rafmacalaba/data-use-mentions-extended huggingface dataset:
oil producers, known for their high subsidies of fossil fuel, the removal of such subsidies is carbon reducing with no harm to long run growth. We also find that both for this group and for individual MENA countries outside the oil group, while a modest carbon tax is not as effective as fossil subsidy reduction in reducing CO2 emissions, a larger carbon tax does confer greater benefits.

We learn that the reduction of fossil fuel subsidies works through the CO2 intensive consumption path and that there still remains residual direct effect of subsidy reduction on reducing CO2 emissions that is outside the consumption path. This is most likely occurring via the production path. However, we do not have the data to examine this second path.

We also learn that the world oil prices do play a role in leading to an increase in the level of subsidy. The higher the world prices, the higher the subsidy, other things being equal. This seems to be the case in the MENA countries and MENA oil producers.

In reaching these and many other findings, we have utilized both some well-known and some novel econometric methods along with data from the International Energy Agency, and the simulation projections produced by the World Bank and International Monetary Fund (CPAT).

To conclude, increased transparency of fossil fuel pricing policies is key to the development of credible policies that citizens could accept and support. Towards this goal, countries with large natural resource basis, which also are the ones with large fossil fuel subsidies, are the greatest beneficiaries of increased transparency. Unfortunately, this is a challenge. As prior research in this area indicates, resource rich countries are often _less_ transparent than average (Mohtadi, et al. 2019, Mohtadi, et al. 2014, Ross, 2001, Ross 2011). International agencies will need to play a role in providing evidence on the effects of policies and in advocating for greater transparency that improves the credibility of national policies and their impact on emissions.

35
|**Ln value added per worker**|1.188***|
|---|---|
||(0.044)|
|**Has unpaid workers (no omit.)**||
|Yes|0.516***|
||(0.083)|
|**Has wage workers (no omit.)**||
|Yes|1.460***|
||(0.119)|
|**Percentage of workers female**|0.999|
||(0.001)|
|**N**|51323|
|**Pseudo R-squared**|.133|

Source: Authors’ calculations based on EcC 2012/13 Notes: *p<0.05; **p<0.01; ***p<0.001.

We now turn to the results of Model 2b and Model 3 estimated on the two waves of the ELMPS data (Table 4). The results on the effect of the firm characteristics do not change appreciably once we add the owner characteristics in Model 3, so for these characteristics we discuss both models for both waves together and then move to the results on owner characteristics in Model 3.

First, we note that firm size plays the same steadily increasing and highly significant role we observed in Model 1, but, as in the case of Model 2a, adding additional regressors attenuates the effect of size. The effects of industry become more mixed and lose significance as more regressors are added to the model. We conclude from this that industry is not a reliable predictor of formality once other characteristics have been taken into account. The effect of region on formality also becomes somewhat mixed when additional regressors are added. Firms in the Alexandria and Suez Canal region are more likely to be formal only in 2012 but not in 2018. Thus, region is also not a reliable predictor of
compare end-of-season recall with weekly work diaries, which are considered more reliable, among farmers in Tanzania and Ghana. They find that end-of-season recall leads to over-reporting of labor use at the individual-by-plot level (‘recall bias’) relative to diary-keeping, but at the same time to undercounting of plots cultivated and of individuals who worked on them (‘listing bias’). In contrast, Gollin (2019) argues that diary methods may lead to under-counting of farm labor given the eclectic set of tasks that ‘farm labor’ comprises. Seymour et al. (2017) find significant differences in the recording of time spent working between recall and diary methods in Uganda and Bangladesh.

Finally, empirical evidence on recall error (and measurement error in general) in **agricultural inputs** data (fertilizer, agro-chemicals, seeds, and others) is scant. Beegle et al. (2012a) find little systematic evidence for recall bias in farmer self-reported fertilizer usage in Malawi and Kenya, though results vary somewhat depending on respondent characteristics. Gollin (2019) argues that farmers likely recall quantities (and prices) of purchased inputs mostly accurately but also points to evidence (e.g. in Ashour et al., 2017; Bold et al., 2017) suggesting that fertilizer and agro-chemical counterfeiting and adulteration may leave farmers unsure about the quality of product they apply to their land.

# 3. Data and empirical strategy

## Data

The study uses three data sets from nationally representative household surveys in Malawi and Tanzania: the Tanzania National Panel Survey (TNPS) 2012/13, the Fourth Malawi Integrated Household Survey 2016/17 (IHS4) and the Malawi Integrated Household Panel Survey 2016/17 (IHPS).

Tanzania NPS 2012/13 is the third wave of the Tanzania National Panel Survey and was collected between October 2012 and November 2013. The NPS sample used a multi-stage clustered sampling design covering a total is 5,015 urban and rural households. With households selected to be interviewed over the course of 14
1177/0959354319857473

- Akyeampong, E., Bates, R. H., Nunn, N., & Robinson, J. (2014). _Africa’s development in historical perspective_ . Cambridge University Press.

- Ali, G.-C., Ryan, G., & De Silva, M. J. (2016). Validated Screening Tools for Common Mental Disorders in Low and Middle Income Countries: A Systematic Review. _PLOS ONE_ , _11_ (6), e0156939. https://doi.org/10.1371/journal.pone.0156939

- Anderson, M., & Magruder, J. (2017). _Split-Sample Strategies for Avoiding False Discoveries_ . National Bureau of Economic Research. https://doi.org/10.3386/w23544

- Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of Other in the Self Scale and the structure of interpersonal closeness. _Journal of Personality and Social Psychology_ , _63_ (4), 596–612. https://doi.org/10.1037/0022-3514.63.4.596

- Ballard, T. J., Kepple, A. W., & Cafiero, C. (2013). The Food Insecurity Experience Scale: Development of a Global Standard for Monitoring Hunger Worldwide. FAO.

- Bandura, A. (1997). _Self-Efficacy: The Exercise of Control_ . Macmillan.

- Banerjee, A., Duflo, E., Goldberg, N., Karlan, D., Osei, R., Parienté, W., Shapiro, J., Thuysbaert, B., & Udry, C. (2015). A multifaceted program causes lasting progress for the very poor: Evidence from six countries. _Science_ , _348_ (6236), 1260799. https://doi.org/10.1126/science.1260799

- Banerjee, A., Duflo, E
atmospheric distortions and similar temporal resolution for all selected periods. We downloaded images (30-meter spatial resolution) with less than 10 percent of cloud cover from mid-to-lateFebruary for all years falling within Bangladesh’s dry season. Although data from the European Space Agency’s Copernicus satellite, Sentinel-2 A/B offers better spatial resolution (10 meters), we did not use it because of lack of availability in earlier years (before 2014).

We used additional geospatial data. First, we use Protected Planet’s World Database of Protected Areas map to classify areas of Cox’s Bazar as wildlife refuges, national parks, or nonprotected (UNEP-WCMC & IUCN, 2021). We also use annual population data (2010-2020) derived from the Worldpop Population Counts unconstrained, UN-adjusted maps at 100m resolution (Lloyd et al., 2019). For estimates of economic productivity, we use the VIIRS 20142020 Nighttime Lights data produced by NOAA. This data has a spatial resolution of 15 arcseconds and is collected monthly. We derive annual estimates of nighttime lights by averaging across months.<sup>7</sup> Finally, we gauge remoteness using the Google Maps API and its static maps of main road locations.

To construct our data set, we draw a random sample of 10,000 pixels across the district and derive zonal statistics of the pixel’s land cover classes for 2010, 2014, 2017, and 2020, the annual population of the 100m grid the pixel falls in (2010-2020), and the nighttime lights score for the pixel (2014-2020). We also determine static characteristics, including whether the pixel is in a protected area, the pixel’s geodesic distance<sup>8</sup> to the nearest border of a camp that opened after Rohingyas arrived in 2017, and the pixel’s geodesic distance to the nearest main road.

# **4.3 Image Processing**

For the image preprocessing, we use some of the in-build <mark>radiometric enhancement techniques in
Roldós (1997). For the case of Guatemala, this means that changes in the index of quality of capital, _zq_ , are computed as a weighted average of investment of the four broad asset groups. These are (1) public and private construction, (2) imported capital goods and investment in machinery and equipment, (3) domestically produced capital goods, and (4) cultivated assets and major improvements to land. The formula used is: where _Ki_ is the respective capital stock and the weights _vi_ are the relative capital rental rates. The index reflects changes in the composition of capital. If _all_ components of the capital stock are growing at the same rate, quality remains unchanged. If _components_ of the capital stock with higher capital rents are growing more rapidly, quality increases. Since data on the rental rates _vi_ is not readily available for Guatemala, estimates of these are, following Roldós (1997), based on the arbitrage relation: where _Pi_ is a price index, _δ Z_ , _i_ the depreciation rate, and _rt_ is the economy-wide real interest rate. The price indices for the respective asset groups are taken from the Morán and Valle (2002) database. In order to take into account the volatility of the real exchange rate, which affects directly the relative price of the four types of capital, and to correct for measurement bias, the final series are smoothed by a 3-year moving average.

# **_Quality of Labor_**

To quantify labor quality, an index _hq_ is computed as a weighted average of labor within different levels of education. This formulation is consistent with the growth accounting literature that makes adjustments for education. It allows a more accurate indication of the contribution of labor to production. The index _hq_ is defined as follows: where _Lj_ is the labor force with education level _j_ (primary, secondary and tertiary) and _wj_ are the weights for the respective schooling level. The weights measure how the productivity effect of schooling varies with the level of education and are
on when firms use IT more effectively, Policy Research Working Paper 7638, World Bank.

- ITU (2017), ‘The challenge on managing digital content’. Paper for the ITU-TRAI regulatory roundtable.

- ITU (2020), ‘Regulatory tracker’. data retrieved from ITU, `https://www.itu.int/net4/ itu-d/irt/` .

- Katz, R. & Jung, J. (2021), ‘The impact of policies, regulation, and institutions on ICT sector performance’, _International Telecommunications Union_ .

- Khandelwal, A. K., Schott, P. K. & Wei, S.-J. (2013), ‘Trade liberalization and embedded institutional reform: Evidence from chinese exporters’, _American Economic Review_ **103** (6), 2169–95.

**URL:** _https://www.aeaweb.org/articles?id=10.1257/aer.103.6.2169_

- Lee, F. C. & Shy, O. (1992), ‘A welfare evaluation of technology transfer to joint ventures in the developing countries’, _The International Trade Journal_ **7** (2), 205–220.

26
# **7. Extensions**

So far, the analysis has focused on the relationship between inequality in access to opportunities and intra-generational mobility, based on one’s assessment of expected socio-economic mobility in the near future. An interesting question to ask is how this relationship may apply, or not, to the inter-generational mobility context. Another angle that can be explored, in addition to the link between perceived IO and mobility expectations, is whether IO is also linked with current preferences for redistribution. For instance, individuals may support more redistribution today if they feel that they may not be able to move up the socio-economic ladder as much because of unequal access to opportunities, as they may be more amenable to the government’s efforts to aid those at the bottom. The inter-generational context is explored in section 7.1, whereas preferences to redistribution are discussed in Section 7.2.

# _7.1. Unequal access to opportunities and inter-generational mobility_

Inequality of opportunity is particularly pernicious to the extent to which it generates immobility (or persistence) not only within a single generation, but across generations, such that children born to disadvantaged parents at the bottom of the income distribution are stuck at the bottom of the income distribution in their generation through no fault of their own, but on account of circumstances that they have no control over.

To investigate the inter-generational mobility context with the LiTS data I rely on responses to the question “To what extent do you agree with the following statement: children who are born now will have a better life than my generation” with responses ranging from“strongly disagree” to “strongly agree” on a five-step Likert scale. About one third of adults in the LiTS sample either agree or strongly agree with the assertion that children will have a better life than the current generation. Interestingly, perceptions of intra-generational mobility and inter-generational mobility are strongly related in the data (Figure 5). Those who expect themselves to be upwardly mobile in the near future are also more likely to
these changes, we present our preferred specification with these modifications in column 8 of Table A5. We see that the coefficient is -0.28, still significant at the usual levels though now at the 10% level.

Table 3 presents the results. In Column 1, we include the management indices, the manager dummy, and only those controls equivalent to our preferred specification in Table 2. We see that the coefficients on the management indices accord with theory exactly. Increasing the value an organization places on the use of information, proxied by the ‘Management Practices: Monitoring’ index, decreases errors substantially. The corresponding coefficient is -0.17, and the p-value 0.014. Similarly, the coefficient on the ‘Management Practices: Other’ index is 0.28, and has a p-value of 0.005. Managers continue to have information equivalent to their employees. These results are slightly strengthened by the inclusion of noise controls and proxies of the marginal cost of information acquisition, as described in column 2. Combining the indicator of de facto authority, the dummy for district government, and the management practices that proxy incentives leaves the qualitative results consistent with the theoretical literature. Once again, these results indicate the primacy of incentives for determining the distribution of information in the public sector.

In column 4 of Table 3, we undertake a first assessment of whether the impact of de facto authority varies by the nature of management practices. We include an interaction between the monitoring management practice index and the district government binary. It is negative, and large in magnitude relative to the baseline impact of de facto authority, but not significant at the usual levels, with a p-value of 0.17. However, we take this heterogeneity analysis further in Figure 4 by plotting the distribution of coefficients of the district government binary as we vary the decile of management practice. The figure makes clearer that the impact of de facto authority is clearly mediated by incentives. At the lowest levels of management practices observed in
|175.89|143.13|81.4|11.16|6.3|
|1994|184.13|141.01|76.6|15.57|8.5|
|1995|190.93|140.31|73.5|20.45|10.7|
|1996|198.15|139.03|70.2|23.29|11.8|
|1997|202.07|135.83|67.2|26.69|13.2|
|1998|206.78|107.09|51.8|32.32|15.6|
|1999|210.14|99.88|47.5|34.67|16.5|
|2000|231.51|93.24|40.3|34.04|14.7|
|2001|239.40|86.50|36.1|36.58|15.3|

Notes: 1. The “traditional formal sector” refers to the urban state-owned and collective units, and the “informal sector” refers to the domestic informal private and self-employed units (mostly enterprises). In the urban employment, except these two categories, there are also the “emerging formal sector”, which includes units (mostly enterprises) with joint, share-holding, or limited liability ownerships (which can be broadly defined as ‘formal private’. Refer to table 8), as well as foreign and Hong Kong, Macao, and Taiwan ownerships and their affiliates. Also, for the “traditional formal sector”, here we only include the “staff and workers”, which refer to the full-time employees but exclude the re-hired retirees.

2. In the Chinese statistics, the “total urban employed” also includes the laid-off or _Xiagang_ workers from state-owned and collectives. These workers have been laid off while still nominally holding an
of some of the more recent theoretical breakthroughs. These authors link worker-level panel data with ...rm-level and industry-level data to obtain a rich dataset that allows them to test many implications of the most advanced trade models (e.g. heterogeneous-...rm models that incorporate heterogeneous labor) for Brazil. In so doing, the authors are able to assess the impact on jobs of Brazil’s trade liberalization during the 1990s while controlling for a number of worker-speci...c, ...rm-speci...c, industry-speci...c, and economy-wide structural reforms. Menezes-Filho and Muendler’s dataset allows them to follow workers throughout the liberalization period and observe the path of their employment histories in greater detail than previous studies. They are particularly interested in the e¤ects of trade liberalization on employment status, type of employment (formal or informal), and job reallocations.

Menezes-Filho and Muendler’s results show that exporting ...rms and ...rms in industries with a “comparative advantage" shed workers more frequently. Moreover, these same ...rms also hire workers less frequently than the average ...rm. Thus, on net, trade liberalization leads to net employment losses in these ...rms. This is surprising given the standard predictions of international trade models that would indicate that these sectors and ...rms would potentially hire more workers when liberalization occurs. Menezes-Filho and Muendler also show that tari¤ reductions and increased import penetration are associated with an increase in the likelihood of a worker transitioning into informality and unemployment, as well as with a lower probability of a transition from informality back to formal employment. Furthermore, they ...nd that trade liberalization in Brazil has been associated with longer reallocation times for workers moving from one formal-sector job to another formal-sector job. Their results are robust to di¤erent levels of exposure to trade, ...rm-level productivity, worker heterogeneity, as well as other general trends
534|4,485|
||Share of tobacco expenditures**|7.4%|5.5%|5.0%|5.2%|5.5%|4.9%|4.1%|3.8%|4.2%|3.8%|4.8%|
||Share of smoker households***|34.5%|32.5%|38.9%|31.6%|41.7%|41.6%|40.5%|35.3%|41.3%|34.0%|37.3%|

_Source:_ Based on national household budget surveys of most recent date (generally 2016).

_Note:_ Deciles are based on household per capita consumption. * Average household per capita consumption in 2016 PPP U.S. dollars; excludes identifiable rents and lumpy expenses. ** Average share of tobacco in household consumption, conditional on the household reporting positive tobacco expenditures. *** Share of households reporting positive expenditures on tobacco.

Page 10
We can thus recover the change in the factor adjustment cost from data on the change in the ratio of input expenditures. This requires no production function estimation. It is a double difference within a firm across inputs over time. Importantly, z can be interpreted as the τ from Hsieh and Klenow (2009), where τ’s are distortions that raise the marginal product of one input relative to the other input. This can be generalized to the case of k variable inputs, in which changes in k-1 relative adjustment costs over time can be estimated.

Note that the price mark-up μ does not appear in equations (4) to (6). This method cannot be used to estimate the price mark-up - or, more generally, any distortion that affects all inputs proportionally (such as the ‘input-neutral implicit tax’ in Hsieh and Klenow, 2009)). As equation (3) shows, estimating the mark-up and the level of the adjustment cost would require production function estimation.

# 3.2 Empirics: Estimating trends in factor adjustment costs from firm-level panel data

The main objective of our descriptive analysis is to apply equation (5) to firm-level data to estimate how factor adjustment costs have changed over time in Indian states.

v Suppose there are k variable inputs, with zist being the adjustment cost of input v (relative to input 1) for a firm. Rearranging Equation (5), θi1 where the ratio of output elasticities, log v , is by assumption fixed over time. θi Now suppose that This equation specifies that the adjustment cost consists of a firm-level fixed component Avis, annual shocks which depend on a set of initial firm characteristics Xi, and an idiosyncratic firm-specific shock eistv . The initial firm characteristics could include, for example, the state in which the firm is located, firm size, sector and age.

We do not observe log (zistv ), but can infer the α′s and β’s – the changes over time –from the ratio of input 1
1 and _c_ = 0 _._ 1 and to the choice of _g_ = _k_<sup>2</sup> ( _i.e._ , the models leading to the highest values for _g_ ) are quite different from those using the other priors. BMA predicts best in 76% to 92% of the subsamples, with the exception of the benchmark priors with _c_ = 1 and _c_ = 0 _._ 1 and the case with _g_ = _k_<sup>2</sup> , where BMA outperforms the highest posterior probability model, the null model and the full model in roughly half the subsamples. BMA is only beaten by the null model in up to 8% of the cases,<sup>16</sup> except for the benchmark priors with _c_ = 1 and _c_ = 0 _._ 1 and the fixed- _g_ specifications, where the null model outperforms BMA in 10% or 12% of the cases.

The marginal information in the data on _g_ , as presented in Figure 8, is quite similar to that for the LPS data, with a slight shift to smaller values and a heavier tail. As a consequence, the pattern of Bayes factors between the models with different priors on _g_ is rather similar to what they were for the FLS data (see Table 4).

# _8.2. Returns to Schooling_

Here we investigate the area of returns to education, where again many potential model specifications have been proposed, and BMA was introduced in this context by Tobias and Li (2004). As these are microeconomic data, the number of potential observations is much larger. We will focus on the log of hourly wages of _n_ = 1190 white males in the U.S. in 1990, which we model as a function of _k_ = 26 possible regressors. In order to simplify the computations, Tobias and Li (2004) always include seven of these regressors in each model and restrict themselves to a smaller model space characterized