rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 6: The relationship between the average rate of November fire occurrences in subforests and the socio-economic characteristics in the neighboring villages.

<!-- Start of picture text --> Number of households<br>% households heads with primary education or better<br>ln(Value of household assets)<br>−0.166**<br>% farmer households<br>% households using inorganic fertilizers<br>0.258***<br>% households using organic fertilizers<br>−0.151*<br>Distance to the main road<br>−0.178*<br>Distance from subforest to the main road<br>Distance to the village’s subforest<br>−.6 −.4 −.2 0 .2 .4<br>Number of days the forest’s average a grid cell is on fire<br>Regression includes all partitions (N=29)<br><!-- End of picture text --> _Note:_ This figure presents the coefficients from regressing average pre-treatment November forest fire occurrences in the treatment forests’ 29 subforests on the neighboring communities’ standardized socioeconomic characteristics, in the pre-intervention period. The dots represent the point estimates, and the horizontal lines represent the 90% confidence intervals. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

trols for each of the treated subforests we not only fitted the weights on pre-intervention outcomes, forest size and precipitation data (as we did in Section 5), but also on all village characteristics as presented in Table 4. After having estimated the treatment effects for all 29 treated subforests between 2014 and 2019, we regress _αsrt_<sup>November</sup> on all village characteristics from the LSMS survey and pre-treatment average of November fire occurrences using the following random effects panel model: Here, _Y_<sup> ̄</sup
# Trickle Down Tax Morale: A Cross-Country Survey Experiment Jonathan Mellon, Tiago Peixoto, Fredrik M Sjoberg, and Varun Gauri

_Keywords: TAX MORALE, CROSS-NATIONAL EXPERIMENT, RETRIBUTIVE JUSTICE, TAX EVASION, TAX AVOIDANCE_ _Classifications: H26 - Tax Evasion and Avoidance; H41- Public Goods; H50 - Government expenditures General_
and Chile, it is generally difficult to avoid errors of _leakage_ (reaching the non-poor) and of _under-coverage_ (not reaching the poor). Improving targeting aims to reduce these errors, resulting in a more effective poverty reduction policy.

The current process for targeting the poor in Vietnam employs a “bottom up” approach. At the aggregate level, overall progress in tracking poverty over time is undertaken by the Ministry of Labor, War Invalids, and Social Affairs (MOLISA) on the basis of an income poverty line<sup>4</sup> applied it to the periodic rounds of VHLSS survey data. Identification of households eligible to receive targeted assistance is undertaken at the village level. Village committees use their own widely varying criteria to compose a list of poor households. Criteria may include food security, housing, assets, etc. This list is submitted for review to a commune-level committee of Hunger Eradication and Poverty Reduction. The committee checks that households on the list are below MOLISA’s income poverty line by conducting simple surveys of randomly selected households from the list. The refined list is then sent to the People’s Committee and the People’s Council, which are empowered to make further adjustments to it as well (Nguyen et al., 2010). Households that make it to the final ‘poor list’ are then in principle eligible for certain social assistance programs such as free health insurance or subsidies.

Previously, Vietnam would redistribute funds by means of geographic targeting. However, it is generally felt that there was a large margin of error in this approach; the “list of poor communes” that MOLISA used to target the poor was constructed somewhat arbitrarily and failed to capture the majority of poor people. One study estimates that although leakage was limited with this practice – about 8% of non-poor were misclassified as being poor – under-coverage was substantial with 80% of the poor not being reached (Baulch and Minot, 2002).

Concerns have also been voiced about the way that funding
main divisions of Chittagong, Dhaka, Khulna, and Rajshahi are population weighted averages for Other Urban and City Corporations estimates.

**Table 15 Alternative approaches for estimating RSE for annual total expenditure at the Division level (Rural/Urban)**

|**Division**|**Ru**<br>|**ral**<br>|**Ur**<br>|**ban**<br>|
|---|---|---|---|---|
||**top-down**|**bottom-up**|**top-down**|**bottom-up**|
|Barisal|4.3%|1.9%|8.0%|3.2%|
|Chittagong|3.5%|2.0%|13.8%|8.3%|
|Dhaka|3.0%|1.5%|8.2%|4.5%|
|Mymensingh|3.3%|1.8%|11.4%|5.3%|
|Khulna|3.5%|1.2%|6.1%|1.9%|
|Rajshahi|3.1%|1.8%|7.0%|3.0%|
|Rangpur|3.6%|1.4%|9.3%|3.3%|
|Sylhet|5.4%|2.4%|9.8%|3.8%|

Note: Urban estimates for the four main divisions of Chittagong, Dhaka, Khulna, and Rajshahi are population weighted averages for Other Urban and City Corporations estimates.

31
variables are in bold. Each cell represents a coefficient estimate from separate regressions. The column heading corresponds to the set of controls for regressions results reported in that column. Control 1: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price). Control 2: Control 1 plus ln (diesel price/litre along OD route). Control 3: Control 1 plus ln(average nightlight luminosity along OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

27
-8-

# The Present Study

This study has a twofold objective:

- a) substantive analysis of real output levels, PPPs, and labour productivity in Brazilian, Mexican and US manufacturing;

- b) a methodological survey of the analytical problems inherent in such an exercise for any group of countries, in order to facilitate the task of researchers who may wish to replicate our approach.

Thanks to the availability of computer technology we have been able to present our methodology and the underlying data in a more or less fully transparenit way. We also offer some shortcuts and guidelines not previously available, and we deliberately tried to reduce the ad hoc element which loomed rather large in previous studies.

The present study is part of a series of comparative industry of origin investigations in which we and our colleagues have been engaged. Other comparisons for the mantufacturing sector for India/USA (Van Ark, 1987) and Brazil/UK (Van *rk, 1988) are available, and others are underway for Japan, Korea, France and the Netherlands. A fourteen country comparison is available for agriculture by Van Ooststroom and Maddison (1985), and for mining in Brazil, Mexico and the USA by Houben (1988).

We used the benchmark year 1975 in order to facilitate comparison with the results of the third phase of the ICP, which is also based on that year. The basic sources for this study are the censuses of manufacturing of the individual countries. These provide information on quantities and gross value of output in considerable detail as well as information on employment and on value added and inputs at national prices. This material is used to derive PPPs for particular products, and relative output levels for the corresponding industries. These are aggregated to derive estimates of value added, labour productivity and purchasing power ratios for 17 branches of manufacturing.

We carried out the quantitative comparison across countries for gross output, and derived PPPs which we also used for inputs and value added in each industry. Although our PPPs are the same for
stratification variable _z_ . _Xi_ ,1 is a set of time-invariant characteristics of the union and of each spouse determined at the time of marriage and not affected by postmarriage outcomes, to avoid reverse causality issues.<sup>11</sup> In our main empirical specification, we control for both spouses’ age at marriage, ethnicity, education, having been fostered before the age of 15, the education and activity of both spouses’ parents, and the location of the union (Dakar and other urban areas, as opposed to rural areas).<sup>12</sup> Prediction of the Risk of Polygamy Based on equation (1), we predict for each union, at each survey wave, the failure function, which represents the probability to become polygamous before time _t_ . In this specific application, we first argue that the failure function, which is a cumulative distribution function, better captures the relevant perception of the risk of polygamy than the hazard rate, which may be interpreted in a continuous setting as the instantaneous transition intensity to polygamy at time _t_ . Second, saving decisions are likely to be made with a medium to long time horizon, rather than be sensitive to instantaneous risks. Third, contrary to the hazard rate, the failure function is a probability, making the interpretation of the magnitude of the effects more intuitive.

The predicted failure function ���,�� is obtained for each union _i_ from the predicted survival ���,��: The predicted failure thus depends on the estimated stratified nonparametric baseline survival, and the predicted hazard ratio, depending on the characteristics _Xi_ ,1 of union _i_ . ���,��, is then included as a regressor in our second step resource allocation equations.

More specifically, the second step of our estimation strategy uses the two waves of the survey collected on average 4.3 years apart and focuses on the subsample of unions that have remained monogamous. We are thus interested in the difference between the predicted
_M M_ where _gi_ = _ti_ and _zi_ / _ei_ = _zi_ / _ei_ if _i_ is an imported good, and _gi_ = _si_ and _zi_ / _ei_ = _zXi_ / _eXi_ if _i_ is an exported good. Data on protection and subsidies from 1998 are used to estimate this model across a cross-section of agricultural goods. The parameter of interest _a_ is recovered as the inverse of the estimate of the coefficient β1.

Concern about the endogeneity of the regressor compels using the appropriate methodology. Quite clearly, import penetration (inverse of _z_<sup>_M_</sup> ) is influenced by protection and the export-tooutput ratio (inverse of _z_<sup>_X_</sup> ) is influenced by subsidies. Further, which sectors are capable of organizing politically is also influenced in part by their expectation of the degree to which they will benefit from tariffs and subsidies. Thus, the regressor _Ii_ . _zi_ / _ei_ is a nonlinear transformation (here product) of two endogenous variables, and is therefore itself endogenous. In order to estimate the coefficient β1 consistently we construct exogenous variables capable of instrumenting the component endogenous variables, and then use the method of Kelejian to instrument the composite regressor.<sup>21</sup> The Kelejian method simply requires us to use not only the instruments linearly, but their higher order terms (quadratic, cross-products) as well.

Constructing instruments for the agricultural data is a nontrivial task due to paucity of data on variables that are truly exogenous. Testing the exogeneity of instruments is thus necessary. Even if instruments pass the test of exogeneity, it is prudent to investigate whether the instruments are “weakly” related to the endogenous regressor. If so, then it seriously affects inferences that may be made from small samples. An important literature has grown around this subject in recent years in response to strong concerns about the endogeneity problem in natural experiments.<sup>22</sup> These studies and others indicate that the first-stage _F_ -statistic may be used to assess whether
be distributed as bonuses to clinical staff, with 30% allocated towards reinvestment in the facility. District health teams approve action plans developed by the facilities for these reinvestments.

Rewards for the quantity indicators are on a fee-for-service basis. Table 1 presents the specific incentivized quantity indicators and their corresponding fees. Services targeting child health include the number of fully vaccinated children under the age of 13 months, and growth monitoring and nutrition services for children under 5. Services targeting women include timeliness and quantity of ANC visits, postnatal care consultations, and contraceptive use. Hypertension services target increasing the number of diagnoses, and the number of hypertensive patients provided treatment. The selected incentives deliberately emphasize preventative rather than curative services to avoid excessive or unwarranted service provision. Following the midterm review of the project, the indicators and corresponding fees were revised starting in 2017.

The quality bonus is a share of the quantity payment calculated according to the quality score and the matrix presented in Table A1 in the appendix. Facilities that scored less than 55% on the quality checklist did not receive any quality bonus while facilities with high scores can double the quantity payments. The quality score is determined by a quality checklist containing both structural quality and clinical indicators measured through review of registries and medical records. The quality checklist for the RHCs contains 93 separate indicators in ten categories.<sup>3</sup> Health house checklists are a subset, containing 60 indicators in 8 categories. The quality checklist includes indicators directly related to the incentivized services and quality indicators related to non-incentivized services such as diarrhea management and curative care for acute respiratory infections.

# 3. Methods and Data

## Difference-in-differences

We employ a difference-in-differences approach to identify the effects on the PBF program as the districts implementing the program were purposively selected. In March 2012, eight rural districts were chosen to be included in the World Bank supported HSIP project, four in each of the Sughd and Khatlon regions. The objective was to select districts that were near regional averages in terms

> 3 The quality
_from Reconciled and Combined Household Survey and Tax Return Data_ . Tech. rept. 21991. National Bureau of Economic Research, Inc.

- Czajka, L ́eo. 2020 (May). _Income Inequality in Cˆote d’Ivoire: 1985-2014_ . World Inequality Lab Working Paper. HAL.

- De Rosa, Mauricio, & Vil ́a, Joan. 2023. Beyond tax-survey combination: inequality and the blurry household-firm border. _The Journal of Economic Inequality_ , Apr.

- De Rosa, Mauricio, Flores, Ignacio, & Morgan, Marc. 2022 (Aug.). _More Unequal or Not as Rich? Revisiting the Latin American Exception_ .

- Fairfield, Tasha, & Jorratt De Luis, Michel. 2016. Top Income Shares, Business Profits, and Effective Tax Rates in Contemporary Chile. _Review of Income and Wealth_ , **62** (S1), S120–S144. ~~e~~ print: https://onlinelibrary.wiley.com/doi/pdf/10.1111/roiw.12196.

- Flores, Ignacio, Sanhueza, Claudia, Atria, Jorge, & Mayer, Ricardo. 2020. Top Incomes in Chile: A Historical Perspective on Income Inequality, 1964–2017. _Review of Income and Wealth_ , **66** (4), 850–874.

- Gasparini, L., & Tornarolli, L. 2009. Labor informality in Latin America and the Caribbean: Patterns and trends from household microdata. _Revista Desar-_ survey

- _rollo y Sociedad_ , **63** .

- Gasparini, Leonardo, & Lustig, Nora. 2011. The Rise and Fall of Income Inequality in Latin America. _Page 0 of:_ Ocampo, Jos ́e Antonio, & Ros, Jaime (eds), _The Oxford_ .

- _Handbook of Latin American Economics_ Oxford University Press.

- Ham, Andr ́es. 2011. La distribuci ́on del ingreso en Honduras: Un an ́alisis de las principales tendencias desde los a ̃
without identify the specific purpose of use. Section 3 describes the new measures of technologies developed using the FAT survey, and provides a conceptual framework for general and sector specific business functions. Section 4 analyzes the level of technology adoption and use for general business functions. Section 5 analyzes the level of technology adoption and use for sector specific technologies. Section 6 describes some of the key barriers and drivers to technology adoption and use. Sections 7 analyzes the relationship between technology use and employment. The last section concludes.

# **2 Standard Measures of Technologies: Electricity, ICT, and Industry 4.0**

Standard measures of general purpose technologies (GPTs) can be linked to different stages of industrial revolutions, following the reference period in which these technologies became available. We organize the information on adoption and use of GPTs in three types according to the period when they were originated and production processes changed: Industry 2.0, 3.0, and 4.0. Industry 2.0 encompasses electricity and generators, which are technologies from the 1880s. Industry 3.0 refers to the ICT revolution, including mobile phone, computer, and Internet. These technologies became available over the 1970-1980 period.<sup>5</sup> Industry 4.0 refers to technologies that in most cases have some digital component, but higher level of autonomy and connection of information across different devices and machines to perform tasks. Among the technologies usually associated with Industry 4.0 are the Internet of Things, Big data analytics and artificial intelligence, 3D printing, advanced robotics, and cloud computing.<sup>6</sup>

## **2.1 Industry 2.0: Electricity**

A large share of firms in Senegal has access to electricity; yet power outages are common across the country. The availability and reliability of electricity and telecommunications services can be decisive in a firm’s decision to adopt a new technology. Table 1 shows the descriptive statistics of access to electricity
indexes are positively correlated (Figure 3.5). Second, the skills requirement approach alone is not enough to classify an occupation as green or non-green. While the correlation is high, there is no simple way to determine the level of skills intensity needed to categorize an occupation as green. Leaving aside concerns about biases from online job vacancy data, the extensive margin of the GSI index classifies many more occupations as green than the GTI index. One could argue that the zero cutoff for the extensive margin could be too low for two reasons. First, employers could be tempted to include green skills regardless of their importance for carrying out tasks, simply because they are important for the firm culture or as a signal of other desired skills. Second, the presence of just one vacancy requiring a green skill would result in a positive GSI. And there might be cases in which a green skill is important for a specific job in a particular firm, but may not necessarily be representative of the common skills demand for the occupation. This issue arises because most occupations have at least one vacancy requiring a green skill. Table 3.6 shows that 723 out of the 773 occupations studied in the U.S. and 106 out of the 159 occupations studied in Indonesia have at least one vacancy requiring one or more green skills. To understand where to place the cutoff, we examine how many occupations would be considered green if we raise the cutoff level and compare this with the results of the GTI index. We do this for both countries as described in Table 3.6 and Figure 3.5. It can quickly be inferred that the skills requirement approach leads to high levels of errors (assuming the task approach is correct). Increasing the threshold to be classified as a green job decreases the false positive (type 1 error) but increases the false negative (type 2 error). In other words, when we increase the threshold, fewer non-green jobs are identified as green (false positive), but
findings hold for the national poverty line where the standard errors of the predicted values are much smaller.<sup>7</sup> Figure 1 repeats the exercise of Table 5 for the ProGres Targeting data and all the poverty lines between the 66th and 99th quantiles of consumption. Panels A and B, respectively, are the normal linear model and empirical error model. The results suggest that Models 1 and 2 predict the poverty rates for different poverty lines well. The predicted poverty rates are within the 95% confidence interval for all the arbitrary poverty lines considered. The predictions are also very similar across the normal and empirical error models. However, Model 3 overestimates poverty, and the predicted poverty rates provided are outside the 95% confidence interval of the survey-based rates for the set of different poverty lines considered. As Model 3 adds variables related to coping

> 6 See also Table A3 in the annex for the full base model.

> 7 The national and international poverty lines are used for illustrative purposes only. As the consumption aggregate used in this paper is not comparable to a full consumption aggregate as usually derived from nationally representative household surveys, the resulting poverty statistics are not comparable to national or international poverty statistics. In our case, these lines are simply alternative thresholds used to test the sensitivity of poverty estimates to different poverty lines.

11
richest prefectures. This qualifies the previous finding of a slight divergence trend in real incomes. The very poorest areas have, after all, gained from the NEN; just not as much as the initially wealthier ones.

- Third, even in the prefectures in which the impacts on real urban wages have been the smallest (e.g., in the western parts of China), the most urbanized prefectures have still seen the largest gains.

- Fourth, impacts on real rural wages have been smallest in prefectures with low rural (i.e., high urban) shares. So, contrary to what might have been expected based on current development thinking (see, for example, World Bank, 2009), rural areas do not appear to have received a significant stimulus from the presence of a large and diverse urban economy in the face of falling transport costs.

- Finally, the initially more remote prefectures – as measured by the aggregate travel time to all other prefectures – have experienced larger impacts on real urban wages and smaller ones on real rural wages. So, the NEN may not yet have had a significant positive impact on remote rural areas.

# **6. Conclusion**

Over the last two decades, China has invested heavily in the upgrading of its road network, constructing a National Expressway Network which is second in its size only to the US Interstate Highway System. The construction of this network has formed an important part of China's national development strategy, which has been increasingly focused on promoting the catch-up of China's lagging inland regions with its prosperous metropolitan and coastal regions. However, although we have found that the NEN has brought sizeable aggregate benefits to the Chinese economy, increasing overall real income by roughly 6 percent in 2007 over the level that would have prevailed in the absence of the network, it has, as of yet, had little impact on regional disparities. The dispersion of the log of real income per worker levels across prefectures is essentially the same as
Table 4: Impacts of Engagement on Payment

||Made at <br>payment|least one<br>within. . .|Total a<br>paid wi|mount<br>thin. . .|Propor<br>bala<br>paid wi|tion of<br>nce<br>thin. . .|
|---|---|---|---|---|---|---|
||1 month|9 months|1 month|9 months|1 month|9 months|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Coefcient|-0.005|-0.002|0.049|-0.575|0.013|0.008|
||(0.018)|(0.019)|(0.642)|(2.011)|(0.017)|(0.015)|
||r0.776s|r0.928s|r0.939s|r0.775s|r0.436s|r0.616s|
|Observations|2584|2584|2584|2584|2584|2584|
|Control Mean|0.301|0.558|6.958|38.343|0.497|0.484|

*** ** * _Notes_ . Standard errors in parentheses; P-value in brackets. p<0.01, p<0.05, p<0.1. Sample includes all compounds that were included in the randomization procedure to assign the tenant-level engagement intervention. Time periods are based on the end date of the intervention (November 2018) and use data downloaded from the Nairobi Water billing data for December 2018, and September 2019 to estimate impacts 1 and 9 months after the intervention respectively. All monetary amounts are in USD