rafmacalaba/datause-encoder-data huggingface dataset:
Mathiassen 2013; etc.). Or the higher share when using the FIES could be consistent with the fact that the measure is also extending the dimensions beyond just the quantity and quality of food consumed to include dimensions relating to anxiety over poor food consumption.

Yet it may also be the case that, in Nigeria, the FIES is poorly aligned with the dimensions of food access that it is trying to capture. The simple consumption model presented above suggests that several more severe food access deprivations, such as undernourishment, coincide with less severe deprivations, such as poor diet quality. Thus, one might expect nearly all of those households with poor food access using a measure that only captures severe food access problems to also have poor food access using a measure that better captures less severe food access problems. However, Table 2 demonstrates that this is not happening: many households have poor food access according to the FIES and adequate food access according to the FCS, as well as vice versa.

# **Section 5. Empirical Strategy**

Based on the predictions from the simple model of consumption presented above, we estimate two empirical specifications that explore the relationship between the FIES and monetary poverty. First, we estimate the difference in the prevalence of poor food access between poor and non-poor populations using the official national poverty line:

# (1) _Poor Access_ _~~F~~ IESi_ = _β_ 0 + _β_ 1 _Poori_ + _εi_

where _Poor Access_ _~~F~~ IES_ is an indicator if the household _i_ is categorized as severely food insecure by the FIES or is an indicator if household _i_ is categorized as at least moderately food insecure by the FIES; and _Poori_ is an indicator if the household is monetarily poor using the national poverty line.<sup>25</sup> The estimate of _β_ 0 represents the share of the non-poor

> 25The specification does not include any regional or month fixed effects. Although fixed effects could be added and the results are qualitatively identical, _β_ 0 in the current specification has a straightforward interpretation that is meaningful to national reporting of food access statistics. Additionally, there are no control variables included in the specification, particularly those that are correlated with monetary poverty. Our goal is not to explain the complicated causal and reverse-causal relationship between

16
First, MAC curves can be displayed differently to avoid their misinterpretation as abatement supply curves. It is indeed possible to display achievable MAC curves together with the corresponding emission-reduction scenarios, also known as _wedge curves_ (Fig. 2). This graphical interpretation makes it explicit that MAC curves are built from emission scenarios, and in particular that it takes time to achieve the _achievable potential_ .

Second, the example studied here shows that the abatement potential and the abatement cost are not sufficient information to schedule emission reduction. The implementation speed is instrumental to investigating emission-reduction strategies, and should explicitly be investigated when building a MAC curve (Appendix B).

Third, our simulations show that aggregated short-term abatement targets may be misleading. They should be completed with credible long-term aggregated targets, or short-term sector- or even technology-specific targets (see also Sand ́en and Azar, 2005; Narain and Veld, 2008; del Rio Gonzalez, 2008; Bosetti et al., 2009; Vogt-Schilb and Hallegatte, 2014).

Finally, the long-term cost, abatement potential and diffusion speed of emission reduction measures is subject to large uncertainties (e.g., regarding the potential for cost reduction due to learning by doing). In the design of a mitigation strategy, we thus recommend to combine a synergy approach with an urgency approach based on a long-term view, as suggested in World Bank (2012, p 153).

The synergy approach identifies the cheapest mitigation options, and those that provide co-benefits in terms of development, economic growth, job creation, local environmental quality, or poverty alleviation. The urgency approach considers the long-term objective (e.g., halving emissions by 2050, or being carbon neutral in 2100) and works backward to identify actions that need to be implemented early to make it possible to reach that goal. Various policies can then be implemented, for instance combining a carbon price to capture low-cost abatement opportunity (e.g., through a carbon tax) with regulations or direct investment to trigger actions where anticipation is critical (e.g., with urban land-use plans, infrastructure, and the development of zero-carbon technologies for the power and transportation sectors).

# **References**

- Bosetti, V., Carraro, C., Tavoni, M., 2009. Climate change mitigation strategies in fast-growing countries: The benefits of early action. Energy Economics 31, Supplement 2, S144–S151.

- CE Delft, 2012. Marginal abatement cost curves for heavy duty vehicles. Background report, Arno Schroten, Geert Warringa and Mart Bles, Delft.

- Climate Works Australia, 2010. Low carbon growth plan for Australia. ClimateWorks Australia Clayton.

- Davis, S. J., Cao, L., Caldeira, K., Hoffert, M. I., 2013. Rethinking wedges. Environmental Research Letters 8 (1), 011001.

- de Gouvello, C., 2010. Brazil Low-carbon Country Case Study. The World Bank, Washington DC, USA.

14
Labor Adjustment Costs across Sectors and Regions<sup>_∗_</sup> Marcio Cruz<sup>_†_</sup> Emmanuel Milet<sup>_‡_</sup> Marcelo Olarreaga<sup>_§_</sup> Keywords: Labor mobility cost, Internal migration, Internet. JEL codes: E24, F13, F16

> _∗_ We are grateful to Rita Almeida, Jorge Alvarez, Erhan Artuc, Mark Dutz, Irene Brambilla, Daniel Lederman, Truman Packard, Jacopo Ponticelli, Jennifer Poole, Guido Porto, Bobby Willig, and participants at workshops at the World Bank, IMF, and ASSA Annual Meeting 2017 for many constructive comments and suggestions. We also gratefully acknowledge support from the Research 4 Development program on Employment funded by Swiss National Science Foundation and the Swiss Development Cooperation, as well as the Knowledge Change Program and the World Bank’s Chief Economist Office for Latin America, under the regional study “Digital technology adoption, skills, productivity and jobs in Latin America”. All errors are ours.

> _†_ World Bank and UFPR. email: marciocruz@worldbank.org

> _‡_ University of Geneva, email: emmanuel.milet@unige.ch .

> _§_ University of Geneva, CEPII, CEPR and FERDI, email: marcelo.olarreaga@unige.ch.
**Table 2e.  Sector Value Added in COMESA  (Percentage unless otherwise indicated)**

||<br>La|<br>bor|||<br>G|<br>DP|
|---|---|---|---|---|---|---|
||Skilled<br>labor|Unskilled<br>labor|Capital|Natural<br>Resources|USD in<br>billion|% of total|
|**Business Services**|||||||
|Air Transport|10.7|49.0|40.3|0.0|0.7|0.4|
|Communication|18.3|24.9|56.8|0.0|5.5|3.1|
|Insurance|36.4|48.7|14.9|0.0|1.7|1.0|
|Business Services nec|32.1|43.6|24.3|0.0|7.9|4.4|
|Financial Services nec|34.1|45.6|20.3|0.0|4.0|2.3|
|Transport nec|12.0|54.9|33.1|0.0|6.6|3.7|
|Water Transport|11.8|53.4|34.8|0.0|0.5|0.3|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|7.1|36.8|56.1|0.0|10.0|5.6|
|Energy and Minerals|1.8|9.5|54.5|34.2|24.2|13.5|
|Food Products|7.2|38.8|54.0|0.0|9.8|5.5|
|Petroleum and Coal Products|1.9|8.9|89.2|0.0|1.0|0.6|
|Other Manufacturing|9.8|49.1|41.1|0.0|5.4|3.0|
|Textile and Apparel|4.8|31.4|63.8|0.0|8.5|4.8|
|Wood and Paper Products|7.5|43.6|48.9|0.0|2.2|1.2|
|**CRTS**|||||||
|Agriculture and Forestry|0.7|60.1|36.9|2.3|28.1|15.7|
|Other Services|27.0|31.6|41.4|0.0|42.0|23.5|
|Trade|10.9|49.9|39.2|0.0|17.3|9.7|
|Utilities|5.1|9.9|85.0|0.0|3.5|2.0|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

44
landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology 102, 99–111. https://doi.org/10.1016/j.enggeo.2008.03.014

- Gómez, H., Kavzoglu, T., 2005. Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology 78, 11–27. https://doi.org/10.1016/j.enggeo.2004.10.004

- Inselberg, A., 2009. Parallel coordinates: Interactive visualisation for high dimensions, in: Liere, R., Adriaansen, T., Zudilova-Seinstra, E. (Eds.), Trends in Interactive Visualization: State-of-the-Art Survey. Springer London, London, pp. 49–78. https://doi.org/10.1007/978-1-84800-269-2_3

- Islam, M.A., Murshed, S., Kabir, S.M., Farazi, A.H., Gazi, M.Y., Jahan, I. and Akhter, S.H., 2017. Utilization of open source spatial data for landslide susceptibility mapping at Chittagong District of Bangladesh—an appraisal for disaster risk reduction and mitigation approach. International Journal of Geosciences, 8(04), p.577.

- Jarvis, A., Reuter, H., Nelson, A., Guevara, E., 2008. Hole-filled SRTM for the globe Version 4 [WWW Document]. CGIAR-CSI SRTM 90m Database. URL http://srtm.csi.cgiar.org (accessed 12.30.18).

- Khan, M.J.U., Islam, A.K.M.S., Bala, S.K., Islam, G.M.T., 2020. Changes in climate extremes over Bangladesh at 1.5 °C, 2 °C, and 4 °C of global warming with high-resolution regional climate modeling. Theoretical and Applied Climatology 140, 1451–1466. https://doi.org/10.1007/s00704-020-03164-w

- Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., Lerner-Lam, A., 2010. A global landslide catalog for hazard applications: Method, results, and limitations. Natural Hazards 52, 561– 575. https://doi.org/10.1007/s11069-009-9401-4

- Lee, S., Ryu, J.H., Won, J.S., Park, H.J., 2004. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology 71, 289–302. https://doi.org/10.1016/S0013-7952(03)00142-X

- Oommen, T., Baise, L.G., Vogel, R., 2010. Validation and Application of Empirical Liquefaction Models. Journal of Geotechnical and Geoenvironmental Engineering 136, 1618–1633. https://doi.org/10.1061/(asce)gt.1943-5606.0000395

- Rabby, Y.W., Li, Y., 2019. An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping. Landslides 16, 633–645. https://doi.org/10.1007/s10346-018-1107-9

- Reddy, C.S., Pasha, S.V., Jha, C.S., Diwakar, P.G., Dadhwal, V.K., 2016. Development of national database on long-term deforestation (1930-2014) in Bangladesh. Global and Planetary Change 139, 173–182. https://doi.org/10.1016/j.gloplacha.2016.02.003

- Reed, P.M., Hadjimichael, A., Malek, K., Karimi, T., Vernon, C.R., Srikrishnan, V., Gupta, R.S., Gold, D.F., Lee, B., Keller, K., Thurber, T.B., Rice, J.S., 2022. Addressing uncertainty in multisector dynamics research. https://doi.org/10.5281/zenodo.6515285

- Regan, H.M., Ben-Haim, Y., Langford, B., Wilson, W.G., Lundberg, P., Andelman, S.J., Burgman, M.A., 2005. Robust decision-making under severe uncertainty for conservation management. Ecological Applications 15, 1471–1477. https://doi.org/10.1890/03-5419

30
# **Comparing Constraints to Economic Stabilization in Macedonia and Slovakia: Macro Estimates with Micro Narratives**<sup>*</sup>

Martin Melecky _World Bank_ Evgenij Najdov _World Bank_

**Key words:** Transition economies, Macedonia, Slovak Republic, macroeconomic stabilization, economic rigidities, New Keynesian Policy model

**JEL Classification:** E30, E58, D11, D21, D53

> * We thank Eduardo Ley, Ivailo Izvorski, Ardo Hansson and Bernard Funck for their comments and suggestions on the earlier draft of the paper. All remaining errors are ours. 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. Contacts: <u>mmelecky@worldbank.org; enajdov@worldbank.org.</u>

1
type of selection exists, the test score distributions of treated and control children tested in school are not comparable. In order to address this issue, we follow Lee (2002) and Angrist et al. (2006) to construct nonparametric upper bounds of program effects (for the matching analysis) on learning by symmetrically truncating the two distributions at some specific quantile. In contrast, unadjusted (selection-contaminated) comparisons of test scores –conditional on positive scores– provide lower bound estimates of program impacts (see Appendix B for details on the methodology for nonparametric bounds of program impacts).

# **_Regression Discontinuity Design_**

The second research design employed in this paper exploits variation in assignment to treatment arising from the discontinuous rule that determines eligibility to the program. As noted before, a proxy-means test designed with the goal of identifying the most vulnerable population that qualifies for various social programs was used to define the target population of FA. The index ( _Sisben_ ) varies between 0 and 100 and is the result of an algorithm that weights households‘ variables associated with their socio-economic wellbeing. Households placed in the first bracket of _Sisben_ (level 1), namely those with scores below 18 and 36 in rural and urban areas, respectively, were considered eligible for the first phase of the program. In principle, the expected discrete change in participation produced by the rule offers an opportunity to estimate the causal effects of the program on education outcomes with a Regression Discontinuity Design (RDD). The intuition behind this strategy is that households that lie just below and just above the threshold are statistically comparable except for their participation in the program. As a result, any discontinuity in the conditional distribution of high school graduation rates and tests scores at the cutoff could be interpreted as the effect of FA.

We used data from SIFA, an information management system that compiles information on a number of operational aspects of the program including historical records of all beneficiaries. When merging it with the _Sisben_ scores, these databases allow identification of the universe of individuals in households that lie below or above (in a smaller proportion) the cutoff of eligibility and participate in the program between 18
# FIGURE 3: SHARE OF TRANSPORT SERVICES IN VALUE ADDED: USSR AND OECD

<!-- Start of picture text --> 15%-<br>NOR<br>NOR<br>10% _<br>USSR U5J8R<br>USSR ISL<br>DNK NOR<br>FR4 Dpa 19L<br>CRC~~~R<br>o%| ~I I F I l<br>8 8.5 9 9.5 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
Table S.3: Summary statistics

||units|mean|std|skewness|
|---|---|---|---|---|
|Loans|USD millions|4,895|25,012|12|
||logs|5.0|3.0|0.1|
|Migrants|thousands|19.7|94.6|16.0|
||logs|7.0|2.6|0.0|
|Distance|thousands km|5.8|4.0|0.6|
||logs|8.3|0.9|-0.9|
|Language|dummy|0.1|0.4|2.0|
|Colonizer|dummy|0.1|0.3|2.9|
|Legal origin|dummy|0.3|0.5|1.0|
|Border|dummy|0.03|0.18|5.27|

The sample corresponds to the benchmark regression reported in column (5) in Table 1. Loans are as of 2000 and migration is as of 1990. The variables language, colonizer, legal origin and border get the value 1 if they are identical for countries i and j, and zero otherwise.

Table S.4: Summary statistics of long-term and short-term bonds by issuer type - ECB data

||Total|Financials|Non-Financials|Government|
|---|---|---|---|---|
||EUR bn||ratios of total||
|Short term|1,599|.505|.049|.445|
|Long term|14,924|.536|.053|.411|

Data refers to outstanding bonds issued by residents in the Euro area, as of 2011. Source: ECB, Monthly Bulletin, April 2013.

4
estimation results show that access to external finance is positively and significantly related to growth and innovation. As an alternative measure, instead of using a dummy variable for measuring access to finance, I use the amount of total investment that is financed through some financial intermediary. The results are very similar. Both measures of access to finance don’t seem to affect the relation between firm’s evolution and its foreign exposure.

**<u>Table 13 All Product Innovation</u>**

||||Pro|duct Innovat|ion|||
|---|---|---|---|---|---|---|---|
|Export/Import|0.193|0.179|0.181|0.124|0.187|0.209|0.121|
||(0.015)***|(0.016)***|(0.015)***|(0.020)***|(0.017)***|(0.020)***|(0.020)***|
|Export Only|0.142|0.124|0.126|0.064|0.124|0.183|0.059|
||(0.022)***|(0.023)***|(0.023)***|(0.031)**|(0.026)***|(0.029)***|(0.032)*|
|Import Only|0.132|0.123|0.126|0.113|0.137|0.108|0.110|
||(0.014)***|(0.016)***|(0.016)***|(0.021)***|(0.017)***|(0.018)***|(0.022)***|
|Foreign (≥%10)||‐0.024|‐0.027|‐0.061|‐0.033|‐0.009|‐0.053|
|||(0.018)|(0.018)|(0.027)**|(0.021)|(0.020)|(0.027)**|
|R&D Ind|0.147|0.156|0.157|0.215|0.146|0.020|0.212|
||(0.025)***|(0.018)***|(0.018)***|(0.014)***|(0.018)***|(0.021)|(0.014)***|
|Foreign ( ≥50%)|‐0.023<br>(0.017)|||||||
|Foreign ( <50%)|‐0.002<br>(0.036)|||||||
|Access to Finance||0.076|||||0.037|
|||(0.012)***|||||(0.015)**|
|% Financed|||0.001<br>(0.000)***|||||
|Multi‐plant firm||||0.021|||0.021|
|||||(0.028)|||(0.028)|
|Total Hrs/Week|||||0.000<br>(0.000)||‐0.000<br>(0.000)|
|log(Markup)||||||0.028<br>(0.013)**||
|Observations|8862|8260|8260|3636|6850|5166|3589|
|R2/Pseudo R2|0.591|0.161|0.160|0.136|0.167|0.0933|0.138|

Robust standard errors clustered by country, industry, and year are in parentheses. All regressions control for log(investment/worker), Non-production worker share, training, age, Log(Labor)t-3. They also control for 2-digit industry, survey year, and country fixed effects. Coefficients for the probit regressions show the marginal effects at mean values. *** p<0.01, ** p<0.05, * p<0.1.

In their detailed analysis of firm dynamics, Dunne, Roberts, and Samuelson (1988) conclude that multi-plant firms are more likely to survive and grow faster than single plant firms. Although their number is small, in the dataset some of the firms are part of a larger firm. To see 25
|**Table 3: Halter**|**, Oechslin and Zweimüller**<br>(1)|**(2014) Benchmark Specification**<br>(2)<br>(3)<br> <br>|
|---|---|---|
||SYS‐GMM|Differenced Equation,2SLS<br>Levels Equation,2SLS|
|Lagged Income|0.998***<br>(0.013)|‐0.040<br>(0.278)<br>0.999***<br>(0.015)|
|Lagged Gini|0.160<br>(0.178)|1.897***<br>(0.661)<br>0.079<br>(0.201)|
|Twice‐Lagged Gini|‐0.671***<br>(0.206)|0.939***<br>(0.201)<br>‐0.754***<br>(0.239)|
|P‐value for Ho: Overidentification|0.022|0.023<br>0.158|
|P‐value for Ho: Underidentification||0.140<br>0.205|
|Cragg‐Donald Wald F‐Statistic||1.50<br>1.67|
|Crit. Val. for 30% Maximal Relative Bias||4.59<br>4.39|
|Sanderson‐Windmeijer Conditional F‐statistics for<br>lagged Gini(twice lagged Gini)||4.15<br>23.46 (10.14)|
|Crit. Val. for 25% Maximal Size||12.82<br>14.00|
|<br>Weak Instrument‐consistent 95% Confidence Sets B<br>|ased on ProjectingAR‐Based|<br> <br>Confidence Sets<br> <br>|
|Lagged Gini||[0.94,...]<br>[...,...]|
|Twice‐Lagged Gini||[‐0.201.35]<br>[......]|
|<br>Weak‐Instrument‐Consistent 95% Confidence Sets B<br>|ased on ProjectingKJ‐Based C|, <br> , <br>onfidence Sets<br> <br>|
|Lagged Gini||[0.37,...]<br>[...,...]|
|Twice‐Lagged Gini||[‐1.181.84]<br>[......]|
|<br>Weak Instrument‐Consistent 95% Confidence Sets B<br>|ased on ProjectingCLR‐Based|, <br> , <br>Confidence Sets<br> <br>|
|Lagged Gini||[0.78,...]<br>[...,0.78]U 0.94|
|Twice‐Lagged Gini||[‐0.04,1.27]<br>[...,...]|
|Period Dummies|Yes|Yes<br>Yes|
|Excluded Instruments In Differenced Equation|�����,�����|�����,�����|
|Excluded Instruments In Levels Equation|∆�����,∆�����|∆�����,∆�����|
|Number of Excluded Instruments|26|12<br>14|
|Number of Countries|88|62<br>88|
|Number of Observations|286|191<br>286|
|_Note: Included instruments areperiod effects in bot_|_h the levels and differenced e_|_quations, and_∆�����_in the differenced equation._|

34
|||•<br>Visionaryleadership|
|---|---|---|
|Campos and|7 countries|•<br>‘Reform readiness’ (ratio of expected gains to costs in reform|
|Esfahani (2000)||proposal)|
|||•<br>Trust between government and entrepreneurs|
|||•<br>Political turnover|

_Source_ : McCourt (2003).

There is a variety of factors here, but political constraints are a thread running through all five studies (such constraints are implicit in Campos and Esfahani’s notion of ‘reform readiness’).

That pattern is quite consistent with the frequency of problems in the implementation of World Bank public service reform projects in the mid-1990s, as identified in World Bank internal evaluation reports:

**Table 6: Problems Encountered in World Bank Civil Service Reform Operations**

|**Problem **|**Frequency in Evaluation Reports**|
|---|---|
|Administrative capacity and financing|21%|
|<br>Domestic reform consensus|17%|
|Project design|17%|
|<br>Government commitment and ownership|15%|
|<br>Overall political and economic crisis|13%|
|<br>Type of lending instrument|5%|
|<br>Supervision or staff continuity|4%|
|<br>Donor co-ordination|4%|
|Bank and countrydialogue|3%|
|_Source:_Nunberg (1997: Figure 13)||

Three of the nine problems listed in Table 6 have a major political dimension: domestic reform consensus, government commitment and ownership and overall political and economic crisis. Moreover, they are among the most frequently occurring problems.

It is consistent with those findings that several studies have found that the key determinants of commitment to reform are internal: ‘domestic policies set the basic parameters of government efforts’, while external support ‘emerges as a sometimes necessary, but far from sufficient, condition for implementation’ (Nelson, 1990: 344; 347). The World Bank’s (1998) _Assessing Aid_ report, despite being devoted to making the case for aid, similarly notes that ‘the lending cum conditionality process works well only when local polities have decided, largely on their own, possibly with outside technical help, to address their reform needs’. ‘Successful reform,’ it concludes, ‘Depends primarily on a country’s institutional and political characteristics’ (World Bank, 1998: 52, 53).

I would like to ask readers to dwell for a moment on the _Assessing Aid_ insight. It has often been assumed that reforms have been driven by donor pressure (perhaps not so surprising when one considers the ubiquity of donor intervention, especially in the structural adjustment era). This underlines Nunberg’s rueful observation, quoted at the start of this paper, that government is a horse that can be brought to water but not made to drink.

Development agencies were quick to grasp the significance of the political economy studies. In 1999, for instance, James Wolfensohn (1999: 9) declared that ‘It is clear to all of us that ownership is essential. Countries must be in the driver’s seat and set the course. They must determine goals and the phasing, timing and sequencing of programs.’ 17
- Williams E.H., Hayc3 R.J. and Smith P.G. (1986) Admissions to a rural hospital in the West Nile district of Uganda over a 27 year period. J Troy Med Hvq 89 193-211.

- World Health Organization. (1978) Lay reDortina of health information. Geneva: WHO.

- World Health Organization. (1980) World Health Statistics Annual 1980-1981. Geneva: WHO.

- World Health Organization. (1987) World Health Statistics Annual 1987. -Geneva: WHO.

- Zimicki S. (1989) Approaches to assessment of the cause structure of mortality: A case study from Bangladesh. Paper presented to the Johns Hopkins University seminar on verbal autopsies, Baltimore, 13-15 March 1989.

31