rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10059

# **Abstract**

This paper uses data on nearly half a million actual shipments from a trucking portal in India to provide evidence on how trucking costs depend on route characteristics and affect the intensive and extensive margins of shipment flows. The empirical analysis using pre-pandemic data (before March 24, 2020) confirms the presence of thick market externalities along a route and spillovers across routes due to network externalities, both of which confer advantages to origins and destinations with larger market sizes. The paper utilizes exogenous variations in value-added tax on gasoline across states to provide causal estimates of the elasticity of shipment flows with respect to trucking costs.

The empirical estimates suggest that a 1 percent increase in trucking unit costs reduces trade flows by 2.8–3.9 percent. On the extensive margin of trade, three eastern states and several smaller territories constitute isolated regions that were largely cut off from the trading networks during the pre-pandemic period. Trucking costs increased by 32 percent during the early post-lockdown period (June 2020 to February 2021). The increase was greater along longer routes. In the short run, the increase in freight rates led to a proportionate decrease in trade flows across states. It pushed a significant number of poorer and remoter states into the ranks of isolated regions.

This paper is a product of the Development Research Group, Development Economics and the office of the Chief Economist, Equitable Growth, Finance and Institutions Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fshilpi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully
|1|
|Sales per worker, in thousand USD (2016)|62.5|130|5.2|14.5|32.8|60.8|117|
|Fixed assets perworker, in thousand USD (2016)|19.8|98.2|(0)|0.7|3.1|10.7|33|
|Value added per worker, in thousand USD (2016)|8|13.7|2.5|4.3|5.9|9.1|16|
|TFP index|6.6|5.2|3.8|45|5.7|7.3|9.5|
|Profitmargin,in%|0.70%|35.90%|-7.50%|0.20%|2.80%|7.60%|13.90%|
7|
|Misiones|331.8|78.0|0|20.2|47.6|
|Rio Negro|47.4|80.0|0|32.3|11.5|
|<br>Salta|73.4|50.0|0|16.7|9.5|
|San Juan|175.3|78.0|0|43.7|34.3|
|San Luis|81.8|50.0|0|21.0|0|
|Santa Fe|N/A.|160.0|160.0|N/A.|N/A.|
|Sant del Est|227.3|50.0|33.3|3.0|0.0|
|Tucuman|262.9|80.0|25.0|_53.6_|18.1|

Source: Fondo Fiduciario In many cases the province contracted the owner of the privatized entity to collect residual assets. The private owners receive a higher percentage of face value for recovering lower quality assets (i.e., loans for which re-payment problems have been most chronic). To the extent that residual balance sheets reflect the face value of assets, such contracts make it impossible for the province to receive compensation for the full value of its residual assets. This is not, of course, to imply that these contracts are a bad deal for provinces -- indeed, it is likely that private entities with the appropriate incentives are best equipped to re-

> 27 Fondo Fiduciario loans data are reported in U.S. dollars. Residual liability data are reported in pesos. Since the exchange rate is pegged so that a peso is worth a dollar, this presents little problem.

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(ii) statutory hypothesis tests are carried out on the parameter estimates; (iii) Ramsey test is done to check model specification; (iv) normality of residuals is tracked through Kernel density plot; (v) all estimates are checked for heteroscedasticity through Cameron and Trivedi‘s decomposition of IM-test; (vi) multicollinearity problems are checked by looking at the variance inflation factor (VIF); (vii) models do not suffer from endogeneity as highly correlated exogenous variables are not used in the gravity equations; and (viii) it being cross-section data, the presence of serial correlation does not appear.

22
such as agricultural equipment. Overall, however, they are not strikingly different than their VG counterparts, which is not very surprising given the poor settings in which these households reside. These results could explain why non-VG households turn to sanitation loans in the absence of getting access to government subsidies.<sup>28</sup>

# **5.2 Sanitation loan conditions and credit constraints**

As formally shown in ACGMS 2019, the sanitation micro-loan program could have motivated sanitation loan and toilet uptake through three features: (i) relaxation of a credit constraint preventing households from borrowing to invest in a toilet; (ii) the lower interest rate on the sanitation micro-loan (as compared to other loans offered by the partner MFI and other MFIs in the area) reducing the cost of investing in a toilet; and/or (iii) the label attached to the loan allowing households who are sensitive to loan labels, to invest in a toilet.

While we do not have the experimental setup to identify the role played by each of these channels directly, we can use the predictions from the formal model laid out in ACGMS 2019 to see whether, based on different pieces of evidence available, we can favour one channel relative to another. To do so, we need to (in addition to impacts on sanitation loan uptake and impacts on toilet uptake) consider impacts on borrowing behaviour in terms of amounts borrowed and types of loans taken, and impacts on other investments. MF client households that decide to take up a sanitation loan have to necessarily change

> 28Note that our study sample consists of MF client households who, given the MFI’s targeting rules, are on average worse off relative to other households in their villages. Table 1 in ACGMS 2019 provides a comparison of the average characteristics of the MFI client households and those for rural households in Latur and Nanded districts. It is nonetheless surprising that households that are officially classified as being VG appear not to be too dissimilar from non-VG households in terms of
354 0.553|**2.621**|||
|1996 SPECT|est|0.064|0.001 0.036|0.567|33 0.192|0.109|
||_t_|0.428|0.085 0.140|**2.393**|||
|1999 SPECT|est|0.242|−0.015 0.387|3.227|38 0.136|0.060|
||_t_|**2.248**|−0.669 0.789|**2.021**|||
|1999 SPECT|est|0.083|−0.006 0.194|0.521|38 0.162|0.088|
||_t_|0.557|−0.271 0.409|**2.298**|||

Notes: 1. See note to Table 2. 2. SPECT=Specific Tariff.

Similar inferences may be drawn about specific tariffs (SPECT) from the estimates reported in Table 4. The association of SPECT with lobbying spending is found for both %PACEXP and RANKPACEXP (except in 1993 when the coefficient on %PACEXP is borderline significant). Curiously, in the 1993 models of SPECT, M/Y and X/Y have signs that are contrary to expectation. For example, X/Y has a positive coefficient, implying that the higher is the export-output ratio, the higher is the specific tariff on that product. It is likely that tariffs are protecting imports of similar products and thus promoting exports of those products. This is an instance of tariffs as export promotion, an idea that was set forth in Krugman (1984).

Table 5 uses data on agriculture tariffs on imports from the European Union for those goods that also benefited export subsidies in the US. These data are from Hoekman, Ng, and Olarreaga
with Eastern Europe, where inequality has been declining lately.

While data availability presents challenges in many developing countries, it poses particular problems in Sub-Saharan Africa. Over the past two decades, data availability and quality have improved drastically, yet researchers are invariably constrained when looking at long-term trends. Levels of inequality are high in the region, especially in the southern countries. Seven of the ten most unequal countries in the world are in Africa (Beegle et al., 2016). This is remarkable given that all other high-inequality countries use income surveys while Sub-Saharan Africa uses expenditure surveys, which produce lower levels of inequality. Furthermore, inequality in the region is “too high” given its relatively widely shared land ownership (Milanovic, 2003). Looking at trends, a set of Sub-Saharan African countries with at least two strictly comparable and recent surveys (mostly in the 2000s) is split evenly into increasing and decreasing inequality (Beegle et al., 2016).<sup>16</sup> Countries with increasing inequality are slightly larger, so around 57% of the population live in a country where inequality rose.

The levels and trends in within-country inequality reviewed thus far have covered a range of different time periods. This section ends by looking at the most recent past which spans the Great Recession of 2008. This is a rather special period, so any change in trend needs to be interpreted

> 15 Székely and Mendoza (2015) is part of a special issue on “Inequality and Human Development in Latin America: A Long-Run Perspective” in the _Journal of Human Development and Capabilities_ (see introduction by Lopez-Calva et al., 2015).

> 16 This pattern of within-country inequality trends is confirmed by Cornia (2014) for a longer time period. Changes in survey design (e.g. urban or national coverage), implementation (e.g. effects of seasonality) or questionnaires (e.g. recall periods for consumption expenditures) can make surveys hard to compare.

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0.4 Tobacco 0.4<br>Oth. Transport 0.4 Garments 0.4<br>Tobacco 0.6 Elec. Machinery 0.7<br>Media Equip. 0.7 Basic Metal 1.4<br>Precision Instr.Paper 0.71.0 PetroleumPaper 1.41.5<br>Basic MetalPetroleum 1.01.0 Media Equip. 2.1<br>Chemicals 3.2<br>Rubber & PlasticsMachinery 1.92.2 Machinery 3.3<br>Wood, not furn 2.5 Rubber & Plastics 4.3<br>Non-metallic metal 3.8 Textiles 4.5<br>Chemicals 4.4 Printing 4.7<br>Fab. metal 4.6 Fab. metal 5.4<br>Textiles 5.9 Wood, not furn 6.8<br>Printing 7.5 Furniture 7.6<br>Furniture 26.0 Non-metallic metal 8.8<br>Food & Bev 34.7 Food & Bev 43.1<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>Graphs by year Graphs by year<br><!-- End of picture text --> 41
- 15 -

**Figure** 8: **Contractual savings portfolio distribution** (OECD countries)

<!-- Start of picture text --> 100% -<br>7 E fl U<br>20<br>80° i .4 263 24 27 30 3<br>70%<br>60%<br>3e8 3 29 2 2 5 22 19<br>50%' X3 3: g<br>40%<br>30%<br>20%31 m 38<br>10%<br>-44 3 -3 4 P 4 7;743 4<br>1982 1984 1986 1988 1990 1992 1994 1996<br>l Cash-anddepo.91 U Bills and bd8 03Loans 0 Sha-es *01her<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

**Figure 9: Contractual savings portfolio distribution (non-OECD countries)**

<!-- Start of picture text --> 100% 2 1 2-i2 14<br>90% 4 _,,,, __29 4<br>80% - 4~~~~~~~~~ l ~~~28<br>70% 3<br>50%<br>49% -<br>40%-C5<br>30%<br>20%<br>10%<br>0%3 3:_<br>1982 1984 1986 1988 1990 1992 1994 1996<br>l CCash and depoas 38 *ilsan050n0998 OLoans 0S6ares 301951<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

In general, not only is the portfolio distribution of contractual savings more skewed towards assets such as stocks and bonds, it is also more
policy objectives and available performance indicators. However, in sum, policies to improve connectivity with global markets could address:

- Traditional barriers to trade (from the negotiation of preferential market access to the reduction of domestic tariffs);

- Customs (efficiency and procedures, including rules of origin);

- Logistics; and

- Transportation and telecommunications (regulatory and infrastructure dimensions, with a greater focus on telecommunications for the offshoring of services, and transportation for goods).

**Table 1. Increasing connectivity with global markets: Policy objectives and performance indicators**

||**Policy objectives**|**Performance indicators**|
|---|---|---|
|**Sup**<br>**at t**|**pressing/reducing obstacles to trade**<br>**he border, including trade facilitation**<br>|•<br>Trade restrictiveness Indices – OTRI, TTRI (WTI 1.1)<br>•<br>Binding coverage and bound rates (WDI)<br>•<br>Share of tariff lines with peaks/specific rates (WDI, WTI 1.6)|
|•|Suppression of quotas and other<br>quantitative restrictions on imports<br>and exports|•<br>MFN applied tariffs – AV+AVE or AV only (WDI, WTI 1.2, 1.3)<br>•<br>Applied tariffs incl. preferences (WDI, WTI 1.4)<br>•<br>Tariff escalation (WTI 1.5)|
|•<br>•|Reduction of tariffs, suppression of<br>tariff peaks, tariff escalation or<br>simplification of tariff schedules<br>Customs modernization and reform,<br>harmonization of procedures and<br>cooperation across borders|<br>•<br>MFN 0 tariff lines / Import value (
general relationship between inequality and growth, from these, and almost certainly other data sets. The final section draws together our conclusions.

# 2.0 Why Might Village Inequality Affect Growth?

There are three conventional classes of explanation: imperfect credit markets, imperfect factor markets, and political economy.<sup>3</sup> In the first class of explanations, credit market constraints tie the ability of households to exploit opportunities for growth to their own resources. As the poorest of households have the fewest resources, holding average village incomes fixed, unequal villages have more resourceconstrained households. If credit markets are fully developed, the relationship breaks down, as the distribution of own resources no longer determines the distribution of household growth rates. A second possible channel is through factor markets. Higher income inequality (or inequality of land, human, or physical capital) may be associated with imperfect competition or other impediments to factor market development that limit opportunities for trade, especially for the poor.<sup>4</sup> The most common channel in the cross-country literature, however, implies the mechanism is through local political economy: unequal communities make different collective choices that affect the growth potential of households. For example, high-inequality communities may adopt more progressive tax structures, as low-income households pressure for redistribution in ways that inhibit growth. This is the conventional taxation-based story offered by Alesina and Rodrik (1994), Benabou (1996), and Persson and Tabellini (1994). Alternatively, the greater homogeneity (equality) of households may facilitate consensus for more efficient tax systems, and higher investment in public goods and services.<sup>5</sup> Localities also play an important role in targeting assistance to the poor, and international evidence suggests that there may be greater leakage, and thus poorer targeting in more unequal communities.<sup>6</sup>

> 3 See Aghion, Caroli, and Garcia-Penalosaand (1999), Lloyd-Elllis (2003), and Perotti (1996) for excellent
picture text --> _Source:_ COVID-19 Trade Policy database, own calculations _._ High and upper middle-income countries, according to the World Bank income classification, enacted more trade policy measures targeting the medical sector than other countries (Evenett et al. 2020). As already mentioned, many of the trade restrictive and liberalizing measures that were adopted to increase the availability of personal protective equipment and medical supplies at the beginning of the COVID-19 pandemic outbreak, were subsequently removed. We consider this dynamic in the empirical analysis.

6
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income) is allocated for consumption, transfers to other economic agents, and savings. The consumption/saving allocation is based on the marginal propensity of consumption, which is based on the data from the social accounting matrix (SAM) used for this study.

The total government revenues are generated through personal and corporate income taxes, excise taxes, import duties, and transfers from other economic agents (i.e., households, enterprises, and the rest of the world).<sup>10</sup> It is allocated for public expenditures, transfers to other agents, and public savings. Public expenditure accounts for government spending on wage and salary of government employees, expenditure and defense, healthcare and education. Since these are the essential services and required to be maintained by the government, the total government expenditure is fixed at the baseline level, following common tradition in CGE modeling. Moreover, the total government expenditure is allocated to various services (e.g., public administration) at the same proportion as in the base case. The remaining government income is saved after deducting the transfers. In CGE modeling, savings of the government is also referred to to public savings.

A government decision on the rates of any tax (income, excise, duty) reverberates throughout the economy through production, consumption, and trade channels. For example, if the government increases production tax rate, it will be passed to intermediate and final consumption as well as exports. It increases costs of production in all sectors and ultimately reduces household welfare. The reverse would happen if the government cuts production taxes. When a carbon tax is introduced and its revenue is used to cut production tax, both taxes affect throughout the economy through production, consumption and trade channels, the model calculates the net effect capturing all interactions between various the production sectors, between production and consumption sectors, between domestic production and imports, between domestic consumption and exports, and every variables in the model (e.g., wage rate
distance between the capital cities of countries _i_ and _j_ .<sup>8</sup> To assess the covariates of the common factors, we consider three candidate variables, namely, (i) total factor productivity; (ii) policy uncertainty, and (iii) the U.S. short-term real interest rate. Crucini, Kose, and Otrok (2011) find that total factor productivity is the leading driver of the business cycle of G-7 countries. They also find a relatively minor contribution of monetary policy. In turn, Baker and Bloom (2013) show that policy uncertainty plays an important role in driving business cycles.

Total factor productivity (TFP) is computed from the standard Solow residual using capital and labor inputs, and is drawn from the Penn World Tables version 9.0. Policy

> 7Such row standardization of the weight matrix facilitates the interpretation of the model coefficients, see Anselin (1988).

> 8The great-circle distance, the shortest distance between any two country capitals, is computed as: _dij_ = _radius× cos_<sup>_−_1</sup> [ _cos |longi − longj| coslaticostlatj_ + _sinlatisinlatj_ ] where radius is the Earth's radius, and _lat_ and _long_ are, respectively, latitude and longitude for country capitals _i_ and _j_ . The latitude and longitude coordinates for each of the country capitals in our sample were collected from the CEPII database.

8
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||
|- Min<br>- Average<br>- Max||4,425.2 (January 2009)<br>5,644.2<br>7,111.6 (April 2011)|

Notes: a. Survey data of a project with 50,000 tonnes capacity by author in 2010; b. the designated diesel price multiplied by the fixed factor 0.92, which is the suggested factor by Hainan project based on the survey of authors.

# **3.2.3 Challenges**

As mentioned in section 2, the current conversion capacity of waste oil-based biodiesel is more than 1.5 million tonnes in China, but most of them are not operated due to lack of collection system from the residential and commercial sectors (Liu et al.2011).

# **3.3 Biodiesel from oil-bearing trees**

# **3.3.1 Resource potential**

There are more than twenty species of oil-bearing tree species that can be used to produce biodiesel feedstock in China. Approximately one-fourth of these species are found in the northern part of the country, while the rest in the southern part, especially in the tropical and subtropical zones (Lv, et al. 2008). The main species include jatropha curcas, xanthoceras sorbifolia, Chinese pisache, varnish tree, sapium sebiferum, S. wilsoniana Sojak, idesia, sumac, euphorbia tirucalli and tung tree. The land productivities of typical oil-bearing trees are shown in Table 6. As in the case of non-grain sugar and starch energy crops, the marginal land available for oil-bearing trees should be in compliance with current land administration policies. It is estimated that, among the available forest barren lands, barren mountains, barren sand areas, and unutilized land resources, there are more than 36 million hectares that 11