rafmacalaba/datause-encoder-data huggingface dataset:
36

# **2) FIh2tionn sand** diseqgnilihrin Fxngennim technical prngre.s.

A second version of the fact that returns to education may vary across countries is the view that the major reason that education increases productivity is that it increases a workers ability to learn new skills and respond to changes (Schultz, 1975, Nelson and Phelps, 1977). Schultz (1975) argues that in a technologically stagnant agricultural environment the production gains from education would be zero, as even the least educated could reach the efficient allocation of factors and only when new technologies and inputs are available does education pay off.

Rosenzweig and Foster (1995) add to the literature which supports this conjecture. Using panel data on agricultural production they fLnd that the returns to schooling to farmers were very low where technological progress was low, but that technological progress increased the return to schooling. The return to primary schooling versus no schooling, (measured as the additional net farm profit) in the average district studied was 11 percent (446 rupees on average profits ), a low figure quite similar to the very low values per year of schooling for agriculture mentioned above. However, the authors emphasize the interaction of returns to schooling with exogenous increases in district wide farm profits. In an area with farm profits one standard deviation higher than the average district the predicted return to primary schooling was 32 percent. The down side of this calculation, however, is that the estimated returns to schooling were negative in all those districts in which growth of profits was 2/3 of a standard deviation less than the mean profits4. In one interpretation, schooling only paid off handsomely in those areas in which the Green Revolution brought technological advances that could be taken advantage of by the more educated farmers while in technologically stable areas education was not important for output.

This explanation might suggest that the reason education appears not to have paid off in Africa, for instance, is that there has been little exogenous change in the technical production functions appropriate to 48 Another way to summarize their results is that since the education return is only barely significant for the average district (t of 2.03) in districts where exogemus progress is only slightly below the mean the return is indistinguishable from zero.
Table 4: Access to irrigation enables transition to dry season horticulture from perennial bananas, causes large increases in dry season labor and input use, yields, and sales; profitability depends on household’s shadow wage

# (a) Dry season

|Culti-<br>vated|Irri-<br>gated|Horti-<br>culture|Banana|HH<br>labor/<br>ha|Input<br>exp./ha|Hired<br>labor<br>exp./ha|Yield|Sales<br>/ha|Prof<br>Shado<br>= 0|ts/ha<br>w wage<br>= 800|
|---|---|---|---|---|---|---|---|---|---|---|
|(1)<br>|(2)<br>|(3)<br>|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|RDD (Site-by-season FE, Spe|cifcation|1)|||||||||
|CA<br>0.005|0.162|0.137|-0.133|70.8|6.3|3.7|73.1|55.5|63.9|9.3|
|(0.041)|(0.024)|(0.024)|(0.037)|(17.5)|(1.5)|(2.1)|(23.2)|(14.5)|(21.0)|(16.5)|
|[0.909]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.082]|[0.002]|[0.000]|[0.002]|[0.573]|
|SFE (Spatial FE, Specifcatio|n 2)||||||||||
|CA<br>0.022|0.171|0.156|-0.142|76.9|4.3|3.2|55.0|49.3|49.1|-3.0|
|(0.044)|(0.030)|(0.029)|(0.035)|(20.7)|(1.8)|(2.6)|(28.5)|(19.2)|(25.7)|(20.8)|
|[0.610]|[0.000]|[0.000]|[0.000]|[0.000]|[0.019]|[0.221]|[0.054]|[0.010]|[0.057]|[0.886]|
|# of observations<br>2,537|2,537|2,536|2,536|2,523|2,527|2,527|2,402|2,527|2,402|2,400|
|# of clusters<br>196|196|196|196|196|196|196|196|196|196|196|
|Control mean<br>0.391|0.058|0.065|0.245|59.5|2.5|3.7|82.3|49.7|76.1|32.8|

# (b) Rainy seasons

|Culti-<br>vated|Irri-<br>gated|Horti-<br>culture|Banana|HH<br>labor/<br>ha|Input<br>exp./ha|Hired<br>labor<br>exp./ha|Yield|Sales<br>/ha|Prof<br>Shado<br>= 0|ts/ha<br>w wage<br>= 800|
|---|---|---|---|---|---|---|---|---|---|---|
|(1)<br>|(2)<br>|(3)<br>|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|RDD (Site-by-season FE, Spe|cifcation|1)|||||||||
|CA<br>-0.092|0.035|0.016|-0.158|8.5|1.1|3.7|-22.6|-13.3|-26.4|-31.8|
|(0.025)|(0.009)|(0.018)|(0.038)|(23.1)|(2.9)|(3.4)|(30.8)|(18.5)|(28.5)|(26.4)|
|[0.000]|[0.000]|[0.371]|[0.000]|[0.714]|[0.710]|[0.276]|[0.462]|[0.472]|[0.354]|[0.228]|
|SFE (Spatial FE, Specifcatio|n 2)||||||||||
|CA<br>-0.053|0.059|0.048|-0.168|9.9|2.1|3.1|-15.4|5.6|-19.4|-27.3|
|(0.027)|(0.012)|(0.025)|(0.034)|(24.7)|(3.1)|(4.5)|(30.8)|(21.6)|(27.5)|(31.9)|
|[0.051]|[0.000]|[0.056]|[0.000]|[0.689]|[0.511]|[0.490]|[0.617]|[0.793]|[0.480]|[0.393]|
|# of observations<br>4,236|4,236|4,235|4,235|4,215|4,223|4,223|4,085|4,223|4,085|4,078|
|# of clusters<br>196|196|196|196|196|196|196|196|196|196|196|
|Control mean<br>0.838|0.016|0.073|0.274|226.7|16.1|15.9|271.5|85.1|239.8|59.5|

_Notes:_ Regression coefficients on a command area indicator for the sample plot (“CA”) are presented above. Specifications control for distance to the command area boundary, its interaction with CA, and log area of the sample plot. RDD specification includes site-by-season fixed effects, and SFE specification includes spatial fixed effects. Standard errors are in parentheses, and p-values in brackets.
The average MFN tariff in Russia has increased between 2001 and 2003. On an un-weighted simple average basis it increased from 11.6% to 12.9%; on a weighted average basis it increased from 11.4% to 14.5%. This average is calculated based on MFN tariffs.

Collected tariffs are less than MFN tariffs because of several exemptions in the Russian tariff structure. Most notably, CIS imports usually enter tariff free (although there are exceptions to this rule), and personal and private imports also enter tariff free for sufficiently small values of imported shipments. We also provide estimates of the tariff rates where we adjust for zero tariff collections on CIS imports. That is, in our formulas for calculating the tariff on a tariff line, we set ad valorem and specific rates on imports from the CIS countries equal to zero to take into account the special trade regime within the CIS _. We call these calculations our estimated collected tariff rates._ We find that overall estimated collected tariff rates are lower than the MFN rates by about 1 percent.

Our overall estimated collected tariff rate was equal to 10.4% in 2001, 10.9% in 2002, and 11.5% in 2003. On the other hand, based on Ministry of Finance and Customs Committee data, the actual collected rate was 9.5% in 2001, 9.7% in 2002, and 9.8% in 2003. The difference can be attributed to the fact that we did not take into account any exemptions other than the CIS free trade zone exemption.<sup>20</sup> We believe collected tariff rates more closely approximate the protection a sector receives and the incentives it faces. Using our estimated collected tariff rates, and based on a Rosstat mapping from the tariff line data of the Customs Committee to the sectors in our input output table, we calculated a weighted average tariff rate for the sectors of our model. The results of this procedure for each sector of our model are reported in table 12a.

Export tax rates are calculated from the 2001 input-output table of Rosstat and are reported in table 12a. Since we do not change export taxes in the counterfactual simulations, these parameters are considerably less important to the results than the tariff rates.

# **Improved Market Access**

Although many in the Russian government argue that improved market access from accession to the WTO is a major reason for WTO accession, Russia has already negotiated most-favored nation (MFN) status on a bilateral basis with most of its important trading partners, so Russia’s exporters will not see an immediate reduction in the tariffs they face and this effect may not be expected to be large. But Russia will have improved rights under antidumping and countervailing duty investigations in its export

> 20 To calculate actual collected rate, we used the Ministry of Finance data on collected import duties as a numerator. As a denominator, we used the overall import volume less import from Belarus as reported by the Russian Customs Committee. The exclusion of the imports from Belarus is determined by the fact that the electronic dataset which we used in the calculations reported import volume without imports from Belarus.

15
# **Can Participation Be Induced? Some Evidence from Developing Countries**<sup>**i**</sup>

**Ghazala Mansuri**<sup>**1**</sup> **and Vijayendra Rao**<sup>**2**</sup>

**JEL Codes: O1, O12, D7**

**Keywords: Participatory Development, Community Development, Decentralization, World Bank Sector Boards:  SDV, PREM**

> 1 Lead Economist, Poverty Reduction and Equity Department, The World Bank

> 2 Lead Economist, Development Research Group, The World Bank
**Table 1: Descriptive** **<u>Statistics at Household Level</u>**

||Total|Small|Medium||Large||East|North|South|West|
|---|---|---|---|---|---|---|---|---|---|---|
|**Area cultivated & output**|||||||||||
|Yield per hectare (US$)|550.619|860.385|492.301|***|298.169|***|521.455|568.117|543.218|573.325|
|Gross profit (US$/ha)|481.321|766.014|431.390|***|245.605|***|455.745|476.319|499.886|488.057|
|<br>Net shadow profit (US$/ha)|379.297|604.028|344.521|***|188.544|***|374.842|375.905|382.191|383.173|
|<br>Net profit (US$/ha)|125.392|136.673|135.245||104.117||83.183|152.326|102.305|172.338|
|Total cultivated area|0.367|0.048|0.180|***|0.878|***|0.416|0.307|0.357|0.385|
|**Input use**|||||||||||
|<br>Family labor days/ha|452.448|765.211|383.676|***|207.532|***|444.476|427.706|474.214|455.307|
|<br>Hired labor (share)|0.391|0.215|0.384|***|0.574|***|0.420|0.429|0.322|0.414|
|<br>Hired labor days/ha|52.681|65.221|46.498|**|46.319||55.394|60.154|34.854|65.634|
|Used chem. fertilizer|||||||||||
|(share)|0.148|0.095|0.142|***|0.206|***|0.043|0.234|0.119|0.210|
|if yes, amount (kg/ha)|16.951|24.557|15.007|*|11.252||5.062|23.253|7.813|34.683|
|<br>Used pesticides (share)|0.078|0.045|0.086|***|0.102||0.032|0.128|0.067|0.092|
|If yes, amount (US$/ha)|1.932|2.741|2.260||0.788|***|1.929|2.753|0.919|2.470|
|Used manure (share)|0.731|0.639|0.754|***|0.799|**|0.525|0.826|0.773|0.796|
|**Marginal products**<sup>**a**</sup>|||||||||||
|<br>Male family labor (US$)|0.666|0.429|0.568|***|0.947|***|||||
|<br>Female family labor (US$)|0.539|0.360|0.477|***|0.783|***|||||
|**Area shares**|||||||||||
|Land with grain|0.497|0.522|0.497|*|0.471|*|0.564|0.600|0.449|0.397|
|Land with tubers|0.243|0.220|0.250|**|0.258||0.163|0.221|0.269|0.310|
|Land with tree crops|0.251|0.245|0.242||0.266|**|0.264|0.167|0.272|0.285|
|<br>Share of vegetable land|0.010|0.012|0.011||0.006|**|0.009|0.012|0.010|0.008|
|**Household characteristics**|||||||||||
|Age of head|46.557|44.286|46.672|***|48.718|***|45.420|45.234|48.965|45.860|
|<br>Female head|0.274|0.329|0.291|*|0.202|***|0.254|0.264|0.328|0.236|
|Primary school completed|0.576|0.570|0.570||0.588||0.601|0.562|0.568|0.574|
|Secondary education|0.067|0.054|0.057||0.091|***|0.076|0.060|0.064|0.069|
|<br>Number of members<=14|2.150|1.975|2.113|**|2.362|***|2.236|2.063|2.047|2.270|
|No of members 15- 35|1.588|1.414|1.585|***|1.767|***|1.537|1.686|1.563|1.584|
|No of members 35- 60|0.861|0.670|0.855|***|1.059|***|0.881|0.791|0.917|0.833|
|No members >=60|0.245|0.212|0.231||0.293|**|0.193|0.236|0.288|0.252|
|Number of observations|3080|1027|1031||1022||741|666|931|742|

_Source:_ Own computation from 2010/11 LTR baseline survey. _Note:_ Stars indicated significance levels for t-tests of the equality of means for each of the variables between terciles (* significant at 10%; ** significant at 5%; *** significant at 1%). Two outliers with inexplicably high levels of fertilizer use are dropped from the largest tercile. a Marginal products of male and female family labor (shadow wages) are calculated using the formula given in the note at the bottom of table 3.

16
public policy interventions would be doomed to ineffectiveness. If pioneering is already common in fragile states, it could be increased to the level appropriate for the state’s level of income.

**Figure 1. Distribution of pioneering firms by income group**

<!-- Start of picture text --> FCS Non-FCS<br>UMIC 44.44 55.56 UMIC 46.47 53.53<br>LMIC 42.63 57.37 LMIC 54.43 45.57<br>LIC 63.23 36.77<br>LIC 55.05 44.95<br>0 50 100<br>0 50 100<br>Pioneers Non-pioneers Pioneers Non-pioneers<br><!-- End of picture text --> _Source:_ Calculations based on the World Bank Enterprise Surveys (2018) _Note:_ LIC = Low Income; LMIC = Lower-Middle Income; UMIC = Upper-Middle Income Pioneers overall tend to be larger, more experienced, domestic rather than foreign-owned, maleowned and run, and a result of a partnership rather than a sole proprietorship. They also tend to be more productive, rely more on training and international certification, and are more frequently encountered in manufacturing. Simple averages across groups of all firms and pioneers in fragile and non-fragile developing countries highlight these differences, which are largely consistent between fragile and non-fragile countries (see Appendix Tables A2 and A3).<sup>13</sup> Firms’ particularities, however, might have little to do with pioneering, and more to do with the sector characteristics where they are active, i.e., the structure of production in fragile markets. For example, there may be a lot of domestic pioneers because foreign ownership is not prevalent in the sectors and markets where they are active. Controlled averages in a simple linear framework allow to net out such influences by looking at deviations from sector averages across countries. Such a calculation highlights how pioneers deviate from their peers across different contexts, which provides some inkling about how fragility interacts with who the pioneers are, and how they innovate.

> 13 With the exception of ‘public ownership’ and ‘skill intensity’, all the mean differences between pioneers in FCS and non-FCS are statistically significant at the 10 percent level.

7
standard financial information and collateral and have a longer credit history that banks can investigate.<sup>1</sup>

# **III.2 Characteristics of firms**

As we see in the above subsection, each financial institutional arrangement has its advantages and disadvantages. Thus the composition of various financial institutional arrangements in the financial system will affect its efficiency in mobilizing savings, allocating capital, and redistributing risks. However, it is hardly convincing to argue that one regime of financial structure is superior to another without taking into serious consideration the characteristics of firms, which constitute the main demand for capital. This subsection analyzes the characteristics of firms in size and risk which, we believe, are two critical factors determining how firms finance their investments.

# **III.2.1 The size of firms**

The size of firms matters for their financing choice because there are economies of scale in financial transactions. Some components of transaction costs that firms incur in raising capital, such as expenses spent on financial auditing, information disclosure, security marketing and selling, contract negotiation and implementation, etc. are fixed. Small firms usually raise a smaller amount of capital than large firms and thus are at a disadvantage in terms of average transaction cost per unit of capital raised (Lin and Li, 2001). Moreover, empirical evidence shows that economies of scale with different sources of financing are different. Public equity financing has substantial economies of scale, followed by public bond issuing<sup>2</sup> . Large companies enjoy some economies of scale when borrowing from banks but much less than in the case of public equity or

> 1 The entry of foreign bank, the increase in competition among large banks and the improvements in risk assessment technology can increase the large banks’ services to medium-size and even small-size enterprises in developing countries (World Bank 2007, de la Torre 2008). However, the basic picture is not changed. The extension of large banks’ lending to SMEs still remains quite limited although other types of services increased substantially. The majority of smaller enterprises at the bottom of the pyramid of firms by size still have no access to bank loans.

> 2 Many empirical studies show that the costs of raising capital through IPO or SEO are very high and the ratio of direct and indirect costs to proceeds in IPO and SEO is much higher for smaller issues (Chen and Ritter, 2000; Lee, Lockhead, Ritter, and Zhao, 1996; Kaserer and Kraft, 2003 ).

13
**Figure 1: Social spending as a share of GDP.**

<!-- Start of picture text --> Venezuela<br>Uruguay<br>Peru<br>Paraguay<br>Panama<br>Nicaragua<br>Mexico 1990<br>Jamaica 2004<br>Honduras<br>Guatemala<br>El Salvador<br>Ecuador<br>Dominican Republic<br>Costa Rica<br>Colombia<br>Chile<br>Brazil<br>Bolivia<br>Argentina<br>0% 5% 10% 15% 20%<br><!-- End of picture text --> When data for 2004 were missing, data from 2001-2003 have been used. Source: CEPAL (2006).

In full similarity, tax revenues also vary significantly across countries: from 7 percent of GDP in Haiti (and 9 percent in Venezuela), to 36 percent of GDP in Brazil (see Figure 2). Tax revenues as well increased significantly during the 1990’s: excluding social security contributions, on average they rose by 15 percent between 1990 and 2004 (see Tanzi, 2000, Gomez Sabaini, 2005, and Figure 2).

On average, countries that had relatively high taxes at the end of the 1970s experienced a faster growth in their tax revenues than those with lower levels, so that they remained above the regional average in the 1990s and 2000s (Bird, 2003). Thus, while countries like Brazil, Uruguay, and Argentina continue to have tax revenues around 25% of GDP, countries like Guatemala, Haiti, Ecuador or Venezuela keep having tax revenues around 10 percent of GDP (Gomez Sabaini, 2005). In looking at tax revenues, however, it must be taken into account that countries with revenues from publicly owned commodities do not need to raise as much revenue as countries with no commodities. This is the case of Venezuela, Mexico, Ecuador, and a few other countries which show relatively low average tax revenues (Tanzi, 2000).

4
-4-

with revenues from the divestment of PEs, can then be utilized more productively to improve the performance of critical PEs and other economic sectors. Second, divestiture is viewed as a tool to improve PE efficiency.6 Although the causal link between ownership and efficiency has not been clearly established, it is argued that private owners and managers would increase efficiency by virtue of the incentives provided by clearer and "atomized" property rights systems, and other factors such as less political interference, a hard budget constraint and the risk of effective bankruptcy, and evaluation on the basis of commercial profitability by self-interested shareholders.7 8. A variety of other factors have also led policy makers to accord greater prominence to divestiture, including: its potential role ill fostering entrepreneurship and capital market development; the relatively limited impact of past enterprise-level reform efforts short of ownership change; the demonstration effects of widespread divestiture efforts in industrialized countries; and its role in reducing the foreign debt of the country, particularly where new owners assume the foreign debts of the PE, or where divestment takes place through debt/equity swaps. Finally, the promotion of "popular capitalism" and the widespread distribution of ownership have also been motivating factors for many governments.

# B. MAGNITUDE AND NATURE OF BANK LENDING FOR DIVESTITURE

9. Between 1983 and June 1989, 71 approved operations in 35 countries contained divestiture components (Table 2).8 Bank involvement is, on the whole, relatively recent: approximately 60% of the operations were approved in 1987 or later; 24 were approved in the past 18 months alone (Graph 1). Slightly over half of all operations are SALs, eleven of which are supported by TALs. Close to 20% of all operations focus exclusively on the PE sector, either for sector rationalization/rehabilitation or as technical. assistance support for PE adjustment operations (Graph 2). These usually 6 The broader theoretical debate of the relationship between ownership and efficiency has been covered in other studies and _s not the focus of attention here. See Richard Hemming and Ali M. Mansoor, Privatization and Public Enteriprises, IMF, OP 56, January 1988; Simon Domberger and John Piggott, "Privatization Policies and Public Enterprise: A Survey," The Economic Record, June 1986.

7 These arguments, however, hold better ground for the divestment of PEs operating in competitive markets rather than monopolistic environments, where a host of other factors complicate the relationship between efficiency and change of ownership.

8 Several operations with divestiture components are under preparation. These include a large number of SALs in sub-Saharan Africa; private sector development operations in Benin, Congo, Madagascar, and Bolivia; and PERLs in Nigeria, Sri Lanka, and Argentina.
<!-- Start of picture text --> Political Support<br>Ddc Sdc<br>Dudc<br>Sudc<br>0<br>1.0 Relative effective protection<br><!-- End of picture text -->

**Figure 1: The political market for government assistance to agriculture**

Moreover, the real incomes of non-farm workers would be lowered by four times as much in the poor as in the rich country. They would be more inclined to join industrialists in opposing price support policies in a poor than in a rich country. Even poor countries with a wealthy landed aristocracy are likely to adopt policies that discriminate against agriculture, since landowners typically are also to some extent industrial capitalists. Anderson’s simulation exercise suggests that if landlords earned as little as one-sixth of their income from industrial capital they would prefer policies which lowered the domestic price of farm relative to industrial products. This helps explain why Krueger (1990b) found agriculture to be only slightly less discriminated against in those developing countries with concentrated land ownership as compared with those with a more even distribution of land.

Other authors have put forth various extensions of the pressure group model to explain agricultural price policy. Swinnen and de Gorter (1994) and Swinnen (1994) develop politician voter models, building on a combination of Stigler (1971), Downs (1957), and de Gorter and Tsur (1991). Their main conclusion can be summarized in five testable propositions put forth in Swinnen (1994) (and tested in Olper, 1998, see below): (i) politicians increase agricultural subsidies as real agricultural income falls, (ii) the equilibrium subsidy increases as a share of agriculture in total output decreases, (iii) the equilibrium subsidy increases as capital intensity increases (inside and outside agriculture), (iv) the equilibrium 5