rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Accurate and timely welfare measurement is a necessary step for effective and informed policy making. Even though there are no official publicly available poverty estimates in the Islamic Republic of Iran, estimates of poverty trends exist in the academic literature. These are calculated either based on the authors’ own assessment of an appropriate national line or according to international poverty lines based on US dollars. Examples of such estimates cover different periods between 1984 and 2009 and are available in Assadzadeh and Paul (2004), Salehi-Isfahani (2009), Mahmoudi (2011), and Maasoumi and Mahmoudi (2013).

According to Salehi-Isfahani (2009), the 1979 Revolution and the subsequent war disrupted economic growth, which contributed to a deterioration of welfare for Iranian households. Using the poverty lines defined in Assadzadeh and Paul (2004),<sup>3</sup> Salehi-Isfahani shows that poverty increased dramatically following the revolution from 29 percent in 1984 to 42 percent in 1989. After the end of the war in 1988, the period of reconstruction and reforms started. The poverty rate fell by more than 30 percentage points, declining from 42 percent in 1989 to 12 percent in 2005. Poverty gap and severity indicators improved as well which may suggest that welfare improved for the population close to the poverty line, but also for those at the bottom of the distribution (Salehi-Isfahani 2009).

The World Bank (2008) analyzed regional dimensions of poverty in the Islamic Republic of Iran in 19982005 using both the international poverty line ($2.9 in 2005 PPPs) and poverty lines in local currency units. Regardless of the choice of poverty line, poverty fell both in urban and rural areas and in most of the provinces. According to Maasoumi and Mahmoudi (2013), who decomposed poverty changes during the period 2000 to 2009 into growth and redistribution components, poverty declined between 2000 and 2004 and increased afterwards both in rural and urban areas. This is in line with findings in Mahmoudi (2011) who
sheets as firms become more leveraged as they deal with the shocks (Benincasa et al., 2024). Weather shocks can also create liquidity shortages, increase loan default, deteriorating credit scores and thus access to future credit (Aguilar-Gomez et al., 2023). Huang et al., (2018) show that extreme weather events are associated with lower and more volatile earnings and cash flows. Exposed firms tend to hold more cash to generate more financial slack and build resilience.

An additional channel that we uncover in this study that has not yet been found in the literature is that of governance. Negative precipitation shocks increase the amount of time senior management spends dealing with the requirements of government regulations. Negative precipitation shocks also increase the likelihood that a firm expects to make bribes _to get things done_ . Findings are presented in table 5. The negative precipitation shock coefficient with regards to government regulations is statistically significant at the 1 percent level regardless of whether ADM1 fixed effects or country fixed effects are used. For bribe expectations, the coefficient is statistically significant at the 1% level for the country fixed effects specification and 5% for the ADM1 fixed effects specification. These findings suggest that weakening economic conditions might lead to more rent seeking behavior. Governments may burden firms with more regulations or extract more rents due to the budget effects of dealing with extreme weather events.

# **3.3 Adaptation**

The 2019 MENA Enterprise Surveys wave includes a green module that contains information on whether firms adopt climate friendly measure (see table A2 indicates which surveys have this information). Benincasa (2024) provides some evidence that firms exposed to extreme weather events are more likely adapt by adopting climate-friendly measures. The replenishment of physical capital damaged due to extreme weather may result in new investments that lead to vintage effects where newer equipment have lower environmental footprint. It is also possible that firms may become environmentally aware and therefore engage in green investments or green management practices. Benincasa (2014) find that firms 12
information when the auxiliary data are aggregated to the subarea level.<sup>32</sup> The estimates produced by the Stata SAE with the traditional bootstrap are less accurate than the baseline EMDI estimates in Sri Lanka, but more accurate in Tanzania. The coverage rate of the traditional bootstrap estimates is 93 percent, which is very close to the target 95 percent rate. This high coverage rate results from two countervailing biases in the variance estimate; the traditional bootstrap overstates the variance by failing to hold the composition of the sample constant across replications, which counteracts the downward bias due to the failure to account for the positive correlation among the sample residuals. Overall, the traditional bootstrap performs quite well in these two settings, in terms of accuracy and coverage, but poorly in terms of efficiency.

The estimates produced by the latest version of Stata SAE with the parametric bootstrap are slightly less accurate, in terms of its correlation with the full census, than those produced by the EMDI package.<sup>33</sup> The Stata SAE estimates are also less efficient than the baseline EMDI estimate in Sri Lanka, as the mean MSE rises from 4.6 to 6.8. In Tanzania, in contrast, the Stata SAE package with the parametric bootstrap yields estimates that are slightly more efficient than the baseline estimates, but because the estimates are less accurate, the coverage rate falls significantly from 74.8 to 64.2 percent.

The fifth row of the table presents results without the heteroscedasticity correction. Disabling the correction brings the mean poverty rate closer to the true 4 percent rate in Sri Lanka, but further from the assumed poverty rate of 20 percent in Tanzania. In Sri Lanka, disabling the correction increases the accuracy of the estimates to slightly above the baseline estimates. In Tanzania, disabling the heteroscedasticity correction has minor effects on accuracy and slightly decreases precision, improving the coverage rate. The limited effects of the heteroscedasticity correction likely results from the negligible explanatory power of the “alpha model”
# **Treatment Effects on WMS and MOPS measures of management**

Table A7.3 reports the estimated treatment impacts on the WMS and MOPS measures. Since these data are only available for a subset of our firms, we report several different specifications. In Panel A, we use all 70 firms for which follow-up WMS data are available (or the 95 firms with MOPS data for the last column). We do not control for randomization triplet fixed effects given that this would result in relatively few triplets being included. Instead, panel A includes no other controls, while Panel B controls linearly for key baseline variables used in the randomization (region, size, employment, labor productivity, and baseline Anexo K). Panels C through E then use the set of 50 firms for which both baseline and endline WMS data are available.

In panels A and B, we find very small and statistically insignificant impacts of either treatment on any of the WMS or MOPS management measures. Restricting to the sample for which we also have baseline data in panels C, D and E results in larger point estimates for the WMS, but the impacts are still far from statistically significant.

Our results show that both treatments resulted in significant increases in the Anexo K measure of management practices, and in each of its five subcomponents. This raises the question of why we do not see such a change in the WMS and MOPS? A first potential explanation is that the WMS and MOPS are only available for subsamples of the data, so that the difference in results could stem from sample composition and sample size. To investigate this hypothesis, Table A7.4 reestimates the management treatment effect regressions for common sub-samples. The first column repeats our estimated impact on the Anexo K measure for the balanced panel. Columns 2 and 3 then consider the 52 firms for which we have both the 2016 WMS and Anexo K measured during and after the intervention. We continue to see a statistically significant impact of the individual treatment on the Anexo K measure
41 them--in relatively morp iuccessful programs in Malaysian and Nicaraguan, midvives received compensation, in the former from the program, in the latter, from clients. Since many traditional oidwives receive remuneration from deliveries, to an extent, family planning acceptance reduces their potential business. Many different remuneration methods have been tried, but the Malaysian program is the only one in vhich midvives are paid based on their recruitment of family planninig acceptors which is verified by supervisors. No pro4ects have recompensed midvives on the basis of continuation rates or paid them based on the amount she forgoes from not attending a birth attributable to a couple year of protection.

A study by Bailey et. al (1982) examined the pattern on side effects, continuation and pregnancy rates associated vith obtaining oral contraceptives from pharmacists, private physicians, and the public sector. They found no significant differences between the continuation and pregnancy rates that prevailed for women vho had initially obtained their orals from one of the three sources. No systematic differenc **e** merged in terms of side effects either. In fact, they found that for one subgroup, older, higher parity vomen, continuation rates vere higher for pharmacy users than for those vho had obtained their supplies from either of the two other sources. They concluded that this subgroup, either vere more highly motivated contraceptors or had interacted more vith their pharmacist.

The family planning services of nurses and physicians vere compared in a Bogota study (Einhorn and Trias, 1978). Vhile there vas no difference in the continuation or pregnancy rates or in the incidence of side effects of vomen vho had seen a nurse compared to vomen vho had seen physicians, the array of methods chosen by these two groups of vomen vas quite different. Those vho sav physicians vere generally prescr4bed more effective methods (the pill, IUD, or

**Al.fp:tamdly Planning Paper Nbole)**
day). Unpaid domestic work is the activity to which men assign the least amount of time (1.9 hours per day). Table 1 presents the average time allocation by type of activity and sex in each country and Figure 1 depicts gender differences by type of activity.

While the analysis of time allocated to productive activities—paid or unpaid work—reveals that men specialize in paid work and women in unpaid domestic activities, the degree of specialization varies widely across countries. On average men dedicate 2.7 more hours per day to market activities than women, but the gender gap ranges from 4.8 more hours of paid work for men in Iraq to 24 minutes in Ghana. Women, on the other hand, allocate 3.2 more hours per day to unpaid domestic work than men, but the difference ranges from approximately 5 more hours in Guatemala, Albania and Turkey to one hour per day in Timor-Leste and Mongolia.

The patterns we observe imply that in most countries, women work more hours than men when all work—paid and unpaid—is accounted for. On average, they spend nearly half an hour more per day than men in productive activities, but the difference increases to 1.1 hours when the sample is restricted to countries where women work more hours than men. Figure 2 shows the gender gap in time allocated to productive activities. The countries with the largest differences are Albania and Uruguay, where women spend 2 more hours per day on productive activities. In the United States,

> 5 See Chapter 5 in the 2012 WDR (World Bank, 2013) and section 3 in this paper for a comparison between our calculations and those presented in the 2012 WDR.

> 6 When this information is missing, the sample of countries is restricted to only those countries with available data.

9
.1812|0.1527|1.20%|1.95%|
|100|0.3600|0.3240|0.3097|1.20%|1.07%|0.3180|0.2862|0.2283|1.20%|2.34%|
|105|0.5064|0.4557|0.4261|1.20%|1.23%|0.4792|0.4313|0.3328|1.20%|2.57%|
|110|1.0000|1.0000|1.0000|1.20%|0.00%|1.0000|1.0000|1.0000|1.20%|0.00%|

Source: SVS and Staff Calculations Note: The values for the RV-04 tables shown here are not updated to any particular year, i.e., are representative of mortality centered around 1999.

# **_3.3. Discount Rates_**

In line with most other studies, the computation of MWRs is performed with two alternative discount rates, the interest rate on government or central bank bonds and the interest rate on corporate bonds. The MWR computed with the first rate is frequently considered to be the most meaningful to the average customer, as it excludes risk and reflects its opportunity cost more accurately. It is also used to facilitate comparisons across countries. The alternative discount rate is also computed, as it may reflect more appropriately the opportunity cost for some consumers, and because it is more relevant from the point of view of the provider.

The risk-free discount rates were obtained by the yield curve of 20 year indexed central bank bonds (the PRC-20) in March of 1999, 2002, 2003, 2004 and 2005 – as indicated below, the annuity sample consists of all annuities sold in those five months.<sup>6</sup> The yield curve for those
# **6. Model and Estimation Strategy**

# <u>Model</u>

The first step involves examining the effects on per capita expenditure from average monthly per capita income, and the share of actual monthly income to average monthly income (as a measure of volatility in monthly income). Determining the effects of income volatility on average per capita consumption is a first step in understanding how policies may have a role in helping households cope with sudden economic shocks. Specifically, following Khandker (2012), we examine trends in average household per capita expenditure and other outcomes using the panel of communities in the SES data, Here _c jm_ is average per capita consumption expenditure across households living in community _j_ at a specific month _m_ . As a measure of income volatility, _s_ represents last month‘s income divided by average monthly income _I._ We also consider a vector of community characteristics _x_ ,<sup>6</sup> and  is a vector of unobserved characteristics affecting outcomes.

Note that  measures the role of time-varying observed household and community characteristics in consumption. On the other hand,  1 measures the effect of monthly share of average monthly income, while  2 measures the effect of changes in average monthly income. If  1<sup></sup> 0 , monthly income variability is not an issue, and monthly consumption is determined by average monthly income. This is the case of perfect consumption smoothing (within a year) where households are able to smooth consumption despite any shocks in monthly income.

> 6 The SES surveys do not have community modules.  So these characteristics are basically averages of household level variables such as household head‘s age and age squared, gender, religion, marital status, years of schooling as well as whether any household member 18 years or older had a chronic illness or disability; household age/gender composition and whether the house was made of solid construction (bricks/cement).

17
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particularly in data terms.

In this paper, we have had access to much better data on regulatory governance and its variation across countries. However, our estimation of models for capacity, like those of Zhang et al and Pargal, is also limited to generation capacity.

> 12 See Stern and Cubbin op cit, p. 38-43 for further details of these studies

> 13 Pargal uses the Calderon –Serven infrastructure investment data set for 9 Latin American countries 1980-98.  This divides electricity investment into public and private but appears, again, only to cover generation.  See Calderon and Serven (2002) for a description of these data.

8
global investors. Large Brazilian firms have faced pressure from investors as well as banks to address deforestation risks and present action plans. For example, the largest meat processing company in the country recently faced the exclusion of its shares from a large asset manager’s funds, as well as warnings by banks against investing in the company due to Amazon deforestation concerns.<sup>23,24</sup> In line with DNB (2020), the credit provided to firms involved in environmental controversies is our proxy for banks' reputational risks. The MSCI ESG Database keeps a record of the controversies of listed companies and assesses the severity of their impacts on the environment by a combination of the scale of the impacts (how widespread it was) and their nature (from minimal to egregious).<sup>25</sup> MSCI defines controversies as single events or ongoing situations where company operations or products allegedly have a negative environmental impact. The database takes into consideration the fact that the impact of some events is indirect or difficult to determine hence having a lesser impact on the final score. In contrast, other events can affect a particularly vulnerable ecosystem, or evidence is found that the company knowingly acted in disregard of the law or the environment, thus exacerbating the relevance of the negative controversy. After the enterprises with environmental controversies have been identified, the information in their annual reports allows us to determine the volume of loans that these companies have received from Brazilian banks.

# **3. Estimating financial risks of biodiversity loss**

## **Physical risks**

_Loss of ecosystem services_ Brazilian banks lend BRL 811 billion to firms whose business processes are highly or very highly dependent on one or more ecosystem services. This amount represents 46 percent of the corporate credit portfolio and 20 percent of the total credit portfolio (Figure 4). In other words, 46 percent of the loans that banks allocate to corporates are subject to potential financial losses due to a disruption of ecosystem services. By analyzing
|-2.5|
|Single male household head|7.4|25.6%|20.7%|-4.8|
|Two-headed household|71.6|31.3%|31.7%|0.4|

Source: authors’ estimations based on PNAD-C 2019. Notes: Pre-fiscal poverty indicates the poverty headcount rate considering market income plus pensions, while the post-fiscal poverty indicates the poverty headcount rate considering consumable income. The proportion of households indicates the number of households in each group as a share of the total number of households in PNAD-C. The household was considered two-headed if there is at least one member that declared to be the spouse of the household head.

# 4.1 Does fiscal policy have the same welfare impacts across racial groups?

Racial disparities are historically high in Brazil and fiscal policies hold the promise of contributing to mitigate such gaps. Therefore, a relevant question is to determine whether the most vulnerable racial groups are better off after taxes and transfers. Table 7 presents the poverty rates for households headed by whites, Afro-Brazilians and non-white, non-Afro-Brazilians. We observe that the impact of the fiscal system is similar across groups, particularly among households headed by Afro-Brazilians and whites. This result indicates a somewhat different trend compared to the previous evidence for Brazil relative to 2008/2009 that the fiscal system used to contribute more to reduce poverty among households headed by whites than Blacks and pardos (Pereira 2016).

**Table 7. Moderate poverty headcount rates considering both pre‐ and post‐fiscal income aggregates, separately by household head’s race**

|**Race of household head**|**Prop. of**<br>**individuals(%)**|**Pre-fiscal**<br>**poverty**|**Post-fiscal**<br>**poverty**|**Change in poverty**<br>**(p.p.
# **2.7 Estimation of Plant-Level Productivity, Marginal Costs and Markup**

To measure plants’ productivity, marginal cost and productivity, we estimate production functions using the panel data of manufacturing plants (Statistik Industri - SI) from 2008 to 2015. The details of the estimation procedure can be found in Section F of the Appendix. Unlike most empirical work using productivity estimates, we observe plant level physical output and input quantities and values. To deflate the total revenue of multi-product plants, capital and intermediate inputs expenditure (including energy consumption), we construct plant-specific output and input price indexes, as in Eslava et al. (2004). This is an advantage over contributions employing industry-level price deflators, which essentially assume that all plants within an industry face similar inputs’ cost and charge the same price.<sup>18</sup> Our approach allows us to obtain a measure of quantitytotal factor productivity (TFPQ). Exploiting quantity information, the latter reflects changes in pure technical efficiency, rather than in revenues.

A key challenge in the measurement and identification of productivity relates to the endogeneity of the firm’s optimal choice of inputs. We follow Ackerberg et al. (2015) and adopt a control function approach with the objective of proxying productivity known by plants’ managers, but unobserved by us, with energy expenditures.<sup>19</sup> Following De Loecker and Warzynski (2012), we adapt the methodology of Ackerberg et al. (2015) and allow plants’ exposure to robots to affect the expected value of future productivity, which in turn might determine differences across exposed and non-exposed plants and affect the estimation results.<sup>20</sup> After estimating the the parameters of the production functions industry by industry, we follow De Loecker and Warzynski (2012) and obtain a measure of plants’ marginal cost and markup, based on the plants’ first order conditions

> 18Foster et al. (2008) discuss the bias arising when using
**Table 6 Fitted output per worker in Russia for a worker with average home country HCI, 2011 US$ at PPP**

|Country of Migrant<br>Worker|Output per worker in<br>home country (2011<br>US dollars, PPP)|Output per worker in<br>Russia<br>(2011<br>US<br>dollars, PPP)|Difference between<br>output per worker in<br>Russia<br>and<br>home<br>country (2011 US<br>dollars)|
|---|---|---|---|
|Armenia|22,915|43,249|20,334|
|Georgia|22,522|44,497|21,975|
|Kyrgyz Republic|11,142|43,494|32,352|
|Tajikistan|13,786|42,034|28,248|

Sources: Output per worker, Penn World Tables; Fitted output in Russia, authors’ calculations The output per migrant worker is between double and three times more in Russia than it would be in the home country of the migrant worker. Most of the difference arises from the much higher capital stock per worker in Russia than in the migrant sending countries. Also note that the output per worker of migrants from each of the four sending countries is fairly similar. This is because the difference between the highest HCI (Georgia) and the lowest HCI (Tajikistan) of these four is only 15 percent and, given the coefficient of 0.4 on the log of HCI in the regression, this translates into a difference in output per worker of only 6 percent holding capital per worker constant. One might argue that, because capital per worker is the main determinant of output per worker, it does not matter how much human capital migrant workers have as long as they migrate to a country with a much larger capital