rafmacalaba/data-use-mentions-extended huggingface dataset:
# **E Age-structured fishery simulation**

I construct an age-structured model of the Peruvian anchoveta fishery in order to illustrate the long-term effects of the closures policy on catch and biomass. In the simulation, I allow anchoveta to grow, reproduce, die from natural causes, and die due to fishing at rates that depend on length (or equivalently, age). The time step is one day. I simulate the population forward 20 years under two scenarios: status quo harvest and counterfactual harvest. Both scenarios have the same harvest rule in tons: at the same level of biomass, tons caught in both scenarios is the same. The only difference between the two scenarios is the proportion of juveniles caught in the status quo scenario matches what I observe in the corrected electronic logbook data, while the proportion of juveniles caught in the counterfactual scenario equals the proportion that would have been caught in the absence of the closures policy.

Each day _t_ , population cohort _c_ changes according to the following four equations. Cohorts contain individuals of the same age, length, and weight. _l_ denotes length in cm of an individual in cohort _c_ , _w_ denotes weight in grams of an individual in cohort _c_ , _a_ denotes age in days, _N_ denotes number of individuals, _Recruitment_ denotes the number of individuals entering the fishery at the smallest length class, _α_ is a constant, _B_ denotes biomass (the anchoveta population measured in tons), _H_ denotes harvest (catch), _t_<sup> ̄</sup> denotes the day the fishing season starts, and _S_ denotes the scenario (status quo or counterfactual).

45
A small private sector in uncontestable markets may be more vulnerable climate shocks. The opportunities for creative destruction are limited, and the lack of competitive forces may restrict innovation or adaptation. Thus, analysis of the private sector is valuable given the unique context. In general, the private sector is an important engine of growth, creating jobs, providing essential products and services, and spurring innovation. Using data for around 9,500 firms across seven countries in MENA, this study finds that negative precipitation shocks are detrimental for private sector firms in terms of lower sales, lower labor productivity, and a lower probability of investing in fixed assets. The seven economies in the sample include Egypt, Jordan, Lebanon, Morocco, Tunisia, the West Bank and Gaza, and the Republic of Yemen. Five countries in the sample have two waves of data (2013 and 2019/2020), except for Egypt and Yemen. Egypt that has three waves of data (2013, 2016 and 2019/2020). The two waves of data for Yemen are 2010 and 2013.

The study also attempts to unmask various channels identified in the literature through which precipitation shocks might affect firm performance in MENA. The literature largely focuses on global or country-specific studies that are outside the MENA region to delineate possible channels. A study in India found that hot days could lead to more absenteeism and lower the labor productivity of workers (Somanathan et al., 2021). Global studies have uncovered infrastructure as another potential channel. Using a global sample of manufacturing firms, Islam and Hyland (2019) find that negative precipitation shocks (droughts) increase the intensity of water outages. Droughts have also been found to increase the frequency of power outages in Latin American cities (Desbureaux and Rodella, 2019; Damania et al., 2017).

Access to finance is another channel by which precipitation shocks could affect the private sector. Using a global sample of firms, Kling et al. (2021) find that frequent climate shocks might impair the ability
2. Firm-level information on input sourcing and sales destinations**

## **2.1 World Bank Enterprise Survey**

Through the Enterprise Surveys portal, the World Bank makes a series of firm-level datasets available to researchers.<sup>5</sup> They are based on surveys of random samples of firms conducted in all developing countries, which are surveyed at semi-regular intervals using the same questionnaire. This survey tool has been developed over several years, based on the experiences with the Regional Program of Enterprise Development (RPED) that surveyed firms in several African countries between 1991 and 1996 and the Business Environment and Enterprise Performance Surveys (BEEPS) which is an ongoing project surveying countries in Europe and central Asia.<sup>6</sup>

> 4 Other, smaller economies in this category include Benin, Djibouti, Lesotho, Mauritania, Mauritius, Niger, and Togo.

> 5 All data is freely downloadable at http://www.enterprisesurveys.org/.

> 6 The older RPED data can be downloaded from the data portal of the Centre for the Study of African Economies at Oxford University: https://www.csae.ox.ac.uk/data. More recent BEEPS data is integrated in the Enterprise - Surveys portal, but additional information and complete data from 1999 to 2016 can be found at https://ebrd <u>beeps.com/data/.</u>

5
earthquake.

# **7. Discussion**

The survey used in this paper made an attempt to be representative of the population in the directly affected areas, and of certain non-directly affected areas. The 2003 census was _de facto_ made obsolete in the wake of the earthquake. So, the sampling probabilities used to determine enumeration sections which were proportional to population size in the census were relatively imprecise. Moreover, the situation in Haiti evolved rapidly. Some camps had at that point been set up but had not yet been registered by the IOM. After a peak in mid-2010, the population in camps has decreased drastically. Many people have been displaced from one area 25
as reported by the mother, and the gaps between the two, over the three time-periods in our sample. Ideal fraction of sons = _idealboys_ + _ideal_ <u>(0</u> _._ 5 _kids∗idealeither_ <u>)</u> if _idealkids >_ 0, where _idealkids_ is the ideal number of total children; _idealboys_ is the ideal number of boys; and _idealeither_ is the number of children that parents are indifferent over the gender of, as reported by a woman. Data: NFHS.

52
<br>structured<br>management<br>practices<br>in 35,000 manufactur-<br>ing plants|The study fnds large dif-<br>ferences<br>in<br>productivity<br>associated with variations<br>in management practices.|
|2019|Bloom,<br>Iacovone,<br>Pereira-<br>Lopez,<br>Van<br>Reenen|Mexico, United States|2014 to 2015|total factor productiv-<br>ity, proft|survey of management<br>practices,<br>combined<br>with data from Mex-<br>ico’s National Survey<br>on<br>Productivity<br>and<br>Competitiveness<br>in<br>2015|Management<br>score<br>is<br>strongly<br>and<br>positively<br>correlated<br>with<br>frm<br>productivity.|
|2020|Karplus<br>and<br>Zhang|China|March<br>and<br>April 2016|energy efciency, total<br>energy use|two-part<br>survey<br>on<br>management<br>and<br>en-<br>ergy<br>management;<br>electricity<br>use<br>and<br>production information<br>from SECA survey|Moving<br>from<br>the<br>25th<br>to<br>75th<br>percentile<br>on<br>management score reduces<br>electricity<br>intensity<br>by<br>40% in value terms and<br>by 33% in physical terms.|

**47**
(as of<br>April 2020)|Log number of citations -<br>normalized by study<br>publicationyear|-|0.000|1.349|
|Spatial<br>Measure|City - Level<br>Sub City - Level|=1 if study uses city-level<br>spatial units<br>= 1 if study uses sub-city-level<br>spatial units|study uses regional level<br>spatial units|0.521<br>0.310|0.500<br>0.463|
|Agglomeration|Density Measure|= 1 if agglomeration is<br>measured by population or<br>employment density|agglomeration is<br>measured by size, such as|0.452|0.498|
|Measure|Market Potential<br>Measure|= 1 if agglomeration is<br>measured by effective density<br>or marketpotential|total employment or<br>population|0.257|0.437|
|Localization|Localization<br>Control|= 1 if a localization control is<br>included (reflecting the size of<br>own industry)|estimations do not include<br>a localization control|0.312|0.464|
||Panel Data|= 1 if study uses panel data|study uses cross-section<br>data|0.590|0.492|
|Data|Firm Data|= 1 if study uses firm-level<br>micro data|study does not use micro|0.352|0.478|
||Worker Data|= 1 if study uses worker-level<br>micro data|data|0.388|0.488|
||Panel Fixed<br>Effects|= 1 if panel fixed effects are<br>included or estimation is in<br>differences|study does not use fixed<br>effects|0.209|0.407|
land registration -- instead of relying on proxies. It should be noted that this is the first study to empirically estimate the relationship between land registration on the one hand and financial development and economic growth on the other.

The rest of the paper is structured as follows. Section II reviews the theoretical and conceptual framework that links land registration to financial development and economic growth. Section III deals with specification and testing of the econometric model using data from Thailand while Section IV focuses on interpretation of the empirical results. Section V draws conclusions emphasising policy implications and areas for further study.

# **II.** A **Theoretical and Conceptual Framework of Land Registration, Financial Development and Economic Growth**

In this section, we review the theoretical and conceptual framework, developed by Byamugisha (1999), to guide the empirical analysis of the effects of land registration on I This paper has been written based on a dissertation by the author submitted as a partial requirement for a Masters Degree in Surveying at the School of Surveying of the University of East London, UK (Byamugisha, 1999a).
A number of studies examine privatization in developing nations. Some conduct cross-country analyses, using data from the transition nations of Eastern Europe (Bonin, Hasan, and Wachtel 2004, 2005) or the crisis nations of East Asia (Nguyen and Williams 2005). In many of the transition nations, control of many of the privatized banks was shifted from state ownership to foreign ownership. Others studies of individual nations – including Brazil (Beck, Crivelli, and Summerhill 2005, Nakane and Bauhmol-Weintraub 2005), Mexico (Haber 2005), Nigeria (Beck, Cull, and Jerome 2005), and Pakistan (Bonaccorsi di Patti and Hardy 2005) – generally find that at least one bank performance measure improved following privatization, although some measures show no change. Some of these studies also find signs of greater prudence in lending after privatization in the form of reduced nonperforming loans or lending (e.g. Beck, Cull, and Jerome 2005, Haber 2005). One study of Argentine privatization finds that cost efficiency improves following privatization (Delfino 2003).

# **3.  The Argentine banking system in the 1990s**

Ownership changes and other organizational restructuring significantly altered the Argentine banking system during the 1990s. The changes included domestic M&As, foreign acquisitions, privatization, and restructuring of state-owned banks without privatization. Most privatized banks were previously provincially-owned, although in some cases, they had been nationally- or municipally-owned. Between 1993 and 1999, assets held by provincially-owned banks fell from about 21.6% to 13.0% of total assets, while assets held by foreign-owned banks grew from 15.6% to 50.3% of total bank assets.

These governance changes were driven both by shifts in government policies and local and regional financial crises. In April 1991, in response to macroeconomic crisis, the Menem administration initiated a radical reform – known as the Convertibility Plan – that pegged the Argentine peso to the U.S. dollar, granted the central bank greater independence, privatized state-owned firms
exploring whether decision-making in the household is more authoritarian or inclusive. Another solution to reducing spousal disaccord may be to reduce the chances of differing interpretations by anchoring respondents’ frames of reference for what it means to be a decision-maker and to make a decision by using vignettes or more specific questions. While, to the best of our knowledge, the impact of such vignettes on reducing gender gaps in reports of decision-making has not yet been tested, it has shown to be promising in other contexts. For example, Montgomery (2017) shows that anchoring response scales for life satisfaction questions reverses gender differences in life satisfaction worldwide.

Enumerator effects may also be of concern in study design, especially when asking sensitive questions. Indeed, we find a correlation between the gender of the enumerator and women’s survey responses, where female respondents are more likely to report that their spouses are involved in decision-making when interviewed by women. This correlation is small and does not negate other findings of the survey, and thus we are cautious making any recommendations on the use of same-gender enumerators, particularly as we are unable to state whether statements given to female enumerators are more or less accurate than those given to male enumerators. We do, however, suggest including controls for enumerator gender when analyzing survey data that could be sensitive to enumerator effects.

While our data suggests that men and women draw on different definitions of decision-making when answering survey questions, future research should confirm this finding through measurement experiments and further unpack it through qualitative work on perceptions of decision-making and household dynamics. Details about how both men and women perceive decision-making and what it means to be a decision-maker may be more easily gathered in qualitative research rather than a structured spousal survey. The results from the qualitative work can be used to design better quantitative surveys. Further research is also recommended to evaluate whether our findings on the mechanisms behind spousal disagreement extend to other regions or contexts. Finally, qualitative or quantitative work on decision-making may wish to pay particular attention to the ways in which assets and
of a road project, but rebounds to pre-rehabilitation levels within three years. These findings are consistent with the suggestion that rehabilitated roads deteriorate again after projects are completed. To further substantiate this, we combine machine learning with thousands of remote sensing data observations to estimate overall road degradation patterns. Doing so, we arrive at a road discount factor of 0.735 per year, meaning that within three years, the road quality of rehabilitated roads on average decreases by 60 percent.<sup>1</sup> Our findings speak back to the literature on the links between transport infrastructure and conflict. Theories on these links vary wildly. On the one hand, road density is often used as a proxy for government control and associated with peace. Bad geography, from this perspective, is seen as an opportunity for rebels and an impediment to international peacekeepers and government security forces (Ruggeri et al. 2016; M ̈uller-Crepon et al. 2021). Faulty transport networks also dampen economic activity that may constitute stumbling blocks to peace and prosperity. On the other hand, transport infrastructure and the ease of access it entails may also increase violence by facilitating the logistics of armed actors, whether rebels or military, and roads may act as magnets for violence (Zhukov 2012; Getmanski et al. 2019; Moreno et al. 2019; Rogall 2021; Voigtl ̈ander and Voth 2022; Gonz ́alez et al. 2023; Raleigh and Hegre 2009). If there are strong hypotheses for both a negative

> 1See section 3.3 for details on the calculation.

3
_ESTIMATING FOOD PRICE INFLATION_ _37_

# Mathematical Appendix

# _B1. Prediction strategy_

The imputation strategy used here is based on an adaption of the Multiple Imputations by Chained Equations (see, van Buuren and Groothuis-Oudshoorn (2011); van Buuren (2012); Murray (2018)). In particular, the original approach has been adapted to allow specify the stochastic properties of the initialization and keep track of cross-validation throughout the imputation process. The adapted code base has been made available, see the links provided in the footnotes of the introduction of the main paper. The code base has also been adapted to allow parallelization of computation. Apart from these practical modifications, the sketch of the algorithm is relatively standard. The algorithm produces multiple likely data sets, which are then pooled into a single imputation. The number of imputations is _M_ = 5, and the _m_ -th imputed price data set is _P_<sup>(</sup><sup>_m_)</sup> where _m ∈_ (1 _, . . . , M_ ). Let _P_<sup>_−d_</sup> = ( _P_<sup>1</sup> _, . . . , P_<sup>_d−_1</sup> _, P_<sup>_d_+1</sup> _, P_<sup>_D_</sup> ) denote the collection of the _d −_ 1 variables in _P_ except _P_<sup>_d_</sup> . Note that for a given _P_<sup>_d_</sup> , the set _P_<sup>_−d_</sup> may itself be incomplete, ( _P_<sup>1</sup> _, . . . , P_<sup>_d−_1</sup> _, P_<sup>_d_+1</sup> _, P_<sup>_D_</sup> ) may be correlated with one another, the relationship
Global demand shocks have been the main force triggering the collapse of inflation in the first half of 2020. They fully accounted for the decline in year-on-year inflation, on average, between the fourth quarter of 2019 and the second quarter of 2020. These demand shocks began to unwind in the second half of 2020, on the back of a strong demand rebound from the global recession, a recovery that was in part fueled by substantial policy support. Price increases were particularly large in sectors in which pent-up demand picked up after the reopening from the pandemic while capacity constraints and supply chain disruptions persisted.

Since the second half of 2021, and in particular following the Russian invasion of Ukraine in early 2022, the drivers of inflation pressures have broadened. Global demand shocks, in part reflecting the release of pent-up demand, accounted for about a fourth of the 6.5 percentage-point increase in year-on-year inflation, on average, between the second quarter of 2021 and the second quarter of 2022; global supply shocks, such as shipping bottlenecks and wage pressures in some countries, accounted for a fifth of the increase; the rebound in global oil prices for a tenth. These findings are broadly consistent with empirical results reported by di Giovanni et al. (2022), Shapiro (2022), and Ha, Kose, and Ohnsorge (2022).

# **VI. Robustness exercises**

In this section, we present a set of exercises to check the robustness of our headline results: (i) using real oil prices and nominal energy prices as alternatives to nominal oil prices; (ii) using alternative measures of global output growth and global inflation; and (iii) presenting variance compositions based on the simple averages and weighted averages across countries (as an alternative to using medians). The results from these exercises are broadly in line with our headline results (see Table 5).

# **VI.1. Oil prices**

Our baseline exercises employ growth of nominal oil prices. We replace them here with
- 29 -

"icore group" of labor intensive products was identified using 1980s production data (see Appendix Table 6) from which four main points can be drawn. First, the position -of most textile and clothing products remained virtually unchanged over the last two decades. These products were produced by highly labor intensive production processes in the 1970s and 1980s, a point that indicates future competitive pressures from developing countries will not be reduced. Second, several other sectors like wood, rubber and leather products had a high concentration of very labor intensive production processes in both the 1960s and 1980s, while some items like processed food shifted from relatively labor intensive production techniques to those that were capital intensive. Third, the recent data show that labor intensive products are generally found in the same general sectors in the 1980s and 1960s. Finally, the magnitude of the coverage of labor intensive products suggest they will continue to place broad based competitive pressure on developed countries in the future as over one-quarter of United States industrial employment nbw originates in relatively labor intensive product sectors.

While the primary objective of this study was to test the predictive power of factor proportions information for future South-North trade, and the utiltiy of this data was established, several points were raised in the investigation that should be subject to further research. Five such issues are as follows:

- (i) Why do such major differences exist in imports of labor intensive pioducts by individual developed countries (see Tables 1 and 2)? In particular, why has the United States import demand for these products been so much greater than that of the European countries or Canada in terms of levels and shares and from Japan in terms of levels?

- (ii) To what extent do domestic policies of developing countries explain the marked differences in their ability to supply these items? As shown (Table 3) labor intensive exports were concentrated in relatively few countries.
Orissa Figure 7a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 7b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Orissa<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>Year<br>Tax Revenue Stamp Rate<br>Figure 8a: Annual Percentage Change<br>in Stamp Duty Revenues and Rates<br>Punjab<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br>Annual % Change<br><!-- End of picture text --> <!-- Start of picture text --> Orissa<br>10000 16.0%<br>9000 14.0%<br>8000<br>12.0%<br>7000<br>6000 10.0%<br>5000 8.0%<br>4000 6.0%<br>3000<br>4.0%<br>2000<br>1000 2.0%<br>0 0.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rs. Lakhs Rate (%)<br><!-- End of picture text --> Punjab Figure 8b: Stamp Duty Revenues and Rates

# **Punjab**

<!-- Start of picture text --> 40000 14.00%<br>35000 12.00%<br>30000 10.00%<br>25000<br>8.00%<
well as a less extreme scenario where the skil ~~l-~~ biased technical change offsets only half of the substitution effect (i-e., « = 1/209). In the former case, technical change fully reverses the substitution effect in Eq. (15). In the latter case where k = 1/205, K—-l/og 1/208 Eq. , ~~WH~~ _ ~~aH~~ — ~~gH~~ (15) becomes ~~ut~~ =To ~~(2)~~ =Io ~~(4)~~ .

Table 3 below summarizes values of the parameters we use in the main simulation. Wage proxies. ~~—~~ We use the wage of a low ~~-s~~ killed young native worker (i.e., wry) as a reference wage, and use proxies for the skill, age and immigrant premia to compute the wage rates of other workers. We use skill premium data from Hendricks (2004) for each country. Average immigrant wage premium for each country is taken from DOP, where they use figures from a number of papers published between 1993 and 2005. We compute age premium ourselves from the American Community Survey data and use this for all countries in our sample. The following are used to calculate the average wages of workers in the other seven groups:

- e wron = (US age premium) xwryn

- e wayn = (skill premium) xwzyn

- e won = (US age premium)x (skill premium)x wry

- e wryr = (immigrant premium) xwzyy

- e wror = (immigrant premium) x(US age premium) xwyy yn e wayr = (immigrant premium)x (skill premium)x wry wn e wor = (immigrant premium) x(US age premium)x (skill premium) x wry7

Productivity and preference parameters. When employment levels (qsaj) and wage rates