rafmacalaba/data-use-mentions-extended huggingface dataset:
_ Monetary values are expressed in Peruvian sol in 2017 prices using CPIs of the WDI database.

# **Propensity Score Matching Results**

The conditional independence assumption requires that the selection be solely based on observables. Any differences in outcomes between treated and control individuals with the same values of observable characteristics are thus ascribable to treatment. The analysis is guided by the hedonic theory of consumption, and the set of covariates includes the following: quartiles of the total number of rooms; a dummy for dwellings with external walls made of low-quality materials, including clay, canes and mud, stones and mud, wood, or rug; a dummy for dwellings with floors made of low-quality materials, including dirt; a dummy for dwellings with roofs made of low-quality materials, including cane and rug with mud, rug, or straw and palm leaves; a dummy for public water network access inside the dwelling or outside the dwelling, but inside the building; a dummy for access to electricity; a dummy for public sewerage access inside the dwelling or outside the dwelling, but inside the building; a set of five dummies for dwelling type, that is, detached house, apartment in a building, apartment in a detached house with independent water access and sewerage, apartment in a detached house with shared water services, and a hut or other type of dwelling; and a set of three dummies capturing geographical location outside Metropolitan Lima (the Costa, Selva, or Sierra). A logit regression of the probability of being a homeowner is estimated over the covariates listed above separately for each year and for Metropolitan Lima and rural areas.

14
|Figure 18. Niger’s Internet market compares poorly with other ECOWAS countries|34|
|---|---|
|Figure 19. Niger’s infrastructure spending needs in the regional context|36|
|Figure 20. Niger’s existing infrastructure spending is relatively low|37|
|Figure 21. Patterns of capital investment in infrastructure, benchmarked against comparator countries|38|
|Figure 22. Underpricing of water in Niger is comparatively less burdensome|39|
|Figure 23. Consumption of infrastructure services in Niger varies by income quintile|39|
|Figure 24. Water subsidies that reach the poor|40|
|Figure 25. Affordability in Niger in comparison to other low-income countries|41|
|Figure 26. Niger’s power and water utilities burden of inefficiency|41|
|Figure 27. Niger has captured relatively low private sector participation|42|

# List of tables

|Table 1. The achievements and challenges of Niger’s infrastructure sectors|8|
|---|---|
|Table 2. Trading across borders in West African countries|10|
|Table 3. West African port indicators|11|
|Table 4. Niger’s international trade|12|
|Table 5. Road conditions along major transit ECOWAS corridors that run through Niger|13|
|Table 6. Niger’s road indicators|14|
|Table 7. Railway indicators for OCBN and select other railways|16|
|Table 8. Benchmarking air transport indicators for Niger and select other countries|18|
|Table 9. Niger's irrigation potential|21|
|Table 10. Benchmarking water and sanitation indicators|23|
|Table 11. Evolution of operational indicators associated with SPEN, Niger|24|
|Table 12. Power indicators, benchmarked against select country groups|28|
|Table 13. High fuel prices escalate domestic power production costs|29|
|Table 14. Benchmarking ICT indicators|31|
|Table 15
cosine function is pursued. The simplest two parameter trigonometric seasonality representation is: where the seasonal amplitude  2 2 and  tan<sup></sup> 1<sup></sup>    measures the phase of the    seasonal cycle, i.e. the location of the peak and trough. The analogue of the seasonal gap is 2λ but since ω will not in general refer to an integer number of months, this quantity will typically slightly exceed the gap as measured by equation (4). Although least squares estimates of  and β are unbiased, λ and ω are nonlinear functions of these unbiased estimates and are therefore potentially biased.

In conjunction with a stochastic trend, the seasonal parameters λ and ω may be estimated from the linear regression: There is no requirement to interpolate over missing data.

Columns 3 and 4 of Table 1 repeat the Monte Carlo experiments using the trigonometric estimator defined by equation (5). The price data are exactly the same as those used in analyzing the dummy variables. For all sample lengths, the bias in the estimates of the seasonal gap is reduced by just under 40 percent relative to the dummy variables method. The degree of “explanation” provided by seasonality is reduced even more substantially. This gives some confidence that the trigonometric specification provides a more reliable means of representing seasonal patterns on short data samples.

# _Seasonality in consumption and its link with prices_

In principle, similar procedures can be followed to examine seasonality in food consumption. Ideally, as with prices, this requires long time series data on daily or monthly food consumption 9
(immediately after the inception of the crisis) to early 1998.1 By contrast, Dollar and Hallward-Driemeier (1998) found little evidence of a credit crunch in Thailand-in the sense of firms with orders but unable to get credit to finance working capital needs. The evidence that they gathered suggests, on the contrary, that constraining factors on output expansion appeared to have been weak perceived demand and the sharp increase in the price of imported inputs resulting from the large nominal exchange rate depreciation that occurred in the months following the crisis.<sup>2</sup> Along the same lines, DworFRecaut, Hallward-Driemeier, and Colaco (1999) analyzed the results of a survey conducted between November 1998 and February 1999 in Indonesia, Korea, Malaysia, the Philippines, and Thailand of 3,700 firms, covering major manufacturing and export sectors. They found that at prevailing interest rates, credit availability in the aggregate was not seen by the surveyed firms as a major constraint on production plans. They concluded therefore that the fall in credit was demand-driven.

Because survey evidence can be seriously biased-for instance, tight credit can reduce demand, and lack of demand may be perceived as the main proW lem by respondents-several other studies have relied on other methodologies. Domac and Ferri (1999b), for instance, used a conceptual framework based on the "credit view" of monetary policy to analyze the existence of a credit crunch in Indonesia, Korea, Malaysia, the Philippines, and Thailand.<sup>3</sup> 'As noted by Ito and Pereira da Silva (1999), if the nominal credit stock is deflated by a composite index consisting of consumer prices and the nominal exchange rate (in order to account for the fact that a significant share of the loans provided by Thai commercial banks and finance companies just before the crisis were denominated in foreign currency and the book value of these loans was recorded at the current exchange rate), the deceleration in the rate of growth of real credit to the private sector
for SSA. During 2005-2011, DRC appeared to have improved, with a market concentration index of 0.36, but in more recent years (2012-2013), concentration has increased again. This seems to coincide with the rise of mineral exports, which account for three-quarters of DRC’s exports, since the mid-2000s. However, a longer historical perspective shows that DRC’s market concentration index has not changed when compared to the 1990s. In fact, the average index of the 1990s was 0.57, which is exactly the same as in 2013. This shows DRC’s long-term reliance on few market destinations. In addition, DRC heavily relies on few export commodities, as reflected by its long-standing product concentration index, which

> 1 Herfindahl-Hirschman market (product) concentration index is a measure of the dispersion of trade value across an exporter’s partners (products). The value of the index ranges between zero and one. A country with a preponderance of trade value concentrated in a very few markets (products) will have an index value close to one.

5
Curtis, V., S. Cairncross, and R. Yonli. 2000. _Tropical Medicine and International Health_ 5 (1), 22–32, January.

Curtis, V., and S. Cairncross. 2003. Effect of washing hands with soap on diarrhea risk in the community: a systematic review. _The Lancet Infectious Diseases_ 3 (5), 275–281, May.

Engle, P., M. Black, J. Behrman, M. Cabral de Mello, P. Gertler, L. Kapiriri, R. Martorell, and M. Young. 2007. Strategies to avoid the loss of developmental potential among 200 million children in the developing world. _The Lancet_ 329, 229–240.

Esrey, S., J. Potash, L. Roberts, and C. Shiff. 1991. Effects of improved water supply and sanitation on ascariasis, diarrhea, dracunculiasis, hookworm infection, schistosomiasis and trachoma. _Bulletin of the World Health Organization_ 69, 609–621.

Faix, R. 1987. Comparative efficacy of handwashing agents against cytomegalovirus. _Infection Control_ 8, 158–162.

Favin, M. 2011. _Endline Assessment of the Enabling Environment in Peru_ . Water and Sanitation Program, World Bank, August 2010.

Galiani, S., P. Gertler, and E. Schargrodsky. 2005. Water for life: The impact of the privatization of water services on child mortality. _Journal of Political Economy_ 113(1), 83– 120, February.

Galiani, S., and A. Orsola-Vidal. 2010. _Scaling Up Handwashing Behavior: Findings from the Impact Evaluation Baseline Survey in Peru_ . Water and Sanitation Program, World Bank, August 2010.

Gibson, L., J. Rose, C. Haas, C. Gerba, and P. Rusin. 2002. Quantitative assessment of risk reduction from hand washing with antibacterial soaps. _Journal of Applied Microbiology_ 92, 136S–143S.

Hendley, J., R. Wenzel, and J. Gwaltney
Policy Research Working Paper 7711

# **Abstract**

Deepening real and financial integration of developing countries into the world economy has prompted renewed interest in the contribution of external shocks to their macroeconomic fluctuations. This paper revisits the issue using four decades of annual data for a large sample of developing countries. The paper implements a conditionally-homogeneous panel vector autoregression with exogenous variables to model GDP fluctuations in these countries. It uses sign restrictions to identify four external structural shocks— demand, supply, monetary, and commodity shocks—and analyzes how their impact on growth is shaped by countries’ policy and structural framework. External shocks are found to account for a small share of the forecast error variance of GDP, especially at short horizons. However, their contribution has been on the rise in recent decades. Further, global monetary shocks have become the leading external source of GDP volatility in developing countries. The paper presents a quantitative assessment of the effects of real and financial opening up, as well as those of commodity specialization, on the impact of external shocks on GDP. The results suggest that increasing openness can account for the increasing trend in the volatility attributable to external shocks, as well as the changing roles of different shocks. Moreover, commodity-intensive developing countries are found to be more vulnerable than the rest to all types of external shocks, not just commodity shocks.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at lserven@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
algorithms to provide scalable analyses of regional growth (Lessmann and Seidel 2017). The question of the inverted-U relationship between the level of spatial inequality in a country and the country’s level of economic development is revisited through an expanded cross-section of regions made possible through the use of remote sensing data on night-time lights. The approach is to first estimate a model relating regional incomes to luminosity for countries with established regional accounts. This relationship is then employed to generate estimates for income in regions in developing 45
settlement.<sup>26</sup> Thus, this selection concern mainly comes from the choice of the government on where to locate the refugee settlement sites. If this choice, which by default is correlated with refugee intensity, creates other factors that are correlated with the outcome variable(s), the analyses should deal with them. Equation (1) includes household-specific (EA-specific) fixed effects that effectively eliminate such factors that are time-invariant.

This leaves me with the potential concern of omitted variable bias from time-varying factors. Absent such factors, the outcome variables in places that experienced high and low temporal variations in refugee intensity during the study period should have followed parallel trends if there was no difference in temporal variations in refugee intensity between them. As the existence of such parallel trends cannot naturally be tested directly, ideally, I would want to give indirect (suggestive) evidence, such as showing its existence going back in time to when there was no refugee in the country. I am not aware of any panel data on household welfare during early times, but there is repeated cross-section information on agricultural income since the 1990s from the Agricultural Sample Survey of Ethiopia. Nevertheless, I cannot provide such suggestive evidence using these data from the 1990s since there was a temporally and spatially varying degree of refugee intensity in Ethiopia owing to its openness to refugees during these times and way before that.<sup>27</sup> However, I partially circumvent this concern by including many (12) household- or zone-level (plausibly predetermined) time-varying control variables ) in equation (1). I discuss and conduct tests to falsify and employ alternative empirical strategy to allay the concern hvv (XX from other potential time-varying variables in sections 7.4, 7.5, and 7.6.

# **5. Descriptive Statistics**

This section presents the summary statistics of the main variables, followed by a description of the association between the two household welfare metrics.

Table 1 presents aggregate summary statistics for
extensively estimated the relationship between “technology shocks” and total hours worked in the United States and some European economies, finding a negative impact on total hours worked in the first year following the shock (Gal ́ı, 1999; Francis and Ramey, 2005; Francis et al., 2014).<sup>4</sup> In this paper, innovations in technology (“technology shocks”) are identified as shocks that drive the largest proportion of long-run developments in labor productivity, while other shocks are assumed to have only transitory effects. Therefore, technology shocks represent any structural development that results in lasting changes in labor productivity. These innovations could boost labor productivity through higher technical efficiency, in the sense of higher TFP in a standard Cobb-Douglas framework. Equally, new technologies may allow for the replacement of tasks previously completed using manual labor with capital, or “robot” inputs, altering the ratio of labor and capital inputs, without necessarily boosting the overall efficiency with which capital and labor are combined (Acemoglu and Restrepo, 2018). In both cases, output per unit of labor input is increased. The identification may capture a mix between labor-augmenting and labor-displacing technologies.

Traditionally, long-run restrictions have been used to identify technology shocks. However, longrun restrictions are susceptible to significant bias from the presence of non-technology shocks which can also drive developments in productivity (Chari et al., 2009; Dieppe et al., 2019, 2021). Instead, the Spectral identification, detailed below, is more robust, particularly when applied to volatile emerging and developing market economy data.

# **3.1 Spectral identification of technology shocks**

The Spectral identification searches for the shock which maximizes the contribution to the variance of productivity at long-run frequencies. This abstracts from the shocks which may instead drive variation in labor productivity at business-cycle frequencies, such as demand and other short-run impact shocks. This approach effectively applies a band-pass filter to the reduced-form coefficients of a VAR containing macroeconomic variables, identifying the spectral density
health services (such as weight checks for young children and prenatal care). There was also a significant (10 percent) reduction in chronic malnutrition. The evaluation found that making the grants conditional on performance improved health outcomes but not education outcomes. There are several hypotheses to explain this. First, improvements in health indicators could be easier to achieve than those in education, perhaps because health indicators were lower at baseline than education indicators. Second, education gains may require more people and long-term efforts (regular attendance at school). It is also possible that perhaps the fact that communities provided education support to almost all children, instead of just those out of school, minimized the effect of the program (Olken, Junko, & Wong, 2011).

A number of lessons from Generasi may apply to the INDH. Having the established architecture and machinery of a CDD approach provided a strong foundation for Generasi. The program was more effective where baseline levels were low, so priority areas should be selected based on low indicators. In addition, performance incentives worked better to affect health indicators. It is important to ensure that the incentive scheme is easy for communities to understand. Furthermore, targets should regularly be reviewed and updated to reflect priority areas for improvement. Given the results and lessons learned from Indonesia, the following design modifications could enhance the impact of the INDH on ECD outcomes for the poorest Moroccan children.

First, in addition to targeting poor areas using the _Haut Commissariat au Plan_ (Higher Planning Commission, HCP) poverty map, the INDH could also target using a nutrition map showing anthropometric data at a commune or other disaggregated level.

Second, the INDH could create a specific program focused on ECD and include a menu of options for community-level ECD services, especially those related to nutrition (for pregnant women and children who are under the age of two) and early childhood care and education services (for children under six years of age). These could include building or revamping health centers, creating community child development
# **1. Introduction**

The interest rate channel of monetary policy is one important transmission channel through which monetary policy affects the real economy. The strength of the pass-through from the monetary policy rate to bank lending rates reflects the effectiveness of monetary policy and how the policy can help manage economic activity and stabilize prices. Central bankers are thus greatly interested in understanding the extent to which changes in the monetary policy rate transmit to lending rates and the factors that affect this transmission.

The interest rate pass-through is a keenly studied economic phenomenon. Because of imperfect information, competition, and other frictions, the pass-through from the monetary policy rate to various lending rates is, in practice, typically incomplete. The broader economic environment and various macro-financial factors may play an important role in different country contexts. Several recent studies summarize the empirical research on the interest rate pass-through. For instance, Sorensen and Werner (2006) survey 22 papers, covering aspects such as the type of data, econometric approach, reference rate, and time period. Andries and Billon (2016) review the empirical literature by comparing methodological approaches across studies and their implications for the estimated pass-through. Several papers also provide insights into the pass-through process from specific perspectives. For instance, Marotta (2009) reviews the literature on short-term business lending rates. Aziakpono and Wilson (2013) focus on the possible asymmetry in the interest rate pass-through, and Grigoli and Mota (2017) survey the pass-through studies for the Dominican Republic. Overall, these literature reviews confirm the heterogeneity in the interest rate pass-through across country contexts and lending rates. They suggest that the transmission of changes in the monetary policy rate to corporate lending rates may be the strongest, while the one to consumer lending rates may be the weakest.<sup>1</sup> Our paper contributes to the literature by conducting, to our knowledge, the first quantitative survey (a meta-analysis) of the empirical literature on the interest rate pass-through. In our meta-analysis, we

> 1 The literature also
Policy Research Working Paper 9321

# **Abstract**

This paper explores the spillover of protests across countries using data on nonviolent and spontaneous demonstrations for 200 countries from 2000 to 2020. Using an autoregressive spatial model, the analysis finds strong evidence of “contagious protests,” with a catalyzing role of social media.

In particular, social media penetration in the source and destination of protests leads to protest spillovers between countries. There is evidence of parallel learning between streets of nations alongside the already documented learning between governments.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 rarezki@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. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team