rafmacalaba/datause-encoder-data huggingface dataset:
recessions. But their identification strategy requires data on government consumption forecast errors, which essentially limits their study to only OECD countries.

Our paper makes three contributions. First, by clearly distinguishing between the state of the business cycle and the fiscal position, we establish that the fiscal position is a unique state that determines the size of the fiscal multiplier. Second, we show the empirical relevance of the transmission mechanisms that underlay the state-dependent effects due to fiscal position: the Ricardian channel and the interest rate channel. Third, compared to previous studies, our sample includes a larger set of countries covering advanced and developing economies, thus providing a general result on the state-dependent effects due to fiscal position.

The rest of the paper is organized as follows. Section 2 presents the econometric methodology. Here, we discuss the IPVAR model, the identification strategy, and the database. We present estimates of state-dependent multipliers in Section 3. In Section 4, we discuss the transmission mechanisms that highlight the Ricardian and the interest rate channels. Section 5 discusses robustness exercises and Section 6 concludes.

# 2 Empirical Methodology

## 2.1 Econometric Model

A standard panel structural VAR (SVAR) estimates a single set of parameters which then yields an “average”or unconditional multiplier. Our objective is to go beyond the unconditional multiplier, and investigate how multipliers can depend on specific macroeconomic conditions, in particular fiscal position of governments. For that, we deploy the Interacted Panel Vector Autoregressive (IPVAR) model where the main innovation, with respect to a standard panel SVAR, is that the model coeffi cients vary deterministically according to conditioning (state) variables. Thus, the IPVAR results in a framework where model dynamics and hence, estimated multipliers are conditional on the state variables. By choosing the conditioning variable to be a measure of fiscal position in the IPVAR, we estimate multipliers that depend on fiscal position.

The IPVAR model, in its structural form, is represented by: where for a given country i in period t, gc represents real government consumption, gdp real gross domestic product (GDP), reer the real effective exchange rate, and ca current account balance (as a share of GDP).

We take government consumption as the fiscal instrument and we track the effects of fiscal policy in terms of GDP. Separately, we check the robustness of our results by tracking fiscal outcomes in terms of private consumption and private investment (Section 4). Real effective exchange rate and the current account are included in the model to account for open economy features that characterize most of the countries in our sample. The matrix X captures additional controls, which include the time-invariant country fixed effects, and U is a vector of uncorrelated, i.i.d. (structural) 4
about commodity stabilization funds that are used to save during commodity booms and dis-save in bad times. In theory, fully saving (dis-saving) temporary increases (declines) in commodity prices should reduce fiscal procyclicality to zero.<sup>29</sup> Given the importance of fiscal rules, we will also assess their effect on fiscal procyclicality. As discussed in Section 2, however, we need to be concerned about the possible endogeneity of fiscal rules. While fiscal rules may reduce procyclicality, the existence of fiscal procyclicality may prompt policymakers to adopt fiscal rules. We thus need an instrument to deal with this problem. We will use bureaucracy quality, also from the ICRG, as our instrument. Bureaucracy quality is viewed by the ICRG as a shock absorber that limits policy changes when governments alternate in power. The bureaucracy tends to function autonomously without major interference from the political process. Since implementing enforceable fiscal rules requires technocrats well versed in economic policy and political stability to enable their success, we would expect bureaucracy quality and fiscal rules to be positively correlated.<sup>30</sup> At the same time, bureaucracy quality should not directly impact fiscal procyclicality since a qualified and stable bureaucracy is not related in an obvious way to government spending and/or tax policy.<sup>31</sup> We will thus run cross-country regressions of the correlation between the cyclical components of real GDP and real government spending on government stability, law and order, fiscal rules, and sovereign wealth funds, using bureaucracy quality as an instrument for fiscal rules in our instrumental variables estimations (two-stage least squares). Results are shown in Table 2.

Columns (1)-(4) in Table 2 report the regressions results with each variable individually.<sup>32</sup> We can see that all four variables have the expected negative sign and We take the average per country of this variable across the sample time interval. The data source is the Sovereign Wealth Fund Institute.

> 29While Fasano (2000) finds mixed evidence, the empirical analysis of Asik (2017) supports our conjecture (at least for oil-rich countries).

> 30Indeed, the simple correlation between bureaucracy quality and fiscal rules is 47 percent.

> 31 Further, as of 2020, public employee compensation varies widely across countries, ranging from 5%-15% in Europe to 30%-50% in Africa, suggesting that factors other than bureaucratic quality come into play. In fact, in the public service literature (see, for example, Dolan, 2002, and the references therein), the main determinant of bureaucrats’budgets is the extent to which their priorities reflect the public’s preferences.

> 32We estimate this regression for the full sample (as opposed to the CE sample). The reason is that, for this particular set of variables, we have only 38 observations in the CE sample (as opposed to 83 in the full sample). Further, if we estimate regressions 1-4 in Table 4 keeping constant the sample at 83 observations, the same results follow.

17
119

# Expenditure 3: Health Expenditure as Percent of Total Government Expenditure

||1975|1980|1985|
|---|---|---|---|
|1 AFRICA|5.60|4.99|5.00|
|(L)|5.60|4.99|5.00|
|(M)|6.72|5.60|6.35|
|1MAngola<br>||||
|1L Benin<br>||-<br>|-<br>|
|1MBotswana|6.34|5.38|4.97|
|1 L Burkina Faso<br>|6.56<br>|5.82|6.21|
|1 LBurundi|7.18|-||
|1 M Cameroon|-|5.11|5.11|
|1 L Cape Verde<br>||||
|1 L Central African Republic||||
|1L Chad<br>|3.75|-||
|1 L Comoros<br>||||
|1M Congo, P.R.|||-|
|1 M Cote d'Ivolre<br>|-|3.95|-|
|1L DjiboutL|.|||
|1 L Equatorial Guinea||-||
|1 L Ethiopia|4.70|3.45|3.09|
|1MGabon||||
|1 L Gambla,The|8.23|7.38|-|
|1 LGhana<br>|8.29|6.96|9.80|
|1 L Guinea<br>||||
|1LGulnea-Blssau|||5.19|
|1 L Kenya|8.01|7.83|6.43|
|1 L Lesotho|5.46|-||
|1 L Liberia|9.26|5.20|-|
|1 L Madagascar||-||
|1 L Malawi|5.80|5.53|6.87|
|1 L Mali<br>|-|3.13|1.69|
|1 L Mauritania|-|||
|1MMauritius<br>|8.15|7.48|7.71|
|1 L Mozambique||||
|1 L Namibia||||
|1 L Niger|.|4.05|-|
|1 L Nigeria|2.25|-|2.16|
|1L Rwanda|6.55|4.55|-|
|1 L Sao Tome and Principe|-|||
|1MSenegal|5.95|4.69||
|1L SierraLeone|4.59|-|-|
|1 L Somalia<br>|5.90<br>|.<br>||
|1 L Sudan|1.56|1.40||
|1 K Swaziland|6.43|7.15|7.50|
# **Appendix Table 2.3: Correlates of Choice of Accounting vs Marketing**

Dependent variable: Chose Accounting rather than Marketing for Insourcing or Outsourcing

||(1)|(2)|
|---|---|---|
|Baseline finance practices|0.003||
||(0.016)||
|Baseline marketing practices|0.011||
||(0.016)||
|Baseline overall business practices|-0.105*|-0.072**|
||(0.060)|(0.030)|
|Way firm spends and generates cash not very firm-specific|-0.017<br>||
||(0.057)||
|Type of customers and customer acquisition strategies not very firm-specific|0.056|0.044|
||(0.056)|(0.046)|
|Believes can verify work of internal accountant|0.168*|0.119|
||(0.088)|(0.073)|
|Believes can verify work of internal marketing worker|-0.147||
||(0.104)||
|Believes can verify work of outside accountant|-0.013||
||(0.077)||
|Believes can verify work of outside marketing firm|0.018||
||(0.079)||
|Believes marketing and accounting workers can be trusted|-0.021||
||(0.053)||
|Believes marketing and accounting providers can be trusted|0.050|0.044|
||(0.052)|(0.049)|
|Owner is Female|-0.144***|-0.126***|
||(0.050)|(0.048)|
|Owner's Age at Baseline|-0.004|-0.004|
||(0.003)|(0.003)|
|Owner has masters or more education|0.015||
||(0.050)||
|Baseline Sector is Construction|-0.181**|-0.182**|
||(0.088)|(0.090)|
|Baseline Sector is ICT|-0.268***|-0.256***|
||(0.081)|(0.080)|
|Baseline Sector is Entertainment|-0.330***|-0.337***|
||(0.121)|(0.118)|
|Baseline Sector is Hospitality|-0.154*|-0.147*|
||(0.086)|(0.081)|
|Baseline registration with CAC|0.026||
||(0.073)||
|Baseline insources or outsources accounting|0.052||
||(0.056)||
|Baseline insources or outsources marketing|0.031||
||(0.061)||
|Full-time employees|0.003|0.007|
||(0.009)|(0.008)|
|insourcing|-0.030||
||(0.050)||
|Sample Size<br>Notes:<br>Marginal effects from probit estimation shown, column 2 shows variables select|253<br>ed by lasso.|253|

Marginal effects from probit estimation shown, column 2 shows variables selected by lasso. Sample restricted to insourcing and outsourcing firms that took up treatment and answer round 2 survey.

55
31 Figure 4 (continued): OECD commodity market welfare reduction indexes, 1980–84 and 2000–04 (percent)

# (b) Western European countries

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1980‐84 2000‐04<br>SoybeanSunflowerRapeseed Egg BarleyTomato Wine Wheat Potato Oat Rice MaizePigmeatSheepmeat MilkPoultry Beef Sugar<br><!-- End of picture text --> Source: Authors’ calculations based on data in Anderson and Valenzuela (2008).
# **Reference**

Aldrich, H. E., & Auster, E. (1986). “Even dwarfs started small: Liabilities of size and age and their strategic implications”, _Research in Organizational Behavior_ , 8:165–198.

Agarwal, R., and Gort, M. (2002) “Firm and Product Life Cycles and Firm Survival”, _American Economic Review_ , 92(2): 184-190.

Austin, J. and D. Rosenbaum (1990) “The Determinants of Entry and Exit Rates into U.S. Manufacturing Industries.” _Review of Industrial Organization_ 5 (2):211-223.

Ayyagari, M., A. Demirguc-Kunt, and V. Maksimovic (2013) “Size and Age of Establishments: Evidence from Developing Countries”. World Bank Policy Research Working Paper, No. 6718.

Baldwin, J. and Yan, B. (2011). “The death of Canadian manufacturing plants: Heterogeneous responses to changes in tariffs and real exchange rates”, _Review of World Economics,_ 147 (1):131-167 Bernard, A., Eaton, J., Jensen B., and Kortum, S. (2002) “Plants and Productivity in International Trade.” _American Economic Review_ 93( 4):1268-1290 _._ Bernard, A. and Sjöholm, F. (2003). “Foreign Owners and Plant Survival”. _NBER Working Papers 10039._ Bartelsman, E., Haltiwanger, J., and Scarpetta, S. (2004) “Microeconomic Evidence of Creative Destruction in Industrial and Developing Countries.”, World Bank Policy Research Working Paper, 3464.

Bloom, N., Sadun, R., and Van Reenen, J. (2012) “Management as a Technology?”, mimeo, ~ <u>http://web.stanford.edu/ nbloom/MAT.pdf</u> Box, M. (2008). “The Death of firms: Exploring the effects of environment and birth cohort on firm survival in Sweden”, _Small Business Economics_ , 31:379–393 Cable, J. and Scwalbach, J. (1991). “International Comparisons of Entry and Exit”. _Entry and Market Contestability: An International Comparison._ P.A. Geroski and J. Schwallbach (eds.). Oxford, U.K. and Cambridge, MA. Blackwell Publishing.

Charness, G., & Gneezy, U. (2012). "Strong Evidence for Gender Differences in Risk Taking." _Journal of Economic Behavior & Organization_ , 83(1):50-58.

Cirmizi, E., L. Klapper, and M. Uttamchandani (2010) “The Challenges of Bankruptcy Reform”, World Bank Policy Research Working Paper, 5448.

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35
- 2.22 -

Flgure 9 highlights the relativo price movemonts in cocoa and maio at domestic and world pricos. (The **prices** of the two other main alternatioeS to cocoa - yams and cocoyams -- follow that of maize farly closely and the graph of their domestic price ratis would be shUar to that of cocoa to maize). The praph brhg out two points clearly -- whilst the domostic prico ratio of cocoa to malze has declhed fairly C eadily from its late 1960* peak, the world price ratio has If anythin drifted upwards, reflecting the steady fall In the real world price of maize, to the point where It now stands at Its nw,st value for over fifty **_yers._** Thus whilt cocoa has steadlly become relatively more valuable than maize in the rest of the world, Ghana has boen attompthg to maintain the domestic real porlce of maize hilst alowi the real price of cocoa to fal.

How does one nterpret the evidenco? Although the relative price of cocoa to other crops has deteriorated, the **pries** of non-traded products such as yams and cocoyams has not faln much In ra tr_m over the past fifteen yars. Had their been a substantial switch Into these non-traded alternatives one might have expected their supply to Ins ease relative to dmnd and to have dopressod their price. Of course, It may be farmers chose to grow maize istead of non.-traded crops, and that the price was prevented from falling by protective trade policies (and, at the Increasingly overvalued exchange rate, the domestic price of iaported crops such as maize and rice might be expected to have fallen rapidly In real _term,_ even faster than the fall In world prices). Even here, though, there Is not much evidenco of a large increase In domestic food productlon. Figuro 10 gives (completo) ovdenco on areas under food crops. The recent rise appears to mahW be a recovery from the poor performance of the mid 19709, and there In no perceptble trond over th period from 1970. Flgure 11 conf Irms that this also hold for maize, which , urnko the other coreals, Is grown In the cocoa producing area.

Given the substantlal irease In population during this period It Is hard to accept that the rural labor force redirected Its efforts from cocoa to alterativo crops. The
Hakobyan (2020) finds that GSP expiration resulted in a decrease in probability of exporting by 0.3 percentage point.

Next, we examine how the impact of suspension varies across countries with different durations of AGOA suspension. In our sample, Cameroon, Guinea-Bissau, Mali, Mauritania and Niger were suspended from AGOA for less than two years. We consider these countries as _short-term suspensions_ . The remaining countries (Burundi, Central African Republic, Democratic Republic of Congo, Côte d’Ivoire, Eswatini, The Gambia, Guinea, Madagascar and South Sudan) have been suspended for more than two years. We consider them as _long-term suspensions_ . We then compare the impact of AGOA suspension for counties with long-term and short-term suspensions by further interacting the triple interaction term with short-term and long-term suspension dummies.

**Table 3: Benchmark results for AGOA suspension effect**

|Dependent variable|lnExports|lnExports|lnExports|Export dummy|
|---|---|---|---|---|
|Sample|Full|Suspended|AGOA eligible|Full|
|||countries|products||
|Method|Triple diff|Diff-in-diff|Diff-in-diff||
||(1)|(2)|(3)|(4)|
|Marginal effect|-39%|-26%|-47%||
|Suspended×Country×Product|-0.501*|-0.304***|-0.640***|-0.020*|
||(0.292)|(0.100)|(0.113)|(0.01)|
|Product-year Fes|Yes|No|Yes|Yes|
|Country-year Fes|Yes|Yes|No|Yes|
|Country-product Fes|Yes|Yes|Yes|Yes|
|Number of observations|584,434|179,246|158,399|584,434|

The marginal effects throughout this paper are calculated as exp(β)−1 if the dependent variable is lnExports. Robust standard errors in parentheses are clustered by country and product. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively. Column (1) reports the results from specification with a full sample and a full set of interactive fixed effects: country– product, country–year and product–year. Column (2) restricts the sample to suspended countries and includes country–product and country–year fixed effects. Column (3) restrict the sample to AGOA eligible products and includes country–product and product–year fixed effects.

17
# <mark>Table 12: Export starters and non-starters three years after the start</mark>

_____________________________________________________________________________________________________________

|Country|Year of|Labor<br>|productivity g<br>|rowth premi<br>|a of export-starte<br>|rs (percent|) [p-value]<br>|No. of<br>||No. of|
|---|---|---|---|---|---|---|---|---|---|---|
|_______________|start<br>_________________|Sales/<br>_________|worker<br>___________|Value<br>__________|added/worker<br>______________|TF<br>________|P<br>observ.<br>______________|<br>_______|starters<br>_____||
|Belgium|All Years|8.8|[0.23]|11.5|[0.14]|-13.4|[0.37]|457||39|
||1999|6.7|[0.80]|26.5|[0.42]|16.0|[0.66]|97||6|
||2000|-8.2|[0.72]|-9.8|[0.34]|-14.5|[0.28]|104||6|
||2001|24.0|[0.05]|13.4|[0.01]|15.8|[0.11]|132||12|
||2002|22.0|[0.15]|23.6|[0.11]|38.4|[0.07]|124||15|
|China|All Years|-3.4|[0.00]|-2.1|[0.14]|-1.2|[0.51]|37,723||893|
||2001|-3.8|[0.01]|-4.2|[0.04]|0.2|[0.94]|18,476||390|
||2002|-3.1|[0.04]|-0.9|[0.66]|-1.2|[0.57]|19,247||503|
|Colombia|All Years|0.7|[0.66]|-1.0|[0.61]|1.1|[0.58]|9,705||157|
||1981|2.8|[0.49]|4.7|[0.36]|0.9|[0.85]|1,805||26|
||1982|2.0|<br>[0.60]|-4.7|<br>[0.30]|1.6|<br>[0.69]|1,910||33|
||1983|-2.0|[0.59]|0.2|[0.97]|0.4|[0.94]|1,997||26|
||1984|0.8|[0.83]|-1.7|[0.70]|-3.4|[0.42]|2,024||37|
||1985|0.4|[0.92]|-2.4|[0.57]|5.7|[0.25]|1,969||35|
|France|All Years|-0.4||-0.7||-0.6||12,496||1,529|
||1993|0.3|[0.76]|-0.9|[0.37]|-0.9|[0.36]|1,268||189|
||1994|-0.1|[0.95]|2.1|[0.03]|1.8|[0.06]|1,338||201|
||1995|1.0|<br>[0.29]|-0.5|<br>[0.64]|-0.7|<br>[0.51]|<br>1,348||189|
||1996|-0.1|[0.91]|0.1|[0.94]|0.1|[0.89]|1,313||151|
||1997|-1.0|<br>[0.37]|-1.9|<br>[0.10]|-1.3|<br>[0.29]|<br>1,358||138|
||1998|-2.0|[0.08]|-1.4|[0.20]|-1.7|[0.13]|1,418||165|
||1999|-0.6|[0.64]|-1.2|[0.37]|-0.4|[0.75]|1,481||163|
||2000|-0.4|[0.67]|-1.9|[0.05]|-0.3|[0.77]|1,482||150|
||2001|-0.9|[0.33]|-1.5|[0.12]|-1.9|[0.10]|1,490||183|

58
These data sets are well-suited to our needs in that they include comparable modules on school enrollment from before and after the onset of FPE, distinguishing between public and private school attendance. Use of integrated household surveys with detailed consumption and expenditure information allows us to highlight changes in the socioeconomic composition of public and private school enrollment over this period. In addition, specific questions on education expenditure provide the basis for examining pre-reform variation in government school fees and secular changes in the – equilibrium price of private schools. The clustered nature of the household survey samples providing information on multiple households in the catchment area of a given public or private – school is central to our empirical strategy to estimate social interaction effects, including the impact of school crowding on the incentive to send one’s child to a government or private school.

Table 1 presents summary statistics from the survey samples used in the regressions in the following sections, separated by the pre- and post-FPE periods. Perhaps the most striking feature of the summary statistics is the _increase_ in real expenditure per household on primary education and primary school fees, and the decline in the average number of children per household enrolled in government primary schools. Note however that these are unweighted statistics; a more detailed comparison of time trends using survey weights to compute nationally-representative statistics is given in Tables 2 and 4.

# **School-level administrative data**

We use test-score data from the Kenya Certificate of Primary Education (KCPE) exam as a proxy for school quality. The KCPE exam is standardized nationwide, administered at the end of primary school by the Kenya National Examination Council, and covers English, Kiswahili, math, science and history. Our combined test-score data set constitutes a panel of all public and private primary schools in Kenya, for each year from 1998 to 2006.

Because we use the test scores as one determinant of school choice, we seek to construct a proxy of the quality of all public and private schools available to a given pupil. We use administrative districts as a conservative proxy of this choice set and collapse test scores to district averages, separately for public and private schools. The household survey data and administrative test9
Policy Research Working Paper 6601

# **Abstract**

The ease of converting and transferring currency is a crucial consideration for firms investing in a foreign economy. The Converting and Transferring Currency data and indicators measure foreign exchange restrictions most relevant for foreign direct investment across economies to identify common policies and benchmark the restrictiveness of economies’ foreign exchange regimes. Of 98 economies included in the analysis, 53 economies maintain generally unrestricted foreign exchange regimes for foreign direct investment. But 24 economies impose moderate to heavy restrictions across most transactions covered by the Converting and Transferring Currency indicators, with another 21 economies imposing administrative or procedural requirements. All high-income economies measured by the Converting and Transferring Currency data maintain unrestricted foreign exchange regimes for foreign direct investment, and the two poorest regions of South Asia and Sub-Saharan Africa are the most restrictive regions on average. Still, there is significant variation in restrictiveness across economies at similar income levels: 38 percent of low-income and lowermiddle-income economies impose moderate to heavy restrictions on transactions covered by the Converting and Transferring Currency data, but another 38 percent of such economies maintain fully unrestricted regimes. The Converting and Transferring Currency indicators are significantly correlated with measures of remittances risk, foreign direct investment, and exports, and especially with inflows of foreign direct investment per capita. The paper concludes by identifying common areas for regulatory reform that could be considered across economies, depending on country-specific conditions and the macroeconomic context of potential foreign exchange reforms.

This paper is a product of the Global Indicators and Analysis Department, Financial and Private Sector Development Network. 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:// econ.worldbank.org. The author may be contacted at janderson9@worldbank.org.

_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