rafmacalaba/data-use-mentions-extended huggingface dataset:
# 4.1. Empirical Specifications

To explain firm performance during the pandemic, we consider several strategic changes: _Stable demand, Stable input, Stable liquidity,_ and _New product_ . As discussed earlier, during the turbulent pandemic, the normal is deterioration in demand, input, and liquidity, and the capacity to make these aspects stable represents firms’ vigorous strategic efforts. Similarly, during the times of crises, innovations are perhaps the most proactive moves that firms could make.

We thus estimate the following three equations to investigate the determinants of firm performance, of firms’ changing circumstances and innovation strategy, and to what extent the changing circumstances and strategy affect firm performance.

Whereas _c_ , _k,_ and _i_ represent country, industry, and firm. _Y_ is firm performance, including _Survival_ , and revenue growth, respectively. _S_ represents firms’ strategic choices. In particular, _S_ includes _Stable demand, Stable input, Stable liquidity,_ and _New product_ . _X_ is firm-level variables, including indicators of firm organizational resources, which include _Sizable, Foreign, SOE, Subsidiary_ ; an indicator of firm leadership, _Log experience_ ; and other controls, such as log firm age. Since the COVID-19 firm surveys were conducted in two waves, we also control for a 2<sup>nd</sup> -wave dummy to control for the general trend. _Z_ represents country-level characteristics, which include the level of GDP per capita (in the log) and _Policy stringency_ . Equation (3) aims to show, after conditioning on firm situations and strategy, how basic firm and country characteristics contribute to firm performance. It also shows whether and how firm strategies contribute to its fortunes.

# 4.2 Baseline Results

Table 4 reports the baseline results, with the first two columns on firm performance, the next four columns on firms’ strategic responses, and the last two columns the combined version for firm performance.

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become urban areas. To the extent that rural (non-farm) economic growth may help create such re-classifications, as successful villages evolve into towns, this process may produce a downward bias in our estimates of the (absolute) elasticities of rural poverty to rural economic growth. The impact on the urban elasticities could go either way, depending on the circumstances of new urban areas relative to the old ones. We have no choice but to use the NSSO/Census classification. The rural and urban population estimates are also centered at the mid-points of the NSSO’s survey periods.

**_National accounts:_** We use private final consumption expenditure and net domestic product and its sectoral components from the National Accounts Statistics (NAS). To mesh the NAS data with the poverty data from the NSSO, we have linearly interpolated the annual national accounts data to the mid-point of the survey period for different rounds.

There has been a rising gap over time between NAS and NSS consumption aggregates.<sup>34</sup> From the point of view of the present discussion, it is notable that the NSS series does not fully reflect the large gains in mean consumption indicated by the NAS from the early 1990s onwards. The ratio of NSS-to-NAS consumption declined from about 70% in 1957 to 60% in 1991, and then steeply to 39% in 2011-12.

We do not know how much of the gap is due to errors in NAS consumption versus NSS survey methods. Until recently, the NSSO’s methods appear to have changed rather little over many decades. That is probably good news for comparability reasons, although it does raise questions about whether their methods are in accord with international best practice. However, it is notable that the MRP rounds of the NSS have helped close the gap between the NAS and NSS consumption aggregates.<sup>35</sup> Regressing the log difference of the NSS mean (  ln<sup></sup> _t_ ) on the log difference of
# 1. **Introduction**

Active labor market programs (ALMPs) aim to increase the employment, employability and income of participants. They have a decidedly mixed track record of raising employment and earnings, and tend to have modest impacts, especially in the short run.<sup>2</sup> Particular types of programs can be effective, however, when they are well‐tailored to local labor markets and implemented effectively.<sup>3</sup> In recent years, there has been greater interest in the potential for ALMPS to offer non‐pecuniary benefits to young participants, such as a reduction in risky behavior and criminal activity. In contexts where unemployment and crime are both high, ALMPs have the potential to improve social as well as economic outcomes. However, little evidence exists regarding the impacts of ALMPS on social indicators such as crime, especially in fragile contexts.<sup>4</sup> This study seeks to fill the gap using unique data collected in Port Moresby, the capital city of Papua New Guinea (PNG).

Port Moresby is a particularly interesting context to study the social effects of active labor market programs because unemployment and crime are rampant, as the creation of formal jobs and other livelihood opportunities have failed to match rapid population growth. A recent report estimated that the homicide rate in the capital city of Port Moresby was 33 per 100,000 persons in 2010, which would rank among the top 50 cities in the world today (World Bank, 2014). This is not surprising: Among young people aged 15 to 24 in Port Moresby, 29 percent were estimated to live on less than $3.10 per day in 2009/10, and 31 percent were unemployed.

To invest in youth and promote their social inclusion, the National Capital District Commission (NCDC) has, since September 2012, implemented the Urban Youth Employment Project (UYEP) in Port Moresby.<sup>5</sup> The project seeks to
.e. there are no Air Quality Management Areas).<sup>13</sup> Lists of all the sampled sub-counties with respective parishes were uploaded onto a portable mapping tool (ArcGIS collector) for precision during the field visits and corresponding GPS coordinates for all potential locations in visited subcounties were obtained ( **figure 1** ).

> 12 Since there is no universal threshold/definition for urban centers (Chomitz et al., 2005; UN, 2017), we adopted the official classification of urban areas in Uganda, i.e., gazetted administrative centers including District Headquarters, Cities, Municipalities, Towns, Town Boards and all Trading Centers with a population of more than 1,000 persons (UBOS, 2001) and all sampled locations met this criterion. This threshold is also comparable to other jurisdictions, e.g., Chomitz et al. (2005) and UN (2017), and adopting a different threshold would not have significantly altered the sampling, in fact even the US census threshold of 288 persons per square kilometer adopted for the parish level firm survey was way below the average parish level population density.

> 13 It is typical for regulatory practice, e.g., UK-EA, to define impact zones for air quality risk assessments prior to establishing monitoring sites.

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# 1. Introduction

Bangladesh is one of the most flood prone countries in the world. Analysis of past flood records indicates that about 21 percent of the country is subject to annual flooding and an additional 42 percent is at risk of floods with varied intensity (Ahmed and Mirza, 2000). Although annual regular flooding has traditionally been beneficial by providing nutrient laden sediments and recharging groundwater aquifers, the country often experiences severe flooding during the monsoon that causes significant damage to crops and properties and has adverse impacts on rural livelihoods and production. A classified list of all floods in Bangladesh reveals the country has experienced 21 _above-normal_ floods, of which 4 were _exceptional_ and 2 _catastrophic_ since 1954.<sup>8</sup> The most recent _exceptional_ flood in 2007 inundated 62,300 sq km of land (42% of the total) and caused severe damage to lives and property. This flood caused 1,110 deaths, submerged 2.1 million ha of standing crop land, completely destroyed 85,000 houses, damaged 31,533 km of roads and affected 14 million people. The estimated loss of assets from this flood alone is US $ 1.1 billion.

In general, the relative severity of the impacts from _above-normal_ floods in Bangladesh has decreased substantially since the 1970s as a result of improved macroeconomic management, increased resilience of the poor, and progress in disaster management and flood protection infrastructure. The 1974 flood was a 1 in 9 years event which resulted in damages of 7.5% of GDP. In comparison, the 1998 flood was a 1 in 90 years event, inundating nearly twice the area but resulting in damages of 4.8% of GDP. The increased resilience of Bangladesh to floods is also apparent when recent GDP and agricultural growth rate trends are examined with respect to the timing of flood events. Until the 1990s, GDP and agricultural growth rates sharply declined following major flood events. However, the relative effects of major floods have diminished

> 8 A flood with inundation area exceeding
work by Beltratti and Stulz (2009) examines how differences in bank corporate governance and country-level regulatory approaches affected bank stock returns in the financial crisis. The main findings are that banks with a board of directors that is less shareholder-oriented and banks that are located in countries with strong capital regulation performed better. Consistent with our results, this study also finds that higher capital is associated with better stock market performance.

The paper is structured as follows: the next section presents the data and the empirical model. Section III contains the main results. Section IV concludes.

# **II.   SAMPLE SELECTION, DATA DESCRIPTION, AND EMPIRICAL MODEL**

# **_Sample selection_**

We construct a sample of banks starting with the all the banks in the Bankscope database that are listed and hence have a stock price. We then exclude banks for which no information is available on capital or other explanatory variables. We also exclude a few banks from countries in the Persian Gulf where the financial crisis followed a different time pattern than the rest of the sample. In addition, since we rely on intra-country variation to identify the relationships of interest, we exclude from the sample countries/dates for which we have less than five banks in the sample. The baseline sample includes a total of 381 banks in 12 economies during the period Q1.2005-Q1.2009. Not all banks enter the sample in every quarter, as the sample is unbalanced.

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.07|0.07|0.55|0.34|
|Mean dep. var. control|2.38|0.00|0.32|0.00|
|SD dep. var. control|0.61|1.00|0.69|1.00|
|T1: _βE_+_βI_+_βG_ =0|0.11|0.03|0.00|0.00|
|T2: _βE_+_βG_ =0|0.25|0.09|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.32|0.09|0.00|0.00|

Note: this table reports results on the agricultural practices performed by the farmers in our study in the 2017 season. Using the full sample of 678 farmers, we run the following regression: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexible_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Follow-up survey conducted in August 2015. The dependent variable in column 1 is a sum of individual dummies. Each dummy takes value of 1 if the farmer performed one of the so-called existing agricultural practices. In column 2, the dependent variable is the standardized index of the outcome in column 1, computed by standardizing each dummy individually, adding them all and standardizing the sum. We use the mean and standard deviation of the control group as reference for the standardized index. In columns 3 and 4, the dependent variables
|
|Adjusted R<sup>2</sup>|0.90||0.96||
|Observations|6,662||||

_Notes:_ Calculations based on ENESTYC 2001. Standard errors are shown in parenthesis. * denotes significance at 5 percent level. The Wald statistic was used to test the equality of the estimated wage and productivity differential. Reference group: women without training.

As can be seen in the table, among workers that received no training, men are 97 percent more productive and earn 54 percent higher wages. These results are consistent with what was previously found on the effects of gender, even among those workers that have received no training. In the same way, the results for education and experience are practically the same as we had before. This shows that our model is robust to the assumptions that we made.

With respect to training, there is no evidence that women who receive either inhouse or external formal training are more productive than those without training. However, even though they are paid 23 percent more, we cannot reject the equality of the wage and productivity differentials. This means that both trained women and their employers share the benefits equally. As for men, those with in-house training are 28 percent more productive, and those with external formal training, are 55 percent more productive. Respectively, men earn 14 percent and 24 percent higher wages. In this case, there is significant evidence that the productivity differential is larger than the wage differential. The results are very similar to the ones we had before relaxing the assumptions.

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blog.<sup>17</sup> As such, while the findings of the experiment are internally valid only for the Development Impact blog, they seem likely to extend more generally to other research-oriented development blogs.

# _5.1. Randomized Encouragement_

We took the 619 graduate student and field staff respondents to our baseline survey that had provided contact email addresses as the sample for this experiment.<sup>18</sup> We stratified the data by three variables thought to potentially influence attitudes towards research methods and interest in the World Bank: whether they were a Ph.D. student, field staff, or Masters student; whether or not they said they read Chris Blattman‟s blog at baseline; and whether or not they said they were involved in a randomized experiment at baseline. Individuals were then randomly assigned to treatment and control within strata, with Appendix Table 1 showing that this succeeded in balancing baseline observable characteristics.

The encouragement then consisted of two emails. The first was sent on April 6<sup>th</sup> , 5 days after the blog launched, thanking this group for participating in the survey and alerting them to the new blog. They were told about the purpose of the blog and some of the topics that would be discussed, as well as saying “We consider you our most important audience for such a blog, and so want to make it something interactive and useful. We therefore very much would like if you check out the new blog, comment on things, and raise any issues or thoughts on things you would like the blog to discuss.”. They then received a second, shorter, email 3 weeks after the blog had been launched, which asked how the blog was doing so

> 17 Chris Blattman traffic statistics based on a blog posting in which he said he had 800,000 page views in 2010.

> 18 We did not use the World Bank or Assistant Professor samples because of the smaller size of these groups, and
also evident in the CSSPA data for 1988/89, but the decline in the percentage of households growing cocoa in the 1988 in East Forest causes a decline in the survey regional sales index in 1988. In contrast, the sharp decline in the West Forest survey data in sales per capita among cocoa-growing households and also in the regional sales index between 1986 and 1987 is not at all evident in the CSSPA production data.

Another inconsistency between the survey and the CSSPA data concerns the distribution of production between the East and West Forest Regions. The survey data indicate that sales in East Forest far outstripped that in the West Forest, while the CSSPA data indicate that production in the West Forest was roughly equal to the East Forest in 1985 and then outstripped production in the East Forest. According to the survey data, cocoa sales per capita was roughly 36 percent higher in East Forest than in the West Forest in 1985. Since the population of East Forest was 66 percent higher than West Forest (based on the survey weights), overall cocoa production should have been 126 percent higher in East Forest than West Forest, extrapolating from the survey data. According to CSSPA data, however, cocoa production in East Forest equaled cocoa production in West Forest in 1984/85. The disparity between the survey and CSSPA data widens in 1988: according to the survey data, sales per capita in the East Forest were 91 percent higher than in the West Forest, with the East Forest having 53 percent more population. Thus, East Forest in principle would have produced 192 percent more cocoa than West Forest. The CSSPA regional production estimates, however, show that in 198 8/89 West Forest produced about 10 percent more cocoa than East Forest.

A sharp break in coffee sales in West Forest between 1986 and 1987 is also clearly apparent in the survey data (table 6). The CSSPA data confirm that there was a fall in coffee production in the West Forest between 1985/85 and 1988/89, though the decline is
</sup> _c_<sup>_, σ_</sup> _c_<sup>_L}_and</sup><sup>_{μH_</sup> _c_<sup>_, σ_</sup> _c_<sup>_H}_.Sinceourdatadonotallowustoidentifyfixedcosts,wecalibrate</sup> them using industry cost reports. Using data from the Generic Cost Model for child care centers provided in Mitchell and Stoney (2012), we obtain fixed cost values of $128.90 and $322.25 a day for low-quality and high quality centers, respectively.<sup>23</sup> Regarding variable costs, we face the usual selection problem in that we do not observe all potential entrants but only those that do enter, and not even for these do we observe variable costs. Identification is therefore based on observed price and quality. We observe prices as reported by centers and observe quality in the high/low quality aggregate fashion described above. Given fixed costs, the observed market shares for high- and low-quality and the price distribution by quality identify variable cost parameters because active firms make non-negative profits and the lognormal distribution is recoverable.

For non-relative caregivers, no additional parameters are left to be identified. For relative care, we have one remaining parameter: the proportion of households with available relative care. We observe some households using relative care (which therefore have it available, or

> 23 The Generic Cost Model for child care centers provided in Mitchell and Stoney (2012) is compiled by the _Alliance for Early Childhood Finance_ to model “budgets for a proposed center and to understand the costs of operating a better quality center”. In the fixed cost calculation we include non-personnel costs for the building (rent/lease, insurance, utilities, maintenance/repair/cleaning) and setup costs (audits, fees and permits, telephone, and Internet). We compute all costs at 2003 prices and assume that the typical center size is two and five classrooms for
that the distribution of restrictiveness (across economies and sectors) is easily made transparent at each stage of aggregation—or “CES nest” —showing which kinds of policy measures drive the overall distribution of STRI values (Figure 3).

The overall distribution of the resultant scores is shown in Nest 4 (bottom right of Figure 3). The mass point at a score of 0 depicts the 315 entirely open cases, whereas the mass point at 1 reflects the 371 instances in which individual economy-subsector-mode combinations are closed altogether.<sup>26</sup> In between these polar cases, in which an economy-subsector-mode is either entirely open or entirely closed, the distribution of restrictiveness scores is fairly spread out. That said, two important features of the incidence of policy restrictions, and the way the algorithm deals with it, are apparent from Figure 3. First, most measures from the “operations and regulatory recourse” categories carry individual scores of 0.25 or 0.125, and the joint application of several such measures leads to a distribution of restrictiveness scores within the interval of [0.25, 0.50] for those subsectors that are not entirely open (see Nest 3). This is a reasonable result. Second, the overwhelming majority of high scores—those around 0.75—arise from the application of some sort of quantitative limitation, rather than outright ownership limitations (Nests 1 and 2).

**Figure 3: Scores by Principal Stage of Aggregation**

<!-- Start of picture text --> Nest 1: Ownership and Control Nest 2: Quantitative Limits<br>0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00<br>Nest 3: Operations/Reg Recourse Nest 4: Overall Scores<br>0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50
In this study, we use a factor analysis approach to construct composite indexes from observed indicators on gender relations reported in the IHDS. Factor analysis uses correlations among measurable (observed) indicators to infer a latent behavior. By creating a single score from multiple indicators, it offers insights into underlying trends that would otherwise be difficult to measure.

Factor analysis starts with all the variables assumed to measure different dimensions of a given concept. It reduces them to a smaller number of variables that are correlated with the original variables but are themselves orthogonal to one another. In arriving at a solution, factor analysis uses only the variance a variable shares with other variables; it then divides this common variance into factors (Child 1990; Conway and Huffcutt 2003; Thompson 2004). The factors obtained thus focus only on what is common to all variables.

In order to analyze these factors, factor-score correlation coefficients (also called _factor loadings_ ) are calculated using regression. The factor score is then estimated as the linear combination of products of the response on each of the original variables and the corresponding correlation coefficient, as shown in the following equation: scoredm = a1x1 + a2x2 + a3x3 + ⋯+ anxn (4) where, x1, x2, x3, ... , xn are original variables such as the ability to make decisions about one’s education, decide whether to have children, and so on; a1, a2, a3, ... , an are correlation coefficients between the score and each of the x ’s. Each of the correlation coefficients takes a value between 0 to 1; they show the extent to which change in one indicator is associated with the change in the overall score. A correlation factor of 0.70, for example, implies that the score increases by 0.70 percent with a 1 percent increase in the value of the original indicator (alternately, one could say that the original indicator increases by 0.70 percent with a 1 percent increase in the score).