rafmacalaba/data-use-mentions-extended huggingface dataset:
with productivity in this extension. This motivates an alternative specification where we use capital (defined as assets reported by the firm in the BvD dataset) to measure firm size in columns (6) and (12). The trust coefficient remains significant, and the interaction term just evades statistical significance with a p-value of 0.15. Taken together, this suggests our results are broadly robust to this alternative definition of firm size.

_Historical Political Institutions as a Driver of Trust._ Our baseline specifications include controls that attempt to capture unobservables that drive both firm behavior and trust, but we do not argue that variation in trust is truly exogenous. Rather, our empirics should be viewed as associations consistent with the model’s multiple predictions and thus support its narrative that legal institutions lower aggregate performance by causing more firms to sort into less productive family management. However, Tabellini (2010) argues that differences in historical institutions can provide exogenous variation in culture (one dimension of which is trust). The intuition is that cultural traditions are inherited from earlier generations, and weak political institutions can affect culture. Once we control for the direct effect of early institutions on contemporaneous outcomes,<sup>28</sup> he argues, the residual variation provides exogenous differences in culture today. The variable capturing early political institutions comes from his paper, and is the first principal component of five variables measuring “institutionalized constraints on the decision making powers of chief executives” in 1600, 1700, 1750, 1800, and 1850 from Acemoglu et. al. (2002).

While Tabellini (2010) a strong stage relationship between early institutions and his measure of culture (F-stat: 12.71), he finds a much weaker positive relationship with trust (F-stat: 4.84). We have a smaller sample of 38 regions which we concord between our two datasets, and find a similarly low first-stage relationship which renders our IV estimates insignificant. Instead, we show the reduced form relationship between early political institutions and
2021 and 2022 surveys. They were collected annually until 2020 and then again in 2023. 31 We use primary education as the deprivation threshold because the majority of the population has secondary education or above. Thise with other studies on Europe, including those of OPHI.

> 32 We could not construct a measure of educational deprivation among children because the SILC survey is not designed to collect educational attainment data for children. Therefore, enrollment rates may vary significantly from administrative data. Thus, we rely on information on the educational level of adults (Alkire and Santos, 2014). 33 In the EU-SILC, the question of unmet needs for medical attention or treatment is only asked for individuals aged 16 and over. Given this, for the educational and health variables we use the same age cutoffs used by Eurostat.

> 34 These vulnerable households were selected following previous evidence on poverty profiles and households vulnerable to shocks in these countries (Prasad et al., 2023; World Bank, 2017, 2018b, 2021, 2023a, 2023b).

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# **3.2 Measuring infiltration**

The random distribution of indoor air pollution monitors in our experiment offers a setting where the ownership of an indoor monitor is not endogenous to household characteristics.<sup>10</sup> To do so we examine the relationship between the indoor pollution measured in the houses of households in Treatment 3 _Outdoor and Indoor_ and the level of outdoor pollution measured by the outdoor monitors closest to the household.

To calculate infiltration rates, we estimate a lagged model with the level of indoor pollution in a given household as a function of the previous six hours of outdoor pollution measured by the nearest monitors (Burke et al., 2021). For each household for which we have indoor pollution data, we run the following specification: To absorb idiosyncratic variation in indoor pollution that may be due to factors unrelated to infiltration (e.g. cooking), we include hour ( _ηh_ ), day ( _γd_ ), and week (∆ _w_ ) fixed effects. These fixed effects are specific to each individual household in the sample and capture household-specific details like the typical times at which they return from work or cook meals.

Our measure of infiltration is the sum over all lags of _βl_ . This provides an estimate of the change in indoor air pollution for a 1 _μg/m_<sup>3</sup> cumulative increase in outdoor pollution over the previous six hours. We include lags because it may take time for outdoor pollution to filter into homes. To assess the sensitivity of our results to our choice of lag structure we estimate several alternative lagged models as in Burke et al. (2021): Our preferred model with six lags of outdoor pollution, one including six lags of indoor pollution and no outdoor lags, one with no lags of indoor or outdoor pollution, and one with ten lags of outdoor pollution. Our estimates of average infiltration are consistent across each of these different specifications (Figure A4).

To understand the drivers of variation in infiltration rates
Their problem is still not a general shortage of health care providers – as in other systems, there is plenty of excess capacity among the workforce. Instead, the inability of these countries to train and retain highly competent providers leads us to believe that a total overhaul of their health care systems is required.

# **Ethical Approvals**

This study used only de-identified secondary data provided by the SDI program and therefore was not classified as human subjects research. Each of the SDI surveys was carried out by the SDI program in collaboration with the Ministry of Health in each country. All survey participants consented to participate in the survey. Details regarding data collection ethical approvals are available in each country survey report. The authors therefore declare that no further ethical approval was necessary to conduct this study.

# **Disclaimer**

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its

**16/30**
114|95|-|-|114|
|Average|105|<br>77|57|57|51|62|

Sources: The Moscow Longitudinal Household Survey, December 1994; author's estimates. *Location: 1- city center, 2 - near the center, 3 **-** far from the center but not outskirts, 4 - outskirts, **_5_** - outside the Moscow Ring Road. Note: The data presented in Table 1.6 and the other value estimates of Moscow apartments used in this work were obtained from the MLHS, 1994 database complemented by the market estimates made by the methods described in footnote 7.

How do the category and location of a dwelling affect privatization? Table 1.7 presents the percentage of the households who have privatized their apartments by type of apartment and its location from the center of Moscow.<sup>8</sup>

**Table 1.7.** Correlation between the category and location of housing and its privatization rate in Moscow (% of households that privatized before 1995)

|Type of Apartment|L|ocati|on fro|m Cen|ter*|Average|
|---|---|---|---|---|---|---|
||1|2|3|4|5||
|Communal apartments|13|10|-|-|-|5|
|Separate apartments, average|65|56|40|33|34|40|
|of which:|||||||
|Khrushchev-era buildings|-|37|32|27|-|29|
|Panel buildings (1970s design)|-|49|39|32|30|36|
|Brick buildings (1970s design)|50|53|40|46|-|45|
|Modern panel buildings|-|51|
even smaller share (27%) of firm owners. In both informal and formal (micro) businesses, women are not equally represented by sector of activity, with female ownership being mostly in non-manufacturing sectors, though these differences are not statistically significant at the conventional levels.

Female-owned informal businesses also operate slightly longer each week than maleowned businesses (62 hours on average vs. 57 hours, Figure 2). These long working hours on the business are likely added to the household responsibilities traditionally expected of women in these countries. Perhaps because of the need to juggle family responsibility and work, or safety concerns,

> 10 All the differences between the informal businesses and micro-enterprises reported in Section 3 are statistically significant at the conventional levels, unless otherwise noted. Table 2 reports the corresponding t-tests. 11 Female ownership information was not collected in the Zimbabwe survey.

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sources into common units, and does so in a way that reflects the fact that different data sources cover different groups of countries with potentially different distributions of governance. However, unlike the UCM, this alternative approach assigns equal weight to all data sources. We do this in four steps:

1. We take our globally representative data sources and standardize each of them to have zero mean and unit standard deviation.  This step converts the globally representative data sources into common units (i.e., the units are z-scores).   We then compute a simple average of these standardized data sources as a preliminary estimate of governance based on only globally representative data sources.

2. As discussed in Section 3, we cannot treat globally non-representative sources in the same way, because the underlying distribution of governance in the different sets of countries covered by these sources is unlikely to be the same as the distribution across all countries in the world. Instead, we standardize each non-representative data source so that it has the same mean and standard deviation as the preliminary estimate of governance from Step 1 _in the sample of countries covered by each non-representative data source._ This step converts non-representative data sources into the same units as the globally representative data sources, allowing the distribution of governance to be different in the different sets of countries covered by non-representative data sources.

3. We construct a simple average of all the resulting standardized representative and nonrepresentative standardized data sources. This simple averaging approach assigns equal weight to all standardized data sources.

4. To make the units of this alternative indicator comparable with those of the baseline WGI estimates, we rescale the resulting average to have zero mean and unit standard deviation.

The results for this alternative aggregation method are reported in Figure 4, using the most recently available data for 2023. The six panels of the figure correspond to the six dimensions of governance. In each panel, we plot the baseline WGI estimate of governance on the horizontal axis, and the alternative equally weighted estimate on the vertical axis. The
at all. Second, when listening to the radio, participants might pay closer attention to the radio messages than they would do otherwise. We do not base rewards on the remembering of content from the show, and the radio component of the campaign occurs during advertising breaks where the contest was not aired. In doing so, we arguably mitigate the concern that people are artificially induced to listen more intently to the materials than they would normally do, an advantage over the Berg and Zia (2017) study. Third, self-selection issues may arise from inherent differences to the radio stations. However, the stations were selected based on listenership, information about the call-in contest, including the timing of the contests (morning and evening throughout May 2016) and the dial-in number of the respective radio station.

> 17 Only phone numbers provided in the baseline survey were able to establish a connection with the dial-in number to avoid unnecessary traffic on the line and restrict cash transfers to the study population. In the rare event of households participating in the call-in contest of the wrong radio channel, they received a text message with the information about their ineligibility for the cash price under this dial-in number together with the dialin number of the right radio station.

> 18 Our baseline data convey that more than 69 percent of the population listened to the radio during the previous day.

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Table 3: Effects by Location in the Productivity Distribution

|DepVar: TFP Index|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Policy Restrictiveness in Services FDI|-1.113**<br>(0.554)||||||
|Restrictiveness*Top 25%|-1.005<br>(0.762)||||||
|Restrictiveness*Bottom 25%|1.169*<br>(0.659)||||||
|Bottom 25% Dummy|-0.168**|-0.0694***|-0.0419**|-0.0757|-0.0503***|-0.0595*|
||(0.0745)|(0.0229)|(0.0177)|(0.0605)|(0.0158)|(0.0356)|
|Top 25% Dummy|-0.00834|-0.104***|-0.0879**|-0.152**|-0.0709*|-0.0587|
||(0.0738)|(0.0326)|(0.0346)|(0.0595)|(0.0380)|(0.0525)|
|Policy Restrictiveness in Manufacturing FDI|-0.120|-0.123|-0.131|-0.0411|-0.223|-0.236|
||(0.807)|(0.748)|(0.737)|(0.763)|(0.736)|(0
26 acute respiratory infection (ARI) treatment variable is therefore only defined for the subgroup of children who experience cough and breathing difficulties coming from the chest. By contrast, many DHS do not include a question on where the respiratory problems originate, and the medical care seeking question is asked for _all_ children with cough _or_ breathing problems. The absence of a question on whether the breathing problems come from the chest prevents us from computing an ARI treatment variable for the DHS that is identical to that of the MICS. We are, however, able to obtain an ARI treatment variable that is consistent across all DHSs by defining ARIs (and thus the sample for which the treatment variable is computed) as having a cough which coincides with difficulties breathing.<sup>28</sup> Other indicators for which we do not achieve full consistency are 4+ antenatal care visits, children under 5 sleeping under insecticide-treated bed nets and measles immunization.<sup>29</sup> In some cases where the original survey questions differ, we try to harmonize indicators using an ex post adjustment of the population (and quintile) rates. Specifically, we flag and subsequently adjust cancer screening and inpatient utilization rates whenever the reported recall period differs from our preferred recall period: 5 years for pap smears and 2 years for mammograms according to WHO recommended screening intervals for our preferred age-groups 30-49 (pap smears) and 50-69 (mammograms) (World Health Organization 2013a, 2014), and 12 months as the most

> 28 ARI treatment rates constructed from WHS data are substantially higher than those obtained from DHS and MICS. We suspect these differences to be due to divergences in the survey methodology: Among other things, WHS data are only available for the _first_ health care seeking response if the _youngest_ child under 5 in the household suffered an ARI, whereas DHS and MICS ARI treatment data come from _all_ health care seeking responses of _all_ children under 5 with an ARI in a household. The HEFPI database therefore does not include ARI treatment data
Table B.8 shows the results from testing various hypotheses about the coefficient estimates.

We find that de-licensing reduces significantly the geographic concentration of non-traded industries but the total effects on import-competing and export-oriented industries are insignificant. The only significant (and positive)<sup>effectof tradereformsongeographicconcen-</sup> tration occurs, surprisingly, for non-traded industries. With respect to the traditional determinants of -aw1iii **i.-** ti we v. find that most mechanisms are stronger for import-competing industries. In particular, import-competing industries;-, **wi** as a result of greater labor pooling potential while the non-traded industries disperse. This<sup>isconsistent withthestory</sup> that greater import competition forces import-competing industries to cut costs via greater use of localization economies.

# **9 Conclusion**

In this paper we analyze the impact of industrial and trade policies on the w1 tii i: of manufacturing industries in India. Our results show the importance of policies **-** in particular de-licensing and FDI liberalization **-** in affecting the spatial distribution of Indian manufacturing. Our study is one of the few that uses a long time span of Indian plant-level data from **1980** to **1999** that allows us to consider these important changes in industrial policies. Several of our results can inform policymakers, particularly in developing countries. Our results show that both de-licensing and FDI liberalization led to a decline in average 1.eration levels, while trade policy had no significant effect. That is, plants respond to some market-oriented policy reforms but not all. Further, our results point out the importance of an hitherto unexplored policy that can affect :a1iii .ti - FDI liberalization **-** and emphasize the need to examine alternative mechanisms that affect plant location decisions.

Our results are also unique in that most studies for developed and developing countries alike have emphasized how market-based forces tend to increase - 1ii.i-tii. Thus it is important to
, and the proportion of the middle-class increased by 20 percentage points. At the household level, poverty reduction has been primarily driven by labor markets, demographics, pensions, and social assistance.

This paper, which uses imputed income based poverty for its analysis, shows that there has been large amounts of poverty reduction in Turkey. Furthermore, the process of poverty alleviation has been uneven across regions. In particular, the central and southern parts of the country (Mediterranean, Anatolian Tigers and Southeastern regions) were significantly more successful in poverty reduction than the Eastern mountainous areas. Poor regions in the East have not succeeded in reducing poverty at a similar rate to the West. In fact, poverty has increased in certain parts of Eastern Turkey. Over time, there are also differences in the speed of convergence, with stronger poverty convergence before and during the financial crisis in comparison to the aftermath. Differences in the speed of poverty reduction is also seen in other countries. For example in China, provinces with higher initial inequality experienced slower speeds of poverty reduction through lower growth and a lower growth-poverty elasticity (Ravallion and Chen, 2007). Yet, the presence of poverty convergence itself in some form is still laudable since poverty convergence has generally not been observed at the cross-country level (Ravallion, 2012). Poverty convergence alongside overall poverty reduction is not trivial. Therefore, Turkey’s experience is unique and serves as an example for understanding why poverty has fallen even when growth has not been shared in the aftermath of the financial crisis.

There are multiple reasons for the presence of sub-national poverty convergence despite the absence of convergence at a global level. One reason is the ability of a national government to coordinate and distribute funds in a targeted manner. A national government will be more familiar with regions that are falling behind or are self-sufficient. For example, in the case of Brazil, social spending has been found to be a primary channel for poverty reduction in the absence of growth (Ferrieira, Leite, and Ravallion, 2010
capital income by making use of technical reserves, which allowed not only financing current expenditures but also maintaining a positive financial result. However, this implies a reduction in the fund accumulated by the system. Based on IESS-provided information, technical reserves diminished from USD 9 billion in 2015 to approximately USD7 billion in 2017.

From the expenditures point of view, recently a steady increase has been noticed. This trend relates directly to two factors: Firstly, an increase in coverage, as described above, and secondly, a sustained increase in the benefit level.

In 2010, the approval of _Ley Cero_ [Zero Law] established an automatic mechanism for updating benefits, which resulted in increases exceeding the inflation rate (Table 2). For that reason, between years 2011 and 2015, the average increase in benefits climbed up to 9% (Figure 13). This, in addition to an increase in coverage, explains the increase in total expenditures. However, in 2015, the inflation rate directly replaced this mechanism for updating benefits. Based on this new rule, benefits experienced an annual increase on average of 3% between 2016 and 2018.

Table 2 Legal increase of contributory <u>pensions up to 2015</u>

|Benefit Amount<br>(as a percentage of basic<br>minimum wages)|Legal Variation<sup>(*)</sup>|
|---|---|
|Lower than 50%|16.16%|
|50-100%|12.41%|
|100%-150%|9.53%|
|150%-200%|7.31%|
|200%-250%|5.61%|
|250% and more|4.31%|

Source: IESS Note: (*) Variation of the benefits established by _Ley Cero_ Figure 13. Benefit on Average Years 2011-2018 <!-- Start of picture text --> 700<br> 600<br>
The WMS evaluation tool elicits management practices through a semi-structured interview covering 7 topics: flexibility, incentives, monitoring, roles, staff involvement, staffing, and targeting. Table A3 details each of the 19 management-related questions, by topic, as well as the 1-5 scoring grid used by our enumerators for each question. These questions provide a holistic assessment of practices related to the topic.<sup>12</sup> We take the ‘Management Practice: Monitoring’ index as a proxy for the organization’s value of the information acquired by officials and the ‘Management Practice: Other’ index, which aggregates the non-monitoring sub-topics of the WMS, as a measure of alignment.<sup>13</sup> The Appendix provides further details on how we constructed our management measures.

Figure A2 plots the distributions of the ‘Management Practice: Monitoring’ and ‘Management Practice: Other’ indices across the organizations we use in our core regressions.<sup>14</sup> Both indices exhibit substantial variation, indicating that de facto management practices vary substantially across the Ethiopian Public Service. For practices related to the organization’s value of the information acquired by officials, the 75th percentile organization has a ‘Management Practice: Monitoring’ score that is 141% higher than the 25th percentile organization. For practices related to the alignment of preferences of agents within an organization, the 75th percentile organization has a ‘Management Practices: Other’ score that is 135% higher than the 25th percentile organization. This variation occurs despite organizations operating under the same broad political environment, the same federal administration, sharing the same national history, and working under the same governing rules over budget allocations, wages, non-wage benefits, promotion schedules and other contracting arrangements. This variation across organizations is consistent with evidence from other management surveys of the public sector (Bloom et al., 2015; Rasul and Rogger, 2018; Rasul et al., 2018).

> 12To provide a sense of the holistic (rather than