|Political Credit Risk Ratings . . . . . . . . . . . . . . . . . . . . . . . .|59|
|---|---|---|
|13|Executive Constraint, Access to Power and Cross Ethnic Group Eco-||
||nomic Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|63|
|A1|Controlling for Lagged Economic Performance . . . . . . . . . . . . . .|81|
|A2|Controlling for Country-specifc Time Trend . . . . . . . . . . . . . . .|82|
|A3|Using Miguel and Satyanath (2011) Data . . . . . . . . . . . . . . . . .|83|
|A4|Using Miguel and Satyanath (2011) Subsample and Country-specifc||
||Time Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|84|
|A5|Country level GDP growth during peace, war and recovery . . . . . . .|85|
|A6|Descriptive Statistics: Conficts and Famine Episodes . . . . . . . . . .|87|
|A7|Civil Confict and Refugee Stock: Robustness to Famine Episodes . . .|88|
|A8|Country level GDP growth during peace, war and recovery . . . . . . .|90|
NGOs, savings funds and self-help groups) and from informal sources (including money lenders, relatives, friends, acquaintances and work), using reports by the household survey respondent.<sup>30</sup> Table 10 reports impacts on overall MFI borrowing, using the credit bureau data, while Table 11 considers impacts on the composition of the MF client’s borrowing portfolio at our partner MFI, using MFI administrative data. We have information on the interest rates charged on all the loan products offered by the partner MFI, but not for the other MFIs reported in the credit bureau data.<sup>31</sup> Thus, the analysis on the borrowing portfolio for the partner MFI could offer insights that would allow us to differentiate between which of two features – interest rate and loan label – could explain our findings. Interestingly, in both tables we observe a very different pattern for subsidy eligible households relative to that for subsidy ineligible households.

We start by discussing our findings for subsidy ineligible households. Table 10 reveals that the average subsidy ineligible MF client household did not increase its overall borrowing from MFIs, either from the implementing MFI or from other MFIs operating in the area. However, in Table 11 we observe a significant shift in MF loan composition: the increase in sanitation borrowing is accompanied by a reduction in the amount borrowed through education loans.<sup>32</sup> It could be possible that subsidy eligible households substitute away from other (more expensive) sources of (non-MFI) credit in response to the introduction of the sanitation loan. To assess whether this is the case, we consider impacts on formal borrowing from non-MFI sources (commercial banks, cooperatives, self-help groups, saving funds and NGOs) and informal borrowing (from relatives, friends, acquaintances and work).

Table 12 presents these findings. For each of these two aggregate outcomes, we consider three different specifications. In the first specification we use as the dependent variable the total loan amount obtained from the
|Manufacturing andrelatedtrades<br>3f,<br>be|
|---|
|Construction<br>_<br>93<br>|
|;<br>65<br>Wholesaleand retail<br>35 a,<br>S2|
|Transportation and storage<br>y<br>65<br> <br><br>|
|Accommodationandfoodservice<br>Bi<br>&<br> <br><br>|
|Variousprofessional acts.<br>o7<br>56 °Bs|
|Otherservice<br>as<br>247<br>|
|61<br>All firms<br>4|
|0<br>20<br>40<br>60<br>80<br>100|
|Percent of firms formal|
|MM ELMPS 2012 inest.<br>MMMM ELMPS 2018 in est.|
|YY)EconomicCensus<br>EstablishmentCensus|
construct two indexes on the import side and the export side, such that: where _im_ ( _i_ , _k_ ) defines total import flows from country _i_ to country _k_ and _IM_ ( _i_ ) the total import flows in country _i_ . With this index, country-pairs with very similar trade partners have an index close to 1. We proceed similarly for the export index.

Using data for 150 countries over the period 1962-2011, we construct a panel data including the above average indexes for each country-pairs and over time windows of 10 years each. Due to missing data, the sample including FDI and BIS indexes are only available for a sub-sample of periods and countries. Table 1 summarizes the number of observations in our sample:

**Table. 1.** Number of observations (country-pairs) in each sample per time window.

|Time window|corr GDP+_TP_|corr GDP+_TP_+_FI_|corr GDP+_TP_+_FP_|
|---|---|---|---|
|1962-1971|983|-|-|
|1972-1981|1586|-|-|
|1982-1991|2241|153|858|
|1992-2001|4957|276|1293|
|2002-2011|6060|633|1572|
|Total|15827|1062|3723|

Throughout this report, we follow the literature assessing the empirical relationship between trade proximity and GDP comovement. First, we test this relationship using total trade proximity and GDP correlation, transformed using logged and filters (HP-filter, BK-filter and first difference (FD)). Using different measures of trade and a variety of controls presented in the next sections, we estimate the following equations: Second, we focus on the specific role of Global Value Chains as opposed to “traditional trade flows” in the strong association between trade and GDP comovement. More precisely, we empirically test the relationship using dis-aggregated trade data and separate trade flows 8
In this study, we test the hypothesis that cognitive skills, as an indicator of human capital overall, are associated with a lower reliance on emissions in aggregate production technology and estimate the subsequent mitigating effect of education quality on the carbon pricing’s effectiveness and economic consequences including wage inequality. We propose a general equilibrium, overlapping-generations model in which: (1) agents are heterogeneous and their choice of skills is endogenous and (2) the average skill level of an economy affects the share of capital relative to carbon-emitting inputs in the aggregate production function. We then present two empirical applications. First, we test how workers’ cognitive skills associate with their industries’ emissions per output using the OECD’s PIAAC data set with industrial emissions from the EU’s environmental accounts data set. We find that cognitive skills are associated with lower emissions per output and lower or negative growth in emissions per output. This finding is consistent with the hypothesis that a higher skilled workforce is not only associated with industries that are able to produce output with less emissions but also able to innovate over time and reduce the amount of emissions required to produce output.

Second, we estimate: (1) our overlapping-generations model’s aggregate production function parameters using industry level data on output, capital, emissions, and literacy skills from the same two sources and (2) our model’s skills cost function conditional on household wealth using the OECD’s PISA data. We find that higher literacy skills are associated with production technology in which capital has a larger contribution relative to emissions in production. In this sense, capital can substitute emissions through more highly skilled labor. This is consistent with technology imbedded in capital (for example, machines) that can produce output with fewer emissions requiring more highly skilled labor.

One result of this finding is that green technology—technology that enables production with fewer emissions—is skill-biased, in that workers with a higher level of literacy skills contribute more to reducing the production function’s reliance on emissions
the Russian invasion of Ukraine and, in some countries, conflict and insecurity. Using high-frequency longitudinal phone survey data from these countries implemented around the same period, in conjunction with face-to-face surveys conducted on the same sample of households before the crisis period, this study documents the dynamics of incidence and use of inorganic fertilizer and farmer’s coping strategies.

The results highlight the varied responses by farmers to the price shocks prevalent in this period. While the share of households using fertilizer remained relatively stable in most of the studied countries, in early 2023 more than 45 percent of fertilizer-using households in Burkina Faso, Ethiopia, Malawi, Nigeria, and Tanzania reported shortfalls in the quantity of fertilizer acquired, reaching nearly 70 percent in Malawi. These fertilizer shortfalls were sizable, with 19
- Diaz-Bazan, Tania (2014). Measuring inequality from top to bottom. Working Paper.

- Douidich, Mohamed, Ezzrari, Abdeljaouad, van der Weide, Roy and Verme, Paolo (2015). Estimating quarterly poverty rates using labor force surveys: a primer. _World Bank Economic Review_ ; Advance Access pulished 2015.

- Himmelberg, Charles, Mayer, Christopher and Sinai, Todd (2005). Assessing high house prices: Bubbles, fundamentals and misperceptions. _Journal of Economic Perspectives_ , **19,** number 4, 67–92.

- Hlasny, Vladimir and Verme, Paolo (2013). Top incomes and the measurement of inequality in egypt. Policy Research Working Paper Series 6557. The World Bank.

26
run the OLS regressions again using different subsets of violent events. Different measures of violence as dependent variables either in sensitivity tests or in order to shed light on potential channels of causation have been used (Besley and Persson 2011; Bazzi and Blattman 2014; Harari and Ferrara 2018; McGuirk and Burke 2020). Using UCDP data, we focus on (1) one-sided and (2) nonstate violence. One-sided violence is defined as ”the deliberate use of armed force by the government of a state or by a formally organized group against civilians which results in at least 25 deaths in a year”. A non-state conflict is defined as “the use of armed force between two organized armed groups, neither of which is the government of a state, which results in at least 25 battle-related deaths in a year”. Using ACLED data, we focus on violence against civilians and battles. Violence against civilians includes sexual violence, attack, and abduction/forced disappearance. Battles include events when the government regains territory, when a non-state actor overtakes territory, and when armed clashes occur.

First, we get strong and robust findings on the relationship between road projects and violence targeting civilians using one-sided violence in UCDP and violence against civilians in ACLED. While the two sources are quite different in the way they define violence against civilians, the estimates are similar and show that the reduction of violence associated with 24