rafmacalaba/datause-encoder-data huggingface dataset:
All regions showed warming but variably but some studies on the Himalayas in Xizang province of China find higher warming rates at higher altitudes (e.g. Liu et al., 2009; Liu and Chen, 2000; Qin et al., 2009; Thompson et al., 2003; Yang et al., 2011). Similar findings are reported by Shrestha et al. (1999) for Nepalese Himalayas and Khattak et al. (2011) in winter (Dec-Feb) Tmax for Pakistan’s upper Indus River Basin.

The rate of warming reported is consistently higher in winter than other seasons in most parts of the Himalayas, namely, the Chinese, northwest Indian, and Nepalese Himalaya (Bhutiyani et al., 2007; 2010; Shrestha et al., 1999; Shrestha and Devkota, 2010). The high mountains and Trans-Himalaya of Nepal exhibit winter warming at the rate of 0.9 °C/decade and 1.2 °C/decade, respectively, as compared to respective annual warming of 0.9 and 0.6 °C/decade in Tmax during 1971-2000 (Shrestha and Devkota, 2010). This seasonal trend (greatest warming in winter, smallest in summer) is also noted by several researchers in the Tibetan Plateau (e.g., Du et al. 2004, Liu and Chen, 2000, You et al., 2008).

Available results from the literature over similar time frames show that the highest rate of warming is in the Yarlung Zangbo River basin (0.29 °C/decade) followed by the northern Mt. Everest region (Mt.Qomolangma; 0.234/decade) during 1971-2004. Warming rates in these regions were higher than both the Chinese average (0.226 °C/decade) and global average (0.148 °C/decade) over the same period (Yang et al., 2006), as well as higher than the all-India average (0.22 °C/decade; Kothawale and Rupa Kumar, 2005) for a slightly different period (1971-2003). In another time frame (1971-2000), the Tibetan Plateau-average trend is 0.24 °C/decade (Wu et al., 2007) and Yarlung Zangbo River basin-average trend is 0.28°C/decade (You et al., 2007). Nepal’s warming rate of 0.4-0.9 °C in Tmax (Tse-ring et al., 2010) during the same period is remarkably high, but is not comparable to those reported above based on Tav.

The following tentative conclusions may be drawn based on the above literature review: (i) The later quarters of the 21st century and recent decades appear to be warmer than earlier periods; (ii) The warming rate is noted to be higher in winter than other periods in the whole region; (iii) The higher altitude Himalayan and Trans-Himalayan zone are reported to be warming at higher rates; and (iv) The Yarlung Zangbo River Basin and Mt. Everest region appear to be warming at higher rates than the rest of the regions.

Despite most literature suggesting a warming trend in the Himalayas, as discussed above, caution is required when comparing or interpreting differences in warming rates over the region.

1
26

# **References and bibliography**

- AEI (Africa Electrification Initiative). 2009. Maputo. ———. 2012. “Institutional Approaches to Electrification: The Experience of the Rural Energy Agencies/Rural Energy Funds in SubSaharan Africa.” Proceedings of the 2011 Africa Electricity Initiative Workshop, Dakar Senegal, World Bank, Washington, DC.

- Anderson, Dennis. 1975. _Costs and Benefits of Rural Electrification: A Case Study in El Salvador_ . World Bank Public Utilities Report No. 5, Washington, DC.

- Asaduzzaman, M., F. Douglas Barnes, and Shahidur R. Khandker. 2009. _Restoring Balance: Bangladesh’s Rural Energy Realities._ Special Report on Energy and Poverty, Energy Strategy Management Assistance Program (ESMAP), World Bank, Washington, DC.

- Barakat, Abul, M. Rahman, S. Zaman, A. Podder, S. Halim, N. Ratna, M. Majid, A. Maksud, A. Karim, and S. Islam. 2002. _Economic and Social Impact Evaluation Study of the Rural Electrification Program in Bangladesh._ Report to the National Rural Electric Cooperative Association (NRECA) International, Dhaka.

- Barnes, Douglas F. 1998. _Electric Power for Rural Growth: How Electricity Affects Rural Life in Developing Countries._ Boulder, CO: Westview Press.

- ———, ed. 2007. _The Challenge of Rural Electrification: Strategies for Developing Countries._ Washington, DC: RFF Press.

- ———. 2011. “Effective Solutions for Rural Electrification: Lessons from Successful Programs.” _Opinion in Environmental Sustainability_ 3: 260–64.

- Barnes, Douglas, and Jonathan Halpern. 2000. “Subsidies and Sustainable Rural Energy Services: Can We Create Incentives without Distorting Markets?” ESMAP Technical Paper No. 10, World Bank, Washington, DC.

- Bernard, Tanguy. 2012. “Impact Analysis of Rural Electrification in Africa.” _The World Bank Research Observer_ 27 (1): 33–51.

- Cecelski, Elizabeth, Ahmed Ounalli, Moncef Aissa, and Joy Dunkerley. 2005. _Rural Electrification in Tunisia: National Commitment, Efficient Implementation and Sound Finances._ Energy Sector Management Assistance Program (ESMAP), World Bank Report No. 307/05, Washington, DC.

- De Gouvello, Christophe, and Geeta Kumar. 2007. “OBA in Senegal—Designing Technology-Neutral Concessions for Rural Electrification.” OBA Approaches, Note No. 14, World Bank, Washington, DC.

- Dinkelman, Taryn. 2011. “The Effects of Rural Electrification on Employment: New Evidence from South Africa.” _American Economic Review_ 101 (7): 3078–3108.

- European Union Energy Initiative – Partnership Dialogue Facility (EUEI-PDF) 2008. “Review of Experiences with Rural Electrification Agencies Lessons for Africa.” Draft paper, Frankfurt, Germany.

Foley, Gerald. 1990. _Electricity for Rural People._ London: Panos Institute.

- Foster, Vivien, and Carmen Briceño-Garmendia. 2010. _Africa’s Infrastructure: A Time for Transformation._ Washington, DC: World Bank.
# V. ESTIMATES OF THE COSTS AND BENEFITS OF CONTROLS

# A. Introduction

Once again, two separate approaches will be taken to estimating costs and benefits. The first, simpler approach will be based on the difference between actual controlled rents and rents predicted by the cross-country model of Malpezzi and Mayo. The second approach will be based on the Olsen cost-benefit model. The parameters used to derive the first set of estimates were taken from Malpezzi et al., and were presented in the previous few pages. The parameters for the second set will be derived here from the household survey data. Each will be compared to the actual current rent paid, PcQc.

Recall that the simple model requires two basic pieces of information:

- (a) the rent currently paid for the current controlled unit, PcQc;

- (b) the rent that the household would pay if they were at their equilibrium demand at market prices, PmQm.

The empirical estimation of the Olsen model discussed in the previous chapter requires four pieces of information:

- (a) the rent currently paid for the current controlled unit, PcQc;

- (b) the rent that the current unit would rent for in the absence of controls, PmQc;

- (c) Lhe rent that the household would pay if they were at their equili'-ium demand at market prices, PmQm;

- (d) the price elasticity of demand for housing, b.

Note both methods require PcQc and PmQm. For both we use the household survey data for PcQc, but we use the cross country model to estimate PmQm in the first instance and demand estimation using a sample of "uncontrolled" Ghanaian renters to estimate PmQm in the second.

The rest of this chapter is organized as follows. First we will describe how we estimate or otherwise arrive at the components just described: PcQc from the household survey; PmQm from a cross country model of housing demand; PmQc using the survey and the method of hedonic indexes; and an alternative measure of PmQm using the survey and demand estimation; and finally the price elasticity of demand. Then we will present the static cost benefit estimates, and evidence sbout their distribution.

Given the rather extreme results presented earlier, i.e., that typical rents are less than two percent of total consumption, and that a month's rent is roughly equivalent to the price of a loaf of bread, readers may reasonably ask the following questions:
the market size in terms of number of firms in the market (terciles), sector (trade, manufacturing and services), profits (quartiles), revenues (quartiles), number of full-time employees (terciles), and number of part-time employees (halves). It is important to note that we control for these stratification variables throughout the analysis. This exercise resulted in the firms being split into 3 sets: i) treatment firms, ii) control firms in treatment markets, iii) control firms in control markets (pure controls). We ultimately obtained 1,099 pure control firms, 817 treatment firms, and the remaining 784 firms as control firms in treatment markets at the time of the intervention. A letter-delivery attempt was made to all 817 treatment firms, and of these intent-to-treat firms only 583 successful deliveries were made, representing an attrition rate of 28%. The remaining firms were closed on the days of delivery. Then 234 new firms, each located in the same market as an attrited firm, were added to the sample as replacement treatment firms for inclusion in the subsequent follow-up survey. We therefore note that out of the original 817 firms randomized into the treatment sample, 583 firms were successfully treated and the 234 firms that dropped out at the intervention stage were replaced by 234 new firms located in the same market. Further, there were 371 control firms (including both pure controls and controls in treatment markets) that dropped out of the sample between the randomization and the follow-up survey and another 42 of the 583 originally-randomized treatment firms that dropped out between the intervention and the follow-up.

Overall the attrition rate for the 2,700 firms identified to be potentially in operation and not registered with the NBR is quite high at nearly 25%. Specifically, we note that of the 2,700 firms determined eligible at the randomization/implementation step, 2,053 of these firms were successfully tracked from the baseline survey to the follow-up survey, with 541 being treatment firms, 643 as control firms in treatment markets, and 869 pure controls. It is worth mentioning that we also surveyed an extra 463 firms in the follow-up survey. Of these replacement firms, 234 were treated as mentioned above, 159 were assigned as pure controls, and the remaining 70 firms as controls in treatment markets. We focus our analysis on the original firms for two reasons: i. for those firms we have baseline business outcomes while we do not have the same baseline information for the replacement firms; and ii. if we include the replacement firms the results are very similar to the ones presented in the paper (see Appendix Table A.1).

In addition to this exercise, in early 2015 we attempted recontacting the 647 firms that were present in the intial randomization sample. We were successful in tracking down and surveying 8
|**Table 5. Effect of earmark**|**ed relations on pr**<br>|**ovision rates o**<br>|**f working cap**<br>|**ital loans (cros**<br>|**s-section comp**<br>|**arison)**<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Earmarkfbt* Riskft|0.034|0.005|-0.031|0.027|0.000|-0.034|
||(0.088)|(0.058)|(0.046)|(0.095)|(0.070)|(0.059)|
|Earmarkfbt|0.201***|0.186***|0.081***|0.207***|0.184***|0.084***|
||(0.025)|(0.021)|(0.021)|(0.025)|(0.027)|(0.025)|
|Earmarkfbt* Riskft*MPft||||0.026|0.013|0.011|
|||||(0.027)|(0.032)|(0.051)|
|Riskft* MPft||||-0.006|-0.008|-0.009|
|||||(0.017)|(0.014)|(0.017)|
|Earmarkfbt* MPft||||-0.021|0.008|-0.011|
|||||(0.018)|(0.030)|(0.027)|
|Observations|231,864|156,705|161,358|231,864|156,705|161,358|
|R-squared|0.527|0.683|0.770|0.527|0.683|0.770|
|<br>Mean provision rate|1.873|1.872|1.891|1.873|1.872|1.891|
|Firm*Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Location*Sector*Month FE|No|Yes|No|No|Yes|No|
|Firm*Year FE<br>Notes: The table shows estimates of OLS|No<br>regressions on our loan|No<br>-level data for the|Yes<br>sample of firms th|No<br>at at any time in ou|No<br>r sample period b|Yes<br>egin an earmarked|

Notes: The table shows estimates of OLS regressions on our loan-level data for the sample of firms that at any time in our sample period begin an earmarked credit relationship with a bank. The dependent variable corresponds to the provision rates of working capital loans. _Earmarkfbt_ is an indicator variable that equals one for all consecutive periods after a firm receives its first earmarked loan from a bank, and zero otherwise. _Riskft_ is an indicator variable that equals one for firms with credit risk at the start of the earmarked relationship above the median and zero otherwise. Credit risk is measured using the rating of the first earmarked loan issued to firm _f_ . _MPft_ is an indicator variable that equals one if the market power in period _t_ of banks operating in firm _f_ 's zip code (measured by the Herfindahl Index) is above the median and zero otherwise. Other controls include the length of the relationship duration of each firm-bank pair and loan characteristics at the time of issuance (maturity, volume and a dummy variable indicating if the loan was collateralized). All regressions include fixed effects for the ten loan rating categories established by regulators, as well as fixed effects for the type of loan (whether the loan has a fixed rate or a rate indexed by Brazil's CDI). Standard errors are doubled clustered at the bank and time level.

23
# _A. Agricultural Marketing in Madhya Pradesh_

Madhya Pradesh (MP) is the second largest Indian state in terms of area and ranks seventh in population.<sup>2</sup> More than 11 million people are directly engaged in agriculture, contributing 30 percent to the state GDP (Economic Survey 2005). With most development indicators below the national average, it is among the lower income states of the country.<sup>3</sup> The State Agricultural Marketing Board facilitated the development of 233 main _mandis_ (government regulated wholesale agricultural markets), with well built storage and display areas, where farmers periodically sell their produce. According to the MP Agricultural Marketing Act of 1972, every farmer is required by law to sell his or her produce in these regulated markets. According to Upton (2003), “transactions outside the _mandis_ were officially prohibited by the government to protect farmers from exploitation by unscrupulous buyers. Open auctions were considered the best safeguard against this.” Nevertheless, an estimated twelve percent of the produce is sold by farmers to cooperative societies and village merchants outside these spot markets.

The _mandis_ in the state are classified into four categories (A-D) based on the annual revenue collected through the payment of _mandi_ tax by buyers.<sup>4</sup> The procurement process in the _mandis_ of different sizes is identical. The buyers in the _mandi_ are all licensed traders; i.e. each buyer needs to obtain a license to operate inside a _mandi_ . These traders are mostly intermediaries and eventually sell the produce to millers, processors and plant owners (involved in crushing or refining of the produce). A trader who buys on behalf of a private company is sometimes called a commission agent because the private company pays him a commission to build a relationship that ensures timely delivery of produce in the future.

According to B. Singh (2005), soybean cultivation in India was negligible until 1970, but it grew rapidly thereafter in response to the domestic deficit of edible oil supply. India

> 2The state has a total area of 308,144 square kilometers and approximately 60 million people (Census of India 2001). It is closest in area to the state of Arizona in the United States.

> 3 In the year 2000, MP had a per capita gross state domestic product of Rs. 7947 and a rural headcount ratio of 37 percent, which was almost 30 percent lower than the national average (Economic Survey 2005).

> 4Every buyer operating inside a _mandi_ pays 2.2 percent of the purchased value as tax to the _mandi_ committee. There are 30 A grade, 25 B grade, 84 C grade and 94 D grade main _mandis_ located in the towns of MP.

6
# **4.2 Access to public support**

Despite efforts of South Asian governments to support firms and their respective workers, access to support programs is very limited, similar to what has been observed in other poor countries (Cirera et al., 2020). Analysis of the BPS data suggests that relative to other regions, South Asian firms are less likely to access firm support programs. Across the sample, only 11% of firms in SAR are likely to have access to public programs, much lower when compared to 25% in EAP and 59% in high-income countries (Figure 7, left panel a). Unreported estimates suggest that in all South Asian countries except Sri Lanka, less than 11% of firms are likely to have received public support. Nearly a third of the firms have access in Sri Lanka compared to 0.7% and 3.7% in Afghanistan and Nepal, respectively. Lack of awareness explains the gaps in access, especially for firms in South Asia and Sub-Saharan Africa (Figure 7, panel b).

Figure 7: South Asian firms’ access to public support.

<!-- Start of picture text --> Regional/by income group comparison Comparison by firm-size<br>60 59 50<br>40<br>40 30<br>30 28<br>25<br>21<br>25 20 19<br>20 18 11 13<br>10 8<br>11<br>9<br>0<br>0 Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>SSA SAR Non-high income EAP High income SAR Rest of the world<br>(excl. SAR)<br>Panel (a): Average probabilities of receiving public support<br>60<br>56<br>48<br>43 44<br>41<br>40<br>26<br>22 23<br>20<br>20<br>16<br>11 12 11<br>8 9 9 8 8<br>6<br>4<br>0<br>I was not aware Too difficult to apply I am not eligible I applied but not received it<br>SAR EAP SSA<br>Non-high income (excl. SAR) High income<br>Predicted probability of accessing support (%)<br>Probability<br><!-- End of picture text --> Panel (b): Reasons why firms do not receive public support There are significant variations in public support access across firm attributes. For example, micro and small firms are left out of public support programs. Only 8% of the surveyed micro-enterprises 14
The summary statistics indicate that (i) acquiring an equity position in a public incumbent firm is more heavily restricted than acquiring private sector firms, (ii) opening a branch, possibly as a less costly alternative to a subsidiary or an acquisition, is not permitted in a number of countries, and (iii) there appears to be a fair amount of regulatory discretion in the treatment of foreign suppliers, thereby giving rise to uncertainty.

**Table 1:  Policy measures in the maritime shipping sector affecting foreign investment**

||BinaryV|ariables||<br>|<br> <br>Continuous|<br> <br>Variables||
|---|---|---|---|---|---|---|---|
||(1)<br>Incidence|(2)<br> Percentage||(3)<br> <br>Mean|(4)<br> <br>Std.Dev.|(5)<br> <br>Min|(6)<br>Max|
|Branch entry not allowed|26|0.40|Equity limit private sector|84.38|25.58|0|100|
|Subsidiary not allowed|2|0.03|Equity limit public sector|70.58|39.20|0|100|
|Licence required|40|0.89|Equity limit JVs|84.31|24.02|30|100|
|Licence criteria not publ avail|3|0.05|Equity limit subsidiaries|83.60|27.28|0|100|
|Licence not autom if crit fulfilled|12|0.18|Nationality employees|36.79|42.59|0|100|
|Regulator not independent|36|0.55|Nationality BoD|5.89|17.28|0|66|
|No right of appeal|13|0.20||||||
|No prior notice of regul changes|38|0.58||||||
|Repatriation of earnings restricted|5|0.08||||||

Source: Own calculations from Services Trade Restrictions Database, data refer to 2008-09. Col.(1) displays the incidence of the measures listed, i.e. a count measure of countries applying that restriction; Cols.(3)-(6) display standard descriptive statistics for continuous variables.

# **3. Data and Empirical Strategy**

We first describe the construction of the Services Trade Restrictiveness Index (STRI) for liner shipping, and then our estimation strategy.

## **3.1  Liner Shipping STRI: Data and Methodology**

In order to incorporate multiple policy barriers to commercial presence as shown in Table 1, we construct a country-pair specific quantitative score (the liner shipping STRI) that reflects the restrictiveness of policy regimes applied at both ends of a given journey. The approach adopted in this paper builds on a relatively long tradition in the literature of quantifying policy barriers (Deardorff and Stern, 2008) of scoring the relative restrictiveness of specific policy measures and then constructing a weighted average of underlying scores. The scoring approach to quantification was first developed by the Australian Productivity Commission and used widely in work undertaken by the OECD<sup>4</sup> ; recent applications to the maritime transport sector were developed by McGuire _et al._ (2000), Kimura _et al._ (2004), Achy _et al._

> 4  See Conway _et al._ (2005), Conway and Nicoletti (2006) and OECD (2008, 2009).

7
that determine the performance of domestic services sectors, may be an important reason why CGE studies assessing the effects of trade reforms may miss the mark.

Notwithstanding the caveats regarding data quality and the limited specificity (accuracy) of the characterization of services trade policies and their effects on the different modes of supply, a basic message that emerges from the current literature is that liberalization of services matters, perhaps much more than trade in goods. However, much depends on how well the characteristics and economic functions of different services are captured, the accuracy of estimated or assumed impacts on costs and prices of services, whether policies create rents or simply raise costs, if there are rents, what share accrues to foreign factors, and whether policy reforms are applied on a discriminatory basis or not. These are all areas where future research should focus and is likely to have a high payoff.

38