rafmacalaba/data-use-mentions-extended huggingface dataset:
direct and dynamic effects on the endogenous variables–is extended along two key dimensions. First, while previous work has focused on the United States, this paper looks at 54 economies including 32 advanced economies and 22 EMDEs. Second, this paper allows for (but again does not require) fat-tailed shocks. That is, it distinguishes between slow-moving and fast-moving volatility, the latter measure reducing the possibility that the estimates of policy uncertainty are contaminated by volatility induced by transitory events or data outliers, and a way to deal with including data from the COVID-19 pandemic.<sup>2</sup> Policy uncertainty is defined in this paper as in Jurado et al. (2015): the conditional volatility of policy shocks or, more specifically, the part of policy that cannot be forecasted by economic agents. What matters for households and firms that make decisions about spending and saving, or producing and investing, is not whether a particular outcome has become more or less disperse (for example, whether sales will rise or collapse) but rather whether it has become more or less predictable (that is, more difficult to predict a rise or collapse in sales).

This paper reports the following findings. First, policy uncertainty shocks have a material impact on real activity and prices. An increase in fiscal policy uncertainty (specifically real government spending uncertainty) is associated with a statistically significant drop in real GDP, private consumption, and fixed investment. Prices also increase. Monetary policy uncertainty (or real interest rate uncertainty) follows a similar dynamic, being associated with a statistically significant decrease in real GDP and increase in prices. There is no _a priori_ theoretical basis for whether uncertainty shocks should act like supply or demand shocks (see Fern ́andez-Villaverde et al., 2015; Fern ́andez-Villaverde and Guerr ́onQuintana, 2020).

Second, policy uncertainty affects advanced economies and EMDEs differently, although given the lower precision in estimating with smaller samples, these should be interpreted

> 2Carriero et al. (2021) refer
|
|Any Trade|0.07|0.26|0|1|224368|
|Any Construction|0.05|0.22|0|1|224368|
|Any Service|0.13|0.34|0|1|224368|

_Notes_ : This table presents summary statistics for rural households using the four rounds of the NSS data. Household consumption is measured in 1993 values.

**Table 1C:** Summary Statistics: Non-Farm Unincorporated Enterprises

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|
|Revenues|15253.28|25981.57|0|800000|139182|
|Value Addition|4449.28|5027.72|0|151220|139182|
|Fixed Assets|117136.3|358223.3|0|6097000|139182|
|Own Account Enterprise|0.92|0.27|0|1|139182|
|Enterprise Expanding|0.32|0.46|0|1|139182|
|Enterprise Stagnating|0.53|0.5|0|1|139182|
|Age _<_ 3 years|0.15|0.36|0|1|139182|
|Any Credit|0.1|0.3|0|1|139182|
|Any Bank Loans|0.03|0.18|0|1|139182|
|Any Informal Credit|0.04|0.2|0|1|139182|
|Any Business Credit|0|0.07|0|1|139182|
|Enterprise Registered|0.21|0.4|0|1|139182|
|
FUNDAUNGO (2013). Atlas de la violencia en el salvador (2009-2012). _San Salvador_ .

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Fuster, J. M. (2013). _The neuroscience of freedom and creativity: Our predictive brain_ . Cambridge University Press.

- Glasswing International (2012a). Estudio Interno sobre impacto de programa extracurricular de seis centros escolares. Technical report, Fundacin Crislida. Glasswing International.

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- Goldschmidt, P., D. Huang, and M. Chinen (2007). The long-term effects of after-school programming on educational adjustment and juvenile crime: A study of the LA’s BEST After-School Program. _Los Angeles: UCLA/CRESST. Retrieved September 8_ , 2008.

- Goleman, D. (2010). Emotional intelligence: Why it can matter more than iq. _Bantam Books_ .

- Gottfredson, D. C., A. Cross, and D. A. Soul ́e (2007). Distinguishing characteristics of effective and ineffective after-school programs to prevent delinquency and victimization. _Criminology & Public Policy 6_ (2), 289–318.

- Gottfredson, D. C., S. A. Gerstenblith, D. A. Soul ́e, S. C. Womer, and S. Lu (2004). Do After-School Programs reduce delinquency? _Prevention Science 5_ (4), 253–266.

- Harmon-Jones, E., P. A. Gable, and C. K. Peterson (2010). The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. _Biological psychology 84_ (3), 451– 462.

- Haushofer, J. and E. Fehr (2014). On the psychology of poverty. _Science 344_ (6186), 862–
withdraw any national standard that might conflict with them. If a manufacturer chooses to produce a product according to these standards, the product carries the CE mark, which implies compliance with the “essential requirements”. On the other hand, manufacturers may use other technical specifications when manufacturing a product provided there is documentation certifying that the product meets the “essential requirements” formulated in the Directives. Nevertheless, anecdotal evidence suggests that the prohibitive costs of the latter option push exporting companies to favor compliance with CENELEC standards (see, for instance, Hanson (2005) and the 2010 U.S. Report on Technical Barriers to Trade). At the global level, the International Electrotechnical Commission (IEC) is the organization that prepares and publishes international standards for the electronic sector.

To examine the impact of the trade liberalization in this NTB, I use the CENELEC-IEC agreement to harmonize European product standards to international norms as a policy experiment. The Lugano Agreement, signed in 1991, and the Dresden Agreement, signed in 1996, sought to expedite the adoption of international standards in the EU as well as to facilitate the adoption of EU standards internationally. This synergy has taken the number of purely European standards as a share of all standards published by CENELEC from 50 percent in the early-1990s to 25 percent in 2008. In this context, the decrease in the share of idiosyncratic standards is a liberalization in an NTB to international trade.

Product standards are an important, albeit often overlooked, actor in the international trade arena. Swann (2010) and WTO (2005) present an excellent overview of product standards in the multilateral system and its impact on trade. The literature regarding the effect of harmonization of standards on international trade is relatively recent. Moenius(2004) provides the first valuable contribution by challenging the commonly held view that country-specific standards act as a barrier to trade whereas harmonized standards encourage trade.<sup>2</sup> In a gravity framework, the author uses a panel data set with data on country-specific and
Table 6 shows the results of the estimation of equation (2), the consumption equation.<sup>17</sup> We first estimate equation (2) by OLS, using the standard specification adopted in these types of poverty analysis. We find that larger households, households with a higher ratio of dependents to working adults, and households headed by a female have lower consumption levels. Households with an older head also have lower consumption levels, although this effect diminishes as the household head ages. The average household head is 44 years old. The education level of the head of household head matters. Literacy in Kiswahili or English is positively correlated with household consumption. Surprisingly, however, the education levels of other household members do not show a significant impact on household consumption.

Households primarily dependent on farming for income generation have significantly lower levels of consumption than households in other sectors. This result is consistent with the finding of the analysis of the national 2000/01 Household Budget Survey (HBS), which shows that poverty is highest among farming households (NBS, 2002). Households primarily dependent on fishing for income generation are neither worse off nor better off, in terms of consumption levels, than households in other occupations. Rural households depend on a variety of income sources. After controlling for household size and number of productive adults relative to the number of dependents, the results on Table 6 show that having such a diversified income base increases household consumption levels. This finding is also consistent with findings of the 2000/01 national HBS. However, nationally 65 percent of households report 3 or more income sources. In our sample of the coastal population, only about half of the households surveyed report having 3 or more income sources. Providing opportunities for income diversification along the coast should therefore increase consumption levels and reduce poverty.

Households with more assets also have higher consumption levels, despite the very low level of ownership of productive assets. Surprisingly, land ownership and the amount of land farmed do not significantly
to decompose the total distributional change into parts that are associated to each of these specific factors.

Using repeated cross‐sections of household surveys for Germany, Poland and Spain from the early 1990s to 2013, this paper shows that this period was characterized by a significant increase in earnings inequality and polarization of occupations. We find that holding everything else constant, the occupational _parameters_ account for at least 44 percent of the increase in the P90/10 earnings ratio over the considered period and group of countries. In terms of the P50/10 earnings ratio, the rise of low‐skill occupations and the decline of mid‐skill ones account for more than its full observed rise; so the model over‐estimates the role of occupational change for the increase of inequality at the bottom part of the distribution. In contrast, the decomposition model shows that simulated occupational shifts explain none or very little of the inequality increase at the top of the income distribution. These findings confirm those 5
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35. Using household survey data in the Comoros, the paper examined the potential impacts of transport connectivity on crop production and market sales. Different types of connectivity, i.e., within- and inter-island connectivity, were considered. By the value chain analysis, first, it was found that in the country, there are significant price differentials among the islands, indicating disconnection among the regional economies in the country. In addition, the differences were found to be attributed largely to high inland and maritime transport costs.

36. More formally, second, regression analysis was carried out. The results indicate that significant untapped economic potential exists in the domestic market. The accessibility to Moroni, the primary market in the country, was found to be of particular importance, indicating the importance of inter-island connectivity among the islands. The crop production can be increased by reducing the transport costs to Moroni or improving the market accessibility in general. The connectivity to Moroni is also essential to encourage farmers to participate in market transactions, stimulating agricultural market sales.

37. The estimation results also show that not only transport infrastructure but also transport and trade services are important. The accessibility to intermediary services, such as cooperatives and collectors, plays an important role to increase crop production and market sales. Thus, it is important to not only invest in transport infrastructure but also promote private sector development in the transport and/or agrobusiness industries.
# **1. Introduction**

Headline conflicts such as major instances of civil war, and their ensuing set-backs for economic and political development, have in recent years been given increasing research attention (Collier 2002; World Bank 2003a). At the same time, pervasive and widespread local conflict characterizes many developing countries such as Indonesia, constituting both a barrier to development and, in some cases, providing the fuel for larger incidents of unrest. Realignments in power and resources, and challenges to existing interests and values, phenomena inherent in the development process, can lead to increased social tensions (Bates 2000). As such, major political and economic transition often finds states and their societies grappling with ways of preventing these smaller instances from escalating into more fully-fledged violent conflict.

Yet to date, systematic evidence on the presence of such local conflict, and on the potential underlying factors that might be associated with this incidence, has been limited. The extent of local violence in a country such as Indonesia (and variations in conflict incidence, type, and impact) suggests that intra-country empirical investigations offer an available starting point to a deeper understanding of conflict dynamics. This paper addresses issues relating to the measurement of conflict, and, using a unique census of villages commissioned by the Government of Indonesia (the Village Potential Statistics – PODES), investigates associated factors that appear to heighten the likelihood of violent local conflict.

Conflict is a feature of life for many in Indonesia. The fall of Soeharto’s authoritarian New Order in 1998 was accompanied by a wave of violent unrest, which appears to be persistent. The old authoritarian regime managed conflict through repression with state structures regulated and vertical in nature (cf Tadjoeddin _, et al._ 2001a), and the army, through its presence in each 2
a subset of OECD countries in the WIOD sample, we look at investment in infrastructure ( _infrainvest_ ) as percentage of GDP available from OECD Stat.

_Connectivity_ looks at procedures and controls governing the movement of goods and services across and within national borders, as well as a country’s ICT infrastructure. It is accounted for by three categories of the World Bank’s Logistics Performance Index (LPI) ( _LPI overall_ , _LPI customs_ , _and LPI logistics_ ), which ranges from 1 to 5 = best. In addition, we include _Internet_ users per 100 inhabitants and the expected time for exporting ( _time to export_ ) and importing ( _time to import_ ) in days by the WDI as measures of connectivity. The latter two are only available from 2003.

_Investment policy_ is measured on the investment side by an index of investment freedom by the Heritage foundation and by FDI inflows as percentage of GDP ( _FDI inflows_ ) from the WDI. The variable _investment freedom_ <mark>serves as a proxy for investment promotion</mark> . The Heritage score ranges from 0 to 100 = highest freedom, and investment freedom measures the ability of individuals and firms to <mark>move their resources in and out of specific activities both internally and across the country’s borders. This variable is mainly based on official government publications of each country on capital flows and foreign investment.</mark> _Trade policy_ is proxied by a country’s share of exports of goods and services as percentage of GDP ( _openness_ ) from the WDI. We also include two measures regarding services trade, namely, its share as percentage of GDP ( _services trade_ ) from the WDI, and the Services Trade Restrictiveness Index ( _STRI OECD_ ) from the OECD.<sup>6</sup> The STRI takes the value from 0 = completely open to 1 = completely closed.

_Business climate and institutions_ are assessed using six indicators. _Property rights_ (The Heritage Foundation) covers the functioning of courts. It measures the degree to which
which are likely to obscure whether a country has a real comparative advantage, or disadvantage, in these products. The present analysis does not attempt to derive revealed comparative advantage indices for agricultural products and other primary commodities. As such, the present analysis is based on 128 three-digit SITC products that include all manufactured goods as well as a number of processed foodstuffs and processed raw materials. Equation (3) is a modified version of the normal RCA formula in that intraMercosur trade is excluded to prevent preferential trade among members from obscuring the true comparative advantage of Mercosur members.

9The issue is essentially whether RTAs foster "high cost" imports at the expense of "low cost' ones. The traditional calculations of trade diversion based on import data infer this from the displacement of imports from nonpartners by those from partners, implicitly comparing partner and non-partner costs by their relative competitiveness in the pre-RTA regional market. The supplementary view developed here makes inferences about 'high" and 'low" costs by implicitly comparing the relative competitiveness of partner and non-partner goods in world markets.
in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 0.14 to 16.64.

# Limitation and Discussion

Assessing the quantitative impact of disasters on populations and economies is particularly challenging in poor and conflict-affected regions, where reliable socio-economic data may be lacking. Our analysis of descriptive statistics has indicated a tendency towards lower nightlight density in rural areas, which could potentially lead to an underestimation of economic activity due to the limitations in spatial resolution. Nightlight radiance in rural settings is typically less intense than in urban centers, and a higher incidence of such readings could result in a skewed sample. Furthermore, sectors like agriculture, which are less dependent on electricity, might not be fully represented by nightlight data. This is particularly true for poorer households in rural areas that may not have access to electricity, rendering them invisible in satellite-based observations. Consequently, vital aspects of a developing economy might be missed when relying exclusively on nightlight data for analysis.

Nonetheless, and despite these limitations, access to advanced earth observation technologies, including regularly updated satellite imagery, presents new avenues for analysis using quasiexperimental approaches. Nightlight satellite data can provide valuable insights into economic activity, primarily enabling the assessment of immediate to short-term impacts. Our case studies highlight the exacerbated effects of floods on nightlight in conflict-affected regions. For instance, we observed a decrease in nightlight radiance following the July 2022 floods in Nigeria, with conflict28
<sup>-3</sup> with North America (Mexico City), and Latin America (São Paulo, Santiago) recording the highest levels i.e. 491μgm<sup>-</sup> 3 -3 -3 , 403μgm , 351μgm r <mark>espectively, whereas most cities in Asia had high PM levels with a number of cities exceeding 300μ</mark> gm<sup>-3</sup> <mark>. These pollutant concentrations were several times higher than the WHO threshold values at the time (i.e. 120</mark> μgm<sup>-3</sup> and <mark>60-90μ</mark> gm<sup>-3</sup> for daily 8-hour O3 and suspended particulate matter respectively).<sup>6</sup> A similar study by Gurjar et al. (2008) classified air quality in 18 megacities based on a Multi-Pollutant Index (MPI) and found poor air quality in 13 of the megacities studied. Work on air pollution trends for Kolkata, Mumbai and Delhi (3 megacities in India) by Gurjar et al (2016) based on historical monitoring data (1991-2012) highlighted the severity of NOx and Particulate Matter ambient concentrations largely coming from transportation and industry. The geographic span of these sampled megacities during various timescales i.e. North America, Europe, Africa, Central and South America and Asia, highlights the historical significance of urban air pollution as a major health and environmental issue for both developed and developing countries, although there has been significant decline in SO2 and Pb levels attributable to regulatory enforcement.

Rapid population increase coupled with air pollution precursors and economic drivers e.g. motorization, combustion energy, industrialization, and other infrastructural developments may lead to inferring that air quality may be deteriorating in fast-growing urban centers in Africa. However, actual evidence is limited given the lack of adequate real-time monitoring data sets. Countries in Sub-Saharan Africa particularly have had minimal
as a key cause of the gender gap across Sub-Saharan Africa (O’Sullivan 2014). This result has also been shown to hold for Côte d’Ivoire; agriculture programs in the country should consider implementing creative solutions such as labor vouchers and directly engaging husbands to reach the last mile in gender-egalitarian poverty reduction (Carranza et al. 2017).

Further, decision-makers in the agriculture sector should focus on better gender integration within export crop cultivation. Indeed, ‘occupational segregation’ across crop types can be considered the key driver of the productivity gap in Côte d’Ivoire: whether one accounts for export crop adoption by households or not determines whether female-headed households face a 21 percent productivity gap or no productivity gap (Tables 7 and 8). In the specific context of Côte d’Ivoire, our results suggest that by targeting export crop female producers, notably by increasing their use of inputs, agricultural policies have been successful at reducing gender inequality.

Moving more women into the export crop sector, which does not appear to have happened over the past decade, could be an avenue to further reduce gender inequality in the country and in the region. Implementing strategies that tailor extension services to female farmers’ needs, facilitate their access to markets and engage husbands as allies to encourage women to “cross-over” to high-value export crop cultivation should be at the top of the agenda in Côte d’Ivoire’s next National Development Plan.

Lastly, our analysis highlights the importance of collecting gender-specific data in household surveys and recording the gender of the main decision maker and/or owner for the important units of economic production in the household, such as agricultural plots and businesses. The 2008 data, where this information is available, show that productivity gaps are larger when conducting the analysis by gender of the plot owner or plot manager. However, our data in 2016 do not allow for an analysis of gender gaps except for by headship. This is a limitation, since most women do not live in female-headed households

> 5
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# APPENDIX 3: **Iustitutional Costs:** Detailed Tables

The cost analysis in the main text considers three levels of VTE: secondary, post-secondary, and teacher training. In this appendix, we show the institutional cost calculations at a more disaggregated level. We also explain the data sources and the assumptions that were necessary to complete the calculations.

For the secondary level, Table A3.1 shows the cost calculations for 5 types of institutions:

- technical high school (Lycee Technique)

- vocational high school (Lycee Professionnel)

- technical college (College d'Enseignement Technique)

- vocational training center (Centre de Formation Professionnelle)

- other training center (Centre de Formation Artisanale, Centre de Formation Professionnelle Specialise)

The main data sources were the 1987 budgets of the Ministry of Vocational and Technical Education and the National Office of Vocational Training (ONFP).1/ While these budget data break down temporary staff salaries and operating expenses (except utilities and communications) by type of institution, only a global figure is available for permanent staff

- 1/ The use of budget figures as data source instead of actual expenditure figures is necessitated because the latter are only available with a lag of several years. However, in the fiscal austerity climate currently present in Cote d'Ivoire, actual expenditures are likely to fall within a couple of percentage points of budgets.