rafmacalaba/datause-encoder-data huggingface dataset:
is intensive in fixed capital, whereas entrepreneurs that require relatively more working capital, will be financed primarily by informal lenders.

In addition, if the transaction costs of accessing formal finance are large, households that need little credit will tend to rely on informal lenders whereas those with large credit needs will be better off incurring the fixed costs in order to have access to a lower cost of capital.<sup>6</sup> Empirical research on rural credit markets faces the problem that the combination of unobserved heterogeneity and of endogenous matching of agents into borrowing from different lenders can create selection biases on the parameters of interest.<sup>7</sup> .

The approach taken here differs from that of Bell et al. (1997), Kochar (1997) and Conning (1996) and resembles Key (1998) in that we estimate the likelihood of borrowing from each source as dictated by the structure of the model. This way, we are able to assess how important enforcement problems vis `a vis transaction costs are in the overall picture of credit markets.

The data used come from a cross-section survey conducted in Thailand in 1997, an interesting country because despite the growth episode experienced in the 1980s, formal credit is still limited in rural areas.<sup>8</sup><sup>_,_9</sup> The estimation results raise several points. First, there are disparities in the cost of accessing different lenders: while the estimated cost of accessing a formal institution is on average US$30, the cost of accessing an informal source is negligible. Second, the cost of accessing formal finance depends on the characteristics of the household, such as the proximity to the bank or whether the household has a savings account. Third, the model suggests that roughly 85 percent of households that resort to the bank are constrained. Thus, most households receive a lower credit amount because if the bank were to advance to them more capital, they would have the incentive to default.

> 6This point is also made in Braverman and Guasch (1986, 1993), Hoff and Stiglitz (1993), Besley (1994), Key (1997), and more generally in Banerjee (2003)

> 7See Chiappori and Salani ́e (2003) for a survey on empirical studies of contract theory, Key (1998) and Banerjee and Duflo (2003) for a review of the econometric issues in credit market studies.

> 8See Gin ́e and Townsend (2004) for a welfare evaluation of the credit liberalization that took place in Thailand from 1975 to 1997.

> 9Using the same data set, Paulson and Townsend (2004) find evidence of credit constraints as wealth, even controlling for talent, contributes significantly to business start-ups.

4
and they are harvested between April and June. The kharif crops are sowed from April to September and harvested in October to March.

**Table 4: Logit Estimates of Work – Rural and Urban (individuals 10-13 years and 14-17** **<u>years vs. all adults)</u>**

|||Rural<br>|||Urban<br>||
|---|---|---|---|---|---|---|
||Total|Male|Female|Total|Male|Female|
|May-Dec.|0.027*|-0.012|0.058**|-0.034**|-0.046*|-0.036**|
||(0.013)|(0.019)|(0.018)|(0.012)|(0.019)|(0.013)|
|Age 10-13|-0.418**|-0.527**|-0.236**|-0.519**|-0.829**|-0.120**|
||(0.027)|(0.031)|(0.042)|(0.032)|(0.048)|(0.029)|
|Age 14-17|-0.243**|-0.344**|-0.129**|-0.269**|-0.516**|-0.005|
||(0.028)|(0.031)|(0.043)|(0.026)|(0.037)|(0.026)|
|May-Dec * Age 10-13|0.012|-0.005|0.047|-0.061|0.041|-0.093*|
||(0.031)|(0.036)|(0.047)|(0.040)|(0.055)|(0.038)|
|May-Dec * Age 14-17|0.027|0.029|0.044|-0.022|0.083|-0.104**|
||(0.032)|(0.036)|(0.049)|(0.032)|(0.043)|(0.033)|
|Observations<br>|11610|6058|5552|12159|6390|5769|
|Pseudo R<sup>2</sup>|0.08|0.22|0.02|0.10|0.24|0.03|
|LogLikelihood|-7143.6|-2809.9|-3779.3|-7509.4|-3177.8|-2871.6|

Robust standard errors in parentheses *significant at 5%; ** significant at 1%.

The estimated model for urban females shows a significant negative effect on the difference-in-difference estimate. In particular the probability of work for 10-13 year old girls relative to women (the control group) in urban areas decreased by 9.3 percentage points after the introduction of the EoCA in April 1991. However, a similar effect is seen for girls in age group 14-17 where the probability of work decreased by 10.4 percentage points. This result might indicate a spill over effect of the implementation of the EoCA 1991. However, we have to be cautious while drawing conclusions from these results because the regression does not control for other factors determining the supply of labor.

# **_4.2 - The difference-in-difference estimate – age 10-13 and 14 years_**

# **Pakistan**

20
divergence between the country groups in the sense that the difference between the index for groups (d) and (a) in 2004 is greater than it is for the whole period. Thus, there is divergence in actual policy over time.

**<u>Table 5. Openness Rankings of Country Groups: GATS vs. Actual Practice</u>**

|Rank|Country<br>Group|Ranked by<br>average GATS<br>commitmentindex|Country<br>Group|Ranked by<br>average EBRD<br>score|Ranked by<br>2004 EBRD<br>score|
|---|---|---|---|---|---|
|1|Group (d)|58.3|Group (a)|2.68|3.44|
|2|Group (b)|45.3|Group (b)|2.41|3.19|
|3|Group (c)|35.7|Group (c)|2.01|2.85|
|4|Group (a)|32.8|Group (d)|1.70|2.24|

_Source_ : EBRD (2004) and authors calculations based on GATS schedules.

There is, of course, also heterogeneity within the 4 country groups, especially groups (a) and (c) (Figure 2). Therefore we also present a ranking of all countries jointly, using the same data as in table 5, which takes account of the information hidden by the averages (see also Annex Table 4). Rather than discuss the results for all countries, in what follows we examine some of the more interesting and extreme cases. In line with its GATS commitments, Hungary is the “frontrunner” in group (a) in terms of applied policy, as measured by the EBRD indices. This is consistent with Hungary’s GATS commitments. However, Poland, with the fewest GATS commitments in group (a) has services policies that are ranked only second to those of Hungary by the EBRD. The four countries in group (b) are all very similar in terms of actual policy performance. Estonia has the highest ranking in terms of applied policies, even though it made the fewest commitments of the three Baltic States. Latvia’s relatively strong use of the GATS is not reflected in its relative ranking in actual policy. In group (c), Bulgaria, Croatia, and Romania follow more or less the same policy path, while FYR Macedonia has been lagging since the mid1990s. This contrasts with the picture painted by the GATS commitments, where Bulgaria and Romania did very little and Macedonia, FYR in contrast made more extensive commitments. GATS commitments and practice thus seem to be reversed for these three countries. Croatia is the outlier, in that it performs well in terms of both GATS commitments and applied policy. The poor policy performance of all four countries in group (d) is striking. Compared to the other transition economies in the sample, these countries have made the least progress in putting in place better services policies. The actual policy performance – uniformly limited – contrasts with the much greater divergences in GATS commitment. Moreover, the relatively ambitious GATS 13
rural transport costs constructed using _TIME Before_ rather than using _TIME After_ . This is the counterfactual solution. In the **fifth stage** , the counterfactual equilibrium values ( _wUBefore_ , _wBeforeR_ ,  _Before_ , _YBefore_ ) are deflated using the cost-of-living index, _U_  _R_ 1  _PBefore_  ( _GBefore_ ) ( _GBefore_ ) to give the corresponding real values. These are then compared with the corresponding "after" values to obtain estimates of impacts. To estimate the overall impact of the NEN on aggregate Chinese nominal and real income, the results are aggregated across regions.

# _3.3. Measuring Transport Costs_

One crucial issue which emerges in the implementation of stages 2 – 4 is the specification of suitable functional forms for the mapping of the travel time estimates in _TIME After_ and _TIME Before_ to our measures of urban and rural transport costs, _TijU_ and _TijR_ . We assume that: where 0   _U_  1 and 0   _R_  1 ,  0 ,  1 ,  0 and  1 are coefficients and<sup>_t_</sup> _ij_<sup>denotes either</sup> _After Before_<sup>_t_</sup> _ij_ or<sup>_t_</sup> _ij_ . Given the strong correlation which exists across prefectures between _tij_ and _d ij_ , where _d ij_ is the straight-line distance separating the centers of regions _i_ and _j_ , the concavity of transport costs implied by equations [8] and [9] is consistent with evidence from the transport economics literature suggesting the presence of economies of distance for goods haulage (McCann, 2005; Fingleton and McCann, 2007). A number of previous empirical NEG studies have used the same functional form to model transport costs, including those of Au and Henderson (2006) and Bosker _et al._ (2010) for China. Crucially, however, these studies have relied on measures of Euclidean (straight-line) or great circle distance rather than network distance or estimated travel times. But it is travel time that is most affected by the stock and quality of transport infrastructure. Evidence of the importance of travel times for Chinese transport costs is to be found in literature on the country's transportation and logistics industries (see, for example, Hong _et al_ ., 2004). Meanwhile, Roberts (2010) has demonstrated that, under reasonable conditions, travel times provide a suitable proxy for generalized transport costs (GTCs), which capture both vehicle operating costs and the 15
6

# Figure 1: The recent rise in protectionist measures

## (A) The stock of harmful measures

## (B) Sectors most often affected

<!-- Start of picture text --> Count Count<br>7000 1000<br>800<br>6000<br>600<br>5000<br>400<br>4000<br>200<br>3000 0<br>2000<br>1000<br>0<br>(C) Most often used harmful measures (D) Average duration of tariff increases<br>Percent Percent<br>25 45<br>40<br>20<br>35<br>15 30<br>25<br>10<br>20<br>5 15<br>10<br>0 5<br>0<br>energy<br>Electrical<br>or steel Domestic appliances<br>trailers and semi-trailers<br>Products of iron Other fabricated metal products Motor vehicles,<br>Import tariff Anti-dumping Financial grant Public procurement State loan<br>2010 2012<br>In place for In place for In place for<br>2009 2011 2013 2014 2015 2016<br>year years years<br>more than 1 more than 2 more than 3<br><!-- End of picture text --> Source: Global Trade Alert. A. The cumulative number of newly introduced protectionist measures. B. C. D. Based on protectionist measures impacting import flows between 2009-2017.
1.3 per year between 1993 and 1999 to 4.2 per year as from 1999 (the second comparison period – 1993 to 1999 – starts a year later to allow for the time required to decide on each petition).

According to a recent survey (June 2003) conducted by the National Society of Industries, 77% of industrial companies that have faced some form of unfair foreign competition consider that they were affected by dumping practices, and 20% by subsidies. Surprisingly,, only less than 5% of these companies have submitted applications to the CDS The authors believe that the limited use of these mechanisms registered during the first years, and the increasing volume of cases submitted for the most part reflects a gradual learning process by companies. Conversely, in countries such as Mexico or Argentina, we see that the number of cases has started to decline. A learning process is also taking place with exporters. In the long term, trade defense measures have the effect of discouraging unfair trade practices.

An increase in the average number of investigations initiated is also observed. It rose from 4.7 cases per year (28 in 5 years) during the first period, to 6.4 per year (32 in 5 years) in the second one. The increase coincides with the political change that took place beginning in 2000, when Valentín Paniagua was sworn in as interim president and succeeded by the current government of Alejandro Toledo in 2001. Of these investigations, antidumping duties were applied to 9 cases in the first 5 years (1.5 per year), while in the second stage, this figure rose to 21 cases in 5 years (4.2 per year ).

In percentage terms, investigations considered founded grew from 32% in the period 1993-2000 to 66% over 2001-2003.

# _(c) Incidence by Sectors_

In terms of sectors, the use of the antidumping mechanism has been diversified. Sixteen different activities requested trade defense measures, eight of which submitted three or more applications. This diversified pattern has not varied significantly between the early period 1993 - 1998 and the most recent period, the most frequent user being the iron and steel sector, with 18% of total cases. Likewise, agriculture, textiles and meters (electricity and water) represent 11% of the total each, while chemical products have a 9% share.

22
Cote dlvoire, Uganda and South Africa. In other countries (e.g., Guinea, Sierra Leone), coverage is largely confined to the capital cities.

Figure **2: International Expansion of Cellular Telephone Systems** <!-- Start of picture text --> Cell Phone Diffusion:<br>% of 194 ITU Countries<br>70<br>50<br>40<br>30<br>20<br>10<br>00<br>1975 1977 1979 1981 1983 1985 1987 1989 1991 1903 1995 1997<br>Data Source: rlU<br><!-- End of picture text --> To explain these wide variations, we employ another version of the diffusion model in equation (4) for the period 1990 - 1999. We include initial income, policy and urbanization, while adding an income growth term to allow for changes during the 1990's. Recognizing that the growth rates of income and mobile phones may be jointly determined, we have tested the impact of mobile phone growth on income growth in a separate instrumental-variables exercise.<sup>6</sup> Finding no significant impact, we have employed a single-equation model (6) for this paper.

logM3 , -log **M l,1**<sup>=A</sup><sup>**_-0logMj,i +O/jloguj,-i +/Alogyj,- +OA logcj,t_**</sup> **_()+_** A4(logy Y_ log Yi, **+** ) where

**Mit** = Mobile telephone subscriptions

U1t = Size of urban population

**Yjt** = Income per capita

**Cit** = Index of competition policy

Table 3 reports the regression result, which explains about 77% of the variation in mobile phone growth rates across 99 countries. All estimated parameters have the expected signs and are highly significant by the conventional criteria. The estimated value of 6Our growth equation replicates a recent specification by Easterly (2000), with the addition of a term to capture the possible effect of mobile phone growth. Results are available from the authors on request.

8
3 (1988), Ahmed et al. (1991), Khundker et al. (1994), Rahman et al. (1995), Ravallion and Sen (1996), Ravallion and Wodon (1999), and Wodon (1997). Among these, Ravallion and Wodon (1999) and Wodon (1997) are the only studies using household level data, but only up to the survey year 1991-92. For inequality, published results are much fewer. Osmani (1982) presented estimations based on surveys conducted between 1963-64 and 1973-74. The contributions of Khan (1986) and Rahman (1988) were based on surveys up to the early 1980's. Rahman and Huda (1992) considered inequality between occupational groups using the 1983-84 HES. The BBS (1998) does report estimates of inequality for the urban and rural sectors as a whole, but these measures are over-estimated because the BBS does not adequately take into account price differentials between areas. Using group data for 1991-92, Ravallion and Sen (1996) also estimated urban and rural Ginis, but they could not investigate in any further detail the impact of household characteristics such as education, land ownership, location, and occupation on inequality (or on poverty) because they did not have access to the household level data.

Section two sketches the big picture as to what happened to poverty and inequality in Bangladesh between 1983 and 1996. Section three analyzes the micro determinants (and changes thereof) of consumption, poverty, growth, and inequality, while introducing the method to compute conditional indices of between group inequality. Section four extends the work on inequality. A conclusion follows.

# I **The big picture: poverty, inequality, and decompositions**

# **_II. 1._** _Methods_

Five rounds of the Household Expenditure Surveys of the BBS are used, corresponding to the years 1983/84, 1985/86, 1988/89, 1991/92, and 1995/96. The five rounds cover from 3,840 to 7,240 households per round. Poverty lines are used not only for computing poverty measures, but also as price deflators for estimating growth and inequality in real per capita consumption, which is preferred to income as an indicator of standards of living. The methodology used for estimating the regional lower (identifying the very poor) and upper (identifying the poor) poverty lines is described in Wodon (1997).
WPS4059

# **The Impact of Policies to Control Motor Vehicle Emissions in Mumbai, India***

Akie Takeuchi<sup>1</sup> , Maureen Cropper<sup>1,2</sup> , and Antonio Bento<sup>1</sup>

## World Bank Policy Research Working Paper 4059, November 2006

_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 polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We thank the World Bank Research Committee and the Transport Anchor for funding, and Alex Pfaff for comments.

> 1 University of Maryland

> 2 World Bank
**Figure 5:  Variation across and within countries comparing the** **_Doing Business_ days (on horizontal axis with each country observation on the 45 degree line) with the Enterprise Survey distribution of days (a box plot for each country)**

# **5a)  Time to get a construction permit (** **_Doing Business_ ) versus days get construction Permit (Enterprise Survey)**

<!-- Start of picture text --> 45 degree line<br>Gap between de jure and<br>de facto<br>0 150 300 450 600<br>Time to get Construction Permit (DB)<br>excludes outside values<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Days to get construction permit (Enterprise Surveys)<br><!-- End of picture text --> 47
**Figure 5 Trends in total health expenditure (as percentage GDP) in the Western Balkans and comparator countries, 1996-2005**

<!-- Start of picture text --> 12.00<br>10.00<br>8.00<br>6.00<br>4.00<br>2.00<br>0.00<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Albania Bosnia and Herzegovina<br>Macedonia, FYR of Serbia and Montenegro<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Croatia Slovenia EU EU-15 EU-12<br><!-- End of picture text --> Source: WHO NHA database (for Western Balkans, Croatia and Slovenia) and WHO HFA-DB 2007 (for EU, EU-15 and EU-12)

# _<u>D1.3 The fiscal space to increase expenditures is extremely limited</u>_

Fiscal space can be defined as “the capacity of government to provide additional budgetary resources for a desired purpose without any prejudice to the sustainability of its financial position” (Heller 2006). While current per capita healthcare expenditure is low, it appears that – at least for the countries for which data are available – the fiscal space for additional expenditures on health is very limited. Most countries in the sub-region already spend a large proportion of their public budgets on health. In 2002, the FYR of Macedonia and Serbia and Montenegro allocated the same share of government expenditure to health as Croatia and Slovenia did (i.e. 14%), even though the latter had tremendously higher levels of per capita expenditure (see Table 4). In addition, all countries in the sub-region spent the same or greater share of total government expenditure on health as the EU-15. This means that if health systems in the sub-region are to expand the quantity and quality of services offered to their populations, the resources for that will need to be sought through either (i) sustained economic growth that generates formal employment and increases total public sector resource availability, or (ii) increasing the cost-effectiveness and efficiency of existing health sector program. It is only Albania that stands out, allocating half of the budgetary share to health that other countries do.

18
Applying the same approach as for the control group, the bottom two rows of Table 1 show that the treatment effect estimated using the counterfactual responses is 45.2 percent for new applicants in the treated group and 41.5 percent for existing applicants. Both of these lie above the experimental treatment effect 95 percent confidence interval, with the estimate for the existing businesses particularly overstated. They appear to be overestimating how much the grant has helped their firm to survive, with their mean expected chance of operating a business of 55 percent more than 20 percentage points below the rate at which the control group existing firms are actually operating. To examine the sensitivity of this result to the heaping at 50 percent, as a robustness check I randomly remove half of the existing firm treated group that report 50 percent. This only raises the mean percent chance of running a business to 55.6 percent from the 55.1 percent reported in Table 1. Hence the heaping at 50 percent is not responsible for the large overestimation of the treatment effect by treated existing firms.

# **4.2 Counterfactuals about the Size of the Firm: Number of Employees and Sales**

Our counterfactual question on employment asks about the number of employees conditional on being in business. Column 2 of Table 2 shows that the control group averages 6.9 (new firms) and 7.6 (existing firms) employees, while the treated firms are larger at 9.7 (new firms) and 10.2 (existing firms) employees. Columns 3, 4, and 5 of this table provide the mean employees given in the counterfactual, a trimmed mean which drops the top 5 percent to alleviate the concerns previously mentioned about the format of this question being susceptible to outliers, and the median response. The last two columns then contain the estimated treatment effect using the counterfactual and the experimental treatment effect respectively.<sup>4</sup> Consider the control group firms. On average they expect that they would have 23 employees had they won the business plan competition, which is more than twice the actual mean level reported by the treated firms. Trimming the mean or using the median still results in a large overstatement. As a result, the estimated average treatment effect of 16 workers using this counterfactual is much larger than the experimental treatment effects of 2.2 to 2.5 workers. Figure 3 examines this overstatement of the treatment effect across the distribution by comparing the distribution of counterfactual employment reported by the control group to the actual distribution of employment for the treatment group. We see

> 4 Note that I use here the difference in treatment and control employment conditional on being in business, reported in the appendix of McKenzie (2015). These estimates correspond most closely to what the expectations questions ask. Alternatively one can combine the elicited probability of operating a firm with the counterfactual number of employees conditional on operating, and then compare these to the treatment effect on unconditional employment reported in Table 3 of McKenzie (2015). This gives similar results.

10