rafmacalaba/datause-encoder-data huggingface dataset:
forward-looking incentive for Indonesian farmers to invest in productive assets. The above effect was strong among poor farmers.

There are some interesting implications. Whether the investment indeed had a dynamic positive impact on agricultural productivity is an important question. Our preliminary result suggests that investments undertaken between 2007 and 2010 have positively impacted productivity gains amid the global food price crisis. This implies that the food price spike seemed to induce an upward shift in the production frontier in agriculture.

Moreover, whether, in the long run, the food price crisis led to a divergence between rich and poor farmers is an important question to answer. Our results indicate that, though large landholders tend to invest in productive assets regardless of price shocks, small farmers capture the price spike as a rare opportunity to relax their liquidity constraint and increased their investments.

# **References**

Carter. M, Little. P, Mogues. T, and Negatu. W (2007) “Poverty Traps and Natural Disasters in Ethiopia and Honduras”, _World Development_ , 35(5), pp. 835-856 Collins. W and Margo. R (2007) “The Economic Aftermath of the 1960s Riots in American Cities: Evidence from Property Values”, _The Journal of Economic History_ , 67(4), pp. 849883 Deaton. A (1990) “Saving in Developing Countries: Theory and Review”, _Proceedings of The World Bank Annual Conference on Development Economics_ , pp. 61-96 Dercon. S and Christiaensen. L (2010) “Consumption Risk, Technology Adoption, and Poverty Traps: Evidence from Ethiopia”, _Journal of Development Economics_ , 96(2), pp. 159-173 25
districts that rarely report a sector. The assumption is that districts that regularly file their expenditures are more likely to report correct numbers. The difference between allowing 1 and 2 years of missing reporting is fairly significant, most likely due to some years generally having fewer reports. For example, the years 2007 and 2011 had fewer than 200 districts reporting across all sectors<sup>10</sup> , the reasons for which are unknown.

Table 3: Comparison of Data depending on Balanced vs Unbalanced

||Bala|nced|Unba|lanced|Unbala|nced 1yr|Unbala|nced 2yr|
|---|---|---|---|---|---|---|---|---|
|Sector|Mean|Total|Mean|Total|Mean|Total|Mean|Total|
|Agriculture|90|720|228|1,827|168|1,343|205|1,643|
|Economy|92|736|230|1,838|170|1,359|207|1,653|
|Education|92|736|230|1,841|171|1,366|207|1,654|
|Environment|71|568|212|1,694|129|1,030|171|1,366|
|General Administration|107|856|234|1,873|176|1,409|213|1,703|
|Health|92|736|230|1,840|171|1,366|207|1,654|
|Housing and Public Facilities|10|80|105|841|38|304|71|568|
|Infrastructure|91|728|230|1,836|168|1,344|206|1,650|
|Social Protection|53|424|203|1,622|119|949|158|1,267|
|Tourism and Culture|59|472|190|1,518|115|920|157|1,256|
|Public Law and Order<sup>_a_</sup>|||231|1,386|||123|738|
|Total||6,056||18,116||11,390||15,152|

Given how the expenditure data are distributed, the unbalanced panel that allows for 2 missing years of reporting is the best compromise between a balanced panel and retaining enough observations across the sectors. To make sure that this does not affect our damage indices too much, we have shown how the disaster descriptives change in Table 4.

Compared with the full set of disasters shown in Table 1, the mean and standard deviation for floods and earthquakes fall. The main reason for the earthquake coefficients to move down is that some big earthquakes hit districts that were later split. One of the more active earthquake areas is the province of Aceh, which has experienced many district splits. This is

> 10General Administration had 202 reports in 2011.

14
are collected by informal parental committees can quickly stop completely once a few parents refuse to pay them - an effect that often occurs in public-good settings if punishment is weak (cf. <u>Fehr and Gächter 2000). In line with our expert discussions, we thus interpret the migration</u> effects as quickly spilling over within schools.<sup>33</sup> To ensure that our results are not driven by local heterogeneity across communities rather than migration, we add a within-community dimension to the original IV’s community-level variation. We interact the network-growth IV with the household’s mean years of education since more educated households can be expected to be better able to respond to the growth-pull mechanism approximated by our IV. The new variable is positively related with migration and statistically significant at the 1% level. The estimated effect of migration on payments to teachers is almost identical to our main estimates (table S.13).

Two motivations for ceasing bribe-paying are plausible: (i) migrant parents being generally less tolerant of corruption due to their experience abroad; and (ii) migrant parents demanding actual cognitive achievement instead of good grades because they have witnessed the unimportance of Moldovan certificates relative to actual skills for success abroad. A full 96% of caregivers replied that education was important to be successful abroad. Yet, there is no significant reduction in the perceived quality of children’s individual schools (table S.14).<sup>34</sup> This gives us confidence that our results are not driven by differences in the cost-benefit analysis of the Moldovan school system between non-migrant and migrant households.

In order to provide some evidence of robustness as well as external validity of our study, we draw on another, less detailed, dataset to show that a similar negative correlation of migration and bribe paying exists also in data independent of ours. The so called _Barometer of Public Opinion_ of Moldova’s Institute for Public Policy, a well-regarded biannual survey which collects individuals’ opinions on a wide range of topics regarding politics, values, and related issues in Moldova, covered informal payments to authorities and migration status in the April 2013 survey.<sup>35</sup> Those individuals with migration experience to the West were more likely to have had contact with the justice system and were more likely to have been asked for bribes for the

> 33. There is no statistically significant correlation between any school budget variable and the migration share of pupils in the household survey. Also, if migrant parents were planning to send their children abroad and therefore stopped paying local teachers, there should be strong differences within schools.

> 34. Alternatively, the main effects of migration on the provision of educational inputs remain unchanged after including the perceived school quality variable as an additional control (available upon request). 35. The sample contains 1100 individuals from 76 communities and is nationally representative of the adult population. All results are available upon request.

16
To examine the effects of the program, Lavy and Schlosser (2005) use a quasi-experimental difference-in-difference methodology (supplemented by instrumental variables as an alternative identification strategy). Their approach relies on the fact that the program was rolled out over time, allowing them to compare learning gains in schools that received it early on with those in schools that received it later. Lavy and Schlosser find that this tutoring program had a positive impact on both the students and the schools benefiting from the program. The program increased the probability of earning a matriculation certificate for tutored students by 12 percentage points, implying an average improvement of 22 percent from the base rate. The targeted schools saw approximately an increase of 3.3 percentage points in the mean matriculation rates, equivalent to an improvement of 6 percent. The program did not appear to affect non-tutored students.

On the other side of the ledger, Lavy and Schlosser note that the average cost of the program is rather high, at $1,100 per tutored student, or around 40 percent of the annual expenditure per high school student in Israel. Nevertheless, Lavy and Schlosser (2005) estimate the program’s internal rate of return at a rather high 20 percent.<sup>22</sup> While this makes the remedial education program less costeffective for increasing matriculation than two other incentive-based programs in Israel examined by the authors, they note this rate of return still makes the remedial tutoring program cost-effective.

# _Private household-financed tutoring in Vietnam_

Private tutoring is very popular in Vietnam, with around 34 percent of the households with children in school sending their children to private lessons. A large majority of these households (90 percent) allocate between 1 percent and 5 percent of the total household expenditure to private tutoring. The proportions of students taking private tutoring are 31%, 56%, and 77%, respectively, in primary school, lower secondary school, and upper secondary school (Dang, 2007b).

Dang (2007b) estimates the learning effects of tutoring and addresses the endogeneity of tutoring with an instrumental variables strategy, which used tutoring prices as the instrument. Using data from the Vietnam Living Standards Surveys for 1992-93 and 1997-98, Dang shows that private tutoring improves student academic performance. After controlling for other individual, household, school and community characteristics, Dang finds that increasing yearly spending on private tutoring from 0 to only 20,000 VND<sup>23</sup> —about US$1.50 in 1998, or about 0.4 percent of mean consumption for households with children in school—has strongly positive effects on performance. It decreases a primary-school student’s probability of having a poor and average academic ranking, respectively, by around 0.01 and 0.04, while increasing the probability of having a good or excellent academic ranking by 0.05. At the same increase in expenditure, the corresponding figures for a lower secondary student are higher at around 0.01, 0.07, and 0.08 respectively.

12
**Table 21: Wage and productivity premiums by occupation, education, union status, and age**

|||All countries|||Anglophone|||Francophone||
|---|---|---|---|---|---|---|---|---|---|
||Production<br>function|Wage<br>equation|Equal premia<br>P-values|Production<br>function|Wage<br>equation|Equal premia<br>P-values|Production<br>function|Wage<br>equation|Equal premia<br>P-values|
||N=1438|N=7726||N=583|N=3210||N=855|N=4516||
|Unionized firm|-0.003|0.01||0.041|**0.112**||0.149|-0.05||
||(0.104)|(0.038)||(0.162)|(0.063)||(0.136)|(0.046)||
|Unionized worker|**0.978**<br>(0.153)|-0.012<br>(0.038)|0.947|**1.755**<br>(0.364)|-0.089<br>(0.059)|0.020|**0.56**<br>(0.163)|0.054<br>(0.045)|0.003|
|Age (32+)|**0.575**<br>(0.121)|0.019<br>(0.026)|0.00|**0.474**<br>(0.147)|0.050<br>(0.044)|0.000|**0.499**<br>(0.152)|-0.014<br>(0.031)|0.002|
|Manager|**1.018**|**0.708**|0.06|**0.864**|**1.046**|0.010|**1.758**|**0.515**|0.762|
||(0.348)|(0.078)||(0.402)|(0.146)||(0.799)|(0.079)||
|Professional|**1.909**|**0.467**|0.54|**1.336**|**0.625**|0.695|1.211|**0.381**|0.840|
||(0.725)|(0.059)||(0.694)|(0.129)||(0.831)|(0.064)||
|Skilled production|**1.006**|**0.197**|0.34|**1.19**|**0.271**|0.786|**1.368**|**0.155**|0.623|
||(0.198)|(0.036)||(0.293)|(0.054)||(0.430)|(0.044)||
|Vocational degree|**1.299**|**0.167**|0.678|**3.303**|**0.171**|0.048|0.419|**0.218**|0.118|
||(0.316)|(0.058)||(1.085)|(0.085)||(0.507)|(0.068)||
|Junior/senior sec.|**0.863**|**0.126**|0.180|**1.532**|**0.221**|0.519|**0.837**|0.062|0.399|
||(0.196)|(0.040)||(0.488)|(0.066)||(0.267)|(0.050)||
|University|**0.807**|**0.521**|0.004|-0.36|**0.466**|0.000|**1.041**|**0.507**|0.157|
||(0.251)|(0.056)||(0.240)|(0.094)||(0.329)|(0.068)||
|H0: Manager coeff=1|||0.96|||0.735|||0.343|
|H0: Professional coeff =1|||0.21|||0.628|||0.800|
|H0: Skilled production coeff=1|||0.98|||0.516|||0.393|
|<br>Joint test: occupation coefficients =1|||0.64|||0.85|||0.72|
|Joint test: occupation premia are equa|l||0.22|||0.07|||0.94|
|<br>H0: Vocational coeff=1|||0.344|||0.034|||0.252|
|H0: Secondary coeff =1|||0.486|||0.276|||0.542|
|<br>H0: University coeff=1|||0.442|||0.000|||0.902|
|<br>Joint test: education coefficients =1|||0.344|||0.000|||0.627|
|Joint test: education premia are equal|||0.016|||0.000|||0.190|
|H0: Unionized workercoeff=1|||0.886|||0.038|||0.007|

Notes: Shares of uinon members, older workers, managers, professionals, skilled production workers, holders of vocational, secondary, and upper-level degrees are calculated as the mean values at the country, industry, size class, and firm age class levels. The estimation of the production function and the wage equation is done jointly, clustering the errors at the firm level. Standard errors in parentheses below coefficients. **Bold** coefficients indicate significance at the 10% level or better. The production function coefficients represent one plus the productivity premium; the wage regression coefficients are semi-elasticities. The equality of premia test is a Wald test of the null hypothesis that the wage regression coefficient is equal to the production function coefficient minus one for the corresponding category.

48
study and that of (Pavcnik 2002) is that the treatments here are identified at the firmlevel as oppose to (Pavcnik 2002) who identifies it at the sector-level. What kind of biased was imposed in (Pavcnik 2002) while aggregating different firms in the same sector? Estimating an specification including Mexican import tariffs as controls, similar to (2) in Table 1, but identifying treated firms at the 4-digit sector level, shows that sectorlevel identification leads to smaller treatment effects. Hence the productivity effects of Chilean integration is higher than the results presented in (Pavcnik 2002).

# 4.2.2 Controlling for Potential Endogeneity of Treatment and Evaluating the Role of Switchers

As mentioned before, if the assumption of exogeneity of the treatment (being integrated within a trade liberalization period) is violated and our treatments are correlated with some unobservable characteristics, the OLS estimated coefficients will be biased. So far we have tried to alleviate this endogeneity problem by including firm-level variables as controls. If the decision to become integrated (treatment) is correlated with any of the observable characteristics used as controls, our results are still consistent. However, the problem of endogenous treatment is especially acute in our case because we have to deal with what is an established finding in the literature: most efficient (and productive) firms self-select into export markets (Bernard and Bradford Jensen 1999, Melitz 2003). It is therefore reasonable to expect a causal relationship from productivity levels to integration status. If this is true, the coefficients presented in the previous Sections may be biased.

Table 1 presents the number of firms that change status in each of the years of the sample distinguishing between those that begin importing or exporting and those that stop importing or exporting. We can see that there is a substantial number of plants, about 20 percent, that switch integration status every year.

In order to tackle this potential endogeneity problem, we re-estimate our model using a reduced sample that includes only firms that remain within the same integration status throughout the period of analysis and exclude “switchers” or firms that at some point change integration status. By excluding the switchers, we artificially avoid the problem of self-select into the treatment.

When we re-estimate our main equation by excluding the switchers we also include a dummy identifying those plants that in t + 1 will switch to a different integration status. The results are reported in Column (1) of Table 2 and are qualitatively similar to the results of our basic model discussed previously in Section 4.2.1. The fact that our 20
become more risk averse. Such investor behavior is reflected in the risk premium which moves in opposite direction with consumption growth.<sup>27</sup> The fact that the investor’s risk aversion is counter-cyclical poses a dilemma for the government that wants to hold _dθT_ constant after time _T_ . The problem for the government in a world with time varying risk averse investor with counter-cyclical properties is that the cost of issuing debt will be high in bad times, precisely when the government does not want to raise taxes and would rather run a primary deficit and finance the deficit through debt issuance. On the other hand, if the hedge is purchased during good times when investor’s risk appetite is great and the risk premium is low, the timing of the purchase coincides with the timing when the government can most afford it. This implies that the hedge must be purchased in good times financed by the primary surplus or seigniorage. If this is deferred to later periods when the situation deteriorates, the debt manager is compelled to purchase future hedges at a high cost, precisely at the moment when it can least afford it.

# **4. Applications: Games Policy Makers Play**

Having set out the analytical framework, we now examine the consequences of uncoordinated mix of fiscal, monetary, and debt management policies. The objective is to illustrate the types of policy switching necessary if sustainability conditions do not hold.<sup>28</sup> We do so by building on Sargent and Wallace (1993) who examined the consequences of uncoordinated fiscal and monetary policies, whereby the fiscal authority chose a course of policy without regard to any coordinated policy goals. The central bank, on the other hand, pursued a responsible monetary policy fighting inflation. Under this uncoordinated policy scheme, they showed how policies became unsustainable unless either of the authorities adjusted its original course of policy. We extend this game by overlaying debt management to the original games under fiscal and monetary

> 27  The time variance of the risk premium reflects the risk aversion of the investor whose marginal rate of substitution of consumption utility falls during good times and rises during bad times.

> 28  Bohn (1991) suggested that if there remained any positive probability that sustainability might not hold, then there should be a complete description of the policy rule that would be followed in the event that sustainability condition were to be violated.

26
# **Political Economy of Reform**

Stuti Khemani<sup>1</sup> World Bank _JEL classification codes_ : H1, H41, H6, E6 _Keywords_ : policy reforms; political economy; institutions; norms; preferences

> 1 This article has been invited by Avinash Dixit as a contribution to the Oxford Research Encyclopedia of Economics and Finance, edited by Jonathan Hamilton, Avinash Dixit, Sebastian Edwards, and Kenneth Judd. I thank Avinash Dixit for his thoughtful comments and helpful suggestions. The opinions expressed here are those of the author and need not coincide with those of the World Bank or its Executive Directors.
Engle and Yoo (1987) “Forecasting and Testing in Cointegrated Series”, Journal of Econometrics, vol. 35, pp. 143-159.

Engle, R. and C. Granger (1987) “Cointegration and Error Correction: Representation, Estimation and Testing”, Econometrica vol. 55, No. 1, pp. 251-276.

Friedman, J. and J. Levinsohn (2002). “The Distributional Impacts of Indonesia’s Financial Crisis on Household Welfare: A Rapid Response Methodology”, _The World Bank Economic Review_ , vol. 16, No. 3, pp. 397-423.

Johansen (1988) “Statistical Analysis of Cointegrating Vectors”, Journal of Economic Dynamics and Controls, vol. 12, No. 3, pp. 231-254.

National Bureau of Statistics (2011) “South Sudan Cost to Market Report”, August 2011.

Stock, J. & M. Watson (1993). "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, vol. 61, No. 4, pp. 783-820.

Timmer (1986) “Getting Prices Right: The Scope and Limits of Agricultural Price Policy. Ithaca: Cornell University Press.

Timmer, C.P. (1996) “Does Bulog stabilize rice prices in Indonesia? Should it Try?” Bulletin of Indonesian Economic Studies, vol. 32 No. 2, pp. 45-74 Varela, G., E. Aldaz-Carroll and L. Iacovone (2012) “Determinants of Market Integration and Price Transmission in Indonesia” Journal of Southeast Asian Economies, vol. 30 No. 1, pp. 1944.

WFP (2014) “ FAO/WFP Crop and Food Security Assessment Mission to South Sudan” Special Report.

The World Bank (2011), “A Poverty Profile for the Southern States of Sudan”. Africa Region, mimeo The World Bank. 2012b. “Agricultural Potential, Rural Roads, and Farm Competitiveness in South Sudan”.

World Bank (2014), “South Sudan Poverty Note: Impact of a Continued Internal Conflict on Food Security and Poverty”, World Bank, mimeo.

The World Bank. (2014a). “South Sudan Trade Strategy Report: Economic Diversification, Regional Integration, and Growth”.

The World Bank. (2014b). “South Sudan Jobs & Livelihoods Report”, forthcoming.

The World Bank. (2014c). “South Sudan: Fostering Competitiveness and Growth”, mimeo.

29
comparability, since the data from developed<sup>and developingnationsare never mixed.</sup> An important caveat is that we do not<sup>have data for all banks in every nation.The BankScope database</sup> covers banks in each nation that control<sup>at least85%of the bankingassets,whichmaydeletea significant</sup> proportion of SMALL banks. As well,<sup>STATE banks in somenations may be significantly underrepresentedin</sup> our data set because some STATE<sup>banks do not file the standardaccounting forms used in the BankScopedata</sup> set. In addition, for the earlier years of<sup>our sample,FOREIGN bank coveragein BankScope may have been less</sup> incomplete. Thus, the STATE and FOREIGN<sup>marketsharesmaybesmallerthanthoseinstudiesthatuse</sup> alternative methods of measuring these<sup>shares, but we use the shares drawnfrom BankScopein order to havea</sup> time series-cross section of STATE and<sup>FOREIGNshares that is consistent with our STATEand FOREIGNbank</sup> efficiency measures and the remainder of<sup>our data set.11While the omissionof some potential community banks</sup> reduces the informativeness of our analysis,<sup>this data limitationdoesnot eliminate the value of thefindings.It</sup> seems reasonable to assume that the<sup>market sharesandweighted-averageefficiencyranks forthereporting</sup> community banks are indicative of the<sup>healthof communitybanksas a wholerelativeto other banks.</sup> The three endogenous variables shown<sup>in Table1 are GDP GROWTH,the annual growth rate of GDP,</sup> SME EMP, the employment share held by<sup>SMEs, and BKLENDGDP, the ratio oftotal banklending to GDP.The</sup> summary statistics for these variables are<sup>for the years1994-2000.Data on GDP and totalbank lending are taken</sup> from the IMF International Financial Statistics<sup>(2002).Data on SME EMPdata are takenfrom Ayyagari, Beck,</sup> and Demirgaic-Kunt (2002) and Klapper<sup>and Sulla (2002).This data is collected from official government sources</sup> and includes all businesses with less<sup>than 250 employees -including services, manufacturing,trade,agriculture,</sup> etc. The data for GDP GROWTH and<sup>BKLENDGDP are available for almost every year for every nation, but we</sup> have significantly fewer observations<sup>for SME EMP.</sup> The key exogenous variables for both<sup>developed and developing nations include SMALL SHARE, the</sup> total market shares held by SMALL banks,<sup>and SMALL COST EFF RANK andSMALL PROFIT EFF RANK,</sup> the weighted-averages of cost and profit<sup>efficiencyranksforSMALLbanks.Thesummarystatisticsforthe</sup> exogenous variables shown are for 1993-1999<sup>becausethese variablesare laggedone yearin theempirical</sup> analysis to help mitigate potential endogeneity<sup>problems.The variablesarecomputedfromthe BankScope</sup> database using information on all the available<sup>banks in each nation in each year,and then converted into national</sup> For example, in some cases our STATE<sup>shares aresignificantlylessthan those reportedin Barth,Caprio,andLevine</sup> (2001), which is based on a survey of bank regulators<sup>and supervisors.</sup> 11