rafmacalaba/data-use-mentions-extended huggingface dataset:
6 The other result that has attracted a lot of attention is the difference between those studies that find capital and energy as complements (Allen elasticity is negative) and those that find the two factors as substitutes (Allen elasticity is positive). The differences between these studies have been attributed to a number of factors: use of time series versus cross-section data (Griffin, 1981), response of output to changes in relative prices (Solow, 1987), and different methods of aggregating capital (Garofalo and Malhotra, 1988).

We also note that there is little information in existing studies on the substitutability between natural resources and other inputs. The Parks (1971) study looks at different inputs used in manufacturing, including materials from the agricultural sector, capital and labor. In terms of Allen elasticity, the author finds a complementarity between capital and agricultural inputs, and a substitutability between agricultural inputs and labor. We could not recover the full elasticity but it is likely to be low. The Moroney and Trapani (1981) study finds substitutability between the inputs, labor and exhaustible mineral resources, in mineral-intensive production processes. The two studies are not comparable; nor do they really throw much light on the substitutability issues we are discussing here.

More recent studies have focused on the substitution between energy and another input, such as labor or capital, using the same definition of elasticity that we employ. Manne and Richels (1992) and Chang (1994) estimated the substitution possibilities between the ‘capital and labor nest’ and energy to be about 0.4; while Kemfert (1998) estimated the same to be about 0.5. On the other hand, Prywes (1986) found the substitution elasticity between the ‘capital and energy nest’ and labor to be less than 0.5. These studies use the variables capital, labor and energy as relating to stock of fixed assets, skilled and unskilled labor, and final energy consumption, respectively.

# **III. Estimation of Nested CES production
# **TABLE II. Correlations between Shocks and Maternal Mental Health Indexes (MHI) in 2002**

|**in 2002**|(1)|(2)|(3)|
|---|---|---|---|
|_Panel A. Descriptive statistics of Maternal MHI_|Mean|S.D.|N|
|MHI-1|-0.02|0.50|1095|
|MHI-2|-0.04|0.98|1095|
|MHI-3|-0.04|0.92|1095|
|_Panel B. Shocks experienced by mothers during pregnancy or_<br>_within thefirstyear after the child was born(in 2002)_|Mean|S.D.|N|
|Crop or livestock loss|0.03|0.16|1095|
|Death, severe illness, divorce|0.13|0.34|1095|
|Birth/new household member|0.06|0.24|1095|
|_Panel C. Correlations between shocks and Maternal MHI in_<br>_2002_|MHI-1|MHI-2|MHI-3|
|Crop or livestock loss|0.34***|0.70***|0.65***|
|Death, severe illness, divorce|0.15***|0.32***|0.29***|
|Birth/new household member|0.13**|0.27**|0.24**|

Table II present summary statistics (mean and standard deviation) of maternal mental health indexes and shocks experienced by mothers of our sample of analysis. These variables are available in the first round of the Peruvian Young Lives Survey (2002). Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Mental health index 1 is the standardized average
# Non-Technical Summary

This paper investigates the extent to which lobbying by foreign exporters and domestic importers (retailers and wholesale fi rms) determines the tariff preferences granted by an importing country. The predictions of the model are tested using data on preferential trade between the U.S. and Latin America. This empirical example is important since Latin American countries display a signi fi cant dependency on exports to the U.S. Moreover, about half of Latin American exports enter the U.S. market through a myriad of preferential schemes (Andean Act, Caribbean Basin Initiative, GSP, NAFTA, etc.) The empirical results suggest that lobbying by U.S. importers and Latin American exporters is an important determinant of the tariff preferences granted by the U.S. to that region. More interestingly, the empirical results indicate that U.S. importers capture a substantial share of the rents generated by tariff preferences. These fi ndings imply that importers are likely to be the engine behind the growth of tariff preferences. Moreover, they also shed a pessimistic view of preferential schemes as a reliable source of gains to developing countries.

1
Poorer Middle Richer Richest<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04 and ENPSF 2011.

Location acutely affects children’s chances of living in a household that uses adequately iodized salt and, therefore, being protected against cognitive deficits. As Figure 7 shows, rates range from 3 percent in Tensfit to 25 percent in North Central. Additionally, there are large differences by rural-urban residence, wealth, and parents’ education. These factors are interrelated. For instance, families that are less wealthy are also less educated.

13
Harding, T. and Rattsø, J. (2005). The barrier model of productivity growth: South Africa. Trade and Industry Policy Strategies Working Paper 5 – 2005.

Joffe, A., Kaplan, D., Kaplinsky, R. and Lewis, D. (1995). Improving Manufacturing Performance in South Africa: Report of the Industrial Strategy Project, University of Cape Town Press, for the Industrial Strategy Project and the International Development Research Centre.

Kerr, A., Wittenberg, M., and Arrow, J. (2014). Job Creation and Job Destruction in South Africa. _South African Journal of Economics,_ 82 (1):1-18.

Kreuser, C. F. and Newman, C. (2018), Total Factor Productivity in South African Manufacturing Firms. South African Journal of Economics, 86: 40-78.

Li, Y. & Rama, M. (2013). Firm Dynamics, Productivity Growth and Job Creation in Developing Countries: The Role of Micro- and Small Enterprises. World Development Report 2013: Jobs.

Mamburu, M. (2017). Defining High-Growth Firms in South Africa. UNU-Wider Working Paper 2017-107. Helsinki: UNU-WIDER.

Matthee, M., Rankin, N., Webb, T. and Bezuidenhout, C. (2018), Understanding Manufactured - Exporters at the Firm Level: New Insights from Using SARS Administrative Data. South African Journal of Economics, 86: 96-119.

National Treasury. (2017). Medium Term Budget Policy Statement. [Online] Available at: <u>http://www.treasury.gov.za/documents/mtbps/2017/mtbps/FullMTBPS.pdf.</u> National Treasury. (2018). Budget Review. [Online] Available at: <u>http://www.treasury.gov.za/documents/national%20budget/2018/review/Chapter%202.pdf</u> Olley, G.
# Figure VII: Withholding Rate Reform

<!-- Start of picture text --> A: Withholding Rate (Full Sample) B: Withholding Rate (Prediction Sample)<br><!-- End of picture text --> <!-- Start of picture text --> 2010m8 2011m8 2012m8 2013m8 2014m8<br>Month<br>2008m8 2009m8 2010m8 2011m8 2012m8 2013m8 2014m8<br>Month Realized Rate Predicted Rate<br>C: Number of Declarations D: Number of Declarations Matched<br>2008m8 2009m8 2010m8 2011m8 2012m8<br>2008m8 2009m8 2010m8 2011m8 2012m8 Month<br>Month<br>Share Sales Tax Declarations Matched With Withholding<br>N Sales Tax Declarations N Withholding Declarations Share Withholding Declarations Matched With Tax Declaration<br>E: Total Share of Card Sales F: Average Share of Card Sales<br>Slope= .004 (.002) ∆ Slope= 0.000 (0.000)<br>Slope= .004 (.005) ∆ Slope= 0.000 (0.000)<br>∆ Constant= -.01 (.013) ∆ Constant= -.01 (.005)<br>2008m8 2009m8 2010m8 2011m8 2012m8 2013m8 2014m8 2008m8 2009m8 2010m8 2011m8 2012m8 2013m8 2014m8<br>Month Month<br>Share Linear Fit Share Linear Fit<br>5 5<br>4<br>4<br>Average Rate Average Rate (%) 3<br>3<br>2<br>2<br>90000 40000 .27 .52<br>.26<br>85000 .51<br>35000 .25<br>80000 .24 .5<br>N Sales Tax N Withholding Share Sales Tax Share Withholding<br>30000 .49<br>75000 .23<br>.22 .48<br>70000 25000<br>.85 .6<br>.8<br
decrease domestic violence to keep a woman from leaving the relationship. Other studies show mixed results on the correlation between labor force opportunities and domestic violence (Vyas and Watts, 2009).<sup>4</sup> This paper looks for evidence of this heterogeneity within a peri-urban population in Bangladesh that is particularly diverse in population and job characteristics. While I will not be able to provide causal impacts of work opportunities, I can take advantage of detailed household survey data to explore the validity of potential stories – both causal and non-causal – that could explain the observed descriptive relationship between domestic violence, household bargaining, and work.

# **3 Empirical Strategy**

## **3.1 Data**

The data for this paper come from a survey of 1395 households outside of Dhaka, Bangladesh that I conducted, along with Mushfiq Mobarak. The survey took place from August to October, 2009. The sampling frame of the survey was every household<sup>5</sup> in 60 villages located in four subdistricts (Savar and Dhamrai in Dhaka District; Gazipur Sadar and Kaliakur in the Gazipur District). On average, each village has 1782 people living in 465 households. These villages are close to Dhaka but not within the city; the average reported travel time into Dhaka is 30 minutes.

This area is a heavy garment-producing area: 34 percent of sampled women between the of 18 and 35 work in the Unlike factories within ages garment industry. garment cities, though, the factories in which survey respondents work tend not to have dormitories. Instead, garment workers commute (commuting an average of 18 minutes one-way) to factories but live in standard household arrangements. Many households are migrants from other districts of Bangladesh<sup>6</sup> ; only 34.1 percent of male household heads and 11.2 percent of female household heads were born in the village in which they are now residing.

> 4Specifically, as detailed by Vyas and Watt, the correlation between
completed secondary= completed F.A/ F. Sc/ I. Com/ ICS/A-Level or Polytechnic Diploma, completed Bachelor’s degree= completed B.A/B.Sc./ B. Com/ B. Ed/ Degree in Engineering/Accounting/Law/Agriculture, completed graduate degree= completed M.A/M. Sc or M.Phil or PhD, religious education=completed religious education or classified as “Islamic scholar”

***Other ECE/Missing includes Class 1+, Prep, Madrasa, and Kindergarten;

****Data was missing for 3 household heads indicating that these household heads did not live in the household

*****Data was missing for 5 children on days per week and hours per day attendance in ECE

CREDI Z-Scores are norm-referenced and standardized against a global population<sup>7</sup> of children from low-and middle-income countries who are from an “advantageous environment.” An advantageous environment is defined as children with maternal educational attainment of secondary or higher and adequate levels of early stimulation (indicated as participated in 4 or more

> 7 <mark>A total of 4,652 children from Bangladesh, Brazil, Cambodia, Chile, Colombia, Ghana, Guatemala, India, Jordan, the Lao People’s Democratic Republic, Lebanon, Nepal and Pakistan were included in the CREDI reference population (24% of the full cross-country sample).</mark>

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.836|
|Endline control mean|0.4|68|0.5|22|0.|541|0.5|84|0.4|04|0.|454|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.010|0.135|0.134|0.264|0.004|0.101|0.068|0.219|0.012|0.136|0.165|0.282|
|Observations|1,550|1,550|382|382|1,550|1,550|382|382|1,550|1,550|382|382|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. FD stands for field day. FD- and non-FD samples consist of actual field day participants respectively non-participants in treatment mws, and their matched controls. Additional control variables identical to those listed in notes of Table 5. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

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# **1. Introduction**

Access to finance plays a key role in the overall growth and development of the private sector (Banerjee & Duflo, 2014; King & Levine, 1993). Lack of adequate finance hampers firms’ day-to-day operations and long-term investments. The problem of inadequate financing is especially severe for small and medium-size enterprises (SMEs) operating in the developing world. The extant literature highlights several drivers of financial access including firm and industry characteristics, and the quality of the institutional and legal environment (La Porta, Lopez-De-Silanes, Shleifer, & Vishny, 1997; Qian & Strahan, 2007).

However, important gaps remain. One such gap relates to the impact of bureaucratic corruption on firms’ access to finance in the developing world. The present paper attempts to fill this gap by using firm-level survey data on SMEs in the manufacturing sector of developing countries. It does so by estimating the impact of corruption faced by firms in obtaining licenses and permits, etc. on the firms’ chances of being financially constrained. Our results reveal a large positive relationship between higher corruption and the likelihood of a firm being financially constrained. That is, higher corruption adversely impacts the firm’s access to finance. We also explore heterogeneities in the corruption-finance relationship. Some of these heterogeneities derive from the specific ways in which corruption impacts informational asymmetries between borrowers and lenders and thereby affects firms’ access to finance. Thus, these heterogeneities serve the additional purpose of partly addressing endogeneity concerns with our main results.

Corruption is a significant economic issue in emerging and developing economies.

It can adversely affect growth, productivity, and overall firm performance by diverting resources from productive to rent-seeking activities, and by increasing the cost of conducting business (D’agostino, Dunne, & Pieroni, 2016; Mauro, 1995; Méon & Weill, 2
trades in New York and dollar denominated bonds. This is very important in Latin America. Our data set includes all these trades.

One technique available to managers is using "cash" (e.g., liquid money-market instruments such as **U.S.** Treasury bills) to buffer their portfolios from redenptions. Holding cash allows managers to meet redemptions without the need to sell less-liquid assets. In principle, this can mute the effect of investor outflows on the underlying stocks. However, managers can also reinforce investors' actions if they increase their liquid positions in times of investor retrenchment. For our whole sample, funds kept an average of 4.4 percent of their net asset value in cash. We then split our sample into two sub-samples, one where on average these funds received inflows, and one where on average these funds suffered outflows. In the inflows sub-sample we find an average cash position of 4.6 percent, whereas in the outflow sample we find an average cash position of 4.3 percent. Average cash positions are remarkably stable. Managers' choice of cash position does not appear to either mute or reinforce investor actions.<sup>13</sup>

# **IV.2. Momentum Trading Results**

In our full sample, we find strong evidence of lag-zero momentum trading at all three levels: whole-fund, manager-only, and investor-only (Table **1,** column **1).** Interestingly, contemporaneous momentum trading is especially strong during crises. In terms of attribution, it is investors that account for the lion's share of the contemporaneous momentum trading at the whole-fund level. Significant lag-one momentum trading is present only in the non-crisis portion of our sample, and it is concentrated at the manager level.14 For robustness, we estimate each cell based only on observations of **Mi,,** within three standard deviations of its mean. This is the reason why, within any column of Table *
regression equation that estimates the elasticity precisely. In emerging markets and developing economies (EMDEs), a 1 percent change in GDP is associated with a 1.55 percent change in nighttime lights. While the elasticity varies somewhat with a country’s income status and economic structure, it does not deviate much from this

> 1A recent survey by Gibson, Olivia, and Boe-Gibson (2020) reviews more than 150 economic studies using nighttime lights and argues that economists seem slower than others to switch to VIIRS data.

> 2For a detailed explanation of the superiority of the VIIRS data, see Elvidge, Baugh, Zhizhin, and Hsu (2013) and Gibson, Olivia, Boe-Gibson, and Li (2021).

> 3The use of nighttime light data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) picked up only after Henderson, Storeygard, and Weil (2012) provided such an elasticity.

2
Figure 2. Growth of Value Added and Electricity Consumption across Sectors, 1996-2016.

<!-- Start of picture text --> 5<br>Agriculture Manufacturing<br>5<br>4<br>High Income Countries<br>4<br>Upper Middle Income Countries 3<br>3<br>Lower Middle Income Countries<br>2<br>2<br>Low Income Countries<br>1<br>1<br>0<br>0<br>-1<br>-1<br>-1 1 3 5 7 9 -1 1 3 5 7 9<br>Electricity Consumption Growth, 1996-2016 Electricity Consumption Growth, 1996-2016<br>5<br>Services<br>4<br>3<br>2<br>1<br>0<br>-1<br>-1 4 9<br>Electricity Consumption Growth, 1996-2016<br>GDP Growth 1996-2016<br>GDP Growth 1996-2016<br>GDP Growth, 1996-2016<br><!-- End of picture text --> Source: Authors’ computations using IEA and WDI data.

Figure 3 depicts a linear relationship between the sectoral value added and electricity consumption using annual data for both growth and level variables. It shows that this relationship is much stronger in highincome countries than non-high-income countries across all sectors and for both growth and level variables. This is possibly due to differences in production technologies and the sub-sectoral composition of these industries across various income groups. High-income countries that use more sophisticated technologies and automated processes in all sectors rely more on electricity than non-high-income countries with large informal and low-productivity sectors. These sectors rely more on low-skill manual labor to perform tasks, and an increase in electricity consumption alone is insufficient to ensure high growth in value added.

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may have been on the run.

To minimize attrition, we collected extensive contact information (all known addresses, plus at least five close contacts), and went to extreme effort to locate each person, wherever they had moved, averaging three to four days of searching per respondent per survey.

We collected data on 92.7% across all endline surveys. Attrition is relatively unsystematic: treatment arms had similar response rates (within 0.4% of the control group) while a test of joint significance of all baseline covariates yields _p_ = 0 _._ 328 .<sup>28</sup>

> 26In this simple case, there is no role for self control skills, _θ_ , in aggression. This is a drawback of adapting the pecuniary crime model, since STYL explicitly teaches men skills to regulate their emotions in charged, automatic situations. In some sense, then, STYL may not only change the underlying value of _σ_ (the extent of one’s desire not to engage in criminal activity) but also one’s ability to ensure that expressed actions conform to the underlying preferences rather than succumbing to immediate temptation or anger. This is functionally equivalent to predictions associated with a larger _σ_ .

> 27The exception is the 100 men in the pilot, which had a single “short run” survey 3 weeks after grants. Actual survey times were, on average, 2.2, 5.7, 55.4 and 61.1 weeks after grants. Surveys were 90 minutes long and delivered verbally by enumerators in Liberian English on handheld computers.

> 28See Appendix A.3 for tracking techniques, response rates by survey wave and treatment group, and

14
Since Proposition 2 shows a necessary condition under which poverty rates estimated from inequalities based on different poverty lines are comparable, it can be used to evaluate the comparability of poverty rates estimated in NISR (2015).

III.2.2 Comparability of the official poverty trend between EICV3 and EICV4 Here we explore the comparability of the poverty rates estimated in NISR (2015). The three conditions of proposition 2 need to be satisfied. Since both EICV3 and EICV4 use the same approach for constructing nominal household expenditures and adopt the same adult equivalent scale, condition (i) holds. Condition (ii) – comparability of COLIs – and condition (iii) – the update of poverty lines, however, _may not_ hold.

In the case of condition (ii), there are three main differences. First, the base months to which the two COLIs are normalized are different. For EICV3, the food COLI uses January 2001 as the base month, while EICV4 uses January 2014 as the base month.<sup>9</sup> Second, the weights of the COLIs differ. For EICV3, the weights for the food COLI are derived from EICV1 (2001) data, while those of the nonfood COLI come from EICV3 data. In contrast, for EICV4, the weights of both food and nonfood COLIs are derived from EICV4 data. Third, the price data used for constructing the COLIs are different. For EICV3, NISR (2015) uses MINAGRI data to estimate the food COLI, and the official CPI data to estimate the non-food COLI. For EICV4, NISR (2015) uses CPI price data for both food and non-food COLIs. It is therefore difficult to guarantee that condition (ii) is satisfied.

<mark>As for (iii), the poverty line used for estimating poverty rates in EICV3 is based on EICV1 data and valued at January 2001 prices (RWF 64,000), while the poverty line used for estimating poverty rates in EICV4 is based on EICV4 data and valued
the population for their future but then converted in discontent after stagnant social mobility, labor market conditions, and poverty (Urzúa 2012; Flechtner 2017). Somewhat paradoxically, protests and social tensions are more likely to improve when countries improve their conditions (e.g. higher economic growth, less inequality) because people raise their aspirations, which may then not be fulfilled (Ray 2006).

Further, the study of international recruits of the terrorist organization of the Islamic State of Iraq and the Syria (ISIS) brings a suggestive example that is consistent with the theory that extreme cases of unfulfilled aspirations may also lead to terrorism. Using personal records from the terrorist organization, a study shows that higher unemployment rates for a given educational level in a given country — an indicator of a lack of opportunities — for 59 countries from which the recruits were

> 9 Adolescents who aspired for an education level and occupations with wages too close to their initial situation had close to three fewer years of education completed seven years later, at age 19, and scored lower on exams of math, English, and Telugu (the local language) between 0.6 and 0.9 standard deviations.

17