rafmacalaba/data-use-mentions-extended huggingface dataset:
## **Figure 14. People in developing economies of Europe and Central Asia tend to rely on family and friends for emergency funds**

<!-- Start of picture text --> 80%<br>60%<br>40%<br>20%<br>0%<br>Other<br>Family and friends<br>Borrowing from a financial institution, employer, or private lender<br>Money from working<br>Savings<br>Percentage of people who raised emergency funds<br><!-- End of picture text -->

### _Source_ : Global Findex database.

_Note:_ The height of the bars represents the percentage of adults aged 15 and above that report being able to come up with the equivalent of 5 percent of Gross National Income per capita in a month.

# Digital Technology Provides Opportunities to Enhance Financial Inclusion

Remarkable progress has been made in expanding financial inclusion in ECA. Account ownership has grown significantly in many countries, and the use of digital payments has picked up. There may be opportunities to enhance financial inclusion and encourage more account owners to use financial services (see box 2, on financial inclusion in the European Union). However, the behavior of account owners in ECA differs from that in other economies, because of issues related to culture, history, and trust.

In some ECA countries, such as Russia and Turkey, the application of digital technologies in financial services has increased. In other countries, such application remains limited. There may be room for growth in the use of digital financial services, such as electronic payments and services associated with e‐commerce.

25
I POLICY RESEARCH WORKING PAPER 2313

# Summary findings

Financial liberalization was expected to make interest rates and asset prices more volatile, with distributional consequences such as reduced or relocated rents and increased competition in financial services. Honohan examines available data on money market and bank interest rates for evidence of whether these things happened.

He shows that as more and more countries liberalized, the level and dynamic behavior of developing-country interest rates converged to industrial-country norms. In the short term, volatility increased in both real and nominal money market interest rates. Treasury bill rates and bank spreads, evidently the most repressed, showed

- the greatest increase as liberalization progressed shifting substantial rents from the public sector and from favored borrowers.

Whereas quoted bank spreads in industrial countries contracted somewhat in the late 1990s, spreads in developing countries remained much higher, presumably reflecting both market power and the higher risks of lending in the developing world.

There was no clear-cut change in mean rates of inflation, monetary depth, or GDP growth. If anything, there was a small average improvement in inflation, but a decline in monetary depth and economic growth, relative to trends in industrial countries.

- - This paper a product of Finance, Development Research Group is part of a larger effort in the group to explore optimal policy under financial liberalization. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Agnes Yaptenco, room MC3-446, telephone 202-473-1823, fax 202-522-1155, email address ayaptenco@worldbank.org. Policy Research Working Papers are also posted on the Web atwww.worldbank.org/ research/workingpapers. The author may be contacted at phonohan@worldbank.org. April 2000. (48 pages)

_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
already explaining much of the impact from soil quality. Increased labor (including non-wage labor or unpaid family members) is also positively associated with higher rice yields, and this is particularly true in larger farms (quintiles 4 and 5), where the effect is greater in magnitude and significance. As expected the findings show that the effect on rice yields from using diverse inputs varied between small and larger farmers.<sup>14</sup> Further research is required to capture the relationship between irrigation and rice yields. While the impact from the dummy for irrigation is positive and significant (and greater in magnitude for the larger farmers), the survey data did not allow capturing factors such as: maintenance, the quality of the irrigation network, or the extent to which existing infrastructure is operational, which are the true drivers of the relationship with yields. Therefore, further research may shed light on the impact of these factors in determining rice production.<sup>15</sup> Not surprisingly, the urea price is negatively associated with urea use, although there are significant differences by farm size (Table 4.3). The consumption of urea in the smallest farmers (quintile 1) is the least affected by the urea price. However, urea consumption of the largest farmers mostly off-Java (quintile 5) is highly sensitive to changes in fertilizer subsidies, as well as quintile 2 farmers that are mostly composed of Javanese farmers.

> 13 The fact that the dummy for Java-Bali is not significant in the quintile 5 suggests that for these larger farms, which are mostly located off-Java, soil quality is not a driver of yields and that soil characteristics are more homogenous.

> 14 One production input that could be controlled for is capital inputs. There is limited information from both surveys to control for investments that farmers may be doing in their fields.

> 15 There may be measurement error in the irrigation variable. The surveys show that about 50 percent of all rice farmers grow rice in irrigated fields ( _sawah_
19 better governance and less corruption in public food provision (Besley and Burgess 2002), and rural educational spending (Reinikka and Svensson 2005).

Mass media can also play an important role in agricultural policy by altering the landscape of political competition. As explained above, the literature on the political economy of agricultural policy suggests that group size (e.g., the number of farmers versus the number of food consumers in the economy) helps determine lobbying effectiveness (Rausser, Swinnen, and Zusman 2011). Mass media alter these political-economy mechanisms between group size and political mobilization by providing more information to larger groups (Kuzyk and McCluskey 2006; Oberholzer-Gee and Waldfogel 2005). Strömberg (2001; 2004a) refers to this outcome as “mass-media competition-induced political bias.” He also shows that mass media bias their information toward groups which are more attractive to advertisers. Typically, this bias benefits urban interests over rural ones.<sup>26</sup> Following Strömberg’s (2004a) theory, Olper and Swinnen (2009) argue that mass media will increasingly weaken the political power of small groups (in rich countries, farmers; in poor countries, consumers) and reinforce that of large groups and groups attractive to advertisers (in rich countries, consumers and urban interests; in poor countries, farmers). Thus, a nonlinear effect of mass media on agricultural policies favors rural interests (and thus higher RRAs) in poor countries but urban interests (and thus lower RRAs) in rich countries. Using the World Bank data set, Olper and Swinnen analyze empirically whether there is evidence of such an effect of mass media on global agricultural and food policies. They find that mass media does indeed have a substantive impact on food policy.

# _3.2.5. Structural-Adjustment Programs and Policy Conditionality_

Another key issue is the impact of international financial institutions (such as the World Bank and the International Monetary Fund) and the policy conditions they impose on developing countries as part of their lending. The
They also found that the regulators often aggressively target their discretionary inspections, to the degree that half of the plants receive fewer than one inspection per year, while plants expected to be the dirtiest may end up receiving up to ten or more. More importantly, they found that discretion in targeting helps enforcement. Inspections that the regulator assigns cause almost three times more abatement than would the same number of randomly assigned inspections. At the same time, the regulator’s information on plant pollution is often found to be poor, and improvements in monitoring can significantly reduce emissions.

In this paper, a unique firm-level data set from India is used to compare and analyze the compliance levels of firms that are inspected versus the ones that self-report. Attempt is also made to identify other factors that may be influencing compliance levels such as size, sector, age, location, etc. The main objective of the paper is to broadly examine the drivers of compliance of firms with regards to environmental regulations. The analysis is based on unique information available on compliance as well as firm and location characteristics of 150 firms and inspection data for 5 years. As India is increasingly moving to encouraging self-reporting, the paper also compares the measurements of air and water pollution to see how these measures differ as reported in the inspection data and when they are self-reported by the firms.

While enforcement is seen as crucial for environmental quality, it is often found to be ineffective and wanting. Regulators are quick to blame resource constraints for not being able to carry out what is on their books. Others offer less charitable explanations, for example, that regulators with wide discretion choose not to enforce the given standards due to corruption, laziness, or incompetence (Stigler (1971), Leaver (2009)). In 1996, the High Court of Gujarat ordered the Gujarat State Pollution Control Board (GPCB) to install a third-party audit system wherein plants from polluting sectors must provide an annual audit report to GPCB. Duflo et al. (2013) evaluated a reform
short and medium term deposits divided by total assets. Funding from core deposits is likely to be cheap. A high ratio would indicate that banks do not need to rely on purchased funds. As noted by Demirguc-Kunt and Huizinga (1988), this may require a large branch network. As economic growth can affect bank performance we control for this, and include annual rate of economic growth as one of the regressors. INF represents annual inflation rate to take its possible impact on bank performance. As real interest rates fluctuated significantly, and they have been high (ex-post), we control for this variable. It is it calculated as: (1 +nominal interest rate/l+inflation rate)-lX100). Nominal interest rate is the annualized rate on three-month government paper and this data was obtained from the Central Bank, as well as the inflation data. We control for market structure by including HHI in the equations. We also create another variable to test whether banks with large branch networks have market power. This variable is BRA ( branch share of each bank in total branches).

# **V.** RESULTS

All three performance measures are estimated using panel data estimation methods. We first tested whether explanatory power was improved by having bankspecific dummy variables. Once this was tested, we applied the Hausman test to determine whether fixed effects or random effects estimation would produce greater efficiency. We first focused on net interest margins and the results are presented in Table 5. In the first column we investigate whether foreign bank ownership is an important determinant of this performance indicator. The foreign bank dummy variable is significant which indicates that foreign ownership matters. The number share is not significant, however. Among variables of interest, it is worth noting DA, which is 13
The findings of the study will have implications for the 2019 Ethiopian proclamation which repeals the 2004 refugee law to grant all refugees in Ethiopia the right to work and free movement, among others. The proclamation could lead to intense competition between refugee and local communities for work and other services and consequently depress the observed positive effects on host communities in most refugee hosting regions. The effect of the policy could be larger in major refugee hosting communities in Ethiopia, particularly in Gambella region, where the number of refugees is as large as the population of the region. This could have a substantial negative consequence on host communities’ livelihood, which may lead to hostilities between host and refugee communities, e.g., in Gambella. Interventions that create more jobs and maintain or build public infrastructure in host communities, such as the World Bank’s Development Response to Displacement Impacts Project (DRDIP), may favor a smooth transition to the new policy environment.

This study has three limitations. First, in the absence of data on net income (gross income minus cost) for all income sources, we could not measure livelihood diversification in terms of net income from each activity, which is the common approach in the literature. However, income is the return of labor allocated to the different activities, which make us to believe that measuring livelihood diversification based on labor is a good proxy for income-based livelihood diversification. Second, as we did not have the price of livestock products, or the value of total livestock production, we measure agricultural commercialization in terms of gross sale of livestock products. We used a similar measure for crop commercialization for the sake of consistency. As these measures do not consider the scale of production, we could not confirm that the observed effect on livestock sale is due to actual market participation. However, total sale is still a good measure for commercialization, whatever is produced.

Finally, we were not able to assess whether the observed effects lead to better livelihood outcomes as our data does not contain information on
fact, the effect of the environment treatment on support for subsidy reform is statistically significant only at a p-value of 0.05 for respondents who perceive themselves to be in the middle quintile.

## **[Insert Figure 13]**

The respondents who perceived themselves to be middle class had lower levels of unconditional support for energy subsidy reforms in the absence of information (as discussed in the descriptive results) than other respondents, but the environment treatment almost entirely closed this gap (see Figure 14). Across each of the outcome variables, the environment treatment substantially increased support among respondents who perceived themselves to be in the middle quintile, but there were no significant effects on other respondents. Multivariate regression analysis shows these individuals who perceive themselves to be in the middle quintile are more likely to have completed university education, to actually be in the middle quintile or top two quintiles of the actual income distribution, to use subsidized energy sources, and to believe the government can assist households; and they are less likely to trust the government and believe they have a right to receive subsidies (see Appendix C).

## **[Insert Figure 14]**

# **5.3 Robustness checks**

Listed below are several robustness checks that were conducted to illustrate the strength of the results of the randomized survey experiment.

41
appears to be random measurement error. For noncognitive skills, we find evidence of systematic measurement error related to acquiescence bias and show that combining questions according to pre-existing scales leads to low internal consistency. Related, the latent noncognitive construct resulting from the factor analysis does not map in the personality domains typically found in developed countries. While the corrected noncognitive constructs are predictive of agricultural productivity, the estimates do not allow drawing clear conclusions about the relevance of specific noncognitive skills. Overall, the best predictions obtained after corrections of different sources of measurement error show that the three types of skills together explain up to 17 % of the variation in yield, with all three skill constructs being significant and with similar point estimates. Technical and noncognitive skills also help predict agricultural practices, though with varying degrees.

In the last part of the paper, we further analyze the different challenges related to measuring skills in household surveys in developing countries, and discuss guidelines on how to address them.

4
# **Table 5: Estimates of** ~~~~ **for Countries without Cross-sectional CPI Data**

|country|Survey<br>period|~~~~|Cumulative<br>CPI‐bias|
|---|---|---|---|
|Burkina Faso|1998‐2003|0.231|‐0.447|
|Cameroon|2001‐2007|0.226|0.040|
|Cote d'Ivoire|2002‐2008|0.223|0.517|
|Madagascar|2001‐2010|0.202|0.718|
|Madagascar|2005‐2010|0.208|0.281|
|Mauritius|2006‐2012|0.167|0.330|
|Nigeria|2011‐2013|0.216|0.878|
|Rwanda|2005‐2010|0.234|0.623|
|Senegal|2005‐2011|0.231|0.627|
|South Africa|2005‐2010|0.205|0.955|
|Tanzania|2000‐2007|0.220|0.942|
|Tanzania|2008‐2012|0.234|0.877|
|Tanzania|2010‐2012|0.236|0.623|
|Togo<br>* Note: There is in<br>D.R. Thus, we use<br>this country.|2006‐2011<br>sufficient data t<br>the average of al|0.239<br>o estimate<br>l Ῡ estimat|0.899<br>Ῡ for Congo,<br>es (0.22) for|

**Table 6: Cumulative CPI Bias, by Country, Sub-Saharan Africa, 1998–2013**

|Country|Survey period|Cumulative<br>CPI bias|Standard<br
), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS3 applied. All columns control for household (HH) FEs, round FEs, and 12 timevarying characteristics. The coefficient estimates for these 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

52
s calculation using data from Proyecto Migracion Venezuela, Revista Semana 2020. Percentage of respondents agreeing with the statements.N(2020-9)= 2,289

> 7 https://www.cancilleria.gov.co/newsroom/news/presidente-duque-anuncia-decision-historica-crear-estatutoproteccion-temporal

> 8 Proyecto Migración also collected data in April 2019 and March 2020. The proportions of respondents agreeing to these statements have remained stable between these waves. We thank Adriana Sabogal and Proyecto Migracíon Venezuela for granting us access to the data. https://migravenezuela.com/

13
subtracted from the sum of household consumption expenditures. If the result was positive it was treated as untaxed unreported income.<sup>38</sup> For almost a third of the sample the unreported income was above zero. The share of unreported income in the simulated disposable income ranged from less than 10 percent in the bottom decile to almost 30 percent in the top deciles. This fits well with the a-priori assumptions about the distribution of non-response. Unreported income was then included in the means test for two allowances<sup>39</sup> : the _state social assistance_ and the _housing subsidy_ , as proxy for unobserved household assets.<sup>40</sup> Third, we assign non-take up probabilities for the three (partially) means-tested benefits and for maternity capital. The share of households eligible for means-tested allowances in the survey data is higher than administrative records suggest. Unfortunately, Rosstat and other external sources do not collect any information about non-take up of benefits, therefore we had to apply a random non-take up correction, per EUROMOD guidelines.<sup>41</sup> The take up probabilities were estimated as the ratio between the caseload recipients reported in administrative statistics and those simulated to be eligible by RUSMOD. Those probabilities (41.3 percent for child allowances up to 16 (18) years, 26.9 percent for state social assistance, 29.8 percent for housing subsidies and 94.3 percent for maternity capital, respectively) were applied at the benefit unit level.<sup>42</sup> This method is rather rough and does not fully reflect the actual take-up issues at play (discretionary power of local social protection offices, detailed assets test), but in the absence of any take-up adjustment, the numbers of recipients of benefits would be inflated leading to an underestimation of poverty.

The remainder of this section compares RUSMOD aggregates with that from official statistics for 2017. For example, the number of employed people in the data appears to be underestimated
Figure H.1. Socioeconomic Well-being – Fuzzy RD Plots with Local-quadratic Fitted Polynomial <!-- Start of picture text --> Well-being (Index) Consumption per Capita (log)<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Labor Income (log) Employed<br>-200 -150 -100 -50 0 50 100 150 200 -200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018) Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>.6 1.6<br>.5 1.55<br>.4 1.5<br>.3 1.45<br>.2 1.4<br>Well-being (Index) .1<br>1.35<br>0 Consumption per Capita (log)<br>1.3<br>-.1 1.25<br>.5 .75<br>.7<br>.45<br>.65<br>.6<br>.4<br>Employed .55<br>Labor Income (log) .35 .5<br>.45<br>.3 .4<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95%
Policy Research Working Paper 8728

# **Abstract**

An asset and liability management framework for managing risks arising from sovereign foreign exchange obligations requires a joint analysis of (i) the external financial liabilities resulting from a country’s sovereign debt and (ii) the foreign exchange assets of its central bank. Governments often issue sizable amounts of debt denominated in foreign currencies, subjecting their fiscal positions to foreign exchange volatilities. Prudent management of a sovereign’s foreign exchange position under an asset and liability management framework enables governments to mitigate risks at the lowest possible cost, hence increasing resilience to external shocks. Based on the challenges associated with the implementation of an asset and liability management framework, this study recommends a practical approach that includes analysis of the foreign exchange positions of central bank reserves and central government debt portfolios and optimization of the net position. The proposed model is tested, using the foreign exchange reserve and external debt data of seven countries (Albania, Ghana, FYR Macedonia, South Africa, the Republic of Korea, Tunisia, and Uruguay). The paper employs quantitative methods to explore the impact of an overarching asset and liability management strategy and integrated approach on the efficient management of foreign exchange risk. It provides policy recommendations on ways to minimize the risk of foreign exchange mismatches and increase the return on foreign exchange reserves.

This paper is a product of the World Bank Treasury. 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/research. The authors may be contacted at ccangoz@worldbank.org and cwang@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