rafmacalaba/data-use-mentions-extended huggingface dataset:
This rapid income growth led to a surge in the demand for home equipment and mobility, hence for infrastructure services such as water, electricity connections, and transport, and for related durables such as electric appliances and vehicles. Yet, as of 2012, access and ownership rates still vary widely, as can be seen in Figure 1, which graphs Latin American average access and ownership rates by deciles, as well as average rates in the worst- and best-performing countries of the sample respectively.<sup>7</sup> For water, Latin American averages, which go from around 71% in decile 1 to 98% at the top of the distribution, mask wide differences, from an abysmal 19% of the poorest decile households in El Salvador having access to a source of water in the house or lot (a measure akin to basic access as defined in the MDGs) to 99% for households of similar income in Argentina. Beyond El Salvador which could be considered an outlier, three countries, Bolivia, the Dominican Republic, and Peru, have barely more than half of the bottom decile households with access to water.

The situation is more critical for sanitation. In 2012, only 1% of households in the poorest decile lived in a dwelling connected to a public sewerage system in Paraguay. With the exception of Chile, where 82% of the poorest households are connected, no country has more than 46% connections, for a Latin American average of 35% in the poorest decile. The range narrows only slightly when moving up the income distribution. At the eighth decile, roughly the lower limit for middle class households according to Ferreira et al. (2013), average Latin American access rates are still only 72%, hovering below 80% for 9 out of 14 countries.

Electricity is clearly the dimension in which the region performs best. As of 2012, regional averages start at 89% in decile 1, and reach 99% from decile 6 up. At the bottom of
, Journal of Behavioral and Experimental Finance, Volume 17.

Fischer, Stanley, Ratna Sahay, and Carlos A. Vegh, 2002, “Modern Hyper- and High Inflations,” Journal of Economic Literature, Vol. 40 (September), pp. 837–80.

Garner T., McClelland R. and Passero W. (2009). Strengths and Weaknesses of the Consumer Expenditure Survey from a BLS Perspective. Unpublished manuscript, available from the Bureau of Labor Statistics, Washington, DC.

GoSS, 2011: South Sudan Development Strategy, 2011-13 – Realising freedom, equality, justice, peace and prosperity for all. Ministry of Finance and Economic Planning, Juba, August 2011.

He, L. (2017). Hyperinflation: A world history. Routledge.

Heltberg, R., N. Hossain and A. Reva. 2012. “Living Through Crises. How the Food, Fuel and Financial Shocks Affect the Poor.” World Bank.Washington, DC Jolliffe D., Seff I. and Fuente A. (2016). Food Insecurity and Rising Food Prices: What do we learn from experiential measures? Draft Manuscript.

15
One of the major limitations of approaches using the co-movement of prices is the lack of a theoretical foundation. A small body of empirical literature rejects the time-series approach on the abovementioned grounds and uses the Enke-Samuelson-Takayama-Judge spatial price equilibrium conditions as its starting point<sup>1</sup> . A seminal contribution is Baulch (1997a), which builds on earlier work from Sexton et al. (1991) and Spiller and Wood (1986). Following the competitive spatial price equilibrium conditions, Baulch (1997a) identifies three regimes in his PBM: in regime I, at the parity bounds, the price difference between two locations is exactly j k kj equal to the transaction cost (pt −pt = tct ); in regime II, inside the parity bounds, the price j k kj difference between two locations is lower than the transaction cost (pt −pt < tct ) and in regime III, outside the parity bounds, the price difference exceeds the transaction cost (ptj − k kj _kj_ pt > tct ), where _tc_ represents the transaction cost of trading goods from _k_ to _j_ . Regimes I and II are consistent with competitive spatial price equilibrium and market integration. In the absence of adequate transaction cost data, Baulch (1997a) uses the observed transaction costs of a single cross-sectional observation and extrapolates them to all periods and all locations. These regimes are characterized by price differences less the extrapolated transaction costs. A switching regression technique is used to estimate the probabilities of the three regimes under the assumption of normally distributed errors within the parity bounds and half normally distributed errors above and below the parity bounds. The PBM is a flexible tool to diagnose market integration that overcomes the limitations of price co-movement techniques.<sup>2</sup> Extensions have focused on relaxing the assumption on the distribution of errors (Barrett and Li, 2002), complementing the analysis with trade flow data (Barrett 2001; Barrett and Li 2002), and shifts in probabilities (Park et al. 2002; Negassa
027|
|||||(0.015)|(0.032)|(0.024)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|102056|102056|102056|102056|102056|102056|
|Firms|11764|11764|11764|11764|11764|11764|
|R-squared|0.121|0.118|0.072|0.121|0.119|0.076|

Notes: Authors’ calculations based on survey data from the Central Statistical Office of Poland. All regressions include firm and year fixed effects. Measures of Germany’s specialization refer to the period 1994-2004. TFPR is estimated using the method in Levinsohn and Petrin (2003). Border is a dummy variable that takes value 1 if a firm is in one of the three regions bordering Germany: Dolnoslaskie, Lubuskie and Zachodniopomorskie. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

Table A.11: Effects of EU accession on firm performance across sectors

|Dep. variable: Log TFPR|(1)<br>All|(2)<br>Low labor-intensity|(2)<br>High labor-intensity|
|---|---|---|---|
|Post 2000 # Exports share Ger (log)|0.017**|-0.003|0.028**|
||(0.008)|(0.015)|(0.011)|
|Pos 2004 # Exports share Ger (log)|0.083***|0.071*|0.090***|
||(0.015)|(0.032)|(0.024
hourly wages and full-time/part-time status for those currently employed.<sup>18</sup> We also differentiate between families with and without children. Table A3 of the appendix presents the sample size for each country.

**In the time usage section of module 4, a randomly selected individual reported time spent in activities during the previous day.**<sup>19</sup> For this paper, we clustered the section’s 40 detailed activities into three groups—leisure, work, and home production—and we report the time allocation between these three categories. Because the time usage section was administered to a randomly selected person inside the dwelling, the final number of husbands and wives who responded is less than the overall sample used in the labor characterization.

**Figure 1: Distribution of time usage by country**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>ALB MNE MKD SRB BIH KOS<br>Home production Leisure Work<br><!-- End of picture text --> _Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey. _Note:_ Each bar represents the average time allocation between home production, leisure, and work of husbands and wives on each labor market state.

**Figure 1 shows the time allocation profile for the marginalized Roma population, without differentiating by gender.** In Albania, husbands and wives spent more time working outside of household than in other RRS countries, about 6.6 percent of total activity time. In Bosnia and Herzegovina and Kosovo, husbands and wives spent less time working, about 3.2 percent and 2.6 percent of the total time, respectively. In terms of time spent in home production, Bosnia and Herzegovina, Montenegro, and North Macedonia reported 33.8 percent, 32.
a}.e or 2 oeCageSs Recmes=? ©<br>o Ss ° * ae &o® ¢ Ss «6<br>=<br>& to) e “8 = S<br>ee<br>6 8 10 12 6 wi a) 4 1<br>‘GDP per capita in 1970, log Gini of exports in 1970<br><!-- End of picture text -->

<!-- Start of picture text --> Source: Authors’ calculations based on data from<br><!-- End of picture text --> <!-- Start of picture text --> Penn World Table and UN COMTRADE.<br><!-- End of picture text --> Source: Authors’ calculations based on data from Penn World Table and UN COMTRADE.

Bringing both sets of literature together, this paper studies the patterns of economic diversification over the past 57 years. The contribution of this paper to the literature is fourfold. First, by taking advantage of longer and expanded data series, this paper updates our understanding of diversification patterns across countries and over time to 151 countries over 57 years. Second, it investigates whether the observed patterns are more likely driven by cross-country characteristics or by within-country trajectories. Third, we expand the study <!-- Start of picture text --> characteristics<br><!-- End of picture text --> <!-- Start of picture text --> or by<br><!-- End of picture text --> <!-- Start of picture text --> trajectories.<br><!-- End of picture text --> <!-- Start of picture text --> we expand the study<br><!-- End of picture text --> 2
, the IHS4 followed the traditional (i.e. business-as-usual) approach of interviewing the most knowledgeable household member(s) to provide information on household members’ ownership of and rights to the same set of assets.

The parallel implementation of the IHPS and the IHS4 offers an opportunity to assess the effects of conducting best-practice individual-level interviews vis-à-vis the business-as-usual approach on the measurement of ownership of and rights to agricultural land among adult household members. Overall, our findings support privately interviewing multiple household members. In the IHS4, 67 percent of women live in male-headed households, and 70 percent in the IHPS, reinforcing the importance of looking within households to better understand gender asset gaps.<sup>8</sup> Malawi is a unique context, where women’s land ownership often exceeds men’s ownership, due to strong matrilineal traditions where family land is passed through the female line. Simple comparisons reveal that women’s land ownership is, on the whole, higher than men’s in both the IHS4 and IHPS, although headship does matter — exclusive reported ownership and rights among non-headed women are significantly lower than for men in the IHS4, while these gaps close in the IHPS.

> 7 The plot-level data used by Kang et al. (2020) stem from the national surveys implemented in Ethiopia and Malawi, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMSISA), including the Malawi Fourth Integrated Household Survey (IHS4), which is in part the subject of our paper. 8 For men, this share was about 89 percent across both the IHS4 and the IHPS.

5
_ft|y_ 1 _, . . . , ys_ ; _ψ_<sup>_MLE_</sup> ) for _t, s_ = 1 _, . . . , T_ , where _ψ_<sup>_MLE_</sup> = _{α, ση_<sup>2</sup><sup>_}_containstheparameters</sup> that pertain to the distribution of the factor. Notice that Λ and Ωare estimated in the first stage. Moreover, the initial factor has density _N_ (0 _, P_ 1) where _P_ 1 = inv(1 _− αα_<sup>_′_</sup> ) and _εt ∼ NID_ (0 _,_ Ω) is the _N ×_ 1 disturbance term.

The estimation of the parameter vector _ψ_<sup>_MLE_</sup> is based upon maximizing the log-likelihood function associated with (1) and (4). Meanwhile the estimated factor, _ft_<sup>_MLE_</sup> , is obtained through a recursive procedure. Specifically, the log-likelihood function associated to the Gaussian density is given by where the quantities _υt_ and _Ft_ represent the prediction residuals ( _yt −_ Λ _at|s_ ) and the predicted variance (Λ _Pt|s_ Λ<sup>_′_</sup> + Ω), which are evaluated by the Kalman filter.

# **4.2 Expanding the series using air quality information**

In this section we propose a simple methodology for expanding the “Google Mobility Index”obtained in the previous section by using air quality-related information. Specifically, we use the Air Quality Open Data Platform for extracting temperature and fine particulate matter (PM2.5) information per city in each country worldwide. Then, the information is averaged per country such as it can be easily associated with the Google Mobility Index over time.

Let’s consider _pj_ and _qj_ for _j_ = _t−J, . . . , t, . . . , T_
the period from January 2010 to April 2021. The shortest panel is the one for Kenya, covering from October 2018 to January 2022.

Average origin prices, price gaps between origin and destination locations, and distances, are reported in Table 2.

Table 1: Main features and coverage of the data

|Country|No. of products|Origins|Destinations|Type of locations|Start period|End period|
|---|---|---|---|---|---|---|
|Kenya|20|10|32|Cities|Oct 2018|Jan 2022|
|Madagascar|9|3|7|Major cities|Jan 2010|Apr 2021|
|Nigeria|25|5|38|Cities|Jan 2001|Jul 2010|
|Rwanda|43|4|13|Districts|Jan 2013|Dec 2020|
|Tanzania|32|5|20|Cities|Jan 2012|Apr 2021|
|Georgia|21|4|6|Cities|Jan 2012|Dec 2020|

Notes: this table describes the main features of the datasets for each country in our sample. Source: authors’ elaboration based on CPI data from countries’ NSO.

> 3Our main sources are Logcluster, a wiki with detailed logistics information about each country, and World Port Source.

> 4As discussed in section 3.2, estimation of pass-through rates relies on variation of prices over time for each product-destination pair.

> 5The data underlying the maps are taken from the Global Roads Inventory Project (GRIP), available at `https://www.globio.info/download-grip-dataset` .

5
Policy Research Working Paper 10408

# **Abstract**

Existing research points to a possible link between slow-onset symptoms of climate change and migration. It is also known that rates of urbanization are fastest in some of the world’s poorest countries, which are incidentally also at greater risk of climate-induced migration. These separate findings suggest that slow-onset climate phenomena such as droughts have likely become a key driver of urbanization across much of the developing world. While intuitive, this link has not been convincingly established by extant research. This study examines the climate-urbanization nexus by constructing a novel measure of urban growth that uses remotely sensed information from the World Settlement Footprint dataset. Relying on panel data that cover the entire globe between 1985 and 2014, the paper shows that drought leads to faster urban growth. The results indicate that a hypothetical drought lasting 12 months is associated with a 27 percent increase in the average annual increment of built-up area. The paper leverages novel data from several Sahelian cities to illustrate that much of this growth takes the form of non-infill development that extends outward from previously built-up localities.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice and the Water Global Practice.. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vchlouba@nd.edu, mmukim@worldbank.org, ezaveri@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors
Respondents face several challenges in the preparation of a sovereign balance sheet. They cited the measurement of asset values (44 percent) as a common challenge followed by data availability (28 percent). Other countries lack a specific mandate (24 percent) or a conceptual framework (20 percent) for developing a more integrated approach to sovereign ALM. Lack of high level political commitment and technical implementation issues (such as IT issues) were also mentioned in a few cases (see graph 8).

<!-- Start of picture text --> Graph 8 ‐ Challenges in developing SALM<br>12<br>11<br>10<br>8<br>7<br>6<br>6<br>5<br>4<br>3<br>2<br>0<br>Data availability Measurement of Conceptual Mandate Other<br>assets framework<br><!-- End of picture text --> Source: Responses to SALM survey The priorities most commonly mentioned for further developing a sovereign ALM are the more fundamental components, such as the institutional framework (36 percent), building capacity to perform the analysis (32 percent) and improving measurement (28 percent). Fewer respondents mentioned expanding the perimeter of assets or liabilities currently covered (12 percent and 20 percent respectively), as well as improving the compilation of data (8 percent). One respondent added the impact of the realization of contingent liabilities on the balance sheet (see graph 9).

28
schooling effect in the United States (see, for example, Neal, 1997, and Figlio and Ludwig, 2000). The importance of availability of private schools in schooling choices has been demonstrated in the developing country context (see, for example, Alderman _et al_ ., 2001).

Data on the presence of public and private schools are available at both the district and the village level. These two measures are moderately correlated, with a correlation coefficient of 0.33. Using district-level information, we measure the percentage of schools that are public in the district of junior high school attendance. For the sample of students from the 321 original IFLS communities, we can measure the share of public schools within 25 miles of the village center. This latter specification can only be estimated on the sample of students that were interviewed in the same sub-district where they attended junior secondary school.<sup>12</sup> The consistency of the two stage least squares estimate is based on the critical assumption that local private school proximity is uncorrelated with unobserved determinants of student test scores. This assumption has been questioned in the US context, where evidence suggests that proximity to catholic high schools is correlated with unobserved determinants of 12<sup>th</sup> grade math and reading test scores (Altonji, Elders, and Taber 2002). However, that conclusion is largely based on the implausibly large differences between OLS and 2SLS estimates of the effect of Catholic schooling on scores, which we do not find in the Indonesia data. Also, the location of American

> included in these sub-samples.

> 12 In principle, considering only students that remained in the same sub-district that that they attended junior secondary school could cause selection bias. To probe this, we utilized the fact that student’s test scores are ranked based on their deviations from annual means, and estimated a Heckman two step model excluding the year the test was taken from the test score equation. The estimated public school premium remained essentially the same in
trademarks, another TRIPS-Plus standard. Finally, the agreement set out minimum protection of geographical names through systems quite similar to those covering trademarks and collective marks. Trade agreements negotiated by the EU and EFTA in this era had similar requirements, though they emphasized somewhat different areas of concern, most prominently in establishing specific systems for recognizing and protecting GIs.

NAFTA was preceded by the formation of the European Economic Area (EEA) in 1994, linking three members of EFTA to the EU’s single-market program. Over time, an essential component of policy making in the EEA has been the adoption of stronger and more harmonized intellectual property standards throughout the region.

The subject matter coverage of PTAs sharply increased after 2000, with the United States and the EU demanding stronger and more comprehensive IPR provisions. Moreover, other new PTAs, not involving the United States or the EU, were negotiated by Japan, Australia, Korea, Mexico, and Chile, among others. These PTAs also included IPRs chapters, though typically with less rigorous standards in certain areas.

It is important to note that most of these IP-related PTAs are legally enforceable, as designated in the World Bank data. Specifically, the database ranks the legal enforceability (LE) of PTAs on a scale of 0, 1, or 2, with 0 essentially indicating that no enforcement language exists, 1 describing weak enforcement language (e.g., “should” or “may”), and 2 identifying those with clear commitment language (e.g., “shall” or “must”). We use this designation (LE = 2) as one means of selecting the treatment IPAs in the econometric analysis.

Figure 1 illustrates the considerable growth since 1990 in the number of IPAs in the World Bank data that feature strong enforceability.<sup>2</sup> The red lines indicate the number of agreements, and the blue lines show the number of countries in one or more of them.<sup>3</sup>
Using monthly survey data from Consensus Economics for a sample of 22 EMDEs and 14 advanced economies in a structural vector autoregressive model, Davis (2014) finds evidence that the introduction of inflation targeting is associated with a statistically significant reduction in the response of 12-month-ahead inflation expectations to shocks in both oil prices and observed inflation. Using market-based measures of inflation expectations for Brazil, Chile, and Mexico, De Pooter et al. (2014) document that long-term inflation expectations became better anchored in these countries over the preceding decade, especially in Chile and Mexico. Although they do not specifically test for the role of inflation targeting, they ascribe this result to recent improvements in the credibility of these countries’ central banks.

IMF (2016) estimates a hybrid New Keynesian Phillips curve using data from a large sample of countries (24 advanced economies and 20 EMDEs). It reports that although the coefficient on lagged inflation (backward-looking expectations) started declining in the early 2000s, there was a reversal in this trend in the aftermath of the Great Recession, with the coefficient returning close to its value in the early 1990s. This study also finds that the sensitivity of inflation expectations to macroeconomic news (proxied by the difference between expected and realized inflation) is negatively correlated with standard measures of central bank independence and transparency, and that expectations become

> 17 Strohsal and Winkelmann (2015) examine the anchoring of inflation expectations, as well as the sensitivity to news shocks, using a sample of four advanced economies. They find that the degree of anchoring did not change during the crisis.

9