rafmacalaba/data-use-mentions-extended huggingface dataset:
level between 1983 and 2002||(0.24)||(0.22)||(0.03)|
|Difference in log of real price of||-0.175||0.210||-0.052**|
|shipments between 1983 and 2002||(0.19)||(0.17)||(0.02)|
|Difference in computer use rates||0.434||-0.227||0.023|
|between 1983 and 2002||(0.67)||(0.59)||(0.09)|
|Number of observations|66|59|66|59|66|61|
|R-squared|0.07|0.14|0.004|0.07|0.17|0.37|

_Source_ : Affiliate (or offshore) employment data are taken from the Bureau of Economic Analysis annual survey of US firms with multinational affiliates for 1983-2002. Low income countries are defined according to the World Bank income categories. Employment data are taken from all workers in the Current Population Surveys Merged Outgoing Rotation Groups for the same period. Import penetration and export share are taken from Bernard et al. (2006). Investment good prices, total factor productivity measures, and the real price of shipments are taken from the NBER productivity database. Computer use rates are taken from October CPS supplements during the sample period. Details for each of the data sources are available in the data appendix.

_Note:_ Robust standard errors reported in parentheses below coefficient estimates.
+ _b_ 6Multi + _b_ 7Manuf + _b_ 8Exporter + _b_ 9Foreign + _b_ 10Govt + _b_ 11Loan + _b_ 12Credit Constraint + _b_ 13Sales Credit + _b_ 14 External Finance+ Country Fixed Effects + _e_ * Significant at the 10 percent level. ** Significant at the 5 percent level. *** Significant at the 1 percent level. _Source:_ Authors' estimations based on data from World Bank Enterprise Surveys (2006-10).

31
rely on cross-sectional survey data due to the constraints of data availability (Adger, 1999; Datt and Hoogeveen, 2003; Carter _et al._ , 2007; Lal _et al._ , 2009). Cross-sectional studies observe a set of units (e.g., households or individuals) at a single point in time, allowing researchers to make comparisons and draw conclusions about prevalence of poverty and inequality. However, cross-sectional data comes with inherent limitations. It only provides a snapshot view of poverty across units at one point in time but does not offer insights into changes over time or the dynamics that drive these changes. This lack of temporal perspective can lead to omitted variable bias and severely limit causal inferences. Recognizing these limitations, many studies have turned to panel data, which observes the same units across multiple time points (Giesbert and Schindler, 2012; Rodriguez-Oreggia _et al._ , 2012; Yamamura, 2015; Keerthiratne and Tol, 2018; Baez _et al._ , 2020; Sedova _et al._ , 2020; Sohnesen, 2020; Cappelli _et al._ , 2021). Panel data allows researchers to track changes over time and explore the temporal dynamics of poverty and inequality; consequently, it also offers a more in-depth understanding of the effects of climate change and natural disasters, which often have a longitudinal nature themselves.

However, the use of panel data is subject to several barriers. In particular, tracking the same households (individuals) over a long period is expensive and poses logistical challenges, especially where households migrate or live in remote locations. In particular, attrition—where households drop out of the panel for various reasons—is a well-known issue affecting the quality of panel data and would require considerable resources investment and advanced survey strategies to address (Schoeni _et al._ , 2013). As an example, the attrition rate in the panel Russia Longitudinal Monitoring Survey (RLMS) is already 40 percent in the first 10 years after it was fielded, but sharply increases
<mark>combine the price information with detailed data on the location of these markets as well as the road network connecting these markets in order to measure the travel-time between each potential market pair. In order to account for changes in maize harvest per capita, we use annual maize production and population data during 1960–2018 and deviation of maize harvest levels from the long-term average in these four countries. Using data on households’ maize production, consumption, and sales patterns from the Integrated</mark> Household Surveys (HIS), we assess the welfare effects of export bans.

<mark>Information on trade restrictions is compiled from government and media reports. During the study period, Malawi had imposed and lifted maize export bans three times. Zambia and Tanzania also imposed similar bans for varying periods, and we account for the policy regimes in the neighboring countries in the analysis. The effects of export bans on relative prices are likely to be heterogeneous across years and over seasons. For example, given the structure of the grain market in Malawi, where farmers typically sell their produce immediately after harvest, the impact of the export ban could be different during harvest time and lean seasons of a given year. The impacts are also likely to vary between good and bad harvest years. The monthly panel structure of the data, along with the variation in the export policy regimes, allows us to</mark> examine the heterogeneous effects of export bans across years and over seasons.

<mark>The results suggest that export bans, in the short run, are associated with lower domestic prices, lower relative prices, and less seasonality in prices in Malawi, after accounting for harvest levels and the existence of trade restrictions in neighboring countries. The welfare analysis we undertake suggests that the majority of Malawian households’ gain, albeit small in magnitude, from low maize prices due to export bans. Even though most of the net-buyers enjoyed gains, net-selling farmers experienced losses due to lower maize prices. On average, Malawians receive
various standard statistical methods and machine learning techniques to detect traffic patterns and to build predictive models of those patterns. The performance of the underlying algorithms varies in many ways that reflect in part their ability to account for network-wide heterogeneity and spatiotemporal relations while maximizing in-sample and out-of-sample prediction (Einav and Levin, 2014a; Lv et al., 2015; Ma et al., 2017). Comparative studies between big data analytics and traditional statistical methods to predict traffic flows point to the superiority of the big data approach which can better account for non-linear relations (Lv et al., 2015; Ma et al., 2017).

Big data analytics and predictive modeling are also increasingly influencing urban management and congestion mitigation efforts (Hampson et al., 2017). The city of Tokyo, for instance, has partnered with a private firm to develop a smartphone compatible app, _Zenryoku Annai!_ , that analyzes nearly 360 million observations every second to generate real-time information on the shortest and least-congested travel routes. A similar intelligent transport system (ITS) in Denmark, _Copenhagen Connecting_ , was implemented to promote transport sustainability through real-time digital traffic control and weather adaptation options. Di Lorenzo et al. (2016) use data from Abidjan, Côte d’Ivoire to develop a CDR-based intelligent platform, _AllBoard_ , which provides local authorities with real-time visualization and a public transit optimization system.

Unlike most machine who tend to focus on learning specialists, primarily predictive modeling from robust data correlations, economists are also concerned with establishing causality between variables. Formally, an econometric model seeking to identify the causal impact of traffic conditions _Ci_ on a given outcome _Yi_ (e.g., health, employment, firm productivity, economic growth, pollution, etc.) for observation _i_ (household, firm, road segment or network, city, metropolitan area, county, country, etc.) can be expressed as: 13
Several factors could in principle drive these differences is managerial quality across firms and then countries. Bloom and Van Reenem (2007) focus on four developed countries (the United States, Germany, the United Kingdom and France) and find evidence that, on average, poor management practices are more prevalent when product market competition is weak and/or when family-owned firms pass management control down to the eldest sons (primogeniture). More recently, Maloney and Sarrias (2017) revisit this issue but using a global sample of countries and focusing on the entire distribution of management quality, not only the mean, as a way to identify which factors are correlated with managerial practices across the convergence path (towards the frontier country, the United States). They find evidence that confirms Bloom and Van Reenem’s result on the importance of ownership as a correlate to explain variation of managerial quality, particularly among less well-managed countries. As a new result, the authors find that human capital of the managers is a key factor driving differences in managerial quality across the whole distribution, particularly among better-managed countries. Overall, these two empirical regularities suggest there might be several distinct dynamics at work across the managerial quality convergence process. In this regard, evidence presented by Maloney and Sarrias (2017) shows that China has substantial space to improve in these two fronts: among Chinese surveyed firms used in their analysis, 54 percent have managers with a degree, against 86.3 percent in the United States and 96.2 percent in Japan; in addition, only 25 percent of the Chinese surveyed firms have a diffused ownership structure (with many shareholders) against 45.7 percent in the United States and 53 percent in Japan.

# **_4. Concluding remarks_**

The current paper followed the comprehensive approach about national innovation systems presented at Cirera and Maloney (2017) and shed light on key innovation patterns and constraints within a selected group of developing East Asian countries (Cambodia, China, Indonesia, Lao PDR, Malaysia, Myanmar
# **Appendix C: Self-reported technical and comprehension difficulties**

Table C1: Debriefing Survey on Technical Issues with ACASI Module

||(1)|(2)|
|---|---|---|
||Mean (=|1 if yes)|
||Malawi|Liberia|
|Was the audio loud enough to hear?|0.99|0.98|
|Was the audio speaking speed okay?|0.99|0.98|
|Observations|866|616|

Note: Questions were asked only to those in the ACASI treatment group (i.e., the FTFI group did not get these questions).

Table C2: Relationship between Reporting Technical Difficulties and Passing Screening

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||=1|if passed|SI screeni|ng<sup>a</sup>|
||Ma|lawi|Lib|eria|
|=1 if said:|||||
|audio loud enough to hear|0.203||-0.043||
||(0.143)||(0.127)||
|audio speaking speed okay||0.016||-0.044|
|||(0.220)||(0.127)|
|R-square|0.002|0.000|0.000|0.000|
|Outcome mean when said no|0.42|0.60|0.75|0.75|
|Observations|866|867|616|615|

Note: Standard errors clustered at individual level are in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively. a
` ` ́` ́` ́ .saesimpovmapyxvar[aw=hhw],area(localityid_n)uniqid(hhid)psu(thepsu)zvar(zvar)///
>eta(normal)epsilon(normal)varest(h3)lnypwcensus(p_hhsize_hh)///
>vce(ell)rep(100)seed(89546)ydump(`sfiles ́)res(`result ́)addvars(pline///
>pline2rdefrdef2rentdefrdef_rentroom2rdef_rentall2)///
>matin(`matadata ́)col(10)bootebeststage(second)aggids(0)indicators(fgt0)///
>plines(5291.52)
note:p_ageomittedbecauseofcollinearity Note:Dependentvariableinlogarithmicform WARNING:76observationsremovedduetolessthan3observationsinthecluster.
``` ``` OLSmodel: ```

|`lny`|`Coef.`|`Std. Err.`|`z`|`P>|z|`|`[95% Conf.`|`Interval]`|
|---|---|---|---|---|---|---|
|`dw_bath`|`.1397767`|`.0200267`|`6.98`|`0.000`|`.100525`|`.1790283`|
|`dw_btype_d3`|`.0496587`|`.0211568`|`2.35`|`0.019`|`.0081921`|`.0911253`|
|`edu_postsec`|`.0816588`|`.0883387`|`0.92`|`0.355`|`-.0914818`|`.2547994`|
|`edu_prim`|`-.8848284`|`.1744707`|`-5.07`|`0.000`|`-1.226785`|`-.5428722`|
|`j_firm_access1`|`.0382874`|`.0129973`
important for several reasons: First, proper valuation and classification of products is important in these countries because they rely to a much greater extent than industrial countries on tariffs as a source of budgetary revenue . **16** Second, the existence of regulations that address technical and other standards and the effectiveness of domestic institutions entrusted with the implementation of these regulatiois is of importance not only to the health of the population and the performance of firms but also to their capacity to export abroad. Weak domestic institutions and non-adherence to intemational standards can result in developing country exports being denied access to foreign markets.

The TPRs contain information regarding a number of institutions. But usually the inforniation is limited to the identification of the existence of a set of rules or regulations and the presence of an institution. For this reason, TPR discussions of these issues was supplemented with a revievw of the needs assessments for trade-related technical assistance prepared for the LDCs following the 1997 HLM. Even so, the institutional discussion presented below is much less systematic than the policy analyses in other parts of this study and a great deal more information is needed to make an assessment of the issues and constraints facing developing countries in this aspect of their integration into the multilateral trading system.

# I1. Customs Valuation, Preshipment Inspection

Developing countries are actively engaged in efforts to bring their customs valuation procedures into conformity with the WTO valuation system by the year 2000, as provided in the WTO Agreement on Customs Valuation. The TPRs typically reaffirm the commitments of authorities to meei: their obligations under the agreements by the stated timetables. In many countries this would involve significant changes because valuation of goods for customs purposes is frequently based on the Brussels definition of value. The issues here involve both the enactment of new legislation, changes in the relevant

> 1<sup>6</sup> Despite the fact that tariff regimes in developing countries are also more likely to contain exemptions.

40
Table 2. Effects of Guerrilla Territorial Control on Night Light Luminosity, Wealth, and Human Capital

||Night Light Arcsine|Wealth Index|Years of Education|
|---|---|---|---|
||(2013)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.186***|-0.121***|-0.279**|
||(0.0247)|(0.0355)|(0.109)|
|Observations|3,652|3,630|3,637|
|Bandwidth (Km)|2.266|2.266|2.266|
|Dependent mean|3.536|-0.0160|6.573|

_Notes:_ The table presents the results of estimating equation 1 for the main outcomes. Column 1 shows the effect of whether a census tract was under guerrilla control on the arcsine of night light luminosity from NOAA. Column 2 uses the standardized score of household wealth as dependent variable in the same estimation. Column 3 shows as dependent variable years of education of the population older than 18 years. The unit of observation in all columns is the census tract. Information from Columns 2 and 3 was obtained from the Population Census of 2007. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with an indicator of whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.
economically significant predictor of slower growth. If he made the same bet for growth fifteen years out (to 2002), it would be a closer call, though a better than fair bet. Beyond that, however, if he was greedy enough to ride the inequality horse in other dimensions, placing money on rising inequality as a marker of lower growth, it would be no better than a crap shoot. The preponderance of evidence we find suggests that inequality did not have a reliable causal impact on growth. We do not believe that higher inequality impeded growth in rural China.

This conclusion rests on several strands of evidence. First, by using household-level data we are able to rule out imperfect credit markets as the source of the causal relationship between observed inequality and growth. Instead, the results using household-level data point to institutional features of villages at the outset of reforms as underlying the correlation between inequality and growth. Important though those institutions may have been, whatever they were, there is little to suggest that they were a _causal_ consequence of inequality. This is most clearly seen when we exploit time-series variation of inequality within-villages (in the panel), and all links between growth and inequality disappear. Even if we keep the cross-section approach, but use village inequality from the 1990s as the basis of predicting growth, we find that inequality ceases to matter: inequality in the 1990s is different from inequality earlier in the reform period. Even focusing on 1987 inequality, the source of a safe bet for growth to 1997, we find that the effect of inequality fades by 2002.

We still learn something about the inequality-related determinants of growth in rural China. For example, our evidence points to the likelihood of growth-impeding polices that were associated with higher inequality. The policies seemed to trap their victims, especially the lower-educated in agriculture (a relative dead end during much of this period), and impeded their movement into more lucrative labor markets. We cannot be sure exactly what these institutions might be, but likely candidates include those
0.015)|
|Has savings|0.226|-0.069**|-0.030|0.189|-0.005|
|||(0.030)|(0.031)||(0.017)|
|Has loan|0.033|-0.010|0.017|0.036|-0.012|
|||(0.013)|(0.018)||(0.008)|
|Observations|871||2,591|1,900|2591|

_Notes._ Column 1 shows ELA only control arm means and column 4 shows control means for females not assigned to the _Goal_ arm. Columns 2–3 and column 5 shows the difference between the control group means and the treatment arm means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. Time spent studying is hours spent studying in the past day, Education aspiration is a binary indicator that the female aspires to a post-baccalaureate degree, Income generating activity is a binary indicator of being involved in an income generating activity in the past year, Personal business is a binary indicator that the female owns her own business, Has savings is a binary indicator that the female has savings. Has loan is a binary indicator that the female has taken out a loan. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

74
**_<u>________________________________________________________</u>_**

private and non-government organizations. It published a second report in May 2001, which evaluated over 50 emerging market economies’ position in the digital economy. A third report is under development with a target release in mid 2002.

_-_ _<u>Bridges: E Assessment Reports</u>_ Bridges provides a compendium of the tools from different sources available and those that are being developed for assessing e-readiness, along with their own analysis. The Reports include:

- _<u>Comparison of E-Readiness Assessment Models</u>_ **- describes the various tools that are available** and what they measure, including the tools' underlying goals and assumptions which shape their outcomes. With this report, we aim to foster informed decisions about approaches to e-readiness assessment, as national governments consider their IT policies and undertake development initiatives.

- _<u>Survey of Who is Doing What and Where</u>_ **- looks at where assessments have already been conducted in the world.** _Updated March 24, 2002._

- _<u>Overview of E-Readiness</u>_ - **bridges.org's overview of the e-readiness processes that was featured in Infodev's 2001 Annual Report** .

- _<u>Consultation report on e-readiness</u>_ **for the South African Development Community - World Economic Forum -** policies and recommendations for improving e-readiness in SADC countries, in a methodology that can be replicated in other areas of the world.

- _-_ -

- _<u>List of other e readiness resources and sample assessments</u>_ **a repository of online tools and resources** for e-readiness assessment and understanding the digital divide.

# _<u>NUA.com How Many Online?</u>_

Nua was founded in 1996. Nua Internet Surveys was acquired in June 2001 by the
# **I. Introduction**

What began as a financial crisis in developed countries led to a severe contraction in global output and trade. This turned the financial crisis into a crisis in the real economy, with serious impacts on workers and their families. The global economy, according to the World Bank, shrank by about 2.2 percent in 2009, from a 3.2 percent expansion in 2008—the first time the global economy has shrunk since World War II.<sup>1</sup> The World Bank estimated that 90 million more people would be living in poverty by the end of 2010 than would have been the case without the crisis.<sup>2</sup> Countries in Eastern Europe and Central Asia are the most adversely affected by the crisis and the growth slowdown was thought likely to shrink GDP by about 4.7 percent in 2009, from a 4.2 percent increase in 2008.

Latvia is one of the hardest hit countries in Eastern Europe; its GDP was projected to shrink by 18 percent in 2009. The objectives of this note are: (i) to estimate the distributional impact of the financial crisis on households in Latvia; and (ii) to assess the distributional impact of several policy reforms undertaken in response to the crisis. To do so, we use a methodology that assesses the impact of the growth slowdown or policy reform through its impact on the sources of household income. The impacts quantified are direct short-run impacts; hence, they do not take into account general equilibrium effects.<sup>3</sup> In most countries, measuring real-time impacts of financial crises or economic slowdowns on households is rarely possible due to delays associated with household surveys; consequently simulation tools are often used to analyze welfare impacts. However, among the genre of welfare simulation tools, considerable variation exists in methodology, data requirements, assumptions, and analyst time requirements.

A computable general equilibrium (CGE) model and a micro-simulation (MS)