rafmacalaba/data-use-mentions-extended huggingface dataset:
9 growth and market access is insufficient to raise food production and farmers’ incomes, beyond those of their parents, agricultural involution would be observed instead (Geertz, 1963).

Figure 3: Fallow land has virtually disappeared, except in Tanzania <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>7.8<br>10 1.0 3.0 0.1 5.0 1.2<br>0<br>Malawi Niger Nigeria Tanzania Uganda Average<br>Share of land in fallow (%)<br><!-- End of picture text --> Source: Binswanger‐Mkhize and Savastano, 2017.

The authors set out to explore the relevance of the BR framework in understanding contemporaneous modern input use in SSA (acknowledging that longer term panel data would be needed for proper testing) and find only partial support. They establish that fallow areas have virtually disappeared (on average the rate of fallow in the six countries is 1.2 percent (Figure 3)), an important finding that has not been systematically documented so far. They also observe a response of modern input use (fertilizer, agro‐ chemicals and improved seeds) to their newly developed exogenous measure of agro‐ecological potential, which is correlated with population density, and controlling for market accessibility (as measured through their newly developed measure of urban gravity), but not with other measures of intensification, such as irrigation or crop intensity. Overall, they conclude that the existing use of organic and inorganic fertilizer is insufficient to maintain soil fertility when fallow practices cease. And the weak response of crop intensity and irrigation is also not consistent with the BR framework. In light of the promising outcomes suggested by the BR framework, the process of intensification across these countries appears to have been too weak according
cellular tower locations, but those results are not presented in this paper.

Once we have generated trips for each user for each day, this data set is stored and is later used as the main input in generating various OD flow outputs which vary mainly by trip _time specification_ and _travel zones_ . For instance, for some purposes, we may require trips taken during the morning commute hours (for example, between 7AM and 10AM) and set the travel zone to cellular tower or _chieftainship_ , which is a fourth–level administrative unit in SL. Given these parameters, we generate an OD matrix where each cell value represents the number of trips from Origin (O) to destination (D) by aggregating trips with the same OD pairs. This process can be easily repeated for different settings of travel zone and trip time parameters.

In addition to OD flows, we also generate _individual mobility characteristics_ as follows: radius of gyration ( _Rg_ ), average number of trips taken per day, and average trip distance. When processing the data, we also assign each user a home cellular tower. This information is useful for some of the analysis we do later on. The procedure we use to detect a user’s home is based on frequency of calls in the hours of the day we consider as home hours.

# **Validation and Adjustments**

In traditional household surveys, study participants are carefully selected using robust statistical sampling techniques to ensure that the results are representative of the whole population. Thus, once the data are analyzed, there is very little validation and adjustments on the data before they can be used. In contrast, CDR data are an example of what is usually referred to _opportunistic data_ , which are the kind of data that are collected for a purpose other than that intended for the analysis. In this case, CDRs are collected by MNOs for billing purposes, among other things. Because of this, it is imperative that the outputs generated from these data be subjected to validations and/
∗∗_</sup>|0.0201<sup>_∗∗∗_</sup>|
||(0.00283)|(0.00600)|(0.00545)|(0.00519)|
|T3: Social pressure|0.0167<sup>_∗∗∗_</sup>|0.0185<sup>_∗∗_</sup>|0.0162<sup>_∗∗_</sup>|0.0111<sup>+</sup>|
||(0.00317)|(0.00676)|(0.00610)|(0.00578)|
|Control mean|0.109|0.109|0.095|0.078|
|First stage f-stat|135,432|28,065.7|28,065.7|28,065.7|
|R<sup>2</sup>|0.001|0.001|0.001|0.001|
|Obs|143,425|31,554|31,554|31,554|
|Test: T1 = T2|0.210|0.752|0.498|0.383|
|Test: T1 = T3|0.254|0.518|0.596|0.349|
|Test: T2 = T3|0.047|0.418|0.317|0.132|

**Notes:** Table shows results for payment outcomes for both entire experimental sample and the subsample for which we have bill data. The outcomes in columns (1) and(2) is an indicator equal to one if the taxpayer has made any paymen to the TRA during the study period. The outcome in column (3) is the share of the total cumulative tax bill the taxpayer made by the end of
Policy Research Working Paper 9763

# **Abstract**

This paper uses a unique survey data set of 105 central banks to investigate whether investment policies for central bank foreign reserve portfolios are linked to the governance arrangements for reserve management. The paper evaluates whether a central bank’s investment decision-making structure impacts how much risk institutions take in their reserve management operations and the level of diversity in their reserve portfolios. Additionally, it explores the implications of the broader governance environment on reserve management. The analysis yields four key findings. First, internal governance arrangements matter for foreign reserve portfolio investment policy; the empirical results indicate that reserve portfolios are more diversified in central banks in which the middle office directly reports to the board. Second, controlling for the level of reserves, the macroenvironment, and the broader governance environment, reserve portfolios are more diversified in central banks where the back, middle, and front offices are separated. Third, the regression analysis also reveals that central banks in countries where the Ministry of Finance has an obligation to cover negative equity have fewer eligible currencies and are therefore less diversified. Fourth, central banks where boards actively exercise portfolio oversight usually have portfolios with more risk and diversification. Portfolios with longer investment horizons, more currencies, and a broader set of asset classes have performed better historically while limiting downside risk. Given that the analysis controls the broader governance environment, the data indicate that any central bank can improve its internal governance regardless of the external governance environment. This paper contributes to the literature on central bank foreign reserves management and on understanding the importance of governance arrangements in investment policy.

This paper is a product of the Treasury Asset Management and Advisory and Capital Markets and Investments Departments. 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 dklingebiel
The last part of the paper discusses the road ahead given the magnitude of the challenge. Based on the observed performance of countries in mobilizing investment in infrastructure, it argues that in a context in which public budgets face strong constraints and large increases in private investment are unlikely to be forthcoming, the solution may lie in other parts of the delivery equation. Specifically, we discuss three promising margins: refocused investment goals, demand management, and interventions to improve public spending efficiency.

The paper is organized as follows. Section 2 reviews the related literature and shows how this paper contributes to it. Section 3 presents the data used in the analysis. Section 4 documents the evolution in infrastructure demand throughout the period in the different countries, and the implications for infrastructure delivery looking at the future. Section 5 discusses the policy challenges and concludes.

# **2. Literature Review**

For decades, the economic literature on infrastructure has mostly focused on aggregate data at the regional or more often the national level. Traditionally, infrastructure has either been measured through supply-side physical indicators, such as electricity generation capacity or kilometers of roads, or demand-side ones such as aggregate electricity or water connection rates. Given the fact that such data ignore both the spatial nature of and the distributional disparities in infrastructure access, the policy relevance of the conclusions have been limited (see Straub 2011 for a critical review).

There were of course micro-econometric studies using household-level data to look at specific local outcomes, such as Gibson and Rozelle (2003), Galiani, Gertler, and Schargrodsky (2005), or Dinkelman (2011), to cite only a few. More recently, the growing use of spatial data has been instrumental in generating a renewal of the field, allowing for example for local (often municipal) level infrastructure usage indicators.<sup>2</sup> However, there have been surprisingly few papers using micro-data to analyze systematically how infrastructure demand at the household level varies along the income distribution. As a result, little
.7 .8<br>Malnutrition rate 2006 Malnutrition rate 2006 DHI 2005 DHI 2005<br>Tot Agr pc exp 2003 Fitted values Tot Agr pc exp 2007 Fitted values Tot Agr pc Exp 2003 Fitted values Tot Agr pc Exp 2007 Fitted values<br>250 600<br>200<br>400<br>150<br>100<br>Tot Agr pc Exp 2007 200<br>50<br>0 0<br>250 600<br>200<br>400<br>150<br>100<br>Tot Agr pc exp 2007 200<br>50<br>0 0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database. Note: Per capita expenditure in Bolivianos.

In addition to these simple correlations, the proposed econometric estimation strategy consists of predicting the probability of pertaining to categories 4 and 5 of VAM in a given year (V), determined by per capita agricultural spending, our key policy variable of analysis (A). Note that this analysis aims at understanding whether and how agricultural spending affects high and very high food insecurity risks. It does not answer the question of whether and how agricultural

> 8 Other explanation mentioned in the context of public spending and welfare levels in Bolivia, see Inchauste (2009), refers to the misalignment between central and local government decisions. Although our paper does not test explicitly this hypothesis, the enormous quantity of agricultural programs, not always clearly delimited in terms of objectives or responsibilities, may be consistent with Inchauste‘s explanation.

11
<u>2022). We use questions about trust in government and beliefs in fairness as proxies for this</u> parameter. Several factors might affect the perceived intensity of climate change effect ( ), including age, gender, level of education, and family composition. The past exposure to adverse climate change tt κκ shocks could also influence . Older individuals may perceive higher risks of future climate change impacts despite shorter future exposure, as they have lived longer and, therefore, have a tt κκ higher probability of having been exposed to adverse climate shocks in the past. More nuanced relations could work through differences in the capacity to adapt to climate change. On the one hand, younger people are more mobile, can tolerate temperature fluctuations, and can more easily escape from disaster-affected areas. On the other hand, as wealth and savings tend to peak around retirement age (Modigliani 1976), older people are able to insure against the impacts of climate change and deal with the financial shocks of natural disasters.<sup>8</sup> We use health self-assessment and information about past exposure to climatic shocks to proxy the effect of this parameter. In the theoretical model, strong discounting of the future (higher ) increases the asymmetry between old and young individuals and may lead to higher optimal investment in climate−change rr action for old individuals. Older individuals experience the intensification of the impacts of climate change sooner and so do not discount them as much as young individuals do. For a sufficiently high , young people are not too concerned about the higher intensity of climate change impacts in their old age. We approximate the future discussion rate with responses to a question that asks rr respondents to select between less money now and more money in a month. rr Willingness to support climate change actions can be constrained by either a lack of awareness of climate change impacts, which may recede with age, or ideological beliefs that may be correlated with age. Political conservatism, which has been associated with climate change
from -100% to +50%. More generally, our analysis of the BPS data suggests that within countrysize-subsector variation of change in sales is larger than the between-groups variation (left panel, Figure 2). Limiting the comparison to firms within South Asia only does not change this conclusion, implying that the shock has a heterogeneous effect on firms within the same size and sector groups (World Bank, 2020c).

<!-- Start of picture text --> Figure 1: Impact of COVID-19 on firms’ operating status and sales<br>Survival: Average probability of business being<br>Average change in sales<br>open or partially open<br>0<br>100<br>94<br>80 82 -20<br>80 -24<br>72<br>66<br>-40<br>60 -46 -44<br>-52<br>-60<br>40 -63<br>-80<br>20<br>-100<br>0 SAR SSA Non-high income EAP High income<br>SAR SSA Non-high income EAP High income (excl. SAR)<br>(excl. SAR)<br>Panel (a): Regional/by income-group comparison<br>100 0<br>88 88<br>84<br>80 80 80 79<br>69 71 -20<br>60<br>-34<br>-40<br>-40<br>40 -47 -45<br>-54<br>-60<br>20 - 63<br>- 66 - 66<br>0 -80<br>Micro (0-4) Small (5-19) Medium (20-99) Large (100+) Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>SAR Rest of the world SAR Rest of the
difference lies in the fact that the PSF questionnaire records the amount of contributions in kind received by the head of the household. This is an important feature since about 20% of the households declare they receive such contributions.

The second important difference between ESPS and PSF designs is that while ESPS varies the length of recall with the type of commodities, PSF gives the respondent the choice of the reference period.

Finally, it should also be mentioned that, aside the consumption survey, the PSF questionnaire is much longer than that of ESPS. Since consumption is covered after the individual surveys, the quality of the consumption data in PSF may be negatively impacted in large households due to respondent and surveyor fatigue. Note, nonetheless, that the extra length of the questionnaire is somewhat compensated in terms of fatigue in PSF by the fact that the burden of the survey is shared among all cell heads.

# **2. Implications for consumption measurement**

Overall, it is difficult to assess how these differences between designs may impact the measurement of consumption. As mentioned, interviewing several adult members of the household is likely to allow PSF to reduce mismeasurement due to asymmetric information within the household. Food consumption may also be better captured in PSF, since respondent in charge of preparing meals and of buying food on markets generally know precisely how much they spend every day, or every week, and this amount is unlikely to change much on a short period of time. On the contrary, household and surveyor fatigue, being potentially more likely to happen in PSF may impact negatively the measurement of consumption. As for variations in the length of the reference period it is difficult to anticipate 8
Appendix 2. Definitions and sources of variables (continued)

|Variable|Definition|Source|
|---|---|---|
|Interest rates,c|onfidence,and uncertainty||
|Shor-term<br>interest rate<br>(nominal and<br>real)|Treasury bill rates or money market rates, with the maturity of three months or less. In countries<br>where 3-month rates are unavailable, shorter maturity rates (including overnight rates) are used. Data<br>are at a quarterly frequency and used as annual averages. The real short-term interest rate is the<br>difference between nominal rate and inflation in the following quarter (as a proxy of expected<br>inflation). Data are taken from different sources, and the data series with the longest time coverage is<br>used as the main series. Then, the main series is spliced with those from other data sources. Data for<br>aggregatedgroups are computed with market-weighted output as a weight.|Bank for International<br>Settlements, Haver<br>Analytics, IMF, OECD,<br>World Bank.|
|Business<br>confidence|Business confidence index, originally taken from the European Commission, OECD, and country<br>sources (including statistical offices, central banks, academic institutions, and think tanks). The series<br>is obtained as quarterly data and used as annual averages. Data from different sources are first<br>standardized and converted to an index equal to 100 in 2015-18. Confidence for aggregated groups is<br>computed with market-weighted output as a weight.|European Commission,<br>OECD, country sources,<br>World Bank.|
|Policy<br>uncertainty|Economic policy uncertainty index, which is based on the frequency of articles in domestic newspapers<br>mentioning economic policy uncertainty. Country-level indices are at a monthly frequency and used as
!-- End of picture text -->

# Fource: Claessens et al., 1997

Germanv and Japan experienced 4-5% TFP growth in the 1950s. and Japan sustained this level through the 1960s (Wolff, 1996); South Korea experienced a 4.8% TFP growth in 1971-1981, Taiwan and Hong Kong - 4.3% in 1966-1976 (Young, 1995).

- 2 The methodology used in calculating the restructuring indicators in Claessens et al., (1997) is identical to the measures described in Section V and the Appendix to this paper. This allows a direct comparison of the prefornance of the fmns in our sample and the whole manufacturing sector in Slovakia. Figure 1 is based on the manufacturing censuses and covers 48%, 64%, 44%, 42%, 92%, 93%, 91% of 1992 manufacturing employment in each (alphabetically listed) country.

4
WPS3747 (wage work _and_ self employment). All individuals aged 15 years and older are assumed to participate in the occupational choice. Following McFadden’s approach to discrete choice behavior (1974) we assume that an individual chooses the outcome that maximizes the utility gained from that choice. It is also assumed that the occupational choice behavior of household heads is different from that of spouses or other members of the household. More specifically, we posit that the decision process is sequential and that occupational choices for spouses and other members depend on characteristics of the household head and on his or her occupational choice.

The microeconomic database of the original Indonesia model is the 1996 SUSENAS SKTIR Household survey on investment and savings. All individuals aged 10 years and older were interviewed on their sources of income. The sample is updated using a cross-entropy approach (Robilliard and Robinson 2003). This method generates new sets of household statistical weights that are consistent with projected population and structure of the labor force for 2002. Changes in the labor force structure are based on observed changes in the 1996 and 1999 SAKERNAS labor force surveys.

The two models are solved separately. The “macro” or CGE model communicates with the micro-simulation model by generating a vector of prices, wages, and aggregate employment variables corresponding to a given shock or policy. Then the micro-simulation model is used to generate changes in individual wages, self-employment incomes and employment status in a way that is consistent with the set of macro variables fed by the macro model. When this is done, the full distribution of real household income corresponding to the simulated shock or policy may be evaluated. Consistency of the microsimulation model with the equilibrium of aggregate markets in the CGE model requires that three conditions hold. First of all, changes in average earnings with respect to the benchmark in the micro-simulation module must be equal to changes in wage 8
_Figure 2 Selected Quantiles and Gini coefficient across Years_ <!-- Start of picture text --> 46<br>2500<br>44<br>1500<br>42<br>1000<br>40 Q10<br>Q50<br>Q90<br>600 38 Gini<br>36<br>300<br>34<br>32<br>2012 2014 2016 2018 2020<br>year<br>Gini points<br>Monthly earnings (log scale)<br><!-- End of picture text --> In terms of inequality, the estimates suggest that it has declined substantially across the years. The estimated Gini coefficient fell from 44.2 Gini points in 2015 to 34.1 in 2019, with a significant increase in 2020. This decline in inequality seems to have been driven by faster growth in the lower and middle sections of the wage distribution and a small decline in the upper section of the distribution.

# **5. Conclusion**

In this paper, we present an imputation strategy that can be used to analyze interval-censored data. Our method proposes that a flexible enough interval regression model can be used to impute interval-censored data, which allows to recover the full distribution of data, and can be further analyzed using standard statistical methods.

The main limitation of our strategy is the assumption of conditional normality, which is required for the estimation of the interval regression model using standard software. The principles of the imputation approach, however, could be extended to allow for more flexible moment specifications, as well as error distributions.
survey were used to classify each household in the panel data set as having no migrants, having one or more members who migrated overseas, or having one or more members who migrated to another Indian state between 1998 and 2003.<sup>18</sup> As noted earlier, only a small fraction of sending households (less than 3 percent) saw members migrating both domestically and internationally for work between survey rounds. I dropped 12 such observations to reach a working sample of 4,783 households.<sup>19</sup> Table 1 displays summary statistics of selected variables. I determined migration status (shown in columns 2 and 3) from the 2003 KMS reports of household members who migrated for work between the 1998 and 2003 survey rounds. In addition, questions in the 2003 KMS enable one to determine the length of time a given household member spent abroad or in another Indian state between survey rounds. I used this variable to measure the duration of a household’s exposure to either form of migration.

Within the three migration categories, I used the 2003 KMS data set to obtain individual-level characteristics for household members between the ages of 19 and 60 who were in the labor force as of 2003 (shown in Table 1, rows 1 to 4). The majority of migrants departing for jobs outside Kerala after 1998 were between 19 and 40 years old. Individuals who migrated for work after 1998 tended to be 5 to 9 years younger than non-migrants. Of these, international migrants were on average 4 years older than domestic migrants. About three-quarters of the adult labor force in Kerala consisted of males; the gender breakout of domestic migrants shows a similar male-female ratio. International migrants were almost all male—close to 90 percent.

In terms of educational level, domestic migrants tended to be more educated than international migrants, who in turn had more schooling than non-migrants. The share of domestic migrants, international migrants, and non-migrants who had completed at least secondary schooling was 68

> 18 Neither the