rafmacalaba/datause-encoder-data huggingface dataset:
39. Another question of interest may be related to heterogeneity between domestic and international shipments. The literature points out an important difference in empty vehicle rate between domestic and international cargos in Europe. To analyze potential structural differences, the sample data is divided by the presence of border crossing, _bordercrossing_ . The results are somewhat mixed but consistently indicate that the estimated impacts of most of the determinants are slightly greater for domestic shipments than international cargos ( **Table 8** ). For instance, domestic shipments generally look more sensitive to shipping speed. The impact of cargo weight is also greater for domestic shipping. All of these effects may provide greater incentive for shipping carriers to consolidate more cargos, reducing empty trucks.

**<u>Table 8. OLS and fixed effects models by border crossing</u>**

||OLS w/o carrier FE|||Carrier F|E models||
|---|---|---|---|---|---|---|
||_crossborder_=0|_crossbo_|_rder_=1|_crossbord_|_er_=0|_crossborder_=1|
||Coef.<br>Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.<br>Std.Err.|
|ln_speed_|-0.437 (0.018) ***|-0.330|(0.026) ***|-0.055|(0.015) ***|-0.036 (0.009) ***|
|ln_dist_|-0.011 (0.005) **|-0.283|(0.009) ***|-0.050|(0.003) ***|-0.010 (0.005) *|
|ln_weight_|-0.677 (0.017) ***|-0.652|(0.025) ***||||
|ln_elev_|0.054 (0.002) ***|0.035|(0.003) ***|0.049|(0.001) ***|0.003 (0.001) ***|
|_D_elev_|-0.138 (0.069) **|1.296|(0.144) ***|-0.271|(0.031) ***|0.034 (0.014) **|
|_cloth_|0.095 (0.066)|-0.167|(0.016) ***||||
|_equipment_|0.239 (0.066) ***|0.061|(0.019) ***||||
|_food_|0.141 (0.067) **|-0.202|(0.016) ***||||
|_mineral_|0.206 (0.067) ***|-0.005|(0.020)||||
|_goods_<br>constant|0.108 (0.066) *<br>4.508 (0.099)***|5.273|(0.146)***|1.727|(0.069)***|1.382 (0.039)***|
|Obs.|40,922|10,215||40,922||10,215|
|R-squared|0.3304|0.4838||0.8897||0.9757|
|F stat.|155.17|49.77||...||...|
|No. of time d|ummy variables||||||
||131|131||0||0|
|No. of carrie|r dummy variables<br>0|0||8149||4869|

Note: The dependent variable is freight rate in logarithm, ln _p_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

40. Finally, one may also be interested in seeing how the observed freight transport costs are spatially distributed and thus, how local economies in the region could benefit from road infrastructure investment. The above-estimated elasticities are important to discuss how they are determined and how they can be reduced. However, there is no spatial dimension. From the data
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Figure 3.2 Indexes of infrastructure growth by region, 1960–2003**

<!-- Start of picture text --> Index of Infrastructure Growth - SSA Index of Infrastructure Growth - SAS<br>2,000 2,000<br>1,800 Paved Road 1,800 Paved Road<br>1,600 Tot. Road 1,600 Tot. Road<br>1,400 Gen. Cap. 1,400 Gen. Cap.<br>1,200 Mainline 1,200 Mainline<br>1,000<br>800 1,000<br>600 800<br>400 600<br>200 400<br>0 200<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Index of Infrastructure Growth - EAP Index of Infrastructure Growth - ECA<br>2,000 2,000<br>1,800 Paved Road 1,800 Paved Road<br>1,600 Tot. Road 1,600 Tot. Road<br>1,400 Gen. Cap.<br>1,2001,000 Mainline 1,4001,200 Gen. Cap.Mainline<br>800 1,000<br>600 800<br>400 600<br>200 400<br>0 200<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Index of Infrastructure Growth - LCR Index of Infrastructure Growth - MNA<br>2,000 2,000<br>1,800 Paved Road 1,800 Paved Road<br>1,600 Tot. Road 1,600 Tot. Road<br>1,4001,2001,000 Gen. Cap.Mainline 1,4001,200 Gen. Cap.Mainline<br>800 1,000<br>600 800<br>400 600<br>200 400<br>0 200<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br><!-- End of picture text --> _Sources_ : As for table 2.1.

# Panel data models

The preceding exploratory data analysis illustrated the importance of historical perspective in understanding countries’ present-day infrastructure position. We use panel data models to integrate that perspective into our formal analysis. As described in the first section of this paper, two different panel model specifications are estimated: an OLS fixed effects model and an instrumental variables specification. Unfortunately, it is not possible to apply either of the panel data models to access to electricity, for which only a single cross-section is available. (However, we do apply a cross-sectional instrumental variables specification to the electricity access data so as to compare them to the OLS estimates. That comparison is reported in table 3.4.) The fixed effects model confirms the importance of income, demography, and economic structure in driving stocks of household services and broader economic infrastructure (table 3.3).

23
# **3. Intervention and Measurement**

This section begins by briefly summarizing the key details of the intervention that individuals will be asked to assess the counterfactual for, before then discussing the survey questions used to measure these counterfactuals.

# **3.1 The YouWiN! Business Plan Competition**

The Youth Enterprise With Innovation in Nigeria (YouWiN!) program is a nationwide business plan competition for young entrepreneurs in Nigeria that was launched in late 2011. Applicants applied online, and a first screening was used to invite the top 6,000 out of almost 24,000 entrants to attend a four‐day business plan training course. These individuals then submitted business plans, which were scored, with the top 2,400 being selected as semi‐finalists. Among the semi‐finalists, those with the highest overall scores were selected as winners with probability one, and then a random draw was used to select the remaining 729 winners from a group of 1,841 semi‐finalists. Winners received a grant which averaged approximately US$50,000 each, which was paid out over a period of a year in four tranches. Three follow‐ up surveys track these firms with the last survey taking place between September 2014 and February 2015. This corresponds to three years after application, and between 12 and 18 months after firms had received their last tranche payment from the program.

Applicants could apply as either a _new firm_ with an idea for starting a business, or as _an existing firm_ with the goal of expanding their business. Random selection was stratified by this firm type, as well as by geographic region and gender. The typical applicant in this experimental pool is aged around 30, has university education, and owns a computer. As such, concerns about the ability of individuals to think about probabilities and alternative outcomes are likely to be less than with less‐educated respondents who are the focus of studies of smaller firms in developing countries. Only 17.4 percent of the sample is female, reflecting that males were much more likely to apply for the program.

In McKenzie (2015), I use this randomized assignment and follow‐up surveys to measure the impacts of this program. This is done separately for the new firm applicants and existing firm applicants, reflecting their different starting statuses. Winning is found to have large treatment impacts on both groups of firms. By the third follow‐up survey, new firm applicants are 37.3 percentage points more likely to be operating a firm, have 5.2 more workers per firm, and are 22.9 percentage points more likely to have a firm of 10 or 5
40 actual share of small enterprises with a formal loan, to Slovenia, where the actual level is 29 percentage points below the predicted level.

**Figure 12: Gap in share of small firms with formal credit across countries**

<!-- Start of picture text --> gap_small<br>50<br>40<br>30<br>20<br>10<br>gap_small<br>0<br>-10<br>-20<br>-30<br>-40<br>Slovenia Malawi Poland Chad Bhutan Brazil Bosnia and ... Argentina Hungary Nicaragua Azerbaijan Lesotho Indonesia Serbia<br>Czech Republic Slovak Republic<br>Micronesia, Fed. ...<br>Russian Federation<br><!-- End of picture text --> Source: Enterprise Surveys, own calculations The Enterprise Surveys, however, also allow a deeper look into the reasons of why firms do not use formal financial services, i.e. whether there are demand-side or supply-side constraints. Table 3 provides an example, comparing low and lower-middle income countries in and outside Sub-Saharan Africa (Beck and Cull, 2014). There is not only a smaller share of firms that have a loan in Africa than outside Africa, but among those firms without a loan, a smaller share of firms applied for a loan in Africa than outside Africa. Considering the reasons for not applying for a loan sheds lights into the bottlenecks that prevent the SME financing frontier from moving outwards in Africa. First, complex application procedures and high collateral requirements point to supply-side constraints, caused either by institutional deficiencies (such as non-existing or ineffective collateral and credit registries) or inefficiencies within the banking system. A higher share of enterprises in Africa points to 40
of the product and the remaining economic rent can be imputed to the unpriced input, i.e. water (the residual claimant).

49. A basic version of the residual imputation approach, the budgeting method, was, for example, used by Chang and Griffin to estimate agricultural costs of irrigation water transfers to urban uses (1992). Linear programming based representative farm models were applied by Hamilton et al. (1989) to analyze the potential of allowing some irrigation to move to hydropower use in critical low flow periods. Taylor and Young (1995) estimated FDB by developing a discrete sequential stochastic program to model the sequential nature of crop production decisions and the uncertainty of water supplies and rainfall. Sunding et al. (2002) used three alternative models to evaluate the impacts of water reduction in irrigated agriculture in California‘s Central Valley for improving water quality in the San Francisco Delta estuary. Based on discrete sequential stochastic programming combined with a hydrological model, Houk et al. (2007) estimated foregone benefits from water transfers from agriculture to instream flows in the Platte River for critical habitat purposes in Nebraska.

50. **Issues with Deductive and Inductive Techniques** . Although conceptually straightforward, in practice several problematic issues arise with the residual imputation approach which so far have not been much examined in the literature (Young, 2005). They include accurately specifying the physical production function, specifying technologies, and assigning correct prices for the non-water purchased inputs as well as the outputs. Furthermore, non-water _owned inputs_ pose a particular challenge. Both production theory and empirical evidence suggest unresolved problems in properly identifying and pricing inputs owned by the firm. Theory suggests that owned inputs such as household labor, equity capital, management and entrepreneurial skills, and land and possibly other (non-water) natural resources should be accounted for. Empirical observations of land market transactions and more elaborate hedonic pricing using farm sales data and econometric techniques show that typical residual imputation studies yield higher valuations than do market transaction-based studies. If, for example, in a long-run context of analyzing FDB the contributions of some of these non-water owned inputs are neglected in calculating the residual claimant for water, then the value assigned to water is erroneously large because it includes returns not just to water, but to the ignored owned inputs. Results from such analyses would yield exaggerated estimates of FDB from decreased water supplies.

51. Similarly, the studies that used the value-added measure from regional input-output models to estimate FDB also produced overstated FDB—because changes in value-added consist not only in the 15
cognitive tasks that are higher order in their complexity, and presumably are associated with larger costs of performing outside of a firm’s central location.

- B. Trends in Offshoring, Trade, Employment, and Wages

In this section we outline broad trends in the data for employment, wages, and the relationship between wages and measures of globalization. In Figure 1, we compare the trends in employment and wages in the manufacturing sector alongside the same trends in the service sector between 1979 and 2002. We present these trends separately for workers performing routine and non-routine tasks. Total manufacturing employment (using the CPS employment numbers) fell from 22 to 17 million from 1979 to 2002, with rapid declines at the beginning of the 1980s and in the late 1990s. Within manufacturing, the labor force has become increasingly high-skilled with a large decline of roughly 6 million in the number of workers in routine occupations, and a modest increase of roughly 1 million in the number of workers performing non-routine occupations.

In contrast, demand for both types of workers continued to grow in the service sector, and many of the displaced routine manufacturing workers may have found employment in the service sector. These trends have important implications for the US wage distribution. As shown in the bottom of Figure 1, where we report the real hourly wage among CPS workers, manufacturing workers enjoyed a large wage premium during the entire period among both routine and nonroutine workers. Insofar as manufacturing provided an opportunity to earn high relative wages – even for low-skill workers – its decline might also have played a role in increasing US income inequality during the period.<sup>10</sup> We turn now to an examination of how offshoring and trade may be related to these employment and wage trends within manufacturing and in the overall economy.

> and non-routine manual (Eye, Hand, Foot), non-routine analytic (General Educational Development, Mathematics), and non-routine interactive (Direction, Control and Planning) tasks. See also the data appendix. 10 See Autor et al. (2008) for a review of these trends. It is worth noting that while the trends in Figure 1 are informative, they do not control for other factors that affect income, such as sex, age, and experience. We redid the trends in wages
well as labor market functioning. Noting that before the intervention counties on both sides of the border followed similar trends and that we can control for other government programs, the fact that the experiment was implemented in Chengdu prefecture but not in neighboring administrative units allows us to implement a triple difference discontinuity design where reform effects are identified by comparing changes in the same village before and after the reform between villages inside and outside the prefecture boundary.

Results suggest that about 3-4 years after it had been completed, rural-urban integration reform had helped to significantly increase tenure security by reducing the incidence of administrative reallocations and expropriation. This led to an increase in the share of arable agricultural land (compared to less intensive pastures) and the share of construction land devoted to economic activities rather than residential use while preserving the level of non-productive ‘waste’ land including forests. Reform-induced increases in levels of market-mediated transfers of construction land as well as formal and informal transfers of agricultural land are consistent with the notion that higher levels of tenure security created a basis for the operation of land markets that transferred land to better uses. While we can only evaluate the impact of the reform package overall, reform-induced increases in the rate of new enterprise startups suggest that labor market reforms had an independent impact as well. These effects are estimated to be more pronounced close to Chengdu city, in line with significant scope for exploitation of agglomeration benefits.

The paper is structured as follows. Section two provides context, discusses salient features of the Chengdu experiment, and introduces analytical methodology and data sources. Section three presents descriptive statistics on economic structure, land endowments and transfers, as well as enterprise start-ups from the village survey, complemented by information on nature and cost of land certification in sample villages. Section four discusses impacts on tenure security, changes in land use, activity in land markets, and formation of new enterprises. Section five concludes with implications for policy and future research.

# **2. Motivation and background**

Between 2000 and 2010, rural-urban land conversion in China expanded at rates that are among the highest in East Asia. As such changes are not easily reversed, this poses vast challenges for China’s development for decades to come. While piecemeal efforts to change this pattern had proved largely ineffective, the rural-urban integration reforms undertaken in Chengdu prefecture followed a more integrated approach that could hold broader lessons. We describe the nature of the reforms and our analytical approach to evaluating their impact.

# **2.1 The challenges of rural-urban land conversion in China**

In the 2000-2010 period, the size of urban areas in China expanded enormously: use of satellite imagery to define ‘urban’ consistently across countries reveals that, with an average annual area expansion of 9.8% for 3
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the most attention given to how GQ traits are developed using project-level records and then paired to district-level conditions.

Empirical specifications use a non-parametric approach with respect to distance to estimate treatment effects. We define indicator variables for the shortest distance of a district to the indicated highway network (GQ, NS-EW) being within a specified range. Most specifications use four distance bands: nodal districts, districts located 0-10 km from a highway, districts located 10-50 km from a highway, and districts over 50 km from a highway. Of our 311 districts, 9 districts are nodal for GQ, 69 districts fall within 0-10 km of GQ, 37 districts fall within 10-50 km, and 196 districts are 50 km or more from GQ.

Our focus is on the non-nodal districts of a highway. We measure and report effects for nodal districts, but the interpretation of these results is difficult as the highway projects are intended to improve the connectivity of the nodal districts. For the GQ network, we follow Datta (2011) in defining the nodal districts as Delhi, Mumbai, Chennai, and Kolkata. In addition, Datta (2011) describes several contiguous suburbs (Gurgaon, Faridabad, Ghaziabad, and NOIDA for Delhi; Thane for Mumbai) as being on the GQ network as 'a matter of design rather than fortuitousness.' We include these suburbs in the nodal districts. As discussed later when constructing our instrument variables, there is ambiguity about whether Bangalore should also be considered a nodal city. The base analysis follows Datta (2011) and does not include Bangalore, but we return to this question. For the NS-EW network, we define Delhi, Chandigarh, NOIDA, Gurgaon, Faridabad, Ghaziabad, Hyderabad, and Bangalore to be the nodal districts using similar criteria to those applied to the GQ network.

# 4 Empirical Analysis of Highways' Impact on Loan Activity

## 4.1 Econometric Methodology

Long-differenced estimations compare district x industry loan activity in 1999, just prior to the start of the GQ upgrades, with loan activity in 2009. About 98% of the GQ upgrades were completed by the end of this time period. Indexing districts with d and 8
Table 3. Policy scenarios

|Scenario<br>definition|Scenario<br>name|Target to be achieved under the<br>scenario|Revenue recycling<br>scheme|Carbon<br>CO|surcharge<br>2, yuan20|(per ton<br>15)|
|---|---|---|---|---|---|---|
|||||2015|2025|2030|
|Base case|B0|58.7% reduction of CO2<br>intensity without carbonpricing|||||
|Low carbon pricing<br>with risingrate|R1CUT|60% reduction in CO2intensity<br>by2030|Cut VAT and capital<br>tax|1.6|18|26|
|High carbon pricing<br>with rising rate|R2CUT|65% reduction in CO2intensity<br>by2030|Cut VAT and capital<br>tax|9.8|108|157|
||R2LUMP|Same cumulative CO2<br>reductions asR2cut|Lump sum rebate to<br>households|9.3|102|149|
|High carbon pricing<br>with flatrate|F2CUT|Same cumulative CO2<br>reductions asR2cut|Cut VAT and capital<br>tax|82|82|82|
|Rising renewable<br>subsidies to reach<br>IEA's New Policies<br>scenario|RRSUB|Renewable subsidies financed<br>by carbon revenue|Carbon tax to offset<br>renewables costs|2.8|36|58|

‐13‐
**7**

found in the theoretical literature<sup>(for a recent survey see Foster, 1984),</sup> including Sen's (1976, 1981) Transfei<sup>Axiom which requires that when income is</sup> transferred from a poor person<sup>to someone who is poorer measurei poverty</sup> decreases, Neither measures<sup>(i) nor</sup><sup>**(ii)**satisfy this condition. It alsohas;</sup> advantages over a number of<sup>alternative distributionally sensitive measures,</sup> such as the Sen index. For example,<sup>the FGT measure is additively separable;</sup> aside from the advantages of<sup>that property for constructing decompositionsof</sup> poverty ("poverty profiles"),<sup>it implies that when any subgroup of the</sup> population becomes poorer, aggregate<sup>poverty will also increase, ceteris</sup> paribus (Foster and Shorrocks,<sup>1987).</sup> We do not have any alternative<sup>data to the HES when estimating the</sup> distribution of consumption in<sup>Bangladesh arcund the mean. So I have little</sup> choice but to assume that the<sup>Lorenz curve from the HES is correct at each</sup> date. It should, however, be<sup>noted that one possible explanation for the high</sup> HES means for the later years<sup>is an undersampling of poor households,which</sup> would also lead to a bias in<sup>the Lorenz curve.</sup> Figures 1 and 2 give the estimated<sup>Lorenz curves and Gini indices for</sup> Bangladesh, for urban and rural<sup>areas respectively, over the three HES years.</sup> The rost notable point here is<sup>how 'ittle relative inequalities have changed</sup> within the rural sector; the Lorenz curves<sup>for these three years are virtually</sup> indistinguishable, and Gini indices<sup>are identical to two decimal places.</sup> Changes in overall inequality<sup>have been primarily due to changes in inequalitv</sup> within the urban sector, and inequality<sup>between sectors. A decline in</sup> inequality in urban areas after<sup>1981/82 is indicated, though it did not</sup> continue after 1983/84. The<sup>same pattern holds for inequality between sectors</sup> (as measured by the urban/rural<sup>disparity in ,means) though doubts have been</sup>