rafmacalaba/datause-encoder-data huggingface dataset:
set, induce too much or too little investment; too much or too little output provision; distort input usage; and distort the timing of investment spending and efficiency gains more generally.

In a sense this is unsurprising. Regulation is a problem of asymmetric information and setting the parameters of the regulatory system is unlikely to result in both an optimal level of cost and service quality. However, through appropriate regulatory design and collection and monitoring of relevant cost and asset data, it should be possible to at least avoid some of the worst imbalances in incentive packages. These imbalances come at a welfare cost and a distributional cost. The welfare cost arises through the inappropriate use of resources. The distributional effect arises because the essence of incentive based regulation is that companies must be induced to reveal information or elicit effort through the provision of profit incentives. If the wrong incentives are put in place, then payment is made for information of no value, a cost that falls directly upon the consumer. Consequently, the quality of the incentive regime is not an abstract matter, but one of significant economic importance.

# **Output based regulation should form the basis for the incentive regime**

It is important to begin by establishing that output-based regulation is the appropriate basis for the incentive regime. In general, businesses exist to provide services to customers. They do not exist to incur costs, although of course that is a natural consequence of service provision. Regulated businesses are no different in this respect to other businesses.

Moreover it is also true that in general if the price of the output increases, the firm is induced to supply more of it, and _vice versa_ . For a regulated business, there must be a financial incentive to continue to provide outputs, and this incentive exists through a number of mechanisms – through explicit quality regimes as well as through the price control determination process. Consequently, an output based regime mimics, to some extent, the value for money offering made by firms in competitive markets.

28
direct interventions in credit markets by governments have generally not been a reliable means of improving firm efficiency.<sup>2</sup> Therefore, it remains an empirical question whether loan facilitation by local governments in China improved firm efficiency.

In a nutshell, we find that both forms of local government activism were associated with higher firm productivity in our sample period, and the effects varied with local income level, industry and firm ownership. Specifically, we find that government activism was helpful in relatively poorer regions and in industries that sold more to the domestic market, but that also relied heavily on innovation. Bank loan facilitation was also especially useful for non-state firms. These are the types of firms that were likely to have poor access to financial, product, and input markets, and thus stood to benefit most from such forms of market facilitation. These results are therefore consistent with a conditional proposition that government activism can help mitigate market failures when and where they are severe.

Our study is also closely related to the large literature on the role of local governments in the rapid growth of China’s collectively owned township-village enterprises (TVEs) in the 1980s and early 1990s. Many authors (Byrd and Lin, 1990; Chang and Wang, 1994; Che and Qian, 1998a, 1998b; Jin and Qian, 1998; Li, 1996; Nee, 1992; Song and Du, 1990; Walder, 1995a and 1995b) argue that local governments contributed critical inputs, such as land, initial collective assets, political connections and human capital to these collective enterprises, helped in securing loans from state-owned banks and sometimes provided political protection for these firms against the predatory behavior of the state.

Even after the mass privatization of TVEs and small state-owned enterprises in the mid1990s, local governments remained active players in China’s local economies (Xu, 2011; Zhou , 2009). Many attribute China’s local government activism to the fiscal policy reforms that provided strong financial incentives for local governments to promote economic growth (Wong, 1992, 1997; Oi, 1992, 1999; Weingast et al., 1995; Li, 1998; Berkowitz and Li, 2000). Several authors have characterized the Chinese government as a “helping hand” to business firms in contrast to the Russian government as the “grabbing hand” during the transition from planned to market economy (Frye and Shleifer, 1997; Li, 1998; Brown et al., 2009). By and large, this literature has viewed the

> 2 See World Bank (2008) for an overview of the evidence.

3
by June 2017. Consequently, parallel to a decline in the average rate level, the dispersion of rates across banks diminished. The standard deviation decreased from 2.1 to 1.4 and the difference between the 90<sup>th</sup> and 10<sup>th</sup> percentile lending rate dropped from 3.7% to 1.9%. These observations suggest that the cap is binding in a sense that most banks had to reduce rates to comply with the new regulation. Price differentiation across commercial banks widely vanished with almost all institutions setting lending rates closely below the 14% cap.

The stated interest rate, however, does not cover the full cost of credit. A more comprehensive measure of the cost of credit is the _annual percentage rate_ (APR), which additionally includes all fees and commissions charged by lenders. The figure below shows the distribution of APRs for a sample of 26 commercial banks in Kenya as of mid‐November 2017. The APR of all banks is higher than the 14% interest rate cap. The average APR stands at 18.5% and the maximum is 27.5%. More than half of the banks charge an APR of more than 18%, indicating that fees and commissions increase the effective cost of the loan by at least 4 percentage points.

**Figure 9: Non‐interest fees and commissions in Kenya**

<!-- Start of picture text --> Annual Percentage Rate (APR) Banks' interest vs fee income<br>120 ‐ 8.6%<br>CBR + 4pp Average APR<br>100<br>80<br>60<br>+6.2%<br>40<br>20<br>0<br>Net interest income Fee and commissions<br>income<br>10 15 20 25 30<br>APR June 2016 June 2017<br>8<br>6<br>4<br>Frequency<br>2<br>0<br><!-- End of picture text --> _Notes: Left hand panel:_ APR of KS 100,000 personal unsecured loan for up to 1 year; 6‐month repayment frequency as of November 2017. _Right hand panel_ : Interest and fee income of ten banks listed on NSE in KSh bn. _Source:_ <u>http://www.costofcredit.co.ke/site/index; Company reports extracted</u> A related analysis of the financial statements of 10 banks listed on the Nairobi Stock Exchange (NSE) reveals that while banks’ net interest income dropped by 8.6% between June 2016 and June 2017, income generated by fees and commissions increased by 6.2%. This 23
Figure 3: A decomposition of FUS contribution revenue <!-- Start of picture text --> 14-<br>12 -<br>10<br>8<br>- 6<br>4 6-<br>2-<br>88 89 90 91 92 93 94<br>-in-- AFUS: actual FUS contributions Series2 -o-- CFUS: counterfactual FUS contributions A~- TFUS: constant tax rate contribution<br><!-- End of picture text -->
classifications, their extensive database is not only uses of market-determined or parallel exchange rates but also develops a _natural_ classification algorithm. Specifically, we use the fine classification of Reinhart-Rogoff that takes values between _1_ and _15_ where higher values indicate a higher level of flexibility in the exchange rate arrangements in place.

The data on _capital controls_ used in this paper is a binary variable collected from the IMF’s _Annual Report on Exchange Arrangements and Exchange Restrictions_ . It takes the value of _1_ in the years when restrictions on capital account transactions are in place and _0_ otherwise (Prasad, Rogoff, Wei and Kose, 2003). The typical problem of this type of data is that, although it captures the presence of controls, it fails to capture the intensity of the controls imposed.

As a result, countries with closed capital account may increase the stringency of those controls by imposing restrictions on current account transactions, multiple exchange rate practices or the surrender of export proceeds while countries with an open capital account may still restrict the flow of capital by imposing other restrictions on cross-border financial transactions (Chinn and Ito, 2007). To capture these aspects, we complement the measure mentioned above with the inverse of the _Chinn-Ito index of financial openness_ which incorporates the different types of restrictions on cross-border financial transactions stated above. We multiply the _Chinn-Ito index_ by -1 to capture the presence of different types of restrictions on cross-border financial transactions. Higher values of this new index would imply more strict restrictions on cross-border financial operations.

The data on intervention in the foreign exchange market is constructed following Levy-Yeyati and Sturzenegger (2007). We aim to show whether FOREX intervention has a lasting effect on the real exchange rate. Although it has traditionally been argued that nominal interventions are unlikely to have a real impact, we examine whether FOREX interventions help to sustain misalignments. According to LevyYeyati and Sturzenegger (2007) we construct a measure of intervention that is not affected by the growth-induced increases in money demand —which in turn may lead to either increases in domestic credit or in international reserves. To calculate such a measure, we construct first the ratio of reserves to broad money ( _M2_ ) for country _c_ in year _y_ and month _m_ , _R2c,y,m_ , 10
<!-- Start of picture text --> Global factor<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>1980 1985 1990 1995 2000 2005 2010 2015<br>2.5 Advanced-country factor Emer4 ging-country inflow factor<br>2<br>3<br>1.5<br>1<br>2<br>0.5<br>0 1<br>-0.5<br>-1 0<br>-1.5 -1<br>-2<br>-2.5 -2<br>1980 1985 1990 1995 2000 2005 2010 2015 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> Figure 3: Estimated factors. Two-standard error bars obtained through country block bootstrap with 10000 replications.

33
# Contents.

|1. INTRODUCTION AND SUMMARY.|p.3|
|---|---|
|2. THE "PACTO": STABILIZATION<br>AND GROWTH RECOVERY.|p.4|
|3. POLICY CREDIBILITY, DEBT REPUDIATION RISKS AND HIGH INTEREST RATES.|<br>p.7|
|4.<br>FISCAL POLICY AND DEBT MANAGEMENT.|p.9|
|4.1 Factors Behind Explicit Debt Repudiation Risk Premia.|p.10|
|4.2 Factors Behind Devaluation<br>Expectations/Exchange<br>Rate||
|Uncertainty.|p.11|
|5.<br>EXCHANGE RATE POLICY, PRIVATE SAVING AND GROWTH.|p.13|
|References|p.17|
|ANNEX 1. Explicit Debt Repudiation. Econometric<br>Results.|p.18|
|ANNEX 2. A Model of Devaluation Expectations/Uncertainty.|p.20|
|ANNEX 3. Devaluation Expectations/Uncertainty.<br>Econometric<br>Estimation.|p.21|

' This paper draws from my contribution to severat World Bank reports on Mexico. I thank Sweder van Wijnbergen and Joaquin Cottani for useful suggestions and cowments and Rebecca Brideau for able research assistance.
Many SADC countries export a more diverse range of products to the SADC region than to the rest of the world, which is indicative of the regionalization of SADC trade. For example, Malawi exported 1008 distinct HS 6-digit products to the rest of SADC, but only 435 to the rest of the world. Mauritius, Namibia, Mozambique, South Africa, Zambia and Zimbabwe, similarly export a more diverse range of products to the rest of SADC.

**Table 4: Export-thickness measures for SADC** **<u>countries, 2008</u>**

||**Total product**|**s to:**||**Average**<br>**to**<br>**each**|
|---|---|---|---|---|
|**Exporter**|**World**|**SADC**|**ROW**|**SADC**<br>**country**|
|Madagascar|1937 (37)|725 (14)|1821 (35)|89 (2)|
|Malawi|1122 (21)|1008 (19)|435 (8)|171 (3)|
|Mauritius|2564 (49)|2057 (39)|1831 (35)|216 (4)|
|Mozambique|1507 (29)|1137 (22)|952 (18)|148 (3)|
|Namibia|3448 (66)|3272 (63)|1703 (33)|611 (12)|
|Seychelles|511 (10)|182 (3)|440 (8)|19 (0)|
|South Africa|4667 (89)|4477 (86)|4286 (82)|2737 (52)|
|Tanzania|2183 (42)|1404 (27)|1672 (32)|214 (4)|
|Zambia|2216 (42)|2127 (41)|538 (10)|353 (7)|
|Zimbabwe|1768(34)|1545(30)|760(15)|290(6)|
|SADC<br>combined|4823(92)|4704(90)|4482(86)|2965(57)|

Notes: Own calculations using HS 6-digit level data obtained from UNcomtrade.

Values for Tanzania are for 2007. Note that the SADC region on the import side includes the SACU members, except for SA who does not declare exports to the other SACU countries. The total number of HS2002 codes in UNcomtrade is 5222. Data is for gross exports and therefore contains re-exports To summarize, intra-SADC trade is low, but this is partly a consequence of low levels of economic development. Once we condition on income levels, SADC countries have experienced an increase in openness comparable to other developing countries. Intra-SADC trade is also found to be relatively high and diversified. Nevertheless, there are some worrying trends. Growth in intra-regional trade has slowed in recent years. Exports from the region continue to decline as a share of world trade. Therefore, in the following sections we look at the role of tariffs and other factors that impede further growth in SADC trade.

12
declined from 14,143 tonnes in 1996 to 10,881 tonnes in 1998 (Figure 17). Likewise, the value of exports diminished from US$43.9 million in 1996 to US$29.0 million in 1998 (Figure 18).

# **Figure 17. Volume of Nile perch exports, 1996-2002:**

<!-- Start of picture text --> 20000<br>15000<br>10000<br>5000<br>Source: Fisheries Department<br>Figure 18. Value of Nile perch exports, 1997-2001:<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>Tonnes<br>1996 1997 1998 1999 2000 2001 2002<br>US$ million<br>1996 1997 1998 1999 2000 2001<br><!-- End of picture text --> _Source: Fisheries Department_ The EU imposed further restrictions on exports of fish and fishery products from Kenya from April 1999 to December 2000, prohibiting all fish from Lake Victoria. Over this period, however, the volume of exports of Nile perch actually increased to 13,667 tonnes in 1999 and 15,438 tonnes in 2000 (Figure 17). However, the value of exports declined to US$26.4 million in 1999, before recovering through 2000 to reach US$34.2 million (Figure 18). This reflects the 45
section. The same applies for column (5) when baseline community wealth – as measured by the median of the household wealth index – is added on top of the baseline electricity access variable. The results are consistent and in fact stronger. One noticeable change that can be observed in the results presented in columns (4) and (5) is that the effect of unimproved sanitation on school enrollment becomes significant for children ages 8–9 years after the inclusion of the two additional covariates. The effect was previously insignificant. For example, if both community electricity access and wealth at baseline are included in the model as additional covariates, the exposure to unimproved sanitation reduces the likelihood of school enrollment by 6 percentage points for all children ages 8–9 years, about twice the size of the estimated effect when community electricity access and wealth at baseline are not accounted for in our preferred specifications. The findings also suggest that the estimated coefficients for both community electricity access and community wealth variables are barely significant or insignificant, predicting school enrollment significantly for two to three subgroups but counterintuitively in the wrong direction – suggesting that the effect of unimproved sanitation on school enrollment is indeed more robust and meaningful than those two variables.

_(ii) Coefficient Stability_ While the discussion above addresses potential omitted variable biases that could arise due to general living conditions confounding our results, other omitted variable bias may underlie our preferred specifications. Our findings in Table 8 address these issues.

Table 8 further examines the robustness of the negative effects of children’s exposure to unimproved sanitation at baseline on school enrollment at the time of the survey, found in Tables 4, 5, and 6. This follows the bounding method introduced in Oster (2019) as well as Altonji, et al. (2005), who developed a method to investigate the coefficients’ sensitivity to potential omitted variable bias. The estimates reported here are calculated using the Stata module _psacalc_ introduced in Oster (2019). Since the bounding method can only be implemented on linear models, we rely on the results from the LPM equivalents for each logistic regression in Tables 4, 5, and 6. The marginal effects, standard error, and model R-squared (in square brackets) for each of the LPM specifications are reported in column (3). Also, as we have shown earlier, the marginal effects estimated from the LPMs are very similar to those of the logistic regression models, reported in column (1). Column (2) reports the marginal effects from the LPM without controlling any covariates we included in our analysis.

First, as suggested by Oster (2019) we hypothetically increase each model’s R-squared in column (5) by a factor of 2.2 and examine the changes on each of the model’s coefficient. Will the negative 14
For companies in other countries the trends of the two measures behave differently over time. Leverage(1) remained at relatively low levels until 1997, after which there was a considerable increase; the average leverage(1) reached close to 60% in 2001. Leverage(2), by contrast, exhibits a reasonably stable trend over the period. Apart from the peak in 1998 the measure has been fluctuating between 20% and 40%. However, the trends over time can be somewhat sample–specific. In the years prior to 1998 there are only a few companies in the ‘other countries’ sample, suggesting that the average leverage presented here is not likely to be a fully representative indicator of transport companies’ capital structure choices across these countries. Overall, it appears that the trends in transport companies’ leverage have been similar across this geographical distinction. The levels of leverage have also been converging towards the end of the period.

**Table 7: Average leverage by geographical area, based on all identified data (%)**

||**Leverage 1 –**|**Market based**|**Leverage 2 – A**|**ccounting based**|
|---|---|---|---|---|
|**Year**|**Asia**|**Other**|**Asia**|**Other**|
|1991|52.3|21.2|48.1|21.6|
|1992|33.2|11.4|43.0|30.6|
|1993|26.7|26.2|40.7|38.3|
|1994|18.9|34.6|46.1|38.4|
|1995|29.4|23.9|48.8|25.0|
|1996|23.2|24.8|38.7|19.6|
|1997|52.3|6.8|53.8|16.4|
|1998|59.6|59.2|60.6|63.9|
|1999|52.8|54.8|53.7|43.9|
|2000|46.3|47.6|39.3|24.8|
|2001|37.6|57.8|34.2|38.5|
|2002|37.1|52.1|30.7|30.0|

Source: Datastream and authors’ calculations.

# **4. A more precise look at the evolution since the 1997 Asian crisis**

In developing countries, the concern with the financing structure of regulated companies started approximately with the slowdown in the interest in project finance resulting from the 1997 Asian crisis. Since this crisis also corresponds to the beginning of the overall slowdown in the commitments made by the private sector to infrastructure investment in developing countries, it is important to make a more precise assessment of the evolution of the situation. Having a high-quality sample of data is essential for this purpose. Indeed, while the relatively high volatility of average leverage levels observed due to the unbalanced full sample does not have much impact on the overall long-term trend, which was presented in section 3, it may interfere with the analysis of the impact. To control for this, results were also produced for the balanced sample which covers a much lower number of countries and companies, but which includes the same companies for each year between 1997 and 2002. The utilities sector is covered in section 4.1 while transport is covered in section 4.2.

11
# Supplemental appendix

## Table <u>S1: Descriptive statistics of household level variables by institution</u>

||All|Individual|New smaller<br>group|Stay in WUG|
|---|---|---|---|---|
|Volumetric priceof reservoir water|0.0607|0.0655|0.0609|0.0565|
||(0.0372)|(0.0389)|(0.0254)|(0.0399)|
|Volumetric priceof reservoir water<br>squared|0.00506|0.00578|0.00433|0.00475|
||(0.00529)|(0.00590)|(0.00401)|(0.00520)|
|No. of parcels|114.5|110.5|121.0|115.3|
||(48.69)|(51.48)|(45.99)|(47.81)|
|Gini coefficient of parcel size|0.261|0.261|0.303|0.244|
||(0.143)|(0.127)|(0.196)|(0.127)|
|Simplicity of water flow direction|0.127|0.126|0.116|0.134|
||(0.0649)|(0.0764)|(0.0540)|(0.0582)|
|Earth canal length|1.003|0.824|0.671|1.298|
||(1.615)|(1.324)|(1.367)|(1.892)|
|No. of ponds per member (pre- reform)|0.714|0.901|0.624|0.590|
||(0.539)|(0.721)|(0.312)|(0.364)|
|No. of pumps per member (pre- reform)|0.852|0.887|1.029|0.746|
||(0.496)|(0.580)|(0.536)|(0.366)|
|No. of surrounding parcels|2.741|2.627|3.120|2.678|
||(1.165)|(1.131)|(1.013)|(1.238)|
|No. of surrounding parcels squared|8.859|8.157|10.72|8.678|
||(6.189)|(5.732)|(6.439)|(6.407)|
|Years knowing owners of surrounding|34.61|34.49|34.12|34.93|
|parcels|||||
||(12.96)|(14.16)|(9.312)|(13.40)|
|Proportion of surrounding parcels whose|0.371|0.356|0.507|0.326|
|owners send their children to the same<br>school|||||
||(0.465)|(0.469)|(0.470)|(0.457)|
|Average schooling years of working|8.881|9.120|9.181|8.548|
|members|||||
||(1.611)|(1.658)|(1.641)|(1.519)|
|Parcel size of paddy|0.134|0.134|0.140|0.132|
||(0.0794)|(0.0826)|(0.0862)|(0.0748)|
|Sandy/high seepage soil dummy|0.0889|0.157|0.0800|0.0339|
||(0.286)|(0.367)|(0.277)|(0.183)|
|Observations|135|51|25|59|

_Note_ : Standard deviations are in parentheses _Source:_ Authors’ analysis based on data sources discussed in the text.

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