rafmacalaba/datause-encoder-data huggingface dataset:
complete financial indicator data in the analysis. Comparisons between the two groups form the basis of most of the statistical analysis that follows.<sup>7</sup>

# **3. Methodology**

For our base results, we rely on simple fixed-effects panel regressions of the following form: where _i_ represents country, and _t_ is time measured in years since 1991. The time variable takes values from one to twelve, and the estimated coefficient _βwb_ thus represents the average growth rate for indicator of financial development _Y_ for countries that received adjustment loans to supports financial reform. We test whether _βwb =βnon-wb_ to assess whether adjustment loans had a beneficial impact on financial sector development. To the extent that the growth rates for the control countries were the same as (or greater than) those for those that received World Bank assistance, the value of that assistance would be questioned. All regressions also include αi, a country-specific fixed effect. Our results, therefore, should be interpreted as changes relative to the country-specific mean for the indicator in question. Later we offer additional, more direct methods for addressing potential selection problems.

_X_ is a vector of macroeconomic and institutional controls that includes inflation, real growth, government budget surplus(-deficit)/GDP and the CPIA index, a broad measure of institutional development. CPIA is the Country Policy and Institutional Assessment done annually by the World Bank to assess the quality of a country’s present policy and institutional framework. The index is based on twenty criteria, which are grouped in four clusters: economic management; structural policies; policies for social inclusion and equity; and public sector management and institutions. All of the macroeconomic and institutional controls are lagged one year in the panel regressions that follow to help mitigate problems arising from the controls and the dependent variable being simultaneously determined.

> 7 A number of the countries in the control group received non-lending services in the sense that the World Bank maintained a policy dialogue with them throughout the period.  A number also had received some loans for financial sector reform prior to our period of study.  These factors could make it harder to find statistically significant differences between the two groups in the regressions that follow.

6
Table A6: Loan application: Additional controls

|Dependent variable:|(1)|L<br>(2)|oan applicati<br>(3)|on<br>(4)|(5)|
|---|---|---|---|---|---|
|Constrained by informal|-0.0506**<br>[0.0209]|-0.0400*<br>[0.0242]|-0.0415*<br>[0.0248]|-0.0413*<br>[0.0250]|-0.117***<br>[0.0412]|
|Years formality|-0.0945|-0.122|-0.124|-0.0890|0.0505|
||[0.0745]|[0.0900]|[0.0919]|[0.100]|[0.244]|
|Originally informal|0.0347<br>|0.0141<br>|0.00520<br>|0.00511<br>|0.00398<br>|
||[0.0325]|[0.0387]|[0.0391]|[0.0402]|[0.0609]|
|Sales growth||0.000866<br>|0.000669<br>|0.000774<br>|-0.000582<br>|
|||[0.000922]|[0.000962]|[0.000968]|[0.00165]|
|Productivity growth||-0.00152*<br>[0.000888]|-0.00129<br>[0.000928]|-0.00136<br>[0.000927]|-0.000467<br>[0.00152]|
|Investment||-0.0375|-0.0354|-0.0349|-0.0654|
|||[0.0242]|[0.0247]|[0.0250]|[0.0439]|
|Local market|||-0.0122|-0.0195|-0.0146|
||||[0.0369]|[0.0371]|[0.0709]|
|LMain<br>~~n~~ational|||-0.00123<br>[0.0322]|-0.00419<br>[0.0324]|0.0182<br>[0.0649]|
|Years manager experience|||-0.000142<br>[0.00102]|-0.000413<br>[0.00102]|0.0000948<br>[0.00169]|
|Government ownership|||0.000321|0.000592|-0.000388|
||||[0.00177]|[0.00179]|[0.00244]|
|Female top manager|||0.0545<br>[0.0506]|0.0313<br>[0.0494]|0.0311<br>[0.0702]|
|Female owner|||-0.00986|-0.00993|-0.0114|
||||[0.0254]|[0.0261]|[0.0410]|
|City 1||||0.0373<br>[0.0689]|-0.0764<br>[0.105]|
|City 2||||0.119*<br>[0.0647]|0.0429<br>[0.0983]|
|City 3||||0.0709<br>[0.0612]|-0.0519<br>[0.0848]|
|City 4||||0.107*<br>[0.0582]|-0.0106<br>[0.0881]|
|Electric outages (N)||||0.000673*<br>[0.000393]|0.00175<br>[0.00177]|
|Electric outages (length)||||0.00301<br>[0.00667]|-0.00419<br>[0.0132]|
|Bribery depth|||||-0.00145**<br>[0.000650]|
|Gifts (share)|||||-0.0000411<br>[0.000553]|
|Constrained by corruption|||||-0.0217<br>[0.0422]|
|Loss from theft|||||-0.000938<br>[0.00632]|
|Constrained by crime|||||0.000290<br>[0.000525]|
|Capacity utilization|||||-0.000147<br>[0000782]|
|Model|Logit|Logit|Logit|Logit|.<br>Logit|
|Time FE<br>|yes|yes|yes|yes|yes|
|Geographic area FE|yes|yes|yes|yes|yes|
|Additional controls|yes|yes|yes|yes|yes|
|Observations|1987|1464|1439|1409|561|
|Pseudo R2|0.196|0.213|0.206|0.211|0.199|

_Notes:_ logit marginal effects. All regressors are lagged once. Unreported additional regressors follow the specification in Column 3 of Table 5. Measures are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

26
the interaction basically amounts to testing that the interaction model fits the data better. Likelihood ratio tests also prefer the interaction model (at the 10% significance level) and the pseudo-R<sup>2</sup> s improve in every column.

– **Figure 3** Average partial effects <!-- Start of picture text --> (a) Fractionalization, size = 0.10 (b) Executive Constraints, size = 0.10<br>1 2 3 4 5 6 7 0 25 50 75 100<br>Executive Constraints (INS0) Fractionalization (ELF)<br>(c) Fractionalization, size = 0.20 (d) Executive Constraints, size = 0.20<br>1 2 3 4 5 6 7 0 25 50 75 100<br>Executive Constraints (INS0) Fractionalization (ELF)<br>1 .04 1 .2<br>)0<br>.03<br>.75 .75 0<br>.02<br>.5 .5 −.2<br>.01<br>Fraction of Data Fraction of Data<br>.25 .25 −.4<br>0<br>Partial Effect of Fractionalizaton (ELF)<br>Partial Effect of Executive Constraints (INS<br>0 −.01 0 −.6<br>1 .03 1 .2<br>)0<br>.75 .02 .75 0<br>.01<br>.5 .5 −.2<br>Fraction of Data Fraction of Data<br>0<br>.25 .25 −.4<br>Partial Effect of Fractionalizaton (ELF)<br>−.01 Partial Effect of Executive Constraints (INS<br>0 0 −.6<br><!-- End of picture text --> _Note(s)_ : Panels (a) and (b) illustrate the results for a nominal size of 10 percent. The break underlying panels (c) and (d) are estimated with a nominal size of 20 percent. The average partial effects shown in panels (a) to (d) are based on column (5) of Table 2 and are computed over the entire range of the variable on the horizontal axis while all other variables take on their respective realizations. The dashed lines are upper and lower 95 percent confidence limits. _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

The estimated effects are both economically and statistically significant across a wide range of situations. Figure 3 plots the average partial effect with respect to one variable of the interaction term over representative values of the other, including a 95% confidence interval. The vertical axis on the right measures the average predicted semi-elasticity of the expected duration with respect to fractionalization or executive constraints. The effect 22
# **2 Overview of the FCS Project Portfolio FY01-13**

**The number of active projects among IDA-only borrowing countries peaked in FY2011, with a total of 1192 projects, from 653 in FY2001 (see Figure 1).** Since FY2011 the number of active projects has fallen to 1119 in FY2013. Across the categorization of IDA-only borrowing countries, the number of active projects in Always FCS countries, also peaked at 348 in FY2011. However, in relative terms, the proportion of projects in Always FCS countries peaked in FY2009 at 31.7%, from 17.8% in FY2001, and was 26.5% in FY2013. The proportion of projects in Partial FCS countries has gradually increased from about 15% in FY2001 to 17% in FY2013, with absolute numbers increasing from 97 to 194.

**The total net commitments associated with active projects has consistently risen and was $48.6bn in FY2013.** While the proportion of net commitments associated with projects in FCS countries has risen from 21.8% in FY2001, it peaked in FY2009 at 34.8% of the portfolio and has since decreased to 30.3% in FY2013. As the proportion of net commitments associated with projects in Partial FCS countries has stayed approximately constant, this growth and decline has mainly come from proportion of net commitments associated with projects in Always FCS countries.

**The trends in disbursements to IDA-only borrowing countries reflect the trends in net commitments.** Disbursements have consistently risen through to FY2013, although the proportion flowing to FCS countries peaked in FY2009.

**Data on the number and size of new project approvals help explain the plateauing of the portfolio (see Figure 2).** The number of approvals per year has decreased since FY2011 and the commitments of approvals peaked in FY2008. Between FCS and non-FCS countries the proportion of approvals of projects (by number) in FCS countries has decreased since FY2008 and the size of the projects approved has proportionally declined substantially.

**The Africa region has the highest number of projects and largest level of net commitments, of all regions, in the FCS IDA portfolio (see Figure 3).** This was the case over the period FY2001 to FY2006, and has continued through to FY2013, with on average 251 projects per year and $7.2bn in net commitments. The South Asia region has the next largest portfolio and with on average 67 projects per year and $2.9bn in net commitments. The other regions have smaller FCS portfolios with between with 21 to 42 projects per year, and $350m to $1.1bn.

**The Sustainable Development Network (SDN) has by far the highest number of projects and largest level**

7
_ESTIMATING FOOD PRICE INFLATION_ _35_ <!-- Start of picture text --> Gambia 2007-01-01 / 2021-08-01 Guinea-Bissau 2015-01-01 / 2021-08-01<br>1. 6 1.1 5<br>1. 5<br>1.1 0<br>1. 4<br>1. 3 1.0 5<br>1. 2<br>1. 1 1.0 0<br>1. 0<br>0.9 5<br>0. 9<br>0. 8 0.9 0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 2021<br>Haiti 2007-01-01 / 2021-08-01 Iraq 2011-03-01 / 2021-08-01<br>2. 6 1.1 0<br>2. 4<br>1.0 5<br>2. 2<br>2. 0 1.0 0<br>1. 8<br>1. 6 0.9 5<br>1. 4<br>0.9 0<br>1. 2<br>1. 0 0.8 5<br>0. 8<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Lao People's Democratic Republic 2007-01-01 / 2021-08-01 Lebanon 2012-01-01 / 2021-08-01<br>1.1 0<br>5. 5<br>1.0 8 5. 0<br>1.0 6 4. 5<br>1.0 4 4. 0<br>3. 5<br>1.0 2<br>3. 0<br>1.0 0<br>2. 5<br>0.9 8 2. 0<br>0.9 6 1. 5<br>1. 0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Liberia 2007-01-01 / 2021-08-01 Mali 2007-01-01 / 2021-08-01<br>2. 2 1.3 5<br>2. 0 1.3 0<br>1. 8 1.2 5<br>1. 6 1.2 0<br>1. 4 1.1 5<br>1. 2 1.1 0<br>1. 0 1.0 5<br>0. 8 1.0 0<br>0. 6 0.9 5<br>20 07 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 0.9 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> Figure A3. Estimated food prices and intra-month volatilities in the Gambia, Guinea-Bissau, Haiti, Iraq, Lao People’s Democratic Republic, and Lebanon.

_Note:_ Price of food baskets in local currency, local-market average, January 2015 =1. _Source:_ Figure prepared by the author for this paper. A Live version of all graphs, including graphs at the subnational level, are maintained in the on line data repository.
accession countries it was 2.2. More generally the ratio tends to be higher in Southern Europe.

Kolev and Saget (2005) provide evidence from countries in South East Europe (SEE) that over a decade after the start of transition period and despite economic recovery in most countries in the region, the employment prospects for young workers still remain dismal. They find that the average youth unemployment rate was over twice the European Union average, and thrice the average adult unemployment rate. Together with high youth unemployment rates, Kolev and Saget (2005) also report the growth of pools of jobless young workers not looking for work and the rising numbers of young workers working in unprotected environments.

O’Higgins (2004) observes that in Poland, Hungary, Czech Republic and the Slovak Republic, the incidence of long-term unemployment among the youth is large and not much lower than the long-term incidence of unemployment among adults. In other countries in Latin America, Eastern Europe, and in the former Soviet Union, evidence from longitudinal data also suggests that young workers experience sustained spells of unemployment.<sup>4</sup>

# _Consequences of youth unemployment_

Does the initial experience of unemployment lead to permanent labor market disadvantage among the youth? The existing empirical evidence is drawn mostly from the experiences of advanced economies. It is also, in large part, inconclusive. In the United States, most studies find that spells of unemployment after leaving school do not result in persistent unemployment later in life. This corresponds to fairly high transition rates from unemployment into employment: 46 percent of unemployed youth are employed one month later. The corresponding transition rates in France, Germany, and the United Kingdom are much lower, ranging from 4 to 14 percent, and there is more evidence that early unemployment leads to persistent unemployment. One-third of the unemployed in France have been unemployed more than a year, compared with 8.5 percent in the United States (Ryan 2001).

The adverse effects of early unemployment spells on subsequent employment outcomes and employment stability have been found to persist for as long as seven years in France, compared with only two years in the United States (Ryan 2001). The degree of persistence appears to be responsive to business cycles; that is, where the local economy is experiencing job growth, the adverse consequences of early unemployment seems less permanent. Early analysis using longitudinal data from the United States (Corcoran 1982 and Ellwood 1982) show that foregone participation lowers future wages. In addition, Burgess et al (2003), using British data, show that the scarring effect of early unemployment tends to be greatest for the least educated and disadvantaged youth.

> 4 Borgarello and others (2005). The countries covered include Albania, Argentina, Georgia, Hungary, Poland, Russia, Ukraine, and Venezuela.

4
One difficulty in parsing the evidence about why firms reporting disliking “policy uncertainty” is disentangling whether it is the fact of deviation _per se_ or whether it is the uncertainty created by “disordered” deals or whether firms are expressing resentment at the uneven access to deals. An interesting finding comes from several of the surveys, which asked a question meant to elicit whether the practice of bribery, if it was regular and predictable, in and of itself constituted an obstacle to business. The firms were given the following vignette: _Musyoka needs to renew a small business license from a local government office each year. Bribes are welcomed. Musyoka usually includes an additional bribe with his applications. When Musyoka had not included bribes, his application was sometimes lost or there were long delays such that the firm had to re-file._ Firms were then asked “Does corruption represent an **obstacle** to the operation and growth for Musyoka's business? _”_

**Table 6:  Are predictable bribes considered an obstacle?**

||Zambia|Mozambique|Mali|Nigeria<sup>a</sup>|
|---|---|---|---|---|
|No obstacle|42.5|32.31|32.31|3.15|
|Minor|23.33|22.31|22.31|4.86|
|Moderate|24.17|23.08|23.08|11.9|
|Major|10|18.46|18.46|42.14|
|Severe|0|3.85|3.85|37.96|
|N|120|130|130|(1800)|

Source: Author's calculations using Enterprise Survey extension modules a) In Nigeria, the question was posed after a series of questions asking about corruption, while in the other 3 countries it was part of extension module looking at issues of gender and the prior experience of the entrepreneur.

Consistently more than half of firms in the three countries in which this was asked in a specialized module about entrepreneur experience regarded this as either no or a minor obstacle to business (Nigeria’s results are quite different but this may be the result of “priming” as it was asked in a module about corruption and hence following a larger 33
# Table of Contents

|1.<br>INTR|ODUCTION<br>.............................<br>I|
|---|---|
|1.1|Multiple Foreign Exchange and Auction Markets in Sub-Saharan Africa.. .<br>. I|
|1.2|The Rationale for Transitiona Foreip Exdcange Auctions in SSA..... .<br>3|
|2.<br>DES|IGN CHARACTERISTCS OF AUCTIONS IN SSA.............<br>4<br>|
|2.1<br>|Duraionand frequency oftheaucdons........................._5_<br>|
|2.2|Pricing............................................. 6|
|2.3|Competitionand thenamueofbids............................6|
|2.4|*Entry and alocative resictions............................<br>7|
|2.5|Informationalrle ............................<br>9|
|2.6|Transpawry............<br>10|
|3.<br>A ST|ATISTICAL ANALYSIS OF MICRO-AUCTION DATA IN SSA .10|
|3.1|Stndard Descriptive Statistics.<br>11|
|3.2|Normalty issues .12|
|3.3|Unit Root Structural Shts and Stationariy Issues..<br>15|
|4.<br>A M<br>AUC|ACRO-ECONOMIC OVERVIEW OF THE SSA FOREIGN EXCHANGE<br>TIONS.17..<br>-|
|5.<br>CON|CLUSIONS AND POLICY LESSONS**...........................**<br>18|
|REFERENC|ES**..............**<br>21|
|TABLE 1:|List of Tables<br>Some design characteristics of SSA foreign exc&ange auctions....<br>.......<br>22|
|TABLE 2:|Average weeldy size of auction variables by aL -on type ............<br>.<br>23|
|TABLE 3:|Measures of normality for fr-differenced auction variables. ....<br>......<br>25|
|FIGURE 1 a|list of Figures<br>,b,c,d:<br>The equilibrium auction price and parallel market premium . ......<br>26|
|FIGURE 2 a|.b,r,d:.<br>Skewed and kurtotic distnbutions of auction exchange race changes . .<br>28|
|FIGURE 3 a|,b,c,d:<br>Cochrane persistence test for the auction exchange rates. ..<br>.<br>30|
|FIGURE 4 a|,b,c,d:<br>Macroeconomic policy indicators and the free exchange rate. ..<br>32|
- **23 -**

Table 8. Household and Individual Sample Statistics

|Variables|Lima, Mean<br>(Standard<br>devlation)||Rural Areas<br>(Standard<br>deviation)|, Mean|
|---|---|---|---|---|
|Household Statistics<br>|||||
|Number**of**households 898|||||
|Consumption per capita (intis) .....<br>..........<br>|**6,900**<br>|(150)<br>|2578<br>|(86)<br>|
|Female hours of work in wage work (yearly)<br>.....<br>|832<br>|(44)<br>|101<br>|(13)<br>|
|Female hours**of**work In self-employment (yearly)<br>|638<br>|(44)<br>|2232<br>|(49)<br>|
|Male hours**of**work in wage work (yea**_,)_.......**<br>|2.171<br>|(61)<br>|594|(29)|
|Male hours**of**work in self-employrnent (yearly) . ..<br>|907<br>|(50)<br>|2724<br>|(51)<br>|
|Childrens hours**of**work In self-employment (yearly)<br>|**53**<br>|(10)<br>|4<br>|(0.1)<br>|
|Total gross revenue from self-employment (Intis) . .<br>|10,700<br>|(600)<br>|9056<br>|(385)<br>|
|Total profit from self-employment (Intis) ....<br>.....<br>|6,300<br>|(400)<br>|7183<br>|(311)<br>|
|Number of children below 7**.....**<br>**...........**<br>|0.84<br>|(0.03)<br>|1.34<br>|(0.03)<br>|
|Number of children below 14**.....**<br>**..........**<br>|1.08<br>|(0.04)<br>|1.39|(0.03)|
|Number of females 1S-70 .... .<br>............<br>|1.79<br>|(0.04)<br>|1.f2 9<br>|O.02)|
|Number of people above 70**.....**<br>**...........**<br>|0.09<br>|(0.01)<br>|0.08<br>|(0.01)<br>|
|Equivalence scale .......<br>................|4|(0.10)|3.7|(0.04)|
|Individual Statistics<br>|||||
|Number**of**females 15-70, 1,611<br>|||||
|Number of males 15-70, 1.539|||||
|Participation rates in<br>|||||
|wage work for females ......<br>.............<br>|0.32<br>|(0.01)<br>|0.07<br>|(0.01)|
|self-employment for females .....<br>...........<br>|0.35<br>|(0.01)<br>|0.85<br>|(0.01)<br>|
|wage work for males ......<br>..............<br>|0.63<br>|(0.01)<br>|0.34<br>|(0.01)<br>|
|self-employment for males .....<br>............|0.35|(0.01)|0.89|**(0.01)**|
|Hours**of**work in<br>|||||
|wage work for females (yearly) .....<br>.........<br> <br>|463<br>|(21)<br>|67|(7|
|seHf-employment<br>**for**females (yearly) ....<br>......<br>|**356**<br>|(20)<br>|**1477**|(25)|
|wage work for males (yearly) .....<br>..........<br>|**1,267**<br>|(32)<br>|387<br>|(17)<br>|
|self-employment for males (yearly) ....<br>.......|529|(27)|**1777**|(25)|
|Wage rate, females (intis per day) ....<br>.........<br>|5.25<br>|(0.40)<br>|7.38|(2.45)|
|Wage rate, males (intis per day) ..............|6.41|(0.20)|2.98|(0-30)|
# The Effect of Agricultural Input Subsidies on Productivity: A Meta-Analysis

Linh Nguyen<sup>1</sup> , Jason Russ<sup>2</sup> , Margaret Triyana<sup>2</sup>

**Keywords** _: systematic review, meta-analysis, input subsidies, developing countries, agriculture_ **JEL codes** _:_ O13 O23 O33 Q18

> 1 Department of Economics, Bates College, Lewiston, Maine, United States of America

> 2 The World Bank, Washington, DC, United States of America
# Table 1: Summary Indicators of WTO Disputes

||-All Co|mplaint||
|---|---|---|---|
|**Complainant**||**Defendant**||
||**Developed**|**Developing**|**Total**|
|Developed|63|42|105|
|Developing|21|11|32|
|Total|84|_53_|137|

||**Comlete**|**d**Pnch_11,_||
|---|---|---|---|
|Complainllant||Defendt||
||**Developed**|**Developing**|Total|
|Developed|9 (7)|7 (7)|16 (14)|
|Developing|8 (8)|2 (0)|10 (8)|
|Total|17 (2)|9 (2)|26 (22)|

_Source:_ World Trade Organization _11_ Figures in brackets refer to number of cases in which complainants were successful.

31