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36
Table 1 lists the country-level variables for the larger 17-nation GI sample, the percentage of survey respondents in each country supporting increased aid, and the percentage of survey respondents who favor aid only to non-corrupt countries. Interestingly, the percentage of respondents who favor aid only to non-corrupt countries is always less than the % who favor aid in general, except in the United States. Table 1 also provides level-2 summary statistics for the GI sample. These rows demonstrate considerable variability across countries in all of these variables, including support for foreign aid.

Table 2 provides bivariate correlations among support for aid and the countrylevel independent variables. Two of the significant correlations in this table show that survey respondents in larger countries tend to be less supportive of increasing aid, even though larger countries already tend to provide less aid. Larger countries tend to spend a larger percentage of national income on the military, consistent with collective action theories of Olson (1965) and Olson and Zeckhauser (1966). Richer countries tend to provide more aid.

# _The Multi-Level Model_

To test the theories outlined above, we use multilevel models that simultaneously estimate individual- and country-level effects. The data in both surveys are hierarchically organized with individuals nested within countries, and information at the both the individual level and the country level is used to determine support for foreign aid. We use the statistical package HLM 6.04 for the analyses (Raudenbush, Bryk, Cheong, and Congdon 2001).

For the WVS analysis, the full specification of level one of the multi-level model is: yij = β 0j + β 1j FEMALE + β 2j AGE + β 3j ANY CHILDREN+ β 4j INCOME+ β 5j IMPORTANCE OF RELIGION + β 6j ATTENDS RELIGIOUS SERVICES+ β 7j MEMBER OF THE WORLD + β 8j NATIONAL PRIDE+ β 9j INTEREST IN POLITICS+ β 10j TV CONSUMPTION+ β 11j BORN IN COUNTRY+ β 12j MILITARY+ β 13j LAWYERS AND TEACHERS + β 14j LEFT-RIGHT POSITION + β 15j POOR ARE LAZY + β 16j POOR CAN 11
78

# **Table A4**

## **Dependent Variable: Growth Rate of Real GDP Regressions with Economic Openness**

|**Years : 1960 - 2000**|**Reg A4.1**|**Reg A4.2**|**Reg A4.3**|**Reg A4.4**|**Reg A4.5**|**Reg A4.6**|**Reg A4.7**|**Reg A4.8**|**Reg A4.9**|**Reg A4.10 **|**Reg A4.11**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2128*****|**0.2100*****|**0.2171*****|**0.2152*****|**0.2029*****|**0.2181*****|**0.2109*****|**0.2161*****|**0.2141*****|**0.2011*****|**0.2199*****|
|**Growth of Labor**|**0.5694*****|**0.5810*****|**0.6170*****|**0.6148*****|**0.5496*****|**0.5425*****|**0.5948*****|**0.6316*****|**0.6283*****|**0.5575*****|**0.5539*****|
|**Initial GDP (1960)**|**-3.8747*** **|**-3.4415*** **|**-3.2582*****|**-3.1453*****|**-3.9135*** **|**-3.1928*** **|**-3.3176*** **|**-3.1794*****|**-3.0907*****|**-3.7969*** **|**-3.0297*****|
|**Economic**<br>**Openness**||**0.9307****|**1.0236****|**0.8193***|**0.8615****|**0.9364****|**0.9767****|**1.0575****|**0.8473***|**0.8994****|**0.9845****|
|**Education Variables**||||||||||||
|**HC15**|**4.3613****|**2.9926**|**2.3752**|**2.0142**|**2.2126**|**3.5653**||||||
|**Sq of HC15**|**-1.0208****|**-0.6858**|**-0.5468**|**-0.4342**|**-0.5976**|**-0.8715**||||||
|**HC25**|||||||**2.5797**|**2.0147**|**1.6447**|**1.8078**|**3.1898**|
|**Sq of HC25**|||||||**-0.6375**|**-0.5030**|**-0.3800**|**-0.5535**|**-0.8414**|
|**Log (Educational**<br>**Quality)**|**0.2933**|**0.3596**|**0.5140**|**0.4745**|**0.3881**|**0.3052**|**0.4147**|**0.5703**|**0.5313**|**0.4414**|**0.3461**|
|**Innovation Variables**||||||||||||
|**Log (Patents)**|**0.1909*****|**0.1921****|||**0.2029****|**0.2013****|**0.1966****|||**0.2082****|**0.2059****|
|**Log (Journals)**|||**0.2351*****|||||**0.2394*****||||
|**Royalties per**<br>**capita**||||**0.000003*****|||||**0.000003*****|||
|**ICT Variables**||||||||||||
|**Phones**|**0.0033*****|**0.0013**|**0.0010**|**0.0028***|||**0.0013**|**0.0010**|**0.0029**|||
|**Computers**|||||**0.0061*****|||||**0.0063*****||
|**Internet Users**||||||**0.0055**|||||**0.0058**|
|**Constant**|**-3.6660***|**-2.8202**|**-3.7620***|**-2.3008**|**-1.7087**|**-3.0310**|**-2.5466**|**-3.5633**|**-2.0873**|**-1.4304**|**-2.7282**|
|**R squared**<br>|**0.6137**<br>|**0.6311**<br>|**0.6665**<br>|**0.6512**<br>|**0.6889**<br>|**0.6338**<br>|**0.6277**<br>|**0.6645**<br>|**0.6487**<br>|**0.6883**<br>|**0.6309**<br>|
|**No. of Countries**<br>*, **, *** denotes significanc<br>Robust standard errors in p|**85**<br>e at the 90, 95<br>arentheses ob|**81**<br>and 99 perce<br>tained using W|**81**<br>nt confidence<br>hite's correcti|**80**<br>level respectively<br>on for heterosced|**78**<br>asticity.|**81**|**80**|**80**|**79**|**77**|**80**|
_23_ _Price Support at Any Price?_ incentives for choosing labor-intensive technologies, making use of relatively low skilled labor. Let us also note, that the process may quickly create a positive feedback: growth of income in rural areas will create additional demand necessary to absorb the newly created output.

In this paper we do not make any particular proposal of the policy mix for accelerating reduction of employment in agriculture. We tend to believe, that such a mix may be created, and may work in the long run (we are talking about a program for 15-20 years). Its main target would be not to replace the market, but to help market solve a socially and politically sensitive problem of moving farmers out of agriculture"<sup>4</sup> .

The main question we try to answer in this section is what would be the impact of a successful policy of speeding up process of employment reduction in agriculture on the income relation and restructuring Polish agriculture. In other words, we try to find out _what would happen to Polish agriculture if resources that may be spent on price supporting are, instead, invested outside the agricultural sector._ The main target of the job promotion program is therefore not to create new jobs, but to encourage and to channel a flow of new investment towards the rural areas and towards low-skilled labor intensive technologies (that fit the labor supply of farmers).

Both experiments are compared with results of the feasible price supporting policy (results of the FAST PATH/OUTPUT REDUCTION experiment).

# 4.2 Alternatives to price support: simulation experiments

Policy costs are calculated differently in the experiments. While the MORE PENSIONERS experiment provokes only higher social security spending, the JOB PROMOTION FOR FARMERS needs financing by taxpayers, at a level comparable with the maximal support agreed under URA.

14 A target for the JOBS PROMOTION FOR FARMERS experiment is more than doubling the average yearly fall of employment in agriculture from 1.6% in the base run to 3.5-4% over a 20 years period. The base run figure is broadly in line with Polish experience in 1967-90 (1.2% yearly fall). Increasing the rate would require a substantial effort, particularly in an economy with high initial urban unemployment rate (that is why we assume that a supporting policy mix is necessary). However, the target rate is similar to rates of reduction of employment in agriculture observed in the most succesful EU countries in the period 1967-90 (Germany 3.8% yearly fall, Belgium 3.5%, Denmark 4.4%, Eurostat data).
||2015<br>|/16<br>|2016<br>|/17<br>|2017<br>|/18<br>|
|---|---|---|---|---|---|---|
|R billion|Guarantee|Exposure”|Guarantee|Exposure”|Guarantee|Exposure”|
|Public institutions|465.9|255.8|475.7|290.4|466.0|300.4|
|ofwhich:|||||||
|Eskom|350.0|174.6|350.0|202.8|350.0|220.8|
|SANRAL|38.9|27.2|38.9|29.4|38.9|30.1|
|Trans-Coledon Tunnel Authority|25.8|21.2|25.6|20.9|25.7|18.7|
|South AfricanAirways|14.4|14.4|19.1|17.8|19.1|11.8|
|Land ondAgricultural<br>Bank of|6.6|5.3|11.1|3.8|9.6|6.6|
|South Africa|||||||
|Development Bank ofSouthern|13.9|4.4|12.5|41|12.3|42|
|Africa|||||||
|South AfricanPost Office|4.4|1.3|4.4|40|o4|o4|
|Transnet|3.5|3.8|3.5|3.8|3.5|3.8|
|Denel|1.9|1.9|1.9|19|24|23|
|South African Express|41.4|O58|1.1|og|og|08|
|Industria! Development|2.0|O2|o4|a2|Os|a1|
|Corporation|||||||
|South AfricanReserveBonk|3.0|-|3.0|=|-|-|
|Independentpowerproducers|200.2|114.0|200.2|1235.8|200.2|122.2|
|Public-privatepartnerships”|10.3|10.3|10.0|10.0|5.6|9.6|

1. A full list of guarantees is given in Table 11 of the statistical annexure in the Budget Review 2. Toto! amount of borrowing ond accrued interestfor the period mode against the guarantee

3. These amount only include the national and provincial PPP agreements
# **X. Annexes**

## **Annex 1**

## **Table A1. Robustness checks: Model Results using 2015 HBS and National Accounts**

|Fiscalelement|National<br>accounts (in<br>millions, old<br>leks)|Household<br>Budget<br>Survey (in<br>millions, old<br>leks)|Ratio total<br>amount HBS<br>/ national<br>accounts|Total amount<br>national<br>accounts/national<br>accounts private<br>consumption|Total<br>amount<br>HBS/HBS<br>private<br>consumpti<br>on|
|---|---|---|---|---|---|
|Total Household Consumption 2015|11,499,303|6,490,000|56%|||
|_Direct taxes and contributions_||||||
|_[Personal Income Tax, no evasion]_|318,030|..||||
|Personal Income Tax (with evasion)|318,030|240,000|75.5%|2.8%|3.7%|
|Social insurance (excl pensions)|..|57,100|..|||
|Pension contributions|..|524,000|..|||
|Social insurance (incl pensions)|598,880|581,100|97.0%|5.2%|9.0%|
|<br>Health insurance|<br>92,010|<br>86,400|93.9%|0.8%|1.3%|
|All contributions|690,890|667,500|96.6%|6.0%|10.3%|
|**All direct taxes and contributions**|1,008,920|907,500|89.9%|8.8%|14.0%|
|_Indirect taxes_||||||
|_[VAT, no evasion]_|1,312,030|997,000|_76.0%_|_11.4%_|_15.4%_|
|VAT (with evasion)|1,312,030|735,000|56.0%|11.4%|11.3%|
|Excise fuel (households, direct effects)||50,500||||
|Excise fuel (households, indirect effects)||6,050||||
|Excise fuel (households, total)|221,929|56,550|25.5%|1.9%|0.9%|
|<br>Excise alcohol/coffee|<br>28,682|<br>27,650|96.4%|0.2%|0.4%|
|Excise cigarettes|<br>131,193|<br>50,800|38.7%|1.1%|0.8%|
|<br>Excise other||..|..|..|..|
|Total excises (no evasion)|449,000|135,000|30.1%|3.9%|2.1%|
|**All indirect taxes**|1,761,030|870,000|49.4%|15.3%|13.4%|
|**All taxes and contributions**|**2,769,950**|**1,777,500**|64.2%|24.1%|27.4%|
|_Social protection spending_||||||
|Old Age Pension|860,710|739,000|85.9%|7.5%|11.4%|
|<br>Disability pension|<br>155,000|<br>106,000|68.4%|1.3%|1.6%|
|<br>Ndihma Ekonomike|44,473|37,000|83.2%|0.4%|0.6%|
|Energy transfers|31,500|28,400|90.2%|0.3%|0.4%|
|Social Benefits/Elderly Services|6,092|12,200|200.3%|0.1%|0.2%|
|<br>Unemployment Benefit|<br>8,000|<br>4,450|55.6%|0.1%|0.1%|
|<br>Family Pension|<br>4,207|<br>11,700|278.1%|0.0%|0.2%|
|**Total social protection**|1,310,000|910,400|69.5%|11.4%|14.0%|
|_Social spending - Education and Health_||||||
|Education|403988|234000|57.9%|3.5%|3.6%|
|Health|,<br>409,841|,<br>232,000|56.6%|3.6%|3.6%|
|**Total social spending**|813,829|466,000|57.3%|7.1%|7.2%|
|**Total spending**|**2,123,829**|**1,376,400**|64.8%|18.5%|21.2%|

37
||3.|Cropping Pattern, Irrigation, and Yields<br>in the Coello District (1987) .....<br>.......|. 33|
|---|---|---|---|
||4.|Cropping Pattern, Irrigation, and<br>Yields in the RUT Irrigation<br>District (1987) ...........................|<br>34|
||5.|Efficiency of Conveyance and<br>Distribution in Coello and RUT||
|||Irrigation Districts ......................|<br>35|
||6.|A Breakdown of the Coello Irrigation<br>District Staff ...................... .....|<br>36|
||7.|Budget for Coello Irrigation<br>District (1987) ...........................|<br>37|
||8.|Budget for Coello Irrigation<br>District (1988) ...........................|<br>38|
||9.|Actual Expenses for RUT Irrigation<br>Districts (1987) ..........................|<br>39|
||10.|Projected Revenues from Water<br>Charges in the Coello Irrigation<br>District (1988) ..........<br>..............|...<br>40|
|APPENDIX 1|Chief<br>Irrig|Characteristics of Coello and RUT<br>ation Districts ........<br>.................|...<br>41|
|APPENDIX 2|Agree<br>the I<br>is De|ment No. 41: General Regulations for<br>rrigation Districts whose Management<br>legated to Water Users Associations ........|<br>45|
|MAPS|IBRD<br>Ir<br>IBRD<br>IBRD|No. 21534- Roldanillo-Unioni-Toro (RUT)<br>rigation District<br>No. 21535-<br>Irrigation Districts<br>No. 21536- Coello Irrigation District||
being fully formal, which makes it a multidimensional concept (Perry and Maloney, 2007; Ulyssea, 2018, 2020).

The literature uses both macro and micro methods to measure informality.<sup>3</sup> Our work measures informality from the firm’s side and relies on microeconomic approaches that produce disaggregate indicators at the sectoral, regional, and enterprise levels. Given the obvious problems of self-reporting by firms about their non-compliance, proxies (e.g., self-employed and microenterprises) are widely used to measure informality, though with the caveat that very small firms can still comply with the law and be formal (Restrepo Echavarria, 2015). Firm-level surveys that include direct questions about the various forms of informality, such as the one used in our work, can be considered a lower bound of the size of informality (Putnin, š and Sauka, 2015), given that some firms might misreport (to avoid being on the radar of the government).<sup>4</sup> However, in most cases, researchers rely on only one aspect of informality ignoring the multidimensional nature of the issue (Restrepo Echavarria, 2015; Perry and Maloney, 2007).

# **2.2 Stylized Facts on Informality**

The informality literature has identified a set of empirical regularities about informality (non-necessarily causal) across developing countries. _First_ , the share of informality in developing countries is large and persistent Ulyssea (2020), accounting for 70%-80% of the labor force and about 20% of GDP (Loayza, 2018). _Second_ , informal firms tend to be a drag on growth not only because these firms have lower productivity (Perry and Maloney, 2007; La Porta and Shleifer, 2014; La Porta et al., 2008) but also because they use valuable resources leading to misallocation and aggregate productivity losses. _Third_ , informal firms tend to employ low-skilled or low-educated managers and workers (La Porta and Shleifer, 2014; De Mel et al., 2010). _Fourth_ , informal firms lag behind in innovation, investment in new capital, and technology adoption, perhaps due to smaller size and lack of complementary factors (intangibles) such as human capital and managerial capabilities.

# **2.3 Informality Types or Views: Exit and Exclusion**

In exploring the root cause of informality in Latin America, Arias et al. (2010) propose two distinct but complementary lenses: informality driven by ‘exclusion’ from state benefits vis-

> 3See Medina and Schneider (2019) for an example of macroeconomic-focused methods, Medina and Schneider (2018) for a survey of macro methods and Elgin et al. (2021) for a comprehensive database using both model-based and survey-based measures of informality that covers more than 160 economies for the period 1990-2018.

> 4Some surveys use indirect questions about the sector in cases where firms might have strong incentives to provide incorrect responses (Putnin, š and Sauka, 2015; Gërxhani, 2004; Reilly et al., 2019).

5
BOX [2: Sector adjlulstme Iend' in ' " , ' i XH~IiAN Ghana's First Education Sector AdJustment Cdit (1987) supported tie government's education reform program to reverse the decid-long deterioration in educational infrastructure and to use recurrent expenditures more efficiently The program is being continued under the Second Educatidn Sector Adjustment Credit (1990). The reform supports the following measures:

* Duration of schooling: Shorten pre-university sehooling from 17 to- 12 years, and increase the length of the school day and the shool year.

* Curriculum: Manufacture a limited number of basic textbooks locally, many of them in local languages, and introduce a simplified syllabus for junior secondary schools. Levy fees for textbooks in secondary and tertiary education.

* Teacher training: In response to a growing shortage of qualified teachers, shorten the teacher training course for new teachers by a year to three years.

* Budget: Audit the Ministry of Education staff,. aboish all posts "filled" by non-existen **t** eachers, and reduce non-teaching staff. HalveAtie room and board'. subsidy for secondary and tertiary institutions, replacing subsidies with scholarships or loans for the most needy students.

MALI: Mali's Education Sector Consolidation Projedt (989) combines a s adjustment program with an investment element that fociuses on primary education and the management of sector resources.

The sector adjustment program establishes the policy and institutional -framework for improving, at all levels of the system:: a:) equt, tyacsd .b) performance and relevance, and c) the cost-effective.management of human, financial and physical resources. For example, student o nto econfdary and higher education are controlled in line with projected budgetary resources and trends in labor market demand. The adjustment program also supports revision of textbooks and teacher training policies, mobilize rate resources, ad restructures the budget in favor of primay education and materials.

Within this framework, the investment element supports the core investments of a long-term development program. It inreases enrollments through a school construction, rehabilitation, and repair program. Additional investment focuses on revising teacher training and primary education curricula, as well as on providing textbooks, pedagogic materials, and in-service training.

Sources: Nicholas (1988), World Bank (1989b).

... ...

9
What is needed are instruments correlated with contractual choice, but uncorrelated with unobserved attributes of the plot, and which also vary across plots within the same household. Such variables are typically hard to come by in farm household surveys. In our data set, though, tenant cultivators report the characteristics of their landlord, including among other things his total landholdings. So, we have data on the plot owner’s landholdings regardless of whether the plot is leased in or owner-cultivated. Since farmers endowed with relatively more land tend to lease out a greater proportion of it, a given plot is more likely to be leased in if it belongs to a larger landowner. Our principal instrument for ∆Lc = Lci in equation 5 is therefore ∆Ao = Aoi − Aoj = Aoi − Acj, where Aoi denotes the total landholdings of the owner of plot i. For owner-cultivated plots (Lci = 0) this will just be the landholdings of the cultivator himself (i.e. Aoj = Acj).<sup>16</sup> Identi fi cation also requires that E [∆Ao∆εc] = 0. In other words, the difference in the total landholdings of the owner-cum-tenant and the landlord of his leased plot should not be correlated with differences in the unobserved attributes of the owner-cultivated and leased plots. We show why this should be the case in the next subsection.

Using the same argument, additional instruments can be constructed from other assets of the landowner (tubewells and tractors) as well as by allowing the impact of relative land endowments on the leasing decision to vary by the landholdings of the tenant-cultivator (interacting ∆Ao with the land endowment of the cultivator Ac). Note that the direct effect of the cultivator’s landowership on his input use has already been removed from equation 5 along with the fi xed effect νc.

# 3.2 Adverse selection and landholdings

To reiterate, our identi fi cation strategy requires zero correlation between the unobserved quality of a plot and the landholdings of its owner, conditional on the household fi xed effect. Adverse selection per se is not a problem for us in this regard. If a plot’s quality is private information to its owner, and some fraction of each landowner’s plots are ‘good’ and the rest ‘bad’ quality, then bad plots would be leased out and good plots selfcultivated, as the latter could never command a rent commensurate with their productivity.<sup>17</sup> While leased-in plots would thus be of lower average quality than owner-cultivated

> 16 Note that, while landowner characteristics may be endogenous in the fi rst-stage contractual choice regression due to landlord-tenant matching (as argued recently by Ackenberg and Botticini, 2002), a correlation between instruments and error term in a fi rst-stage regression does not invalidate identi fi cation of the second stage regression parameters.

> 17 Generalizing to a continuous distribution of quality, there will be a criticial quality level above which the owner will not want to lease out the plot (see Wilson, 1980, for an exposition).

10