rafmacalaba/data-use-mentions-extended huggingface dataset:
factors ensure that multicollinearity is not a serious problem. First, as the model is offered as<sup>a heuristic</sup> device for attracting policy-makers attention to particular points of entry to<sup>reform, this is</sup> not a particular drawback provided that any correlation is positive. If policy<sup>credibility</sup> were to deteriorate as resource predictability improved, the resulting combined<sup>effect</sup> would be hard to interpret. However, in all the survey results analyzed to<sup>date the</sup> correlation where it exists has been positive. Second, multicollinearity<sup>is a sample</sup> phenomenon as well as a theoretical one. A good rule of thumb is to find variables that are theoretically relevant (for meaningful interpretation) and that are also statistically<sup>non</sup> multi-collinear (for meaningful inference). As the correlation matrix in Appendix<sup>6</sup> shows, the correlation coefficients between the explanatory variables<sup>in the Bolivia data</sup> are low.<sup>54</sup> The term "Institutional Rate of Return" has been coined deliberately to echo<sup>the more</sup> common Internal Rate of Return. Its purpose is to draw attention to the heterogeneous 54 In addition to presenting this matrix, Appendix 6 further explains how survey responses were converted to a binary mode thus lowering correlation among the explanatory variables.

29
Dist. to town (km)|14.940|13.970|15.910|-1.940|
|Dist. to bus station (km)|4.240|4.980|3.490|1.490|
|Dist. railway station (km)|25.130|25.590|24.660|0.930|
|Dist. to post office (km)|1.940|1.990|1.880|0.110|
|**Panel B: Pradhan's Characteristics**|||||
|Earlier contested|0.158|0.137|0.179|-0.042|
|Held position before|0.474|0.442|0.505|-0.063|
|Up to high school|0.263|0.263|0.263|0.000|
|High sec. & above|0.342|0.263|0.421|-0.158|
|Higher education|0.163|0.137|0.189|-0.052|
|SC|0.537|0.579|0.495|0.084|
|ST|0.116|0.116|0.116|0.000|
|OBC|0.126|0.105|0.147|-0.042|
|OC|0.216|0.200|0.232|-0.032|
|Hindu|0.563|0.484|0.642|-0.158|
|Muslim|0.089|0.084|0.095|-0.011|
|No. of obs.|190|95|95||

_Note:_ Author’s own calculation from 2014/15 REDS follow-up survey. To test difference in means, p
residential/commercial|Passengers and freight vehicles (both distinguished<br>according to traditional combustion, hybrid, plug in<br>hybrid and electric). The composition of vehicles is<br>determined by a Leontief function of a range of costs<br>(including battery, O&M, fuel and carbon costs)|Partly endogenous|
|**C3IAM**|transport, industry and<br>residential/commercial|Intercity passenger transport, urban passenger<br>transport and freight transport. Urban passenger<br>transport is divided into public transport (bus and rail<br>transit), taxi, and private vehicles (cars and electric<br>bikes). Intercity passenger transport is divided into<br>private vehicles and four main business intercity<br>passenger transport (road transport, railway<br>transport, aviation and waterway transport). Freight<br>transport is divided into five main types: road<br>transport, railway transport, aviation transport,<br>waterway and pipeline transport.|Exogenous|
|**BET EMF33**|Transport, Power, Heat|Passenger and freight|Exogenous|
|**AIM-CGE**|Industry, residential,<br>commercial, transport|Road, Railway, Waterways, Air transport|Exogenous|
|**GEM-E3-ICCS**|Firms, households|As regards the transport modes, the sectorial split<br>follows and further extends the classification used by<br>the GTAP database. This extension splits the GTAP<br>data in passenger and freight transport by mode and<br>further disaggregates themodesintorailand roadas|Exogenous|

16
to the production process (Loayza and Raddatz, 2010). At a sector level, growth in services has been found to be major contributors to poverty reduction in Brazil, Indonesia, and India (Ferreira, Leite, Ravallion, 2010; Suryahadi, Suryadarma, and Sumarto, 2009; Ravallion and Datt 1996 and 2002). Since agricultural jobs tend to be very labor intensive and is a common line of work for the poor, growth in this sector has also associated with poverty reduction in Indonesia and China (Suryahadi, Suryadarma, and Sumarto, 2009; Montalvo and Ravallion, 2010).

In the case of Turkey, regional poverty reduction is primarily explained, in GVA terms, by growth of agricultural sectors in the East.<sup>6</sup> The positive impact of agricultural growth in the East might be partially due to the shift towards agriculture during the financial crisis as jobs in the services and industrial sectors became scarcer as a result of the economic slowdown. Surprisingly, growth of value added of the services sector was not found to impact poverty reduction, even though construction and real estate have been high growth sectors. One reason for this may be due to the access of gross value added data, which is limited to 2006 to 2011. Moreover, GVA excludes subsidies, and the Turkish government gives substantial amounts of subsidies to the services and industrial sectors. When using employment levels as a primary determinant, employment growth in the services sector is found to contribute to poverty reduction in both the East and West during the period 2006 to 2013. In terms of the impact of services to poverty alleviation, the diverging trends between GVA and employment indicators relating to this sector is noteworthy. These difference may in fact be due to the previously explained data related issues; however, another possibility is the relatively higher level of wages in the services sector compared to low-skilled agricultural and industrial jobs. As a result of this discrepancy in wage levels, switching to the services sector (measured by employment) might
**Figure 3 Unit Values of Sri Lanka’s Apparel Exports to the EU-15 and the United States**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>EU-15 Unit Values (Euro/Kg) US Unit Values ($US/Dozen)*<br>unit values<br><!-- End of picture text --> _Source:_ EU-15: Eurostat; United States: U.S. International Trade Commission (USITC). _Note:_ For products exported to the United States, volume data were “not available” for four products,and for five products the volumes were reported in “numbers.” Both sets are measured in numbers that were converted into dozens.

On the other hand, the unit prices of Cambodia’s main export products are lower than the world average. For EU-15 exports, unit prices in 2005 were lower than in India, Sri Lanka, and Vietnam, but higher than in Bangladesh, China, and Pakistan (Tewari 2008). One of the reasons for the low prices of Cambodian apparel exports is that Cambodian apparel manufacturers concentrate on the production of basic products, while other countries export higher-value products. Unit prices went down after the end of the MFA, with U.S. import prices experiencing sharper declines than those of the EU. Between 2004 and 2008 the average price of apparel exports to the United States fell by 25 percent, from $52 to $39 per dozen, while the average price of apparel exports to the EU-15 declined by 7 percent, from €13.4 to €12.5 per kilogrambetween 2004 and 2008,according to the U.S. International Trade Commission (USITC) and Eurostat data (Staritz and Frederick 2011a) (
2013 May‐2014 May‐2015<br><!-- End of picture text --> _Source:_ Elaborated from data available at EIA 2015.

The picture is similar if we consider the commodities indexes for fuel and food (figure 2). The fuel energy index, which combines fuel products, shows that its trend overlaps with the oil trend up until the 2008 global financial crisis when we start to see more volatility and a certain divergence between the two trends. The food price index is naturally less associated with changes in the oil price but nevertheless is correlated with it. In particular, during the period that we consider more closely (2010–14) both the food and energy indexes show high levels and high volatility. In their reform efforts, governments faced increasing fiscal pressure and increasing 8
) entitled a participant to one raffle ticket. Tickets were distributed twice. The first distribution took place in early September, and was based on savings as of August 19. The second distribution took place in November, and was based on savings as of October 22. By varying the way in which tickets were distributed, we sought to manipulate the information that club members had about

> 8 Among farmers in the control group, nobody requested an ordinary or a commitment account during the savings training at baseline. According to OIBM administrative records, eight farmers in the control group had commitment accounts by the end of October 2009 (opened without our assistance or encouragement), but none of these had any transactions in the accounts.

> 9 By design, funds in the commitment account could not be accessed before the release date. In a small number of cases OIBM staff allowed premature withdrawals of funds when clients presented evidence of emergency needs, e.g. health or funeral expenditures.

> 10 Notice that members could have revised the initial allocation of funds made during the initial account opening process when they visited the bank after the crop sale. However, we find no evidence of this behavior in practice (analysis not reported) using data from the club funds allocation sheets.

8
and Weil 2012) and this paper will take advantage of this to use VIIRS as a proxy for economic activity.

The settlement layers used in the analysis are from WorldPop (WorldPop 2013) (Worldpop 2016) and CIESIN (Facebook Connectivity Lab and Center for International Earth Science Information Network ‐ CIESIN ‐ Columbia University n.d.). Both sets are high resolution layers that show where people live through a combination of satellite images and census data. There are two reasons for including the settlement layers. Firstly, the data are used as a way to clean the VIIRS data by excluding cells that are unlikely to contain economic activity and population. The use of secondary data to improve recognition of human activity has previously been done for poverty (Jean, et al. 2016) and for urban markets (Baragwanath, et al. 2019). Secondly, the population data are used to distinguish between rural and urban areas.

For the disaster modeling, we model three disaster types, earthquakes, typhoons and floods, by following closely the methodology from Skoufias, Strobl and Tveit (2017) for earthquakes and floods and Strobl (2012) for typhoons. These damage indices are then combined with the other data sets and two types of analysis are performed on this combined set. The first type is one case study for each hazard type. The choice fell on typhoon Haiyan, which hit the Philippines in 2013, the 2016 Aceh Earthquake and the 2017 floods in Southern Thailand. All three disasters caused significant local damage and were for the most part spatially and temporally separate from other disasters. The case studies consist of a simple graph showing the difference in mean nightlight values for the nightlight cells that were hit by the hazard and the cells that were not hit over a period starting 12 months before the hazard and until 12 months after. The second analysis moves the focus from specific events to a national level and hazards that occurred between June 2012 and March 2018. To find any short and medium‐term impacts that each disaster
sampled areas, the R<sup>2</sup> appear to consistently range from about 0.74 to 0.95, as shown in Table 1.

Because geospatial data are particularly predictive of population density (Leasure et al, 2020, Engstrom et al, 2020) and population density is systematically related to economic welfare (Castaneda et al, 2018), geospatial data can help “fill in” two-stage household surveys with model-based predictions, boosting efficiency and accuracy. Most of the studies that have compared geospatial estimates with direct survey estimates find that the model-based estimates are more accurate, although the comparisons are not shown here. There is also evidence that prediction accuracy is highly dependent on the strength of the training data, suggesting that partial registries that collect proxy indicators may be a valuable supplement to household survey data when publicly available geospatial data can be linked. Finally, outof-sample estimates are generally less accurate than in-sample estimates, and occasionally very inaccurate. This suggests that there are benefits from including as many target areas as possible in the sample, and from additional research and tools to improve out-of-sample prediction accuracy.

At the same time, the early literature has utilized a dizzying number of approaches to integrating survey and geospatial data. Many of these papers train a CNN model directly to imagery, others use machine learning methods applied to specific features, while others use linear mixed models. Those that either use deep learning or tree-based machine learning either ignore or may not properly estimate or evaluate uncertainty, and many of the papers ignore the well-established statistical literature on small area estimation. The following section explores these issues in greater detail.

12
# **2.1 Early impacts of the pandemic**

**We hypothesize that urban household enterprise income fell, few coping strategies were utilized, and women, youth and the poor were disproportionately affected by the COVID-19 shock.** The HFPS data do not allow us to observe the extent of income declines, though there is a measure of their prevalence. We expect that income declines were more prevalent among urban household enterprises than urban households that rely on other sources of income. We also expect that the disproportionate negative income impacts on women, youth and the poor that were observed in other studies will emerge in these data. Finally, given the scarcity of public and private support such as insurance or safety nets in urban Sub-Saharan Africa (Guven et al. 2022), we expect that few household enterprises were able to access these tools or to use them to offset the impact of the pandemic.

**To analyze the early impact of the pandemic on enterprise income and use of coping mechanisms, we pool information from the first wave of the HFPS for the 13 countries in the sample and estimate linear probability models.** The sample is restricted to people who were employed and report information on income decline. Table 1 shows the month of the lockdown, month of the first survey wave, and the share of respondents that are household heads. Most countries locked down in March or April 2020, while most data were collected one to three months after the first lockdown. Due to slight differences in the questions included in each country survey, the subset of countries differs by definition and across empirical exercises. We use the household sample weights.

**We carry out four empirical exercises to better understand the initial impacts of the crisis** . We estimate each model for the sample derived from each of the two definitions of household enterprises described above. In each case, observations are weighted using the survey weights included in the HFPS.

**First, we examine if the incomes of urban households
animus on a black candidate: Evidence using Google search data,” _Journal of Public Economics,_ Volume 118, Pp; 26-40 <mark>Thaler, Richard H.</mark> 2016. "Behavioral Economics: Past, Present, and Future." _American Economic Review_ , 106 (7): 1577-1600.

Udry, Christopher, 2003, “Fieldwork, Economic Theory and Research on Institutions in Developing Countries,” mimeo.

White, Howard, 2008, “Of Probits and Participation: The Use of Mixed Methods in Quantitative Impact Evaluation,” _IDS Bulletin_ 39:98-109 Woolcock, Michael, 2021, “On Folklore and ‘Folklore’ (QJE): Forging Dialogue, Reconciling Tradeoffs,” _Development Impact Blog_ , February 23<sup>rd</sup> World Bank, 2021, “Helping Communities to Gain the Ability to Collect and Analyze Their Own Data,” Spotlight 1.1. _World Development Report 2021, Data for Development._ The World Bank Group, Washington DC 23
that result from withdrawing model-based entry barriers relative to those stemming from removing the World Bank’s DBI’s measure of entry taxation. Since, as we showed, the interaction between distortions exerts a nontrivial effect on the magnitudes of partial liberalizations, we compute these differentials both in the context of an active interaction, preserving the underlying idiosyncratic distortions, and in a context with no misallocation, as in the majority of existing studies on entry regulation.

Concretely, letting _τi_<sup>_E,DBI_</sup> denote the entry barrier in country _i_ according to the DBI, we compute the following two TFP differentials: The productivity elasticity of distortions _γi_ is unchanged and remains to be given by the WLS estimate of the regression defined in equation 18.

Figure 13: TFP Gains: Model-Based versus World Bank’s Doing Business Indicator Measures of Entry Barriers <!-- Start of picture text --> Remove Entry Barrier with Misallocation Remove Entry Barrier No Misallocation<br>Kenya Kenya<br>Salvador<br>ColombiaChile<br>Ethiopia GhanaSalvador India Peru BulRomaniagColombiariaHungaryLatvia Chile Mal Portugal a SpainysiaItalyFranceBelgiumFinland Ethiopia Ghana IndiaPeru Bulgaria RomaniaHungaLatvia Malaysia Po r tugalySpItalyFranceBelgiumainFinland<br>6 7 8 9 10 11 6 7 8 9 10 11<br>log GDPpc log GDPpc<br>0.25 0.25<br>0.15 0.15<br>Difference TFP gain 0.05 Difference TFP gain 0.05<br>−0.05 −0.05<br><!-- End of picture text --> Note: The left panel of the figure shows the differential TFP gain between removing entry barriers measured as in the model relative to entry barriers imputed from the World Bank’s Doing Business Indicator. Formally, the left panel illustrates _T F PT F P{{τ_ 0 _i_<sup>_E_</sup> _<u>,γ</u>_<sup>_,γ_</sup> _<u
# **6. Conclusions**

Our paper is motivated by three main questions. First, is there a pattern of volatility across commodities over time? Second, is there a long-run trend in commodity price volatility? Third, are there identifiable breaks in commodity price volatility? For example, do we see changes in volatility when the world went from relatively open (1820 – 1913) to closed (1914 – 1949) and then to open (1950-2009)?

Drawing on a large unbalanced panel of monthly prices of 45 individual commodities and 5 commodity price indices we address these issues by implementing two tests for detecting structural change. Both tests have the advantage of not imposing a priori the dates of potential structural breaks. While both tests are based on CUSUM-type test statistics, they differ in how they establish the threshold above which a change in volatility is considered a structural break. The Kokoszka-Leipus test uses an estimate of the long-run variance of the entire time series to determine such a threshold level. As a result, it is a more stringent test resulting in fewer breaks and by and large only three periods in history are associated with breaks in volatility in most (but not all) commodities: the two world wars and the collapse of the Bretton-Woods system. The Inclan-Tiao (IT) test, in contrast, considers only the properties of the time series since the last identified break in volatility. As a result, threshold volatilities are re-defined every time a break is found. This results in less stringent thresholds and therefore more breaks in volatility – both upwards and downwards. Therefore, even though break-points may overlap, the IT test, constructed to be more sensitive, picks up more frequent shifts. However, the historical breaks coincide under both methodologies.

The main conclusions are as follows. First, the timing and number of breaks in volatility vary considerably across individual commodities. This result cautions against broad generalizations and the use of commodity price indices to analyze changes in volatility. Second, the three most significant breaks common to most
|[0.19]|[0.18]|[0.18]|
|FMXGDP ((3)-(6): sum of lags)||-0.09|||0.06|0.08|
|||[0.16]|||[0.08]|[0.11]|
|AGXGDP ((3)-(6): sum of lags)||-0.46|||-0.11|-0.11|
|||[0.36]|||[0.18]|[0.20]|
|NRXGDP - MFGSERV|0.08||0.12||0.10||
||[0.18]||[0.09]||[0.14]||
|DEINDU - MFGSERV|0.11|0.09|0.01|-0.01|0.00|0.00|
||[0.20]|[0.18]|[0.24]|[0.23]|[0.28]|[0.27]|
|FMXGDP - MFGSERV||0.11||0.15||0.13|
|||[0.20]||[0.10]||[0.16]|
|AGXGDP - MFGSERV||-0.27||-0.01||-0.06|
|||[0.36]||[0.19]||[0.21]|
|Country, Year FE|N|N|Y|Y|Y|Y|
|Observations|115|115|462|346|462|346|

_Notes:_ Robust SE (clust. at the country level in (3)-(6)
serious trade-offs in terms of their own personal needs (e.g., employment, education, leisure, personal care) and those of their families. Children’s diets and nutrition-related health behaviors are heavily dependent on mothers’ time allocation to a range of economic and nutrition- and health-enhancing activities (Black et al., 2013).

Efforts to formulate policies to address issues relating to income-generating activities, time allocation of domestic work, and gender disparities require accurate data on men’s and women’s time use. The revised statistical definition of ‘work’ which includes unpaid care and domestic activities (ILO, 2013), and Sustainable Development Goal Target 5.4 to “recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies” have provided additional momentum for investments in time use data collection.

These analytical objectives, and the design of effective policies and programs for addressing gender disparities in both paid and unpaid work that they aim to inform, rely on the accurate measurement of men’s and women’s time use. The existing and persisting weaknesses in individual-level data and statistics on time use (Chatzitheochari et al., 2017; Harvey and Taylor, 2000; Juster et al., 2003; Kan and Pudney, 2008; Kes and Swaminathan, 2006) are a major bottleneck hindering progress towards these policy research goals. Time use surveys or time use modules as part of multi-topic household surveys are key sources of the required data. It is most common for these surveys and modules to elicit respondents’ recollection of their activities during the reference period, typically the previous 24 hours leading up to the interview (Fisher, 2015).<sup>2</sup> The shortcomings in terms of cross-country availability, comparability, and quality of individual-level data on time use can in part be traced back to underinvestment in rigorous methodological research for the development of scalable and accurate survey methods (Daum et al., 2019; Kilic et al
<!-- Start of picture text --> Figure 15. Incidence curves with respect to baseline, by 2030<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2030<br>EU-IDN FTAAP TPP-15<br>0 50 100 0 50 100 0 50 100<br>Percentiles of Per Capita Income<br>a: Growth b: a + Sectoral Reallocation c: b + Wage Premia d: c + Food prices<br>Growth Incidence Curve with respect to Baseline, 2030<br>TPP-11 RCEP<br>0 50 100 0 50 100<br>Percentiles of Per Capita Income<br>a: Growth b: a + Sectoral Reallocation c: b + Wage Premia d: c + Food prices<br>3<br>2<br>1<br>% change, w.r.t. Baseline<br>0<br>3<br>2<br>1<br>% change, w.r.t. Baseline<br>0<br><!-- End of picture text -->

# 6. Conclusions

With the flurry of new, more complex bilateral and regional PTAs, it is become more important to carefully assess their economic impacts. This paper has provided macro- and micro-estimates of the impacts of various important agreements currently negotiated or potentially negotiated by the largest economy in Southeast Asia – Indonesia. To that end, it has employed state-of-the-art simulation methods, including a dynamic multi-country global CGE model and a micro-simulation tool linking the macro-economic results to the household level.

The results suggest that of the PTAs under investigation, the EU- CEPA would yield the largest macro-economic gains for Indonesia in terms of income, output and exports. This large impact is mainly driven by the high expected reductions in trade barriers and the high share of international trade between Indonesia and the EU