rafmacalaba/datause-encoder-data huggingface dataset:
WPS3795 economic performance and their association with indicators of market power such as concentration and regulatory restrictions on entry and competition. The empirical results are mixed, with some studies finding generally unfavorable effects from market power (e.g., Elsas 2005, Karceski, Ongena, and Smith 2005, Cetorelli and Strahan 2006), and others finding favorable effects (e.g., Petersen and Rajan 1995, Cetorelli and Gambera 2001, Bonaccorsi di Patti and Dell’Ariccia 2004, Cetorelli 2004).<sup>12</sup>

# **4. The lending infrastructure**

In this section, we turn our attention to the lending infrastructures of nations and how they affect the feasibility and profitability of using the different lending technologies in SME financing. The lending infrastructure includes the information environment, the legal, judicial and bankruptcy environments, the social environment, and the tax and regulatory environments. All of these elements may affect SME credit availability by influencing the extent to which the different lending technologies may be legally and profitably employed. The final element, the regulatory environment, may also restrict SME credit availability by constraining the financial institution structure.

# **A.  The information environment**

An important aspect of the information environment is the accounting infrastructure. Strong accounting standards and credible independent accounting firms are necessary conditions for informative financial statements, which are key to the financial statement lending technology. These are also important for many components of loan contracting associated with financial statement lending and some of the other lending technologies to a lesser extent. For example, covenants based on financial ratios are not feasible if the ratios calculated from the financial statements are not reliable. Indices of global accounting standards indicate considerable variation across countries both between developed and developing economies and among developed economies (e.g., La Porta, Lopez-de-Silanes, Shleifer, and Vishny 1998).

Another important aspect of the information environment is the sharing of information. Commercial and consumer credit bureaus provide formal organizational mechanisms for the exchange of payment performance data. Credit bureaus have also been found to reduce the cost and time to process loans and the

> 12 Some studies also found that higher measured market power is associated with higher SME loan interest rates (e.g., Hannan 1991, Berger, Rosen, and Udell forthcoming).  Although this finding may appear to support the SCP hypothesis, it may also be consistent with the alternative hypothesis of an expansion of relationship lending if relationship loans tend to have higher interest rates on average than transactions loans (which may or may not be the case).  Again, much of the difficulty in interpreting the results arises because the lending technologies are not identified.

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are usually transitioning to the service sectors, regulating dirty industries or improving technologies and consequently reducing pollution levels.

When looking at the subset of cities in the first phase of development, some interesting examples arise. The first thing we notice is that all these cities are market towns and they are located primarily in Sub‐ Saharan Africa and South Asia regions. For example, we find cities like Kigali (Rwanda), Bujumbura (Burundi), Kabul (Afghanistan), and Lilongwe (Malawi). Their economies focus on agriculture and incipient industrial activities. Second, we find that all these cities are also in the list of cities that we identify as the top 10 most competitive (Table 1). Bujumbura, Kabul, and Lilongwe are outperforming their nation in terms of employment growth, while Kigali is a competitive city in terms of both, employment and per capita GDP growth. They are on the verge of development, growing fast and starting to face the negative externalities associated with growth. Third, none of these cities is in the list of top competitive cities in terms of productivity. This comes as no surprise, considering that most of the cities that do well in this area are often located in more developed nations. However, some of the cities in this phase are still performing well in terms of productivity. For example, Kigali’s average productivity growth is above Rwanda’s average productivity growth by 1%.

The turning point of this predicted relationship between pollution and growth comes at $1,693.<sup>16</sup> We define an arbitrary interval of +/‐ $100 around the turning point and look at the cities that fall in this interval. We find that most of these cities are located in South Asia. Some examples include Chittagong and Khulna (Bangladesh) and Patna and Bokaro (India). They are mostly industrial cities, focusing on a variety of light and heavy industries. Although none of these cities is included in our list of top 10 competitive cities (Table 1) some still show good performance. For example, during this 10‐year period, Patna and Bokaro show an annual average GDP growth that is between 2 and 3 percentage points above India’s average GDP growth. Furthermore, most of these cities are outperforming their nation in terms of employment growth.

> 16 This is a relatively low level of per capita GDP compared to what other studies have found. For example, Grossman and Krueger (1991) find that the turning point comes at $4,119, while Panayotou (1993) finds a turning point of $3,137. However, most of these studies focus on emissions or concentrations of sulfur dioxide. Stern and van Dijk (2016) use PM 2.5 concentration levels and per capita national GDP and still find a relatively high turning point of $3,336. However, the fact that these turning points are higher than our findings is not surprising. Harbaugh et al. (2002) find that the location of the turning point is sensitive to not only the specific pollutant but also the sample used and the specification of the model.

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they help to make inroads in the gap in incomes between the rich and poor. Indirect taxes are slightly regressive: the bottom 40 percent of the distribution contributed about 4.95 percent of total indirect tax collections, compared to their share of 4.78 percent in disposable income. This regressivity at the lower end of the income distribution largely reflects the impact of excises because VAT and fuel tax are progressive.

On the spending side of fiscal policy, social spending is not only progressive but also contributes to large reductions in poverty and inequality which nonetheless remain very high. Direct transfers are progressive in absolute terms, since they effectively target the poor (who are largely children and old age pensioners and benefits are means tested), and are sizable in terms of GDP, all of which leads to reductions in poverty and inequality. In fact, South Africa performs very well when compared with other middle-income countries: it achieves the most “redistribution” compared to the other middle-income countries in the CEQ analysis but that levels of inequality and poverty that remain are far higher than in other countries.<sup>65</sup> Our analysis suggests that while there is some scope to improve the targeting of certain social programs like free basic services, cash transfer programs are already well targeted and substantive. Education and health spending also benefit the poorer parts of the income distribution relatively more than the rich. However, there are concerns about the quality of such spending, which suggests that more could be done to improve the quality of such services to ensure that education and health spending maximize their potential in reducing poverty and inequality.

In sum, fiscal policy already goes a long way toward achieving redistribution. Nevertheless, the level of inequality and poverty in South Africa after taxes and spending remains unacceptably high. In fact, the level of inequality in South Africa after taxes and spending is still higher than the level of inequality in other middle-income countries before they begin to use fiscal policy interventions. More can and needs to be done to improve the quality of service delivery. But South Africa’s fiscal deficit and debt indicators show that the fiscal space to do so is extremely limited and little room remains on the macro front to spend more to achieve even greater redistribution. Addressing the twin challenges of poverty and inequality going forward in a way that is consistent with fiscal sustainability requires higher and more inclusive economic growth to support fiscal policy. This would be particularly important in addressing the need for jobs and higher incomes, especially at the lower end of the income distribution, helping narrow the gap in incomes between the rich and the poor and reinforcing the effectiveness of fiscal policy.

> 65 See Lustig (2014).

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Table 11: Intergenerational transmission. Convergence / Divergence.

||**Years**|**school**|**Asset ind**|**ex Pctile**|**Incom**|**e Pctile**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||∆_Y_|∆_Y_|∆_Y_|∆_Y_|∆_Y_|∆_Y_|
|**_Panel A: Original hous_**|**_eholds_**||||||
|_Treat_|-0.282|-0.0788|1.892|-3.000|0.889|-7.220*|
||(0.219)|(0.337)|(1.858)|(2.521)|(2.965)|(3.830)|
|_Y_(_G_1)1997||-0.657***||-0.681***||-0.997***|
|||(0.0636)||(0.0457)||(0.0564)|
|_Treat ∗Y_(_G_1)1997||-0.031<br>(0.0723)||0.092*<br>(0.0520)||0.113<br>(0.0746)|
|Constant|1.270*|6.942***|2.515|38.73***|59.71***|93.42***|
||(0.679)|(0.717)|(6.794)|(5.755)|(8.744)|(7.655)|
|Observations|1,442|1,442|1,432|1,432|939|939|
|R-squared|0.056|0.266|0.027|0.275|0.081|0.486|
|_Mean C Y_(_G_1)1997|2.86|2.86|37.00|37.00|44.24|44.24|
|_Mean C Y_ (_G_2)2017|8.32|8.32|45.04|45.04|56.08|56.08|
|**_Panel B: New househo_**|**_lds_**||||||
|_Treat_|0.298|0.833***|6.918***|4.035|5.950**|6.579*|
||(0.192)|(0.270)|(2.269)|(2.860)|(2.549)|(3.629)|
|_Y_(_G_1)1997||-0.657***||-0.862***||-0.833***|
|||(0.0469)||(0.0436)||(0.0524)|
|_Treat ∗Y_(_G_1)1997||-0.139**||0.014||-0.078|
|||(0.0555)||(0.0572)||(0.0644)|
|Constant|4.061***|8.173***|-16.51**|30.07***|11.61|55.85***|
||(0.705)|(0.625)|(6.932)|(5.891)|(9.296)|(7.894)|
|Observations|1,891|1,891|1,880|1,880|1,354|1,354|
|R-squared|0.089|0.391|0.054|0.357|0.069|0.423|
|_Mean C Y_(_G_1)1997|2.71|2.71|38.89|38.89|42.46|42.46|
|_Mean C Y_(_G_2)2017|7.76|7.76|40.51|40.51|42.36|42.36|

This table analyzes the inter-generational mobility relation to the parent’s (Generation 1, G1) distributional position to understand how poorer or richer households manage to change their distributional position for specific outcomes and how _Treatment_ affects this relation. Inter-generational mobility is defined as ∆ _Y_ = _Y_ ( _G_ 2)2017 _− Y_ ( _G_ 1)1997, where _Y_ ( _G_ 2)2017 and _Y_ ( _G_ 1)1997 are defined as the outcome of the offspring (G2) or parents (G1) observed in 2017 or 1997, respectively. The outcomes of interest considered are: (1) years of schooling; (2) a durable asset index formed with a principal component that considers ownership of cars, vans, TV, radio, sound equipment, blender, refrigerator, electric grill, gas stove, microwave oven, firm floor, electricity, drainage and water access in the property; and (3) monthly income. For column (1) the level of years of schooling is used by itself. For columns (2) and (3), the percentile of each outcome is calculated relative to all the individuals inside the same 10-year window age range. The unit of observation are individuals in relevant age groups, as described in section IV and their parents. Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

31
Even among the more precisely defined category of VOs some organizations function purely on voluntary commitment, with no budget. Others have well paid full-time profesdonal staff. Their commitments span the range from pure relief operations to development advocacy and the mobilization of politically active people's movements. The participants range from saints to scoundrels. of VOs will often be overlooked if attention is focused on individual organizations. Only in the aggregate is it possible to appreciate fully the sector's contribution to mobilizing voluntary energy in support of a broad range of social agendas; its role in serving as a marketplace of ideas and of social and institutional innovations; and its function in the promotion and preservation of democratic institutions, in part as a counter weight to the centralizing and controlling tendencies of the state. These contributions are best appreciated within the dimensions of development that have so often been overlooked.

The strongest VOs and POs respond to a good deal more than financial incentives. Their distinctive development contributions are found in the fact that they are not the same as goverament organizations or businesses. At the same time they are not immune to financial incentives, which if wrow)y applied have the potential to destroy the volur.ary nature of all but the most strongly self-awsre of sary data elusive, and the potential for damage substantial If there is one central message of this analyis, it is that no resposible donor should undertake to wasdist or 'use' NGOs as a part of its development programming un!ess it is prepared to make a substantial investment in understanding their nature and their distinctive roles. Donors cannot automatically assume that exisdng staff experieuce and training prepare them to play a constructive role in helping NGOs become more effective in their 32 -
already a violation against the theoretical proposition that pre-commitment to the scoring rule is important to implement the optimal mechanism. Five criteria were used for evaluation: tariff level, concession period, construction period, return, and revenue transfer quota to the government (Table 7). A one-month advance of the delivery of the infrastructure seems too costly compared with the total expected construction period of four years.

Table 7. Weights on Multiple Criteria in Brescia–Milan Toll Road Auction

||Weig|hts|Implied cost<br>of one point|
|---|---|---|---|
||Original|Revised|(euro mil.)|
|Operation procedure|20|||
|Technical evaluation|12|||
|Tariff level|18|26|10–15|
|Concession period (year)|16|24|30–40|
|Construction period (month)|15|22|2–3|
|Economic return|10|15|...|
|Revenue transfer quota|9|13|...|
||100|100||

Source: Torta (2005).

These episodes are sufficient to realize the fragility of multidimensional auctions against ambiguity of evaluation weights and political interventions. Transparency in setting and assessing requirements is essential. It is not recommendable that too many dimensions are used as evaluation criteria. They will create more room for rent-seeking activities, whence corruption and collusion. If necessary, auction theory may support the two-stage approach with a few criteria. However, note that how to evaluate those criteria should be predetermined before inviting bids (Che, 1993). Ex post adjustments would be especially inappropriate. Therefore, good governance and sound government policies are required to implement multidimensional auctions successfully.

# **5. EMPIRICAL MODEL AND DATA**

To estimate the impact of multidimensional auctions on renegotiation, the following two-stage least squares technique is employed (e.g., Wooldridge, 2002). The basic reason is that the decision of whether or not to use a multiple criteria rule is endogenous in the sense that the multidimensional format might be selected in particular auctions with certain unobserved characteristics, which would in turn affect the 15
19

# Table **S.** Formal (Schedular) and Effective Tax Structure in the Malagasy Model (1988) (Effective tax **rates** in parenthesis)

||Value<br>addedb|Taxes<br>imports|Taxes<br>exports|VAT<br>(TUT)|Excise tax<br>(TC)|
|---|---|---|---|---|---|
|Agriculture (39)'|1230|53.3 (40.3)|15.0|10.2 (1.6)||
|Energy (2)|60|30.3 (7.3)|-|15.0 (0.8)||
|Processed food (7)|220|62.0 (5.7)||15.0 (1.3)|30.0 (3.7)|
|Light industry (3)|104|55.3 (37.6)|-|15.0 (0.9)|30.0 (3.3)|
|Heavy industry|274|55.3 (44.8)|-|15.0 (0.4)||
|Othermanuf.(1)|34|46.0 (44.8)|-|15.0(0.9)||
|Tranport(5)|143||-|15.0 (1.3)||
|Commerce (11)|357||-|15.0 (1.2)||
|Services (23)|730||-|15.0 (1.2)||
|Non-market (6)|194|-|-|-||
|Actual tax collection"||156.7|43.8|40.3|28.6|

All tax rates in percent are formal rates as defined in the notes to this table.

a. Figures in parentheses are sectoral shares in GDP.

b. 10 billion FMG c. Tax revenues are from Guillaumont et. al. (1990) and IMF (1990). Total tax revenues in table 4 are 331 billion PMG.
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Table 4: Returns to Schooling and Skills from IV – Male Employees 22-45 years <u>(Instrument=1 if age in 1981 between 6 and 13 years)</u>

|Variable|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Years of schooling|0.117|0.108|0.117|0.117|
||(5.5)|(3.2)|(3.5)|(3.0)|
|Experience|0.019|0.018|0.019|0.019|
||(1.2)|(1.1)|(1.2)|(1.1)|
|Experience squared|-0.0002<br>(0.5)|-0.0002<br>(0.5)|-0.0003<br>(0.6)|-0.0003<br>(0.6)|
|Standardized IALS|-|0.045<br>(0.6)|-|-|
|Standardized SCS|-|-|0.029|0.029|
||||(0.4)|(0.4)|
|Standardized NSCS|-|-|-|0.003<br>(0.0)|
|Father >= Post-Secondary|0.191|0.178|0.205|0.201|
||(2.1)|(2.0)|(2.2)|(1.4)|
|Constant|4.95|5.05|4.95|4.95|
||(15.0)|(11.2)|(10.9)|(9.4)|
|Centered R<sup>2</sup>|0.194|0.202|0.221|0.221|
|N|490|490|467|467|
|First Stage:<br>|||||
|Shea’s partial R<sup>2</sup>|0.274|0.165|0.168|0.145|
|F-value|(182.7)|(95.7)|(93.0)|(78.1)|
|[p-value]|[0.000]|[0.000]|[0.000]|[0.000]|
|Endogeneity Test:|||||
|Durbin-Wu-Hausman statistic|2.22|1.65|1.81|1.75|
|[p-value]|[0.137]|[0.200]|[0.179]|[0.186]|

Note: Theoretical sampling weights included; _t_ -values in parentheses The results in Table 4, for the group of compliers of basic or secondary school age in 1981 are again drastically different. Overall, the IV estimates of the coefficient of years of schooling are now lower compared to the corresponding OLS estimates (Table 2). Controlling for the two types of cognitive skills decreases the coefficient of years of schooling by about 27 percent compared to the standard specification in column 1. At the same time, one standard deviation increase in the score increases earnings by about 13 and 9 percent for schooling cognitive and non-schooling cognitive skills.

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Table 4: Effects on Temporary Incentives on Timing of 1<sup>st</sup> Prenatal Visit

||(1)|(2)|(3)|
|---|---|---|---|
||Intervention Period|Post‐Intervention<br>Period I<br>(Jan 2011 – March 2012)|<br>Post‐Intervention<br>Period II<br>(April – Dec 2012)|
|**A. Weeks Pregnant at 1**<sup>**st**</sup>**Pren**|**atal Visit**|||
|Treatment|‐1.47**|‐1.63**|‐2.47**|
||(0.71)|(0.75)|(1.02)|
|Large Sample_p_‐value|0.04|0.03|0.02|
|Wild Bootstrapped_p_‐value|0.08|0.03|0.03|
|Control Group Mean|17.80|17.90|20.10|
|Sample Size|769|1,296|710|
|**B. First Prenatal Visit Before**|**Week 13 of Pregnancy**|||
|Treatment|0.11**|0.08**|0.08**|
||(0.04)|(0.04)|(0.04)|
|Large Sample_p_‐value|0.01|0.02|0.04|
|Wild Bootstrapped_p_‐value|0.03|0.05|0.06|
|Control Group Mean|0.31|0.34|0.27|
|Sample Size|769|1,296|710|

Notes: This table reports LATE estimates of the treatment effect of the modified fee schedule on indicators of the timing of the 1<sup>st</sup> prenatal visit. The differences are estimated from 2SLS regressions of the dependent variable on actual treatment status instrumented with clinic treatment assignment type. The _p_ ‐values are for 2‐sided hypothesis tests of the null that the difference is equal to zero. We present both the _p_ ‐value computed for large samples and a Wild bootstrapped _p_ ‐value that is robust in samples with small numbers of clusters (Cameron et al. 2008). Our Wild bootstrap procedure assigns symmetric weights and equal probability after re‐sampling residuals (Davidson and Flachaire 2008) and uses 999 replications. Column (1) reports the results for the sample observed in an 8‐month intervention period (May 2010 – December 2010). Column (2) reports the results for the sample observed in the 15‐month period following the end of the intervention (January 2011 – March 2012). Column (3) reports the results for the 9‐month period after the change in the coding of the first prenatal visit (April 2012 – December 2012). Standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

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