rafmacalaba/datause-encoder-data huggingface dataset:
# Table 3.3

Catevorical funxl1bility in Indonesia

|- R .<br>-|Change in<br>-<br>~~~~~|C<br>Sector aid<br>~aid|oefficient on<br>Other aid|Toaad|Ratio|
|---|---|---|---|---|---|
|- 0:-<br>- -|-<br>~~~|~~~~(1)<br>(2)|C3)|(4)|m(4}()|
|Agriculture|0.10|0.09|0.05|0.13|1.41|
|Industry|0.46|0.37|0.08|0.44|0.97|
|Transport<br>and tourism|0,21|0.18|0.02|0.20|0.96|
|_Socia_<br>_._<br>sectors|0.10|0.07|0.12|0.19|1.86|
|Other|0.14|0.18|0.27|_0.45_|3.23|
|-Current<br>expenditure|0.00|n.a.|n.a.|0.17|.n.|
|Total|1.00|0.88|-0.53|1.58|_1.58_|
|-Revenue|0.00|-0.29||||

-Source: Pack and Pack (1990)

||**CateoricaI fun**|**ibility**in Kena-<br>.-|
|---|---|---|
||hange-m_-i_<br>Sector aid|Coefficient:on<br>L<br>-ther -aid<br>Total ad<br>-Ratio|
|-:~~~~~~~~~~id|||
|:Agriculture|0.18<br>0.11|<br>4-.10<br>-019_1.-0_|
|Industry|-<br>033 -<br>0.05|-0.18<br>-0.21<br>0.63|
|Public<br>-works|_0.15_<br>0.07-|<br>0.09<br>_0.31_<br>_2.04_|
|Social<br>sectors|0.22<br>-0.02|0.01<br>---.22<br>0.97.|
|O- .e-r<br>-|0.11<br>0-X--<br>--<br>0<br>:<br>---<br>O<br>.00.|t<br> <br>A.00<br>0.<br>0.98|
|:::C:urrent<br>expenditure|- :<br>-0.00<br>-<br>.a.|n.a.<br>.<br>0.06<br>na.|
|Total:|1.00--<br>0.22-|<br>-0.18<br>098<br>0.98|
|Revenue|_--0.60_|-:0;4|
|Source: Ekman|-and Metell f(1993)||

versus actual values but which is left implicit by Pack and Pack's methodology. A better approach would be to incorporate the restriction into the model and estimate by three stage least squares.11 This approach is laid out more filly below.

56
<!-- Start of picture text --> 24.0%<br>22.5%<br>22.0% ae<br>20.0%18.0% 18.9%Qo~ ~ “== “s OS SS<br>18.2% ‘“<br>S<br>16.0% “S<br>‘<br>14.0% 14.5%° SQ S<br>13.6% NSS<br>12.0% oe<br>10.0% 10.6% 10.2%<br>8.0%<br>2011 (nss) 2015 2016 2017 2018 2019<br>—— Approach 2 (2011) = — Approach 2 (2017) -== Approach 1<br>Raw CPHS consumption ¢ Currently in povcalnet<br><!-- End of picture text -->
Policy Research Working Paper 10907

# **Abstract**

How reliable are public debt statistics? This paper quantifies the magnitude, characteristics, and timing of hidden debt by tracking ex post data revisions across a comprehensive new database of more than 50 vintages of World Bank debt statistics. In a sample of debt data covering 146 countries and 53 years, the paper establishes three new stylized facts: (i) debt statistics are systematically under-reported; (ii) hidden debt accumulates in boom years and tends to be revealed in bad times, often during IMF programs and sovereign defaults; and (iii) in debt restructurings, higher hidden debt is associated with larger creditor losses. The novel data is used to numerically discipline a quantitative sovereign debt model with hidden debt accumulation and an endogenous monitoring decision that triggers revelations. Model simulations show that hidden debt has adverse effects on default risk, debt-carrying capacity and asset prices and is therefore welfare detrimental.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 shorn1@worldbank.org; dmihalyi@worldbank.org; philipp.nickol@vwl.uni-due.de; and csosapad@nd.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Figure A1. Evolution of research on macro-financial linkages

|Macroeconomi|cs||Finance|
|---|---|---|---|
|Studies|Approach|Approach|Studies|
|Fisher (`33)<br>Keynes (`36)|Qualitative discussions of<br>Narratives of|macro-financial linkages<br>debt-deflation|Mills (1867)<br>Haberler (`37)<br>Marschak (`38)|
|Hicks (`37)<br>Modigliani (`44)<br>Tobin (`58)|Keynesian approach|Financial<br>intermediation<br>Financial repression|Gurley-Shaw (`55)<br>Goldsmith (`69)<br>Shaw (`73)<br>Cameron (`67)|
|Friedman-Schwartz<br>(`63)|Monetarist approach|Portfolio theory<br>Capital structure<br>irrelevance<br>Q ratio|Markowitz (`52)<br>Modigliani-Miller (`58)<br>Tobin (`69)|
|Sims (`72, `80)<br>Lucas (`76)<br>Sargent-Wallace (`75)<br>Sargent (`81)<br>Kydland-Prescott (`82)<br>King-Plosser-Rebelo (`88)|VARs<br>Lucas critique<br>Monetary arithmetic<br>Rational expectations<br>Real business cycle<br>models|Efficient markets<br>Asset pricing<br>Derivatives-arbitrage|Fama (`70)<br>Merton (`73)<br>Black-Scholes (`73)|

<!-- Start of picture text --> Mills (1867)<br>Haberler (`37)<br>Marschak (`38)<br><!-- End of picture text --> <!-- Start of picture text --> Fisher (`33)<br>Keynes (`36)<br><!-- End of picture text --> <!-- Start of picture text --> Gurley-Shaw (`55)<br>Goldsmith (`69)<br>Shaw (`73)<br>Cameron (`67)<br><!-- End of picture text --> <!-- Start of picture text --> Hicks (`37)<br>Modigliani (`44)<br>Tobin (`58)<br><!-- End of picture text --> <!-- Start of picture text --> Friedman-Schwartz<br>(`63)<br><!-- End of picture text --> <!-- Start of picture text --> Markowitz (`52)<br>Modigliani-Miller (`58)<br>Tobin (`69)<br><!-- End of picture text --> <!-- Start of picture text --> VARs<br>Lucas critique<br>Monetary arithmetic<br>Rational expectations<br>Real business cycle<br>models<br><!-- End of picture text --> <!-- Start of picture text --> Efficient markets<br>Asset pricing<br>Derivatives-arbitrage<br><!-- End of picture text --> <!-- Start of picture text --> Fama (`70)<br>Merton (`73)<br>Black-Scholes (`73)<br><!-- End of picture text --> <!-- Start of picture text --> Sims (`72, `80)<br>Lucas (`76)<br>Sargent-Wallace (`75)<br>Sargent (`81)<br>Kydland-Prescott (`82)<br>King-Plosser-Rebelo (`88)<br><!-- End of picture text --> 46
comparable manner, which allows us to evaluate the representativeness of our sample and employ inverse probability weights to adjust for sample selection (i.e., non-response) bias.<sup>12</sup> The process by which we constructed the country-specific sampling frames that were used to facilitate the survey is described in Section B of the Appendix. The master sampling frame consists of 54,990 host government officials, development partner staff, civil society leaders, private sector representatives, and independent experts from 126 low- and lower-middle income countries and semi-autonomous territories.<sup>13</sup> Altogether, 6,731 individuals participated in the survey. However, for the purposes of this study, we are interested in a specific subsample of respondents: the 1,244 host government officials from 121 low- and middle-income countries who (a) participated in the survey, (b) reported having direct interactions with the World Bank, and (c) subsequently evaluated the agenda-setting influence of the World Bank. Each of these respondents was asked to evaluate the level of influence that the World Bank exerted on the host government’s decision to pursue specific reforms over a specific time frame (during the respondent’s tenure in a self-identified public sector position and institution) and in a specific policy area (the respondent’s selfidentified area of policy expertise).<sup>14</sup> These respondents evaluated the influence of the World Bank on a scale of 0 (no agenda-setting influence) to 5 (maximum agenda-setting influence). Given that our sample may not be representative of the population of interest (i.e., the sampling frame), we employ inverse-probability weights to account for unit non-response.

The data from the _2014 Reform Efforts Survey_ are valuable in that they capture the experiences and observations of public sector officials from the governments that the World

> 12For each low- and lower-middle income country, we defined our population of interest as those individuals with direct knowledge of how government policies and programs were prioritized, designed, and implemented between 2004 and 2013. See Section 2 of the Appendix for more details.

> 13The specific inclusion criteria and procedures employed to develop the sampling frame are described in detail in Parks et al. (2015).

> 14These specific reforms were identified by respondents themselves in a prior survey question that asked them to identify up to three specific problems that government reforms (in the respondent’s area of policy expertise) sought to address. In an earlier set of survey questions, each respondent was asked to select his or her primary policy area of focus (from a fixed list 23 different policy areas) and anchor on a specific public sector job and institution (with a defined start year and end year) in a given country between 2004 and 2013. Respondents were then asked to evaluate the influence of specific donors from the vantage point of his or her policy area of focus and country in that particular position and time period.

12
|Experienced power<br>outage Y/N|0.137**|0.100**|0.213|0.061|0.234|0.243**|-0.254|-0.118|-0.297**|-0.114|
|---|---|---|---|---|---|---|---|---|---|---|
||(0.065)|(0.044)|(0.163)|(0.099)|(0.214)|(0.104)|(0.228)|(0.094)|(0.127)|(0.111)|
|Experienced losses<br>due to crime Y/N|0.128**|0.003|0.330**|-0.185*|0.144|0.057|0.092|-0.113|-0.360|-0.111|
||(0.059)|(0.050)|(0.145)|(0.103)|(0.118)|(0.083)|(0.185)|(0.219)|(0.241)|(0.179)|
|Website Y/N|0.217***|0.349***|0.083|0.397***|0.440***|0.486***|0.085|0.056|0.477***|0.187|
||(0.058)|(0.040)|(0.144)|(0.100)|(0.139)|(0.079)|(0.174)|(0.094)|(0.180)|(0.128)|
|_cons|15.119***|15.088***|16.862***|15.482***|9.799***|10.211***|12.197***|11.906***|13.253***|13.461***|
||(0.359)|(0.232)|(0.791)|(0.537)|(0.570)|(0.522)|(1.033)|(0.418)|(1.478)|(0.837)|
|_N|12,970|30,398|1,893|5,082|2,454|6,697|531|3,902|895|5,525|
|Adjusted R2|0.812|0.768|0.808|0.784|0.735|0.705|0.862|0.831|0.435|0.270|

Note: .01 - ***; .05 - **; .1 - *. Coefficients from the estimation of an OLS model are reported. Standard errors are clustered at the level of the strata; rescaled weights are applied in the regressions. The model also includes controls for the within-country region in which the firm is located, the sector in which it operates and the year in which the survey was carried out.

**_Table 8 Starting informally and subsequent performance: exporting status_**

||**(1)**<br>**All fir**|**(2)**<br>**ms**|**(3)**<br>**Young**|**(4)**<br>**firms**|**(5)**<br>**Sub-Saha**|**(6)**<br>**ran Africa**|**(7)**<br>**Middle East an**|**(8)**<br>**d North Africa**|**(9)**<br>**Sou**|**(10)**<br>**th Asia**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Y = Pr. firm is an**<br>**exporter**|**Female**<br>**participation**|**No female**<br>**participati**<br>**on**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participation**|**No female**<br>**participation**|**Female**<br>**participati**<br>**on**|**No female**<br>**participation**|
|Began operating<br>informally|-0.069***|-0.003|-0.043|0.031|0.008|0.006|-0.090|0.021|0.042|0.012|
||(0.025)|(0.014)|(0.073)|(0.035)|(0.028)|(0.019)|(0.111)|(0.038)|(0.049)|(0.018)|
|Female top manager<br>Y/N|-0.016|-0.003|-0.041|-0.041|0.003|-0.010|0.010|-0.084|0.000|0.006|
||(0.012)|(0.012)|(0.045)|(0.034)|(0.020)|(0.014)|(0.047)|(0.053)|(0.018)|(0.012)|
|Log of age of firm|-0.013|-0.004|-0.041|-0.017|0.020|-0.005|-0.006|-0.022|0.058***|-0.001|
||(0.012)|(0.005)|(0.038)|(0.017)|(0.016)|(0.008)|(0.034)|(0.021)|(0.018)|(0.010)|
|Log of size|0.016***|0.029***|0.015|0.037***|0.010|0.021***|0.072***|0.054***|0.048***|0.032***|
||(0.005)|(0.003)|(0.012)|(0.007)|(0.009)|(0.005)|(0.023)|(0.012)|(0.007)|(0.004)|
|Part of a larger firm<br>Y/N|-0.006|0.010|-0.069*|-0.017|-0.015|0.019|-0.026|-0.068|-0.049*|-0.003|
||(0.016)|(0.009)|(0.041)|(0.021)|(0.028)|(0.013)|(0.042)|(0.046)|(0.025)|(0.009)|

25
24

- qualitative learning about the determinants of behavior of clients and service providers is used in project design (ready models can be found in the existing experience with beneficiary assessment, focus group interviewing and functional task analysis, and funding could often be arranged through the Project Preparation Facility);

- quantitative output and process targets are developed;

- particularly disadvantaged groups are identified and targeted where feasible (in this connection, special consideration should be given to the wider use of growth monitoring along the lines practiced in the Tamil Nadu nutrition project, not solely as a way of targeting supplementary feeding, but as a way of identifying the particularly disadvantaged for targeting of the whole available range of PHN and other services); and

- MIS systems are developed which feed back performance data to field workers as well as upwards to managers.

In addition, it is recommended that PHRHN, the TDs and the CDs should jointly

- identify several countries and projects for systematic experimentation with outreach systems which promote learning from clients and the adaptation of project interventions during implementation on a decentralized basis (ready models are available in the existing experience with social marketing and T&V); and

- identify one or two countries and projects where a special effort would be made to develop private sector provision of services and/or IEC.

Supervision. The appropriateness of learning processes as opposed to blueprints in PHN service development at the periphery suggests a more important role for supervision, as cGmpared to preparation and appraisal, than has been the de facto case in the Bank in the past. Yet, in a period of budgetary stringency and reduced supervision coefficients, it is clear that the time staff can put into field supervision will be strictly limited, and will, in many countries, decline. Under these circumstances, it is recommended that

- project staff encourage more emphasis in project designs on strengthening the Borrower's own supervision process, with technical assistance as necessary, thus minimizing Bank staff's own technical assistance role during implementation;

- project staff work with Borrowers to develop a routine reporting format, to be completed by the Borrower before the supervision mission, which would be tailored to the individual project, but would cover at least civil works progress, procurement and
# **REFERENCES**

Adams, Richard, McCarl, Bruce, Segerson, Kathy, Rosenzweig, Cynthia, Bryant, Kelley,

- Dixon, Bruce, Conner, Richard, Evenson, Robert, Ojima, Dennis. “The economic effects of climate change on US agriculture.” in Mendelsohn, Robert and Neumann, James, eds. _The_

- _Impact of Climate Change on the United States Economy_ , Cambridge, UK: Cambridge University Press, 1999.

- Boer, G., G. Flato, and D. Ramsden (2000), “A transient climate change simulation withgreenhouse gas and aerosol forcing: projected climate for the 21<sup>st</sup> century”, Climate Dynamics **16** , 427-450.

- Dubin, Jeffrey A., and McFadden, Daniel L. “An Econometric Analysis of Residential Electric Appliance Holdings and Consumption”, Econometrica, 1984, Vol.52, No.2 , pp. 345-362.

- Emori, S. T. Nozawa, A. Abe-Ouchi, A. Namaguti, and M. Kimoto (1999), “Coupled oceanatmospheric model experiments of future climate change with an explicit representation of sulfate aerosol scattering”, J. Meteorological Society Japan **77** , 1299-1307.

- Houghton, John, Yihui, Ding, Griggs, Dave, Noguer, Maria, Van der Linden, Paul, Dai,Xiaosu, Maskell, Kathy and Johnson, Cathy, (eds.) _Climate Change 2001: The Scientific Basis,_ Intergovernmental Panel on Climate Change: Cambridge University Press, 2001.

- Kurukulasuriya, P. and R. Mendelsohn. 2006. **“** Crop Selection: Adapting to Climate Change in Africa **”** World Bank Working Paper (forthcoming).

- McCarthy, James, Canziani, Osvaldo F., Leary, Neil A., Dokken, David J. and White, Casey, (eds.) _Climate Change 2001: Impacts, Adaptation, and Vulnerability_ <u>, Cambridge:</u> Cambridge University Press, <u>Intergovernmental Panel on Climate Change, 2001.</u>

- McFadden, Daniel L. “Conditional Logit Analysis of Qualitative Choice Behavior”, in P. Zarembka (ed.), _Frontiers in Econometrics_ , Academic Press, 1973.

- McFadden, Daniel L. “Chapter 1. Discrete Response Models”, University of California at Berkeley, Lecture Note, 1999.

- Mendelsohn, R., A. Basist, A. Dinar, F. Kogan, P. Kurukulasuriya and C. Williams. 2006. “Climate Analysis with Satellites Versus Weather Station Data” <u>Climatic Change</u> (forthcoming).

- Pearce, D. _et al._ 1996. _“_ The Social Costs of Climate Change: Greenhouse Damage and Benefits of Control” in _Climate Change 1995: Economic and Social Dimensions of_

12
Table 2b: Shares of plants with financial loans by state and sector

||||ASI|share|||||NSS|share|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1989|1994|2000|2005|2010|Average|1989|1994|2000|2005|2010|Average|
|A & N Islands|0.74|0.63|0.73|0.67|0.63|0.68|0.34|0.27|0.14|0.09|0.12|0.19|
|Andhra Pradesh|0.52|0.72|0.86|0.86|0.85|0.76|0.09|0.10|0.07|0.08|0.11|0.09|
|Assam|0.81|0.83|0.83|0.76|0.80|0.80|0.02|0.10|0.06|0.10|0.03|0.06|
|Bihar|0.46|0.67|0.61|0.59|0.54|0.58|0.09|0.06|0.03|0.03|0.03|0.05|
|Chandigarh|0.93|0.96|0.99|0.96|0.97|0.96|0.12|0.25|0.06|0.07|0.02|0.10|
|Dadra & Nagar Haveli|0.99|0.95|0.91|0.90|0.92|0.93|0.03|0.08|0.08|0.02|0.07|0.06|
|<br>Daman & Diu|0.96|0.95|0.88|0.90|0.90|0.92|0.10|0.13|0.13|0.01|0.06|0.09|
|Delhi|0.95|0.92|0.93|0.93|0.92|0.93|0.04|0.12|0.04|0.16|0.04|0.08|
|Goa|0.95|0.96|0.92|0.90|0.88|0.92|0.25|0.13|0.09|0.16|0.15|0.16|
|Gujarat|0.97|0.97|0.97|0.96|0.95|0.96|0.09|0.11|0.07|0.07|0.05|0.08|
|Haryana|0.94|0.95|0.95|0.97|0.95|0.95|0.15|0.18|0.10|0.13|0.06|0.12|
|Himachal Pradesh|0.92|0.92|0.91|0.87|0.89|0.90|0.35|0.10|0.10|0.09|0.04|0.14|
|Jammu & Kashmir|0.89|0.87|0.90|0.92|0.90|0.89|0.07|0.05|0.03|0.08|0.07|0.06|
|Karnataka|0.92|0.92|0.93|0.93|0.93|0.93|0.08|0.11|0.07|0.08|0.09|0.09|
|Kerala|0.82|0.82|0.80|0.85|0.76|0.81|0.11|0.11|0.16|0.17|0.13|0.14|
|Madhya Pradesh|0.91|0.91|0.91|0.93|0.95|0.92|0.16|0.08|0.04|0.03|0.02|0.07|
|Maharashtra|0.93|0.92|0.94|0.94|0.94|0.93|0.16|0.17|0.12|0.15|0.09|0.14|
|Manipur|0.36|0.64|0.48|0.29|0.42|0.44|0.04|0.02|0.03|0.08|0.07|0.05|
|Meghalaya|0.50|0.57|0.63|0.75|0.78|0.65|0.02|0.01|0.01|0.02|0.01|0.01|
|Nagaland|0.80|0.70|0.36|0.35|0.35|0.51|0.02|0.03|0.04|0.15|0.13|0.08|
|Orissa|0.80|0.83|0.86|0.89|0.86|0.85|0.06|0.02|0.03|0.07|0.07|0.05|
|Pondicherry|0.96|0.86|0.80|0.86|0.78|0.85|0.23|0.32|0.13|0.18|0.12|0.20|
|Punjab|0.98|0.97|0.96|0.95|0.95|0.96|0.15|0.11|0.06|0.07|0.04|0.09|
|Rajasthan|0.96|0.96|0.96|0.93|0.93|0.95|0.13|0.08|0.05|0.06|0.03|0.07|
|Tamil Nadu|0.86|0.85|0.85|0.84|0.87|0.85|0.10|0.13|0.12|0.10|0.10|0.11|
|Tripura|0.70|0.56|0.55|0.45|0.40|0.53|0.07|0.10|0.01|0.15|0.02|0.07|
|Uttar Pradesh|0.90|0.92|0.90|0.91|0.89|0.90|0.10|0.07|0.04|0.06|0.02|0.06|
|West Bengal|0.86|0.73|0.81|0.84|0.87|0.82|0.07|0.09|0.04|0.07|0.03|0.06|
|Nationwide|0.84|0.87|0.90|0.90|0.89|0.88|0.10|0.09|0.06|0.08|0.06|0.08|

Notes: See Table 2a.
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9 <!-- Start of picture text --> Real Sales AAA Real Sdes AA Real Soles A<br>(Se. 1989 tOO) (Sop. 1989 ' 10O) (Sop. 1989 * 100)<br>tie-__eso_ _ _t_ o00 ito<br>7C<br>II.- 7 _07_0JWl71Jt_2 Hf~~~~~~v h=.tO 11_ 0 a.t4o<br>A." km-g IN4 SWOO A- 1 41 8 Sip-t 41 W*42 i40164 W4 1t 1.041 jwt4 N"2 1.049 fM-4Q w4 SeW40 Jo-ft goW4t SW10. J042 lagOl<br><!-- End of picture text --> Figure 1 <!-- Start of picture text --> Productivit A<br>(S. 1959 *'00)<br>70e<<br>II* M<br><!-- End of picture text --> <!-- Start of picture text --> Productivity AAA Productivity AA Productivit A<br>(sp isat = tOO) (Sep. 19S9 100) (S. 1959 *'00)<br>so SJF4o t 70e<<br>tic * OH- - II* M<br>Unit Labor Cost AAA Unit Labor Co-st AA Unit Labor Cost A<br>(198S * 100) ($M 1959 * 100) (Sq 1989 - 100)<br>100_ _ _ - _ _ _ _J__ _ 1_ _ _ _ _ _ _I t 11-<br>Figure 2<br>Figul 3 '<br><!-- End of picture text -->
17 Figure 6: The extent of government interventions during the 2010-11 food price spike

- (A) Decline in protection rates, 2010-11 (B) Increase in protection rates, 2010-11

<!-- Start of picture text --> Percent Rice Wheat Maize Percent Rice Wheat Maize<br>0 90<br>60<br>-20<br>30<br>-40<br>0<br>-60 -30<br>-80 -60<br>Tanzania Bangladesh Ecuador Nigeria Nepal Pakistan India Uganda Ethiopia Yemen, Rep.<br><!-- End of picture text -->

- (C) Change in EMDE protection rates, 2010-2011

- (D) Change in LIC protection rates, 2010-11

<!-- Start of picture text --> Percent<br>Percent 10<br>0<br>0<br>-5<br>-10<br>-10<br>-20<br>-15<br>-30<br>-20<br>-40<br>-25<br>-50<br>-30<br>-60<br>Rice Wheat Maize<br>-35<br>Rice Wheat Maize<br><!-- End of picture text --> Source: Ag-Incentives Database.

Note: Estimates based on the methodology described in Annex 6.2. Changes in the rates of protection are presented in the form: _Ti_ = ∆ t/(1 + _t_ 0), where t is the initial rate of protection (positive if an import tariff or export subsidy) and ∆ t is the change in this rate of protection. If the change in the rate of protection is negative during a period of rising world prices, countries are seeking to insulate their markets from the increase in prices. If it is positive, policymakers are compounding the increase in world prices with an increase in protection, which may be due to the correction of past “errors”: If domestic prices fall below policymakers’ desired long-run level of protection, or if a policy that insulated the domestic market from world markets and a subsequent exogenous shock—such as a harvest shortfall—has caused the domestic price to rise relative to the world price.

C.D. Median and interquartile range in the change for protection rates for rice, wheat, and maize in EMDEs (C) and LICs (D).