rafmacalaba/datause-encoder-data huggingface dataset:
25

# SUMMARY AND CONCLUSIONS

45.

45. The RMSM is primarily a planning tool and thinking aid. It calculates the amount of investment, imports, and external borrowing that will be needed in order to sustain a targeted real growth rate in GDP and exports. Except in the constrained savings case, consumption and savings are residual items which float up or down as a consequence of the relative changes in the other national accounts aggregates. If consumption declines or if external borrowing increases too quickly, this is a sign that the desired rate of growth - is unacceptable or that structural changes are needed before society can sustain that amount of growth without damage.

46.

46. Structural change, defined as a shift in the composition of the supply and use of goods, is extremely easy to manipulate within the model. The amount of investment needed to fuel domestic output is controlled through a marginal propensity to invest or an ICOR. The level of imports needed to support investment, consumption or intermediate uses is controlled through a set of demand elasticities. Export performance is controlled directly by means of a real growth rate. If desired, marginal changes in consumption and savings rates can be constrained within politically acceptable boundaries.

47. The clarity of the model is its main strength but also a source of weakness. The simplicity of the linkages in the RMSM, the lack of prices and other key policy variables, and the number of exogenous variables presents the modeler with numerous challenges. The planner can describe the desired changes and trends but he or she cannot be sure that these will be consistent with the existing arrangement of price and other incentives in the economy. In consequence, the planner cannot indicate how prices and policies should be changed. 1<sup>9</sup> l9. This charge is not unique to the RMSM but holds for all similarly constructed two-gap models, including the original, as well as many other types of planning models. See for example the comments of Kindleberger and Herrick (1977).
- [115] McGuirk, E. F. & Nunn, N. Transhumant Pastoralism, Climate Change, and Conflict in Africa. National Bureau of Economic Research Working Paper 28243 (2021).

- [116] Schon, J. & Koren, O. Introducing AfroGrid, a unified framework for environmental conflict research in Africa. _Scientific Data_ **9** , 116 (2022).

- [117] Stearns, J. & Vogel, C. The Landscape of Armed Groups in Eastern Congo: Fragmented, politicized networks (2017). URL https://reliefweb.int/report/democratic-republic-congo/ landscape-armed-groups-eastern-congo-fragmented-politicized.

- [118] OECD and Sahel and West Africa Club. _Urbanisation and Conflicts in North and West Africa_ (2023). URL https://www.oecd-ilibrary.org/content/publication/ 3fc68183-en.

**Acknowledgments.** We thank Jonas Geldmann for sharing METT data, Vivek Sakhrani for sharing asset wealth data, Sahiti Sarva for facilitating the transfer of asset wealth data, Reece Thornley for sharing the Chinko Nature Reserve shapefile, and Mathias D’haen for sharing the Siniaka-Minia Reserve shapefile. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. SD and GE acknowledge generous funding from the Knowledge for Change Program, whose donors are the Swedish International Development Cooperation Agency (SIDA), Agence Fran ̧caise de D ́eveloppement (AFD) - French Development Agency, the Government of Japan, and the European Union.

**Data Availability.** All data is available in figshare at https://doi.org/10.6084/m9. figshare.25560351 [52]. The only exception is the raw asset wealth data, obtainable directly from Atlas AI. However, we provide in figshare the asset wealth values for each protected area and year in our study, along with the code used to derive these values from the raw data.

**Code Availability.** All replication code is available in figshare at https://doi.org/ 10.6084/m9.figshare.25560351 [52].

**Peer-Reviewed Version of Article.** This article was also published in the _Proceedings of the National Academy of Sciences_ : https://doi.org/10.1073/pnas. 2401814121

**Author Contributions Statement.** SD contributed conceptualization, methodology, investigation, and writing. GE contributed conceptualization, methodology, investigation, visualization, funding acquisition, and writing. PH contributed conceptualization, methodology, visualization, investigation, and writing.

**Competing Interests Statement.** SD, GE and PH declare that they have no competing interests.

**Declaration of Generative AI and AI-Assisted Technologies in the Writing Process.** During the preparation of this work the authors used GPT-4 in order to

24
WPS3377

# **Resolving Systemic Financial Crises: Policies and Institutions**

Stijn Claessens, Daniela Klingebiel, and Luc Laeven *

**Abstract:** We analyze the role of institutions in resolving systemic banking crises for a broad sample of countries.  Banking crises are fiscally costly, especially when policies like substantial liquidity support, explicit government guarantees on financial institutions’ liabilities, and forbearance from prudential regulations are used.  Higher fiscal outlays do not, however, accelerate the recovery from a crisis.  Better institutions—less corruption, improved law and order, legal system, and bureaucracy—do.  We find these results to be relatively robust to estimation techniques, including controlling for the effects of a poor institutional environment on the likelihood of financial crisis and the size of fiscal costs. Our results suggest that countries should use strict policies to resolve a crisis and use the crisis as an opportunity to implement medium-term structural reforms, which will also help avoid future systemic crises.

## World Bank Policy Research Working Paper 3377, August 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ________________________________________________________________________* Claessens is with the University of Amsterdam and CEPR, Klingebiel is with the World Bank, and Laeven is with the World Bank and CEPR. We would like to thank Jerry Caprio, Asli Demirgüç-Kunt, Charles Goodhart, Richard Herring, Patrick Honohan, Edward Kane, Augusto de la Torre, and other participants in the conference “Systemic Financial Distress: Containment and Resolution” held at the World Bank, October 8-9, 2003, for very useful comments and Guillermo Noguera for excellent research assistance. Email addresses: stijn@fee.uva.nl, dklingebiel@worldbank.org, and llaeven@worldbank.org.

1
|Bulgaria|Na|57.7|57.6|41.2|41.9|
|---|---|---|---|---|---|
|Czech Republic|11.8|9.6|7.4|6.8|6.1|
|Estonia|12.4|7.0|5.6|4.9|6.2|
|Hungary|22.9|20.9|19.4|17.9|20.8|
|Latvia|38.9|30.6|24.9|19.0|21.9|
|Lithuania|32.6|25.3|16.6|15.0|15.1|
|Poland|33.8|27.6|22.3|17.7|15.0|
|Romania|Na|na|36.5|32.9|32.2|
|Slovakia|22.1|18.2|13.7|11.8|11.1|
|Slovenia|5.1|5.1|5.1|6.7|6.1|
|**Low work intensity**|**2005**|**2006**|**2007**|**2008**|**2009**|
|European Union (27 countries)|10.3|10.5|9.6|9.0|9.0|
|New Member States (10 countries)|Na|na|na|na|Na|
|Bulgaria|Na|14.7|15.9|8.1|6.9|
|Czech Republic|8.8|8.9|8.6|7.2|6.0|
|Estonia|9.4|7.0|6.2|5.3|5.6|
|Hungary|9.5|13.0|11.3|12.0|11.3|
|Latvia<br>|8.1|7.0|6.1|5.1|6.7|
|Lithuania|9.5|8.3|6.4|5.1|6.9|
|Poland|14.2|12.3|10.0|7.9|6.9|
|Romania|Na|na|8.4|8.2|7.7|
|Slovakia|6.6|6.2|6.4|5.2|5.6|
|Slovenia|8.6|6.9|7.2|6.7|5.6|
|**Anchoredpoverty (anchored at 2005 at-risk-of-poverty line)**|**2005**|**2006**|**2007**|**2008**|**2009**|
|Bulgaria|Na|||na||
|Czech Republic|10.4|||7.2||
|Estonia|18.6|||7.8||
|Hungary|13.4|||8.8||
|Latvia|19.8|||9.2||
|Lithuania|20.7|||9.6||
|Poland|22.2|||5.7||
|Romania|Na|||na||
|Slovakia|13.5|||4.9||
|Slovenia|Na|||na||

_Source:_ World Bank staff calculations using data from the European Union Statistics on Income and Living Conditions (EU-SILC).

# **3. Data**

This paper’s primary data source is the European Union Statistics on Income and Living Conditions (EUSILC) household survey, in particular data from 2005 through 2009. The reference population is all private households and their current members residing in each of the national territories. The survey covers the entire population living in private households and excludes collective households such as boarding houses, residence halls, and hospitals. Although the questionnaires, sample designs, and implementation vary across countries, the EU-SILC is semi-standardized: each EU Member State adheres to common standards overseen by Eurostat, the statistical office of the European Union. Moreover, there is a large set of Eurostat-defined “target variables” that are included in each Member State’s EU-
Regarding the number of lags, we use two annual lags in the benchmark specification. This number of lags was obtained from standard lag selection tests (Akaike information criterion, Schwartz information criterion, and Hannan-Quinn criterion).

Under the identification assumptions described above, we estimate the parameters of the model using a two-step procedure in which we first estimate the reduced form coefficients by OLS equation-by-equation and recover the impulse-response functions (IRF) to each of the structural shocks using the reduced form coefficients and the variance-covariance matrices of reduced form errors derived from these coefficients. The confidence bands for the IRF were estimated by parametric bootstrapping assuming normally distributed reduced form errors.<sup>8</sup>

# **3. Data**

The main variables used in the paper are the following. Real GDP per-capita corresponds to the GDP per capita in constant 2000 U.S. dollars and was obtained from the WDI. The reason to use this series instead of the PPP adjusted ones, despite the reduced international comparability, is that it has more recent coverage than the measures from the Penn World Tables and longer coverage than the PPP series produced by the World Bank. The terms-of-trade index is the ratio of export prices to import prices computed using the current and constant price values of exports and imports from the national accounts component of the Penn World Tables (version 6.1) and updated using the terms-of-trade data from WDI. To reduce concerns about structural breaks we focus on the post Bretton-Woods period, 1974-2000.

> results but most of them cannot reject the null of no cointegration. As the power and size trade-off of the different tests varies with the cross-sectional and time-series dimension of the panel (see Pedroni, 2004), we focused on the statistics with that have the largest size (so tend to over-reject) and highest power at short time dimensions. Those tests, corresponding to the panel and group t-statistics derive by Pedroni (1999) clearly do not reject the null of no cointegration.

> 8 The procedure can be briefly described as follows: (i) we use the estimated variance-covariance matrix of the reduced form errors to simulate a random realization of the perturbations; (ii) we use the initial values of the different variables, the baseline coefficients, and the simulated perturbations to simulate a new set of observations for the variables in the VAR; (iii) we use these simulated observations to estimate a new set of coefficients; (iv) we repeat this exercise 500 times; (v) we compute the IRF for each set of coefficients obtained from the bootstrapping; (vi) we build a 90% confidence interval for the IRF by taking the 5<sup>th</sup> and 95<sup>th</sup> percentile of the empirical distribution of the IRF on a point-by-point basis.

8
**Figure 10: Scatterplot of ln[Area planted] and ln[Export Values] (1990-2012)**

<!-- Start of picture text --> 2012<br>2008 2009 20102007 2011<br>2003200420052006<br>2002<br>2001<br>2000<br>1999<br>1998<br>1997<br>1996<br>1995<br>1994 19931992<br>1991<br>1990<br>4 4.2 4.4 4.6 4.8<br>ln(Area Planted ha.)<br>8<br>7<br>6<br>ln(exports in MM USD)<br>5<br>4<br><!-- End of picture text --> A plot of residuals in Figure 11 of exports and R&D (after controlling for area planted) suggests that even once area planted is controlled for, there is a strong relationship between R&D and exports. Thus even after controlling for important input factors that appear similarly correlated such as area planted, R&D spending appears to remain an important determinant of export values.

**Figure 11: Residuals scatterplot of ln[R&D spending] and ln[Export Values] (1990-2012) controlling for Area Planted**

<!-- Start of picture text --> 1996<br>1997<br>1998<br>2008<br>1995<br>20091994 1999<br>2012 20072010<br>20032004<br>2011 2001200620052002<br>2000<br>1993<br>1992<br>1991<br>1990<br>-4 -2 0 2<br>ln(R&D in MM USD) - residuals<br>Residuals Fitted values<br>Fitted values - excl 2011+<br>.5<br>0<br>-.5<br>ln(exports in MM USD) - residuals<br>-1<br><!-- End of picture text --> We now investigate the association between aggregate industry-level exports and joint public-private spending on knowledge capital via multivariate OLS regression. Our estimation approach includes values of bottled wine exports, the public policy-supported aggregated intangible expenditure types (R&D, 20
# **Assessing Effects of Large-Scale Land Transfers: Challenges and Opportunities in Malawi’s Estate Sector**

Klaus Deininger<sup>a</sup> Fang Xia<sup>b</sup> a World Bank, Washington DC b Research Institute for Global Value Chains, University of International Business & Economics, Beijing JEL Codes: J20, J21, J22, J23, J24, Q10, Q11, Q12 This paper would not have been possible without the support from the Ministry of Lands in particular by Atupele Muluzi, Ivy Luhanga, Charles Msosa, and Davie Chilonga as well as digitization of estate leases under Henry Kankwamba and Dan v.Setten. We also thank Daniel Ali, Blessings Botha, Thabbie Chilongo, Mercy Chimpokosero-Mseu, Alejandro de la Fuente, Time Fatch, Thea Hilhorst, Maxwell Mkondiwa, Valens Mumvaneza, Sam Katengeza, Richard Record, and two anonymous reviewers as well as the editor of this journal for helpful comments that helped to greatly improve the quality of the paper. Funding support from DFID and the German Government (BMZ via GIZ) is gratefully acknowledged. The views presented are those of the authors and do not necessarily represent those of the World Bank, its Executive Directors or the member countries they represent.
The market access measure is a comprehensive metric of proximity to destination localities’ population, with more importance attached to localities that take less time to reach. Estimating travel times between localities requires data on the road network (including road type and travel speeds for all road segments), detailed information on the precise geolocations and timing of the obstacles (including the separation barrier wall), and on checkpoint traversal times. To construct these data we processed obstacle and road maps provided by UNOCHA and conducted repeated interviews with UNOCHA officials to obtain estimates of the time cost of traversing each checkpoint at any given time during our sample period. We calculate the minimum travel time between each locality and the governorate capitals in the West Bank via the road network, and recalculate optimal routes and attendant travel times for all of the configurations of obstacles observed each year throughout the 2005-2012 period.

We show that year-to-year changes in market access positively predict changes in nighttime light emissions (NTL), which serves as our preferred measure of local economic performance given the absence of spatially disaggregated GDP measures in the West Bank.<sup>1</sup> According to our preferred estimates, a 10% improvement in market access increases local output by 0.6%, assuming a lights-to-GDP elasticity of 0.3 (cf. Henderson et al. (2012)).<sup>2</sup> A back-of-the-envelope calculation suggests that in the absence of road blocks, GDP per capita in the West Bank would have been 4.1% to 6.1% higher each year over our sample period. These ceteris paribus estimates must be interpreted with caution as they depend on the assumed GDP-to-lights elasticity as well as the choice of distance decay parameters, which govern how much weight is attached to destinations that take more time to reach. Moreover, they abstract away from other distortions and uncertainty that may have attenuated the economy’s output response to changes in market access.

The main threat to identification is the potential endogeneity of market access, which could result from omitted variables bias. To address this concern we use georeferenced data on fatalities to control for local conflict intensity, arguably the most likely confounder of a relationship between local economic performance and mobility restrictions. In addition, we construct an instrument for market access, notably the number of obstacles located in a radius between 10 and 25 km away from the locality. These obstacles are orthogonal to local conditions yet an important determinant of local market access. Our IV estimates are substantially larger than our simple OLS

> 1NTL are by now widely accepted as a credible proxy for local GDP/economic performance (Chen and Nordhaus (2011), Henderson et al. (2012), Pinkovskiy (2013), Alesina et al. (2016), and Storeygard (2016)). Henderson et al. (2011) furthermore confirms that log differences in NTL is a suitable proxy for growth in GDP.

> 2A 10% improvement in market access yields a 1.9% increase in lights emissions.

3
2 policies, especially real appreciation and real overvaluation'. The evidence from Sub-Saharan Africa (see table (1.2)) points to episodes of dramatic real appreciation over the seventies and the first half of the eighties. The real appreciation is clearly related 0o the high rate of fiscal expansion and the increased domestic absorption (negative of the resource balance) where both indicators respectively rose by 2.1 and 1.4 percentage points of GDP betwoen the first two periods (table (1.2)).

On the face of these expansive macroeconomic policies, SSA experienced a severe negative external shock, where the value of this aggregate shock turned from a small but positive average of 0.1 to a negative at -5.3. The terms of trade component of this shock also reflect the same effect, with the TOT index declining from 106.3 in 1973-81 to 91.6 in 1982-85. Also external finance available to SSA dropped by more than 50% over the two perious. Therefore, as the RER is appreciated, the major fundamentals call for an equilibrium depreciation of the RER. Another evidence in support of this view is the tremendous expansion of parallel markets in SSA and the rising black market exchange rate premium which is directly related to real overvaluation and economic distort irns in general (table (1.2)).

Another possible explanation of Africa's economic performance emphasizes the exogeneity of the determinants of economic growth in Africa, especially the effect due to the observed sustained worsening TOT for SSA and the declining external finance available to it. Regardless of the extent of domestic policy accommodation to external shocks, "The results of these unfavorable TOT have been increased indebtedness which, in its turn, has given birth to crippling debt I A frequently cited analysis of the economic crisis of SSA representing this tradition in the World Bank's Berg Report (World Bank (1981)).
14 Claeson 2004), the ‘Countdown to 2030 for Maternal, Newborn, and Child Survival’ (cf. Victora et al. 2015), the SDGs,<sup>6</sup> the STEPS<sup>7</sup> indicators and other NCD indicators used to track progress relating to the UN General Assembly’s 2011 Political Declaration on NCDs,<sup>8</sup> and the ongoing discussions relating to the measurement of service coverage in the context of UHC<sup>9</sup> (cf. Boerma et al. 2014a; Boerma et al. 2014b; Hogan et al. 2018). We include 18 indicators of health service utilization, of which 12 are preventative and 6 curative. The other 28 indicators are health and anthropometric outcomes for both adults and children.

# <u>Data search and data sources</u>

We set out to assemble as large a data set as possible of household surveys. To this end, we undertook inventories of the microdata catalogs of the International Household Survey Network<sup>10</sup> and the World Bank,<sup>11</sup> the Institute of Health Metrics’ Global Health Data Exchange,<sup>12</sup> and several household survey collections. We also searched for household surveys online. This search identified 1,153 surveys from 193 countries – see Figure 5. The surveys include country-specific surveys as well as multi-country household survey collections, notably the DHS, the ECHP, the Eurobarometer, the European Health Interview Survey (EHIS), the LSMS, the Multi-Country Survey Study on Health and Responsiveness (MCSS), the MICS, the Reproductive Health Survey (RHS), the STEPS, the World Bank’s Europe and Central Asia Household Health Survey, and the WHS. Table 4 summarizes the key details of these survey ‘families’. For 863 of the surveys identified, we were able

> 6 The SDG indicators are listed at <u>https://unstats.un.org/sdgs/indicators/indicators-list/.</u>

> 7 STEPS survey reports and fact sheets are available at <u>http://www.who.int/ncds/surveillance/steps/en/.</u> 8 The indicators being used to monitor progress on the NCDs are listed at <u>http://www.who.int/nmh/global_monitoring_framework/en/.</u> 9 See also the discussions of UHC service coverage indicators at <u>http://www.who.int/healthinfo/universal_health_coverage/UHC_Meeting_Nov2015_Report.pdf.</u> 10 http://www.ihsn.org/.

> 11 http://microdata.worldbank.org/index.php/home.

> 12 http://ghdx.healthdata.org/.
**Table 1: Regional and Income Representation of Countries (1960)**

|**Region**|**Number ofCountries**|**Low Income 1960**|**Low Middle Income 1960**|**High Middle Income 1960**|**High Income 1960**|
|---|---|---|---|---|---|
|**EAP**|12|5|3|3|1|
|**ECA **|10|0|4|6|0|
|**LAC**|15|0|1|11|3|
|**MENA**|8|0|5|3|0|
|**OECD **|17|0|0|2|15|
|**SAR**|5|5|0|0|0|
|**SSA **|33|16|14|2|1|
|**Total**|**100**|**26**|**27**|**27**|**20**|

# **Selection of Indicators – Why They Are Important**

The indicators selected impact on women’s economic empowerment but are not intended to be exhaustive. Although the categories overlap, they can be broadly classified under the following categories:

**Table 2.  List Of Questions Tracked From 1960 To 2010**

|**Category**|**Question**|
|---|---|
|**Property Rights and Access to**<br>**Assets**|Do unmarried men and unmarried women have equal ownership<br>rights over immoveable property?|
||Do married men and married women have equal ownership rights<br>over immoveable property?<br>Do sons and daughters have equal inheritance rights over<br>immoveable property from their parents?<br>Do female and male surviving spouses have equal inheritance rights<br>over immoveable property?<br>What is the default marital property regime?<br>If joint titling exists for married couples, is it the default for marital<br>property?|
|**Legal Capacity: Restrictions on**<br>**Agency and Mobility for**<br>**Married Women**|Can an adult married woman legally be designated as "head of<br>household" or "head of family" in the same way as an adult married<br>man?|
||Does an adult married woman legally need permission from her<br>husband in order to get or keep a job or pursue a trade or profession;<br>or can a husband legally stop his wife from working?|

8