rafmacalaba/datause-encoder-data huggingface dataset:
# **Panel G – 1994 Start Ages**

Start ages from the 1994 census are used to calculate all intensity measures. The timing of this survey is problematic in the sense that the MTI implementation had already begun at this time. This along with any anticipation of the forthcoming FPE program could alter the decision to enter school in 1994.

# **Panel H – Three Part Trend**

The district-specific linear trends are replaced with a set of district-specific trends that are allowed to change slope at two points, in 1978 and in 1987. On time entrants are partially treated beginning with the 1978 cohort, and fully treated beginning with the 1987 cohort.

# **Panel I – Regional Trends**

The district-specific linear trend is replaced with a region-specific linear trend.

# **Panel J – No Trends**

All trend variables are removed from the estimating equations.

# **Panel K – Only Zones in All Rounds of the DHS**

Data are restricted only to zones with observations in all three of the rounds of the DHS survey. This includes 25 of 30 zones in the non-MTI regions, and 48 of the 60 zones throughout Ethiopia.

# **Panel L – Zones with Fewer than 4,000 Organized Violence Deaths (1989 to 1991)**

Data from the four zones with the highest level of pre-independence violence, three of which are in the non-MTI sample, are removed from the sample. These zones contain more than 75 percent of all deaths included in the data over this time period.

# **Panel M – Zones with Fewer than 500 Organized Violence Deaths (1989 to 1991)**

Data from 13 zones with more than 500 deaths related to organized violence in the pre-independence period, eight of which are in the non-MTI sample, are removed from the sample. These zones contain more than 96 percent of all deaths included in the data over this time period.

# **Panel N – Zones without High Intensity of Famine (1985)**

Areas of Ethiopia (Tigray, Afar, Somali regions or the zones of Gonder) from which over 90 percent of individuals who registered with international shelters and camps at the height of the famine are removed from the sample (USAID, 1987). This includes 15 of the 30 zones in non-MTI regions.

A.45
- Hallward-Driemeier, Mary, Bob Rijkers, and Andrew Waxmen, 2010. “Women in Crisis: Firm level Evidence on the Labor Market Impacts of the Indonesian Crisis.” Mimeo. World Bank.

International Labour Organization, 2010a, “Global Employment Trends: 2010”

- International Labour Organization, 2010b, “Women in Labour Markets: Measuring Progress and Identifying Challenges”

- International Monetary Fund and International Labour Organization, 2010, “The Challenges of Growth, Employment, and Social Cohesion”, Background discussion paper.

- Ha, Byung-jin, Caroline McInerney, Steven Tobin, and Raymond Torres: “Youth Employment in Crisis”, ILO discussion paper 201/2010

- Leung, Ron, Marco Marco Stampini and Désiré Vencatachellum. 2009. Does Human Capital Protect Workers against Exogenous Shocks? South Africa in the 2008-2009 Crisis. IZA working paper no. 4608.

- Lim, Joseph. 2000, “The Effects of the East Asian Crisis on the Employment of Women and Men: The Philippine Case.” _World Development._ 28(7), p. 1285-1306.

- Khanna, Gaurav, David Newhouse, and Pierella Paci, forthcoming, “Fewer jobs or smaller paychecks? Aggregate Impacts of the Crisis in Selected Middle-Income Countries”

- Lokshin, Michael and Martin Ravallion, “Welfare Impacts of the 1998 Financial Crisis in Russia and the Response of the Public Safety Net,” _Economics of Transition,_ vol. 8 no. 2.

- McKenzie, David, 2004, “Aggregate Shocks and Urban Labor Market Responses: Evidence from Argentina’s Financial Crisis”, _Economic Development and Cultural Change._ 52, p. 719–758

- McKenzie, David, 2003, “How do Households Cope with Aggregate Shocks: Evidence from the Mexican Peso Crisis”, _World Development_ , Vol. 31 no. 7.

- Sabarwal, Shwetlena, Nistha Sinha, and Mayra Buvinic, 2011. “How do Women Weather Economic Shocks: What We Know” Economic Premise no. 46. World Bank

- Scarpetta, Stefano, Anne Sonnet and Thomas Manfredi. 2010  “Rising Youth Unemployment during the Crisis: How to Prevent Negative Long Term Consequences on a generation?”, OECD Social Employment and Migration Papers. No 106. OECD. Paris.

- Schultz, Theodore, 1975, “The Value of the Ability to Deal with Disequilibria”, Journal of Economic Literature, Vol. 13 No. 3, pp. 827-846

- Seguino, S. 2009. “The Global Economic Crisis, Its Gender Implications, and Policy Responses.” Paper prepared for _Gender Perspectives on the Financial Crisis_ Panel at the Fifty-Third Session of the Commission on the Status of Women, United Nations

24
officers in government banks cite fear of prosecution for corruption as a reason for their rigid lending decisions. Finally, Beck et. al. (2007) find that a supervisory strategy that forces banks to disclose accurate information to the private sector tends to lower the degree to which corruption of bank officials is an obstacle to enterprises raising external finance. To the best of our knowledge, this study is unique in that it links corruption and the quality of the regulatory environment to the demand for financial products by enterprises.

This paper is organized as follows. Section II describes the data sources used to test the hypothesis, and analyzes general patterns of enterprise finance across countries and regions. Section III presents the estimation and results while Section IV focuses on tests of robustness. Section V concludes.

# **II. Data and Main Variables**

The hypothesis that a heavy regulatory environment drives enterprises to finance informally is tested using enterprise-level data from the World Bank Enterprise Surveys.<sup>3</sup> The data used for the analysis cover approximately 3500 enterprises in 29 countries - 27 countries in Central and Eastern Europe as well as Ireland and Spain. We only include enterprises with external sources of credit to finance working capital needs. Surveys were conducted in 2005. Below we describe the main variables used in this paper. A more detailed description is provided in Table 1. Table 2 summarizes the main variables by country.

# Dependent Variable

We test the hypothesis that enterprises may choose to finance their operations exclusively through informal creditors in order to avoid regulatory scrutiny. Informal finance is defined as any financial contract or financing arrangement that is provided by lenders that are not regulated financial intermediaries. The survey data captures the percent of

> 3 www.enterprisesurveys.org

4
For the growth database, we use the Penn World Table PWT 6.2 as our baseline data source.<sup>14</sup> Hence, the “growth” database covers 187 countries over the same period as the fiscal data base, i.e. 1972-2004. Taking into account missing data, the sample includes 5380 observations hence 87% of the potential number of observations (=6171=187 countries*33 years).

Turn now to the construction of growth and fiscal events as defined in section 3 and in the annex A.3. Due to the availability of GFS data since 1972, we have a shorter time-series than HPR. This implies that we cannot use periods as long as HPR who used GDP data going back to 1950, leading them to choose 8-year periods, i.e. _n_ = 7 and giving them events over the period 1958-1992 (GDP data available until 1999). As mentioned above, due to the limitations imposed by the availability of fiscal data, we choose 5-year periods for both fiscal and growth events, i.e. _n_ = 4 . This means that the exercise covers the period 1977-2000. 15 Because there is missing data, we have also imposed that data be available for 4 out of the 5 years entering each “window”. If this condition is not satisfied, a missing value is entered for that “window”.

Having computed the fiscal and growth events on their respective databases, we merge the two into a final dataset (see annex A.2 for details). The resulting database includes 107 countries (84 developing countries, i.e. all non- High Income OECD countries), over 1977-2000. This leads to 1452 observations hence 57% of the potential number (=2568=107 countries*24 years).

As indicated in the annex A.3--where we carry out sensitivity analysis with respect to the selection of parameter values in defining events--for this sample and for the parameter values selected here, we get 58 growth events and 95 fiscal events (see table A.4 for the number of events obtained when we change parameter values over the sample). This is our benchmark data set over which exploration takes place. As discussed in table A.4, the number of events is relatively insensitive to a range of plausible parameter values. Nor are changes in the pattern of events surprising when we change parameter values.

Figure 1 plots a subset of events in this benchmark case: 25 fiscal events are simultaneous or followed by a growth event, 23 fiscal events are preceded by a growth event. The residual (47) events that are neither followed nor preceded by a growth event are not shown in the figure (also see the details in table A.4).

> 14 Using WDI database is an alternative. However, as shown by HPR, this does not affect the results.

> 15 When we define n=8 instead of 5, the period under study shrinks to 1980-1997 and we get 52 fiscal events and 18 growth events.

12
# **5 Conclusions**

This paper analyzes the important, yet often ignored, link between capital adjustment costs and the choice of fuels used by manufacturing firms. We formulate a structural model that accounts for the short run complementarity between fuel inputs and corresponding fuel-using capital stocks. Based on this model, we estimate, for each type of fuel-using capital, its frictionless stock that would be observed in a steady state. The observed deviations between actual and frictionless capital stocks reveal the level of adjustment costs faced by firms in our data.

Our econometric estimates show a significant variation in the optimal response of capital to changing fuel prices across different fuel-using technologies. For all these technologies, we find a significant gap between the frictionless and observed capital stocks, which indicates significant costs to capital adjustment. Furthermore, the shape of the investment response function shows a region of inaction when capital is above its frictionless level; this suggests there are prohibitively large costs to capital divestment. Our estimates of capital adjustment costs are an order of magnitude larger compared to earlier studies that rely on implicit estimation based on lagged values of output and fuel prices. Based on these findings we conclude that our approach may capture more realistic dynamics of fuel substitution which are currently missing from both econometric analysis of fuel substitution and from the energy-environment component of CGE models.

# **References**

- Acemoglu, Daron, Philippe Aghion, Leonardo Bursztyn, and David Hemous (2012) ‘The Environment and Directed Technical Change.’ _American Economic Review_ 102(1), 131–66

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- Ang, BW, XY Xu, and Bin Su (2015) ‘Multi-country Comparisons of Energy Performance: The Index Decomposition Analysis Approach.’ _Energy Economics_ 47, 68–76

- Atkeson, Andrew, and Patrick J. Kehoe (1999) ‘Models of Energy Use: Putty-Putty versus Putty-Clay.’ _American Economic Review_ 89(4), 1028–1043

- Bjørner, Thomas Bue, and Henrik Holm Jensen (2002) ‘Interfuel Substitution within Industrial Companies: An Analysis Based on Panel Data at Company Level.’ _The Energy Journal_ 23(2), 27–50

- Br ̈annlund, Runar, and Tommy Lundgren (2004) ‘A Dynamic Analysis of Interfuel Substitution for Swedish Heating Plants.’ _Energy Economics_ 26(6), 961–976

19
binomial regression model alone.<sup>29</sup> We take this as evidence that the AIJ investment process was influenced by the same factors that describe bilateral aid flows.

Though the mixed regime models are conceptually similar to selection models, there are differences related to how errors associated with the two processes are specified. With this in mind, we estimate an alternative model which retains the same probit selection structure as in the dichotomous model, but which uses a heterogeneous Poisson component, consistent with count data, for the investment equation (Greene 1997). Key estimation results are given in table 6.

In general, the results from the count-based model are consistent with the dichotomous model discussed earlier. The estimated correlation between the Poisson and selection equations is statistically significant, suggested that the two components of the model are not independent. Because the model has been recast in terms of the project count data, the parameters associated with the investment equation take on a different scale. Even so, for the most part, variables that were statistically significant determinants of investment outcomes in the dichotomous model are significant for the count model as well and take on the same signs. The single exception is the subsidy variable, which remains positively associated with investments but not statistically so.

The bilateral aid selection equation remains in probit form and consequently, the parameters retain the same scale. Generally, the estimated parameters coincide with the estimates from the dichotomous model, with the exception of the corruption measure, which is no longer statistically different from zero.

# 7.2. <u>An alternative model of transaction costs</u>

Evidence from the previous sections suggests that the selection models, motivated by descriptions of AIJ approval procedures, are consistent with the data. Moreover, in the case of the zero-inflated count models, the role of determinants associated with bilateral aid in reducing AIJ investment is tested and found to be significant. Generally however, selection is asserted in the estimated models rather than directly tested.<sup>30</sup> In this section, we develop a related model that does not necessarily imply selection. The model is motivated by evidence of high transaction costs in related markets and potentially provides an alternative explanation for observed investment flows. Later, we discuss the implications of this alternative explanation for policy.

> 29 Detailed estimation results are given in annex table 4. 30 Heckman (1979) discusses this general point.

18
**<u>Table 4: Factor market participation by region and farm size group</u>**

||||<br>**By r**|<br>**egion**||||**By fa**|**rm size**||
|---|---|---|---|---|---|---|---|---|---|---|
||**Total**|**Center**|<br>**North**|<br>**South**|**East**|**West**|**<50**|<br>**50-120**|<br>**120-500**|**> 500**|
|**Land market**|||||||||||
|Leases land|0.84|0.85|0.84|0.82|0.84|0.84|0.72|0.87|0.87|0.85|
|if yes, lease paid ($/ha)|105.34|110.24|107.16|92.54|96.65|107.96|85.62|105.16|108.38|117.02|
|Wants to lease more (y/n)|0.85|0.87|0.86|0.80|0.84|0.84|0.71|0.89|0.89|0.94|
|Wants to buy land (y/n)|0.78|0.80|0.82|0.70|0.82|0.76|0.56|0.86|0.85|0.89|
|if yes, price ($/ha)|1,432|1,483|1,355|1,340|1,353|1,512|1,353|1,440|1,413|1,548|
|<br>if yes, med. price ($/ha)|1,111|1,388|1,111|1,111|1,111|1,111|1,111|1,250|1,111|1,389|
|Wants to sell land (y/n)|0.03|0.03|0.04|0.03|0.05|0.05|0.06|0.02|0.03|0.00|
|if yes, selling price ($/ha)|3,142|4,627|1,369|1,728|1,852|3,254|4,787|1,848|1,206||
|if yes, med. Price ($/ha)|1,111|1,389|1,111|1,111|1,111|1,111|2,500|1,806|1,111||
|**Assets**|||||||||||
|Area cultivated (ha)|418.06|339.87|822.50|289.66|467.01|445.07|20.36|86.68|256.49|2,118.21|
|<br>Own tractor/equip|0.84|0.84|0.84|0.86|0.78|0.86|0.82|0.88|0.88|<br>0.72|
|if yes, value ($)|187,991|183,610|268,727|149,566|198,787|167,105|21,033|64,220|179,167|785,646|
|<br>.. if yes, value/ha ($)|774.79|787.09|835.41|551.16|705.56|1023.28|1212.75|672.42|680.82|609.29|
|**Credit markets**|||||||||||
|Ever received credit|0.55|0.56|0.57|0.51|0.58|0.54|0.34|0.49|0.69|0.84|
|if yes, years since last|2.29|2.28|1.96|2.82|2.01|2.07|4.78|2.85|1.57|0.78|
|<br>size (US$)|75,709|62,469|149,683|45,655|74,600|89,671|6,946|17,547|53,253|253,041|
|interest rate|7.59|7.71|7.36|7.28|9.69|6.96|11.47|8.31|7.04|5.44|
|used for working capital|0.94|0.93|0.95|0.94|0.92|0.91|0.79|0.95|0.97|0.98|
|<br>Would want to borrow|0.78|0.79|0.83|0.72|0.78|0.81|0.62|0.84|0.84|0.89|
|if yes, amount|140,530|116,669|188,375|130,829|148,939|184,438|34,508|74,299|128,475|446,956|
|interest rate|6.08|6.18|6.76|5.32|6.55|5.84|5.20|5.89|6.64|6.75|
|Credit constrained?|0.54|0.54|0.55|0.53|0.62|0.50|0.56|0.67|0.46|0.33|
|**Support received/needed**|||||||||||
|<br>Got advisory service (y/n)|0.18|0.19|0.20|0.14|0.13|0.20|0.14|0.19|0.19|0.22|
|<br>if yes, from Gov’t|0.49|0.50|0.49|0.44|0.50|0.49|0.41|0.48|0.53|0.53|
|Would want more TA|0.91|0.90|0.92|0.92|0.94|0.93|0.88|0.93|0.90|0.93|
|**Priorities for gov’t to**|||||||||||
|<br>... regulate input prices|0.29|0.30|0.24|0.31|0.24|0.30|0.34|0.28|0.26|0.25|
|<br>... procurement/markets|0.25|0.27|0.26|0.24|0.24|0.23|0.24|0.26|0.25|0.26|
|<br>... provide credit|0.15|0.14|0.17|0.15|0.14|0.15|0.12|0.16|0.16|0.16|
|... tax reduction|0.12|0.11|0.12|0.13|0.14|0.10|0.11|0.10|0.12|0.15|
|No. ofobs. (farms)|2,251|1,059|336|464|124|268|619|790|501|341|

_Source:_ Own computation from MAPF/WB/EU small producer survey.

_Note:_ As discussed in the text, a farm is considered credit constrained in 2022 if it indicated that it would want to borrow but did not have access to credit in this year. Area cultivated at the farm level reported for 2022.

23
|Bottom 25%∗<br>(10)<br>-0.369*|(0.189)<br>-0.499**<br>(0.189)<br>-0.086<br>(0.102)<br>-0.120<br>(0.078)<br>0.003<br>(0.007)<br>0.001<br>(0.001)<br>58<br>0.172<br>0.238|61.|reports the p-values of<br>ne control (25 percent|
|---|---|---|---|
|Efects<br>Trimming Tail 5%<br>(9)<br>0.055|(0.055)<br>0.134**<br>(0.056)<br>0.034<br>(0.038)<br>0.012<br>(0.023)<br>0.001<br>(0.002)<br>-0.000<br>(0.000)<br>53<br>0.144<br>0.035|mine in 1959-19|ce. (4) The last row<br>m 25 percent of fami|
|on<br>||a|an<br>to|
|Interacti<br> <br>(8)<br>0.094|(0.060)<br>0.157***<br>(0.058)<br>0.031<br>(0.039)<br>0.027<br>(0.028)<br>-0.000<br>(0.002)<br>-0.000<br>(0.000)<br>57<br>0.110<br>0.142|at Chinese F|istical signifc<br>was in the bot|
|Full Sample<br>(7)<br>0.091|(0.057)<br>0.154***<br>(0.056)<br>0.034<br>(0.038)<br>0.029<br>(0.024)<br>57<br>0.143<br>0.136|ing the Gre|% and 1% stat<br>, if the county|
|(6)<br>0.072|(0.056)<br>0.152***<br>(0.050)<br>58<br>0.126<br>0.031|e Control dur|indicate 10%, 5<br>e the value of 1|
|Bottom 25%∗<br>(5)<br>-0.422**<br>(0.182)<br>0.023<br>(0.111)|-0.079<br>(0.102)<br>-0.159**<br>(0.076)<br>0.002<br>(0.007)<br>0.001<br>(0.001)<br>58<br>0.156|sence on Famin|*, *** respectively<br>variable, which tak<br>se.|
|Efects<br>imming Tail 5%<br>(4)<br>0.077<br>(0.055)<br>0.052<br>(0.037)|0.032<br>(0.038)<br>0.017<br>(0.024)<br>0.001<br>(0.002)<br>-0.000<br>(0.000)<br>53<br>0.104|nd Guerrilla Pre|ot reported. (3). *, *<br>variable is a dummy<br>e value of 0, otherwi|
|Separate<br>le<br>Tr<br>(3)<br>0.117**<br>(0.058)<br>0.018<br>(0.042)|0.029<br>(0.039)<br>0.036<br>(0.029)<br>0.000<br>(0.002)<br>-0.000<br>(0.000)<br>57<br>0.080|of Factions a|Intercepts are n<br>, the dependent<br>961), and take th|
|Full Samp<br>(2)<br>0.114**<br>(0.056)<br>0.021<br>(0.040)|0.033<br>(0.037)<br>0.041<br>(0.025)<br>57<br>0.113|on Efects|nthesis. (2).<br>(5) and (10)<br>ring 1959-1|
|(1)<br>0.101*<br>(0.055)<br>0.041<br>(0.036)|58<br>0.079|d Interacti|s are in pare<br>In Columns<br>e famine du|
|_Power Structure_<br>YRD<br>Guerrilla<br>YRD×GuerrillaNo [_b_0]|YRD×GuerrillaYes [_b_1]<br>_Initial Conditions_<br>Ln_GVOPC_52<br>Ln_pop_52<br>_Geography_<br>Share of Plain (%)<br>Distance to Xiamen<br>(Km)<br>Obs.<br>Adjusted-_R_2<br>_H_0 : _b_0 = _b_1|Table 10: The Separate an|Note: (1). White Standard Error<br>the hypothesis that_b_0 = _b_1. (5)<br>counties sufering the most sever|

34
the tax base, and the tax liability. These tabulations also decompose this information by gender, the location of the asset (domestic versus foreign), the type of asset, the industry sector, and many wealth brackets. In addition, we use information from the personal income tax and its four components: rental income, capital income, business income, and labor income. The income tax tabulations include the number of filers and taxpayers and information on the asset value, debts, and net worth. This information is also reported by brackets of total income.

The extensive scale of Argentina’s asset disclosures and the detailed tax tabulations allow us to utilize straightforward methodologies to unpack the effects of the policy changes. Furthermore, we examine the distributional implications of the enforcement policies using the Pareto interpolation methodology to characterize, visualize, and estimate distributions of income or wealth. In particular, we use the generalized Pareto interpolation method developed by Blanchet et al. (2022) to flexibly reconstruct continuous distributions of income and assets and obtain precise series of the income and asset distribution. This method is substantially more precise than the alternatives commonly used in the literature and can often be more precise than non-exhaustive individual microdata.

# **3 The effectiveness of Argentina’s enforcement initiatives**

This section examines the effectiveness of Argentina’s tax enforcement initiatives. Section 3.1 sheds light on the prevalence and nature of tax evasion. Section 3.2 discusses the distributional patterns of tax evasion. Sections 3.3 analyzes the fiscal externalities that asset revelations have on the wealth and capital income tax bases. Finally, Section 3.4 discusses why the 2016 policy was so much more effective than previous enforcement efforts.

## **3.1 Revealing foreign and domestic assets**

Figure 2 illustrates the number of individuals who filed wealth tax declarations. The series is indexed to 100 in 2015, before the 2016 amnesty. This figure reveals several noteworthy findings. First, neither the 2009 nor the amnesties in 2013–15 had any noticeable impact on the number of people reporting their assets to the tax authority. In stark contrast, there was a remarkable 310% surge in the number of taxpayers who declared ownership of foreign assets in 2016. This substantial increase reflects the success of Argentina’s tax amnesty in encouraging individuals who possessed foreign assets to disclose them. Second, this surge in the reporting of foreign assets continued even five years later, while the number of 8