rafmacalaba/data-use-mentions-extended huggingface dataset:
-100-

Industrial Statistics, and this leads to use of non-commensurate valuations In construction of world indices of industrial production. There is therefore need for both improvement and standardisation in this field. Of course, it is not easy to modify the scope of detailed data collection in censuses, but at least some better guidelines on the problems of reconciliation with the national accounts should be provided in the summary volumes of the census reports. The US General Summary volume has made a start on this, but its analysis could be greatly improved given the wealth of input-output material available in that country.

# b) Scope of National Accounts

Our investigation revealed a major difference between Mexican and Brazilian national accounting practice in estimating activity in the infermal manufacturing sector. The Mexican estimates for such activity add 38 per cent to ce.asus definition of value added, whereas the Brazilian national accounts make virtually no such imputation, in spite of evidence from employment statistics that such informal activity is probably as large proportionately as in Mexico. This means that comparative real product estimates must be particularly wary of such differences in national accounts coverage. In the long run, improvements will require increased manpower resources in national statistical offices (in this case, particularly in Brazil) and increased scrutiny by international agencies with the vocation and the funds to carry out such a task (which in practice means the World Bank).
|0.08|0.09|0.01|0.08|-0.00|
|**Household owns a refrigerator/freezer**|(0.02)|(0.02)|(0.02)|(0.01)|(0.02)|
||0.07|0.08|0.01|0.07|-0.00|
|**Household owns an air c/fans**|(0.01)|(0.01)|(0.02)|(0.01)|(0.02)|
||0.42|0.45|0.03|0.45|0.03|
|**Household owns a radio**|(0.02)|(0.03)|(0.04)|(0.03)|(0.04)|
||0.83|0.85|0.01|0.84|0.01|
|**Household owns a mosquito net**|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
||0.46|0.47|0.01|0.46|0.00|
|**Log of perca residential area**|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
||083|083|-000|082|-000|
|**Roof is made of concrete/metal sheets/tiles**|.<br>(0.02)|.<br>(0.03)|.<br>(0.03)|.<br>(0
in effects for girls versus boys.

> 21In the Appendix, we discuss that our main results are no different if we exclude or include the states for which we do not have DISE data prior to 2005, we are reassured that the sample for which we have data prior to 2005 is not systematically different. The results are reported in Appendix Table S3. Limited data for a few states is also available for 2001 and 2002 but the coverage is not as expansive. Since data for many states and many variables is not available, we do not use these years.

> 22The study lost 0.7 percent of the sample schools as new districts were carved in 2004 and we are unable to use their pre-trend data. The study also checks the consistency of results when excluding those schools without pre-trends in Appendix Table S3.3.

23Appendix S3 on robustness tests further substantiate our identification by showing in a year-by-year comparison of early versus late districts, that the decline in enrollment occurs in 2006 after the early phases are treated. Appendix Figure S4 depicts this in a graph. We also conduct a placebo test to rule out pre-trends in our results for children’s employment to ensure credibility of our findings. The results are discussed in Subsection D3 in the online Supplemental Appendix.

15
Table 4: Plant level productivity, markup, value added and revenue.

||(1)<br>TFPQ|(2)<br>Marginal cost|(3)<br>Markup|(4)<br>Value added|(5)<br>Revenue|
|---|---|---|---|---|---|
|ETR _×_ secondary|0.029***|-0.086***|0.051***|0.028***|0.041***|
||(0.005)|(0.020)|(0.010)|(0.006)|(0.004)|
|Observations|53,517|53,517|53,517|53,517|53,517|
|R-squared|0.999|0.747|0.744|0.885|0.914|
|Plant FE|yes|yes|yes|yes|yes|
|Industry-year FE|yes|yes|yes|yes|yes|
|Other technologies|yes|yes|yes|yes|yes|

The table presents OLS estimates of the relationship between plants’ exposure to robots, productivity, markup, value added and revenue. The dependent variables are TFPQ (1) the log of plant level real marginal cost (2), markup (3) real value added (4) and real revenue (5). Secondary is the 2006 share of plants’ employment with secondary education. Other technologies are capture by an index of innovation activities in 2006, interacted with year fixed effects. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</
- **, , , , and** , “Tax audits as scarecrows: Evidence from a large-scale field experiment,” _American Economic Journal: Economic Policy_ , 2021, _forthcoming._

- **, Rodrigo Cenia, and Maria Sauval** , “Factura Electronica y Cumplimiento Tributario: Evidencia a Partir de un Enfoque Cuasi-Experimental,” IADB Working Paper IDB-DP-561, Inter-American Development Bank 2017.

- **Bolt, Wilko, Nicole Jonker, and Corry van Renselaar** , “Incentives at the Counter: An Empirical Analysis of Surcharging Card Payments and Payment Behaviour in the Nether–

- lands,” _Journal of Banking and Finance_ , 2010, _34_ (8), 1738 1744.

- **Burgess, Robin and Rohini Pande** , “Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment,” _American Economic Review_ , June 2005, _95_ (3), 780–795.

- **Calonico, Sebastian, Matias D Cattaneo, and Rocio Titiunik** , “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs,” _Econometrica_ , 2014, _82_ (6), 2295–2326.

- **Carrillo, Paul, Dina Pomeranz, and Monica Singhal** , “Dodging the Taxman: Firm Misreporting and Limits to Tax Enforcement,” _American Economic Journal: Applied Economics_ , 2017, _9_ (2), 144–164.

- **Dalton, Patricio, Haki Pamuk, Ravindra Ramrattan, Daan van Soest, and Burak Uras** , “Payment Technology Adoption and Finance: A Randomized-Controlled-Trial with SMEs,” CentER Discussion Paper 2018-042, National Bureau of Economic Research, Inc 2018.

- **Das, Satadru, Lucie Gadenne, Tushar Nandi, and Ross Warwick** , “Does going cashless make you tax rich? Evidence from India’s demonetization experiment,” IFS Working Paper W22/03, January 2022.

- **Davidson, R. and J. G. MacKinnon** , Oxford University Press, 1993.

-
price database. Coincidentally, the size of the GAEZ grids and the sparsity of the market geo-location data prevent us from finding multiple markets within any grids.

Finally, we compute expected per capita income gains by taking the product of crop prices and the simulated production gains for each crop-grid. We aggregate this for all grids ( _G_ ) within a country to estimate an expected change in income for individuals at the poverty line. That is, for a representative farmer at the 1.90 USD extreme poverty line (2011 ppp) within a specific country, we compute the expected percentage income growth ( _E_ ( _θ_ )) from all crops combined.

Here, _AEPL_ represents the annualized extreme poverty line (i.e., 1.90 * 365 USD), and therefore _E_ ( _δθc_ ) represents the expected change in income for farmers around the poverty line from adopting groundwater irrigation to reduce the crop water deficit.

# **6 Results**

Table 4 presents the simulation results we obtained using the two different approaches— SD and SRHS—when all three conditions are met. Each row in the tables corresponds to a unique combination of model and crop choice.

The mean gains in production are much higher than the median gains, suggesting the distribution of production gains is right-skewed. Therefore, we use median production gains to discuss our results.

Using the two approaches, our simulated median gains in production for different crops range between 29.57% and 33.28% for barley, 76.35% for millet, 107.96 and 123.26% for maize, 108.09% and 114.64% for rice, 89.49% and 129.42% for sorghum, and 27.97% and 53.08% for wheat. Refer to the map of production gains in Figure 6 and Figure 7 for the SD and SRHS approaches, respectively.

Note we were not able to run simulations for millet using the SRHS approach, due to very few grids being irrigated
Table 1 gives the regional breakdown of the headcount index for selected years. Figure 4 gives the series for three regions accounting for 95% of those living below $1.25 a day (in 2008). For East Asia, the rate of poverty reduction has been about double the average, at slightly over 2% points per year; for South Asia, it has been slightly less than 1%, and for Sub-Saharan Africa it has been about zero.<sup>14</sup> However, there are clear signs of a positive trend emerging in Africa in the 2000s, at a similar trend to South Asia. The late 2000s have seen progress against poverty—with both falling incidence and falling numbers of poor—in all six regions of the developing world (Eastern Europe and Central Asia, Latin America and the Caribbean, and the Middle-East and North Africa, in addition to those in Figure 4).

So the developing world as a whole has been maintaining robust progress against extreme poverty, judged by the $1.25 line, and there is clearly cause for optimism. However, as Chen and Ravallion (2012) also show, there has been less progress in getting people over $2 a day. A

> 12 This line is derived in Ravallion et al. (2009).

> 13 The estimates of absolute poverty measures used here are from Chen and Ravallion (2012), which discusses data and methods. (The update for 2010 gives 20.8% below $1.25 a day, representing 1227 million people.) A fuller discussion of the absolute measures (with various tests of robustness) can be found in Chen and Ravallion (2010); on the relative measures see Ravallion and Chen (2011).

> 14 The coefficients are -2.24% (s.e.=0.14) for East Asia, -0.88% (0.04) for South Asia and -0.09% (0
# _Potential data quality issues_

Finally, our data contain firms that report their results at different times of the year. This can potentially impact the results, as we examine changes in profitability over time. To ensure that this does not distort our results, we have as a precaution estimated an additional regression setup for only those firms that declare results on a calendar-year basis (approximately 70% of our total firm population).

# **Data**

Data with regard to the characteristics of employees are based on SCB/RAMS, which covers virtually all employed Swedes (roughly four million observations per year) for the period 20042012. We make use of information regarding place of work, annual wage income as well as age and educational attainment in order to:

- Estimate the share of young workers per firm in the pre-reform period.

- Exclude marginal workers from the sample (the bottom 10 percentiles in terms of wage income). This exclusion is due to our use of the share of young employees as a treatment intensity variable. Marginal workers with very low incomes will hence be overweighed in the treatment variable relative to their wage sum. This exclusion mitigates this problem.

- Estimate our firm skill intensity control variable based on the average level of SUN2000-standard educational attainment among firm employees pre-reform (a higher value indicates more education).

This information is then matched with SCB/Structural business statistics, which provide firmlevel accounting information to create our final panel data set.

In the creation of this data set, we exclude the following categories of firms and organizations:

- All public sector institutions.

- Firms that are deemed inactive (net turnover of less than 100,000 SEK per year). This exclusion is primarily motivated by very low turnover (often due to missing or erroneous data) severely distorting our profitability measure.

- Firms that lack basic accounting information regarding operating income and net turnover.

- Firms that lack matched RAMS information regarding employee characteristics (age and education).

10
**Figure 12: Average expenditure in medical consultation on the month before the survey, by type of centre**

<!-- Start of picture text --> 3,472.60<br>SESPAS (Public) Private<br>2,273.30 2,270.60<br>2,075.40<br>1,809.40 1,764.30<br>1,425.60<br>896.2 952.4 988.5<br>Q1 Q2 Q3 Q4 Q5<br>Source: ENDESA survey, 2007<br>DR pesos<br><!-- End of picture text --> As it is shown in the graph, people from the first quintile<sup>11</sup> is spending an average of RD$ 896 (US$ 23) when they go to a SESPAS public center for examination. This confirms that there is a share of the poorest population that is not totally covered by the fully-subsidized regime. The main implication is that those that have to attend to the doctor may be spending most of its monthly income in health services, even when they use the public system (the upper income boundary for this quintile is RD$ 1,904 a month).

People from the middle and higher strata (Q4 and Q5) are on average paying 68% more than people in the lower quintiles (Q1-3) when they opt for a public centre for a check up<sup>12</sup> . One of the reasons for this divergence may be that the richer strata of the population are not subsidized, which affects averages, or that they “consume” more medicines and are more able and willing to pay for additional analysis and medical check-ups in order to obtain a better diagnosis. In any case, the narrower difference in cost the upper intermediate class (Q4) is facing when they are choosing between public or private health is likely
-|---|---|---|---|
|Industrialproducts|9.0|0.75|0|26.8|809|4,126|8,592|
|**EU-15**||||||||
|Industrialproducts|3.7|0.93|0|22.0|23|4,464|750,172|

Note: International peaks are defined as tariff rates exceeding 15 percent _ad valorem_ . The format of the UNCTAD TRAINS file identifies maximum and minimum tariff rates within six-digit lines that represent averages for HS nine-digit original tariff schedules. Source: derived from the EU”s and Bulgaria's Applied Tariff Rates by HS 6-digit products in 2003 (average for 6-digit tariff line in % from UNCTAD TRAINS) Bulgaria’s tariffs are not only higher but also offer protection to different products than in the EU (Annex Table 4). Yet, although at the six-digit HS level there are some differences reflecting dissimilar ‘protectionist’ preferences, they are not extreme. Both Bulgaria’s and the EU’s MFN applied tariff schedules offer larger tariff protection to unskilled labor intensive products. Paradoxically, Bulgarian successful exports to EU markets—clothing and footwear—are also highly protected domestically. So are they in the EU, albeit levels of tariff protection are lower. For instance, clothing is protected in both, but tariff rates on clothing imports in Bulgaria (24 percent) are twice as high as in the EU (12 percent).<sup>32</sup> On the other hand, there is one unequivocal difference. Bulgaria provides high tariff protection to low processed industrial goods such as glass and glassware, paper products, fertilizers, modified starches, carpets and other textile floor coverings, whereas the EU protects relatively more such products as trucks, bicycles, road tractors and video cameras.

In all, however, the two schedules for industrial products are not dissimilar.
usually have access to the public health system through the contributory regime, when they are employed. However, given the high levels of informality of the Dominican economy, there is a large share of the working population that has either to opt for the partially subsidized regime, or to recur to private insurances. Moreover, imperfections in the public system, together with a lack of incentives to improve quality standards causes that some of the people in the lower strata prefer to get treatment in private hospitals.

> 10 WHO country health statistics, 2010

[15]
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dropped several hyperinflation observations, such as Zimbabwe), and was available for a large range of developing countries.<sup>6</sup> However, since domestic price movements might be endogenous with respect to poverty (see below), we use an international price index to identify exogenous externally driven variation in domestic prices. To do so we construct an international price index with country-specific weights based on consumption (calorie) patterns for nine commodities for which international prices were available. This has the effect of creating an international price series that has cross-country as well as time series variation. For example, rice accounts for around 70 percent of caloric intake in Bangladesh, so international rice prices receive a very high weight in Bangladesh’s index, while wheat, maize, sorghum, and so on get very low weights. This approach helps minimize the weak instrument problem stemming from the fact that international price transmission may be highly variable across countries for a variety of reasons.

# _Agricultural Indicators_

In section V, we chiefly focus on agricultural production indicators to test for agricultural supply response and to circumvent some of the issues with the POVCAL data raised above. These indicators are mostly sourced from the FAO (2004). This set includes value-added measures of all agricultural production as well as food and crop production components. Growth in crop production can also be decomposed into increases in production per hectare and increases in land area harvested, often referred to as the intensive and extensive margins, respectively. We also examine another common indicator of the intensive margins: cereal yields.

# _Descriptive Statistics_

Table 1 shows some summary statistics for some of our key indicators, all of which are measured as annualized changes or percentage changes across consecutive surveys. The statistics are broken up by regions to illustrate one other serious limitation of POVCAL data, namely, that it is overpopulated with countries from Latin America (approximately half the sample), while there are only about 50 observations (i.e., first differences in poverty) each for sub-Saharan Africa
I1 are not significant)."' A positive sign for leverage can be expected in the Q-regression as leverage increaLses the value of the tax-shield advantages derived from debt financing, thus increasing the relative value of a firm. Leverage may not, however, have a positive coefficient for the profitability regression since we use operating income as our profitability-measure, which is not influenced by the tax advantages of increased interest payments. Alternatively, for both the Q- and profitability-regression, leverage may have a negative coefficient as, according to some agency models, leverage can be negatively correlated with Q and profitability (see Harris and Raviv, 1991, for a review of the relationships between leverage, and Q and profitability).

We run the regressions in three steps. First with the Herfindahl index (or the top 5 investors) only; second with the Herfindahl index but also including the shares for the various categories of investors to investigate their individual roles; and finally-to investigate the role of banks-also including one dummy, which is one when an investment fund sponsored by a firm's main bank has an ownership stake of more than 10%. The explanatory power of the regressions is good, with R<sup>2</sup> s between 0.14 and 0.17 for the Tobin's Q regressions and between 0.08 and 0.10 for the profitability regressions. The lower R<sup>2</sup> s for the profitability regression likely reflect the fact that current year profit is a poor guide to future profitability and valuation. **12** Furthermore, the panel-regressions document not only the cross-section variation in the variables, but also their variation over time. Lower R<sup>2</sup> s could thus be expected. Annual cross-section regressions give R<sup>2</sup> s comparable to those obtained in similar cross-section regressions.<sup>13</sup> Leverage has a positive and significant coefficient for Tobin's Q. Leverage
communities had access to improved sanitation.<sup>13</sup> Access to improved sanitation had increased only very slightly, by about 1 percentage point in both treatment and control communities by endline. Figure 3 presents the percentage of households with no sanitation, unimproved sanitation, and improved sanitation in the baseline and endline by treatment status. It similarly shows small improvements in sanitation in both treatment and control communities.

**Figure 3. Households’ sanitation status**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>No Sanitation<br>50%<br>Unimproved Sanitation<br>40% Improved Sanitation<br>30%<br>20%<br>10%<br>0%<br>Control-BL Control-EL Treatrment-BL Treatment-EL<br><!-- End of picture text --> Table 8a also shows that although more than 40 percent of the households in the sample had access to improved sanitation before the program, another 40 percent of households lacked access to any form of sanitation. These households, many of which are particularly poor, defecate in open spaces. The percentage of households with no access to sanitation of any sort declined by about 4 percentage points between the baseline and endline. Again the decline in treatment and control communities appears similar.

The endline data specifically asked households whether they had constructed a toilet in the past two years. This variable is reported in Table 8a and indicates that more toilets were built in treatment communities than in control communities. Approximately 16 percent of households

> 13 Improved sanitation is defined according to the WHO/UNICEF (2012) Joint Monitoring Programme for Water Supply and Sanitation (JMP; http://www.wssinfo.org). Improved sanitation facilities include: a) a flush toilet or latrine that flushes to a sewer, septic tank, or pit; b) a ventilated
# Introduction

The Russian Federation is a major producer and exporter of natural gas and crude oil. A number of authors (see Oomes and Kalcheva, 2007 and World Bank, 2005 among others) argued that – following the more than 300 percent increase in the oil price (from about 25 USD to 110 USD per barrel) between 2002 and 2008 – the economy had been showing symptoms of the Dutch disease. Increased revenues from oil exports pushed up the real exchange rate and hurt the competitiveness of the tradable sectors; manufacturing and other exportables shrunk, while services and non‐tradables expanded.<sup>2</sup> More recently the Russian economy has been facing the opposite situation; between July and December 2014 the oil price halved from around 110 USD to less than 60 USD per barrel (World Bank, 2015). The consensus is that the oil price will stabilize at the current lower levels (the average price in 2015 was 50 USD per barrel, and 42 USD in 2016) reflecting structural changes in the global oil markets rather than cyclical and short term adjustments.

The main objective of this paper is to assess the distributional impact of this terms of trade shock. Recent macroeconomic data suggest that a reverse Dutch Disease may be at work. In 2015, the exchange rate depreciated by more than 30% and terms of trade losses were large, equivalent to about 7% of GDP. Assuming that these income losses will be putting pressure on prices of non‐traded goods and services and will require related adjustments in the labor markets, this paper tries to identify who are the winners and losers, and to estimate the changes in income distribution and poverty.

Two difficulties arise in the estimation of the distributive impact. The first is that a household survey for the period after the shock is not yet available. However, and this is even more important, even if survey data were available, they may not provide an adequate counterfactual given that, simultaneously to the oil price drop, the Russian economy