rafmacalaba/data-use-mentions-extended huggingface dataset:
in the PICS-BEEPS data. The second question is addressed by testing whether the estimated effects for Serbian firms are different from those for firms in other countries.

The 2003 Serbia PICS survey covered 408 firms; the 2002 Serbia BEEPS survey covered an additional 230 firms. About half of these firms were in services, about 10 percent were in construction, and the rest were manufacturing firms of varying sorts. Two thirds of the sample were small and medium-sized firms (employing 100 or more persons). Over half (57 percent) of the sample was composed of new private firms, 29 percent were sociallyor state-owned, and the remainder had been privatized. Almost one-quarter of the sample were exporting, and about one in ten had significant foreign ownership. Because of the relatively small Serbian sample, the estimations of pool manufacturing, construction and services firms and employ sector dummy variables; the results are similar if the regressions are done separately by sector. Table 3 shows the results of the TFP estimations.

**Table 3: TFP regressions using firm-level data – Serbia vs. other PICS-BEEPS countries**

||**49 country**<br>**surveys**<br>**Serbia**|**Serbia different**<br>**from 49 others?**|
|---|---|---|
|Fixed capital|0.374**<br>0.394**|No|
|Labor|0.693**<br>0.590**|No|
|Privatized|0.359**<br>0.599**|No|
|New private|0.359**<br>0.846**|Yes*|
|Foreign-owned|0.205**<br>0.282|No|
|Exporter|0.216**<br>0.089|No|
|Capital city|0.089**
overall pattern of health and COVID-19 results remains elusive.

Despite these negative effects, there were also several indications of improved household outcomes due to the HSP. Consistent with the subsidy program relaxing the household’s budget constraint, midline survey data indicate that there were reductions in credit usage among treated participants (Panel E, Table 5): treated participants reported taking fewer loans before and during Jordan’s COVID-19 lockdown by 20 and 34 percentage points, respectively. We largely view this outcome in a positive light considering the high debt burden in the sample: control households hold average total rental debt of 806 USD PPP at endline.

Finally, there are mixed midline estimates on child outcomes. Children in treated households 25
value of the Kakwani index corresponds to a progressive tax or transfer, while for regressive taxes or transfers the index takes on negative values. Deciles are defined based on disposable income plus taxes for direct taxes, disposable income minus transfer for direct transfers and pensions, and pure disposable income in other cases.

# **Box 1: Progressivity of taxes and transfers – main principles**

<mark>One of the ways of determining the effectiveness of different programs in reducing inequality is through the</mark> analysis of _progressivity_ of programs. In this analysis, the degree of progressivity is determined by the share of benefits (or taxes) going to different population groups (such as deciles) relative to their respective share of income. Thus, a program can be deemed to be progressive if the share of benefits (as a proportion of pre-fiscal income) decreases with income; a tax can be deemed to be progressive if the share of tax (or the tax rate) increases with income. Progressivity, thus defined, is known as _relative_ progressivity. Alternatively, a program can be deemed to be _absolutely_ progressive if the per capita amount decreases as income rises.

The CEQ analysis relies mainly on the concept of relative progressivity, which is also consistent with the principle that a transfer or tax is progressive (regressive) if applying that tax or transfer alone results in, conditional on no re-ranking, a less (more) unequal distribution than that of pre-fiscal income.

One of the (relative) progressivity indicators is the _Kakwani_ index, defined as the difference between the concentration coefficient of the tax and the Gini for market income in the case of taxes, and as the difference between the Gini for market income and the concentration coefficient of the transfer in the case of transfers (Kakwani, 1977). Thus, the Kakwani index will be positive (negative) if a tax is globally progressive (regressive), where global progressivity (regressivity) is in terms of the concentration curve associated with the particular tax lying everywhere
759***|0|431,436|
|Asset ownership|-0.021***|-0.085***|0|-0.284***|-0.347***|0|264,680|
|Literacy|0.003|-0.011***|0.0014|-0.238***|-0.252***|0.0014|694,463|
|Not employed in the<br>agricultural sector|-0.004|-0.040***|0.0002|-0.158***|-0.194***|0.0002|450,521|
|Access to electricity|0.017***|0.021***|0.4559|-0.316***|-0.311***|0.4559|697,574|
|Piped drinking<br>water|0.002|0.013**|0.0929|-0.132***|-0.122***|0.0929|595,934|
|Improved sanitation|0.003|-0.022***|0|-0.220***|-0.245***|0|592,937|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the results of regressing an indicator variable of being poor on (ranked) residuals of covariates</u> of poverty (see Section 3b in the paper for more details). The residuals are determined by conditioning out household size from the covariates of poverty, including years of schooling, asset ownership/index, literacy, access to electricity, etc. Ranked residuals are used for
pre-program earnings so as to account for the so-called “Ashenfelter Dip” problem, namely, that program participants often exhibit a pre-program dip in their earnings that is not seen in comparison group data. The two-stage matching procedure generates a similar preprogram earnings dip pattern in both the treatment and comparison groups, although aligning the groups in terms of preprogram earnings would not necessarily guarantee that post-program comparisons are valid.

> 10 Recent research has shown that bootstrapping does not lead to valid inference about standard errors for nearest neighbor matching estimators, so the standard errors reported in this study would not be valid. However, correcting them using the alternative standard error estimators suggested in Abadie and Imbens (2005) or using kernel smoothed estimators for which bootstrapping is valid probably would make little difference.

18
201|29,702|4,593|15,549|32,281|42.6|55.6|50.5|

Source: IMF Direction of Trade Statistics. Notes: Oil imports are excluded from the South Asia’s data. All Middle East, the post-Soviet Republics and Eastern European countries are categorized under “Other Developing Countries (which excludes South Asia); East Asia also includes the Rep. of Korea, Taiwan (China), and Hong Kong (China). China is among the “Other Developing” category, as it is not traditionally identified under East Asia.

The above indicates that South Asia has gradually adjusted its direction of trade as it has evolved through both domestic reform and international competitive conditions. In the process, the South Asia region, as a trading partner, has also relatively increased in importance among regional members. But how significant is this emergence and can the South Asian countries be characterized as “natural trading partners”?

Table 2 provides a review of intra-regional trade between 1981 and 1998 at an aggregate level. The overall (official) intra-regional trade as a share of total trade has remained below 5 percent throughout this period. However, these aggregate figures are biased by India’s trade patterns, the dominant economy in the region, and therefore do not reflect the extent of intraregional trade of the smaller countries. There are, in fact, large differences in the relative importance of South Asian markets for individual member countries of the South Asian Association for Regional Cooperation (SAARC).<sup>8</sup> For example, Bhutan and Nepal trade disproportionately with the region, with shares of regional trade as high as 71 percent and 33 percent, respectively, in 1998 as seen from Table 2. Due to the landlocked nature of these two countries, trade takes place primarily with India. While Nepal has made some efforts to reduce its

> 8 See Annex Table A1.

7
9597|8|61|0.0073|Yes|

Note: Granger causality is based on 5% significance level; "No" indicates that we fail to reject the null hypothesis: x does not granger cause y.

> 19 Data is from 1997q1 to 2019q4. We use standard splicing method to expand our CPI 2011-12 inflation series which starts from 2012 onwards. Prior to 2012, CPI-IW inflation is used for each component—food, fuel, and core. 20 India being a net importer of fuel, its base price is determined globally, and retail prices by the base price and taxes. During the earlier period in the analysis, the retail price was administered.

10
Policy Research Working Paper 10805

# **Abstract**

This paper examines the potential distributional effects of a tax increase on processed and ultra-processed foods. Using data from the most recent Brazilian consumption survey (POF 2017/2018), it analyzes the welfare changes that households would experience when facing increased costs for these products. Using an extended cost-benefit analysis model to assess net income effects, the paper considers three distinct dimensions: changes in product expenditure, changes in medical expenditure, and changes in years of life lost. The findings suggest that the tax increase would have a progressive impact, benefiting households at the lower end of the consumption distribution in all three dimensions. Overall, the study highlights the potential for targeted taxation policies as a “triple win” to address health concerns, promote greater equity, and increase fiscal revenues.

This paper is a product of the Health, Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bernardocoelho@worldbank.org, civins@worldbank.org, and riunes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
early stages of the use of harmful coping strategies by farmers (three to eight months following the main harvesting season), and prior to the phase where live-saving interventions become necessary (nine to eleven months following the main harvesting season). Moreover, seasonal monitoring data are available earlier than LEAP and LIAS data, enabling the detection of a failed start of a season, as well as rainfall deficits prior to the final phase of crop development. Jointly, monitoring and early warning data could therefore enable a timely drought response<sup>59</sup> . In contrast, interventions through HRD appeals are typically associated with time delays as funds are raised, leading to response interventions during the time where coping strategies are near their exhaustion and emergency assistance is required (eight to eleven months following the main harvesting season). The timing of HRD appeal funding makes the HRD process particularly well-suited to addressing emergency needs that may arise when PSNP FCB funds are exhausted early.

# V.2 The welfare benefits of a timely drought response

The impacts of droughts on household welfare have been studied in the academic literature. In particular, a body of research in Ethiopia has demonstrated that droughts have significant adverse impacts on household consumption and may cause households to lose valuable productive assets. Droughts also negatively affect farmers’ health, leading to a higher incidence of morbidity caused by the development of a range of diseases.

Investigating the impacts of droughts on household consumption, Dercon, Hoddinott and Woldehanna (2005) show that experiencing a drought at least once in the previous five years lowers per capita consumption by 20%. Moreover, the authors show that 41% of households lose productive assets and 77% of households experience a loss of income and consumption as a result of droughts. In a similar spirit, Yamano, Alderman and Christiaensen (2005) showed that crop losses associated with droughts result in reduced consumption, affecting the growth of Ethiopian children particularly in the 6-24 months age group. Estimates suggest a 50% crop loss results in a reduction
# Table (G4) Impacts of the Program on Altruism and Trust

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Trust in Syrians|-0.45|(0.46)|[0.37]|4.75|(3.37)|1,102|
|Dictator game, Altruism to Jordanians|-0.51*|(0.28)|[0.37]|1.88|(1.77)|1,102|
|Dictator game, keep|0.30|(0.31)|[0.37]|2.24|(2.03)|1,102|
|Incentivized Dictator game, Altruism to Jordanians|-0.43|(0.53)|[0.37]|1.95|(1.98)|375|
|Incentivized Dictator game, keep|0.97|(0.60)|[0.37]|2.29|(2.26)|375|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: trust in Syrians reflects how much of the total endowment the respondent chooses to share with a Syrian refugee game partner in a hypothetical trust game. The respondent is told they (hypothetically) have 10 JD and can share as much of it or as little of it with their game partner
work every day, but I usually work every three days” or “I typically work four days a week”) may produce inaccurate estimates of season-long labor.

Having postulated that irregularity in work schedules is likely to contribute to inaccuracy in labor measurement and that errors increase as schedules become more irregular and having shown evidence of few systematic patterns in smallholder work schedules, we turn to the question of how people arrive at the labor figures they report.

Several possibilities are raised in the cognitive psychology literature. First, people might infer their labor by extrapolating from salient episodes of work, such as the busiest workweek. Second, they might base their inferences on the most recent workweek. Third, as de Nicola and Giné (2014) find in the case of earnings, people might attempt to calculate a total from their knowledge of averages. In all cases, the seasonwide total is built on the basis of some subset of the season.

To examine which subset of the season may be used as the reference period for their seasonwide inferences, we use the weekly data. Here, we compare the recall data with the seasonlong extrapolations based on the person-level average, peak, recent, and harvest work periods in the weekly visit and weekly phone data. These estimates are presented in table 6. Unlike de Nicola and Giné (2014), it appears that farmers in our data do not base season-long estimates on the average workweek. This finding is consistent with the idea that it may be difficult for people to calculate an average mentally in the absence of a clear, regular work pattern. There is little evidence that respondents are inferring their season-long labor based on their average weekly labor at the harvest, the scaled total of which is much higher than the reported data in recall NPS and recall ALT. Nor does it appear that respondents are making inferences based on their busiest week, the totals of which would be two or three times higher than those reported in the recall survey designs. Instead, although none of these reference periods
Suivi de la Pauvreté au Sénégal » (ESPS) surveys, authors' calculations. Note: Summary statistics of total consumption per capita, by household types. In the first two columns, the sample size is reported between parentheses. In the third column, the standard error of the difference is reported.

# **3. Poverty and inequality**

We now compare the values of poverty and inequality indices obtained with the two surveys.

Two poverty lines are considered. The lowest, « nutrition », line corresponds to the level of expenditure necessary to purchase enough food to attain a minimum caloric intake of 2400 kcal per adult equivalent and per day. Since in PSF we do not have a reliable set of price data, we use the poverty lines computed by Ndoye et al. (2009), that we update using the 13
2008 round. We check robustness of results to two alternative strategies to account for the missing years.

We use plant level outcomes from the Indonesian survey of manufacturing plants with at least 20 employees Statistik Industri (SI henceforth). SI is administered by Indonesia’s Badan Pusat Statistik (Central Agency on Statistics). The survey has extensive coverage: it is administered as a census every 10 years starting from 1996 and is very close to a census in the remaining years. Such coverage ensures high representation even at low levels of sectoral and geographic disaggregation. The data allow for different groupings of firms into sectors, based on definitions of Klasifikasi Baku Lapangan Usaha Indonesia (KBLI), a classification mostly compatible with ISIC Rev.3. We use 5-digit sector levels as our primary specification; however, our results are also robust to an alternative specification using 3-digit sector classifications. For the main analysis we use data from 2002 to 2014, the last year of data which allows us to compute TFPQ estimates. We also use the change between 2000 and 2002 to check our identifying assumptions.

Following Cali et al. (2021) we use SI data to estimate the plant-level performance measures: TFPR, TFPQ and markup. Unlike most existing studies using industry-level price indexes to deflate nominal variables, the granularity of our data allows us to calculate plant-specific output and input price indexes. This mitigates the bias deriving from inputs' price heterogeneity across plants and allows us to disentangle technical efficiency (TFPQ) and markups. To estimate plant-level production function parameters, we follow the control function approach and timing assumptions of Ackerberg et al. (2015), which are useful to mitigate the simultaneity bias affecting simple OLS coefficients. We slightly modify the standard estimator to allow for the presence of adjustment costs in materials. Then, using the estimated production functions, we calculate plant-level markups based on a model of profit maximization by firms, as in De Loecker and Warzynski (2012). Finally, we combine our estimates of TFPQ and markup to obtain a
# Authors' Note

To ensure intertemporal consistency, this investigation employs a concept of trade "affected" by nontariff barriers that has not been used in previous World Bank studies. The shift was required to conform with NTB surveys undertaken by other organizations in the mid-1960s. The "affected" trade concept holds that an NTB applied to one or more tariff lines within a four-digit SITC group affects all trade in the group since exporters often modify trade to halt the spread of barriers to their own (related) products. The World Bank has normally utilized a measure based on the share of imports actually covered by nontariff barriers. For reasons explained in the text these coverage ratios are lower than the "affectet' trade ratios. However, the key point to note is that we apply the exact same affected trade measure in 1966 and 1986 so our standard of NTB measurement is held constant over time.

There is one other important aspect in which the present study differs from previous World Bank analyses of nontariff barriers. In computing NTB indices, we have utilized a somewhat broader group of nontariff barriers than has been previously employed by the Bank, also in order to achieve comparability with data drawn from the 1960s. This would influence the levels of NTB indices in 1966 and 1986, but should not affect our analysis of trends (i.e., changes in nontariff barrier use) since the indices have been calculated for the same group of measures. As a result of these methodological changes the empirical results presented in this study are not directly comparable with those of other World Bank investigations.
10 August. Because the mean differences between contract prices<sup>were not statistically</sup> significant, Gilbert (1997, p.46) concluded that the argument<sup>that producing countries</sup> gain in terms of higher export prices by selling forward<sup>should be judged as "not</sup> proven".

We further compared different futures positions to spot with<sup>the same data. Table</sup> 2 shows that only the averages of the two most remote positions<sup>were significantly</sup> different from spot. Figure 4 plots the difference of the third, fourth,<sup>and fifth</sup> positions against spot; if spot is December, then the third corresponds<sup>to the</sup> following March. The three series are obviously closely<sup>related and the difference</sup> between further and nearby positions can be either positive<sup>or negative. Based on this</sup> analysis and other studies, the evidence for a statistically significant<sup>forward premium</sup> is at best inconclusive.

_Table 2. Cocoa Price Analysis of Variance_

|Position|Average price<br>(£/mt)|Average difference<br>from spot (percent)|
|---|---|---|
|Spot|834.2|0.0|
|Position 2|853.9|2.4|
|Position 3|858.1|2.9<br>|
|Position 4|859.9|3.1<br>|
|Position 5|877.5|5.2|
|Position 6|889.9*|6.7|
|Position 7|899.3**|7.8|

_Source:_ LIFFE monthly data from December 1990 through May 1997.

_*F-statistic for n = 1,
846**|Yes*|
|Foreign-owned|0.205**<br>0.282|No|
|Exporter|0.216**<br>0.089|No|
|Capital city|0.089**<br>0.398*|No|
|Country-survey dummy variables included|Yes||
|Sector dummy variables included|Yes||
|Number of observations|15,050 firms from 51 country<br>surveys||

* Significant at the 5 percent level; ** significant at the 1percent level; heteroskedastic-robust standard errors

The estimations for Serbian firms show that new private firms in Serbia have productivity levels that are 85 percent higher<sup>15</sup> than the benchmark socially-owned firms, and the productivity of privatized firms is 60 percent higher. The estimations based only on the Serbian firms in the sample do not show that foreign ownership and export activity are associated with high TFP levels, but this result is probably due to the relatively small sample size of the Serbian surveys. The last column in Table 3 compares the effect of the firm characteristics in Serbia to those in other countries: there is no evidence that Serbia is any different from other countries, with one exception — new private firms are even more strongly associated in Serbia with higher TFP than they are in other countries. Thus Serbia provides positive evidence in support of the general pattern found internationally, and no evidence against it.

These findings extend to the other three measures of performance used above.<sup>16</sup> New private firms in Serbia are more likely to have higher employment growth, to have reinvested in profits, and to have invested in new plant, than state/socially-owned firms; Serbian exporters are also more likely to have invested in new plant. Moreover, there is no evidence that Serbian firms are any different from their counterparts in other countries 8