rafmacalaba/datause-encoder-data huggingface dataset:
While the case-study is a practical approach for collating data on national regulations, it is not suitable for measuring all aspects of the investment climate even if one’s sole purpose is to create country-level variables. For instance, if one wants to know how long it takes on average for imported and exported goods to clear customs or how much money firms lose on average due to power outages, a firm survey is likely to be the only feasible approach. Further, firm surveys provide probably the best (the most representative) basis for reports on “the voices of the firms” (Batra et al., 2003) with respect to growth obstacles. The case-study and the firm surveys are thus best viewed as complementary methods, generating different kinds of complementary information.

When, then, are firm survey data most useful? We have already mentioned one obvious use of firm data, namely to construct aggregate investment climate measures (or any aggregate variable, for that matter). In some cases, the researcher’s goal cannot be reached by relying on aggregate data. It may be that the researcher has to analyze differences across firms within an economy, in order to estimate, say, productivity dispersion - a conventional indicator of the degree of competition – or to find out whether credit constrained firms invest less than unconstrained ones. It is arguably in cases like this, when the _heterogeneity_ across firms is of central interest, that firm data is most useful. Unless firm data are already available (which is often not the case in SSA), there is then really no substitute for the firm survey.<sup>5</sup> Indeed, perhaps the most striking general result that has come out of the work on the African firm survey data thus far is that there is substantial heterogeneity in performance across firms within countries. For instance, Bigsten et al. (1999a) analyze data on manufacturing firms in four African countries and report that for one quarter of the firms profit to capital ratios are more than four times lower than the median, while for another quarter of the observations profit rates are more than four times higher than the median (and this is not a result, primarily, of cross-country differences). A similar finding of large variability across firms holds for most of the key performance variables in the data (e.g. productivity, export intensity, investment).

The policy implications of heterogeneity in firm performance are potentially far-reaching. For instance, recent research has shown that there are important knockon effects on labor market outcomes, which, as discussed above, provides the link

> 5 Industrial census data may exist but are typically much less detailed than survey data.

6
headed by men as compared to households headed by women but this difference is not very large and the number of households headed by women is relatively small. Depending on the size of households, double poverty increases with household size. It displays its maximum when the household is composed of 10 or more people with a percentage of 7.3% against 0.9% for those living in households with less than 4 people. Interestingly, households that are poor according to non-monetary criteria but non-poor according to monetary criteria seem to experience higher poverty the smaller is the household size. This might be explained by the fact that smaller families have fewer assets than larger families, income being equal. By grade level of the household head, 23.8% of people living in households headed by someone with no education are at risk of at least one form of poverty. Double poverty affects 5.5% of those households headed by people who never attended school. Also, the higher the educational level of the household head the lower is the risk of poverty as one would expect. Overall and considering urban and rural areas together, in 2007 3.8% of people were below the poverty line according to both CWI and expenditure criteria. This can be considered as the core of poverty in Morocco while 82.8% of the population is above all forms of poverty.

# [Table 10]

## **6.3 Multidimensional poverty over time**

As already discussed and in order to conduct the MCA analysis over time, we used the weights from the NSLS 2006/07 for both time periods considered. We also followed exactly the same methodology we used for the 2006/07 MCA for the 2000/01 data. The multidimensional poverty line for 2000/01 is equal to 60% of the CWI median value and amounts to 1.1 in rural areas and 0.6 in urban areas.

The analysis of CWI and expenditure poverty over time shows that the two forms of poverty declined significantly between 2001 and 2007 (Table 11). This decrease is more pronounced with the multidimensional approach as compared to the monetary approach at the national level. The rate of multidimensional poverty decreased from 23.9% in 2001 to 12.1% in 2007, an average annual decline of about 12%. Income poverty has decreased annually by an average of about 9.5%, from 15.3% in 2001 to 8.9% in 2007. Urban and rural areas show different patterns. Poverty in rural areas is much higher and the decline over the 2001-2007 period has been steeper. Multidimensional poverty in particular shows that rural poverty declined by more than half, from 42.35 to 18.3%.

[Table 11]

## **6.4 Robustness analysis**

16
**<u>Table 8: Labor allocation hours for household agriculture by women 10 years and older</u>**

||(1)|(2)|(3)|
|---|---|---|---|
||Overall|Less Biomass Areas|More Biomass Areas|
|Age inyears|0.133***|0.138***|0.126***|
||(0.007)|(0.008)|(0.011)|
|Age Square|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|
|Head Education: Snr. Prim or more|-0.142***|-0.029|-0.360***|
||(0.048)|(0.059)|(0.082)|
|HH Size|-0.031***|-0.038***|-0.042**|
||(0.010)|(0.012)|(0.017)|
|Real Value of Assets '000|-0.005***|-0.005***|-0.004***|
||(0.001)|(0.001)|(0.001)|
|Wet Season & Wet Area Interaction|0.181***|0.176***|0.229***|
||(0.036)|(0.041)|(0.074)|
|DrySeason and Dimba Area Interaction|0.623***|0.582***|0.596***|
||(0.121)|(0.144)|(0.220)|
|Owned & uncultivated last season|-0.150***|-0.090|-0.167***|
||(0.043)|(0.061)|(0.061)|
|Total hectares of trees|0.224|1.041|0.184|
||(0.479)|(0.934)|(0.559)|
|Predicted Real AvgWage for Women|-0.003***|-0.003***|-0.003***|
||(0.000)|(0.001)|(0.001)|
|Number of <5yr children|0.256***|0.245***|0.289***|
||(0.036)|(0.043)|(0.061)|
|Married for age 12+|0.563***|0.585***|0.462***|
||(0.055)|(0.067)|(0.094)|
|Rural Centre|-0.036|-0.385***|0.514***|
||(0.081)|(0.109)|(0.129)|
|Rural South|-0.032|-0.455***|0.340***|
||(0.082)|(0.113)|(0.123)|
|Wet Season Dummy|0.740***|0.642***|0.891***|
||(0.058)|(0.071)|(0.104)|
|ADMARC Market Distance KM|0.003***|0.002***|0.016***|
||(0.000)|(0.000)|(0.003)|
|Distance to Asphalt Road KM|0.006***|0.001|0.010***|
||(0.001)|(0.001)|(0.001)|
|Elevation|-0.000***|-0.001***|0.000|
||(0.000)|(0.000)|(0.000)|
|Biomass(m3/ha)|-0.005***|0.003|0.000|
||(0.001)|(0.005)|(0.002)|
|Price of Maize Grain MK/Kg|0.045***|0.085***|0.000|
||(0.012)|(0.015)|(0.019)|
|Recent illness/injury|-0.215***|-0.213***|-0.209**|
||(0.049)|(0.060)|(0.083)|
|Doyou suffer from a chronic illness|-0.176**|-0.219***|-0.172|
||(0.070)|(0.085)|(0.125)|
|Other Members ChronicallyIll in HH|-0.153***|-0.113***|-0.282***|
||<br>(0.032)|<br>(0.038)|<br>(0.057)|
|Productive Assets Index|8.078***|6.945***|11.463***|
||(0.831)|(0.952)|(1.709)|
|Constant|-2.028***|-2.156***|-2.388***|
||(0.262)|(0.330)|(0.437)|
|Observations|12740|8399|4341|

**Standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%**

44
3

# **1.  Introduction**

The objective of this paper, which is drawn from the recently completed Financial Sector Assessment Program (FSAP) work in Chile undertaken by a joint World Bank-IMF mission, is to assess the extent to which Chilean firms have access to sufficient and adequate sources of funds. Access to finance has become an important issue for policy makers throughout Latin America. Small and medium enterprises (SMEs) in particular complain indeed that their lack of access to adequate sources of financing is an obstacle to their growth. Chile represents an interesting case study since it has one of the most developed financial markets of the continent, and thus with great potential for using products suited to the needs and risk characteristics of SMEs.

# **_Overview of the Chilean Corporate Sector_**

Chile’s corporate sector is highly pyramidal, with just above 200 generally exporting, efficient, and well managed “mega large firms” at one end of the spectrum, and one million, mostly non exporting, labor intensive, and unprofitable, micro firms at the other end. Chile’s 6,469 large and mega large firms account for only 1 percent of the number of formally registered firms, but generate about 78 percent of total corporate sales and 96 percent of exports. Among them, there are just above 200 mega large firms dominating the scene. At the other end of the spectrum, the country’s 535,537 formal micro firms account for the vast majority of firms (82 percent), but contribute only 3 percent of corporate sales and less than 1 percent of exports. However, micro and small firms are the largest generators of employment, accounting together for 70 percent of total private sector jobs. These numbers ignore more than 500,000 informal micro firms.
# The Implementation of Industrial Parks: Some Lessons Learned in India

Yannick Saleman<sup>*</sup> <mark>Luke Jordan</mark><sup>†</sup>

## **JEL classification**

<mark>D78 - Positive Analysis of Policy-Making and Implementation</mark> <mark>H11 - Structure, Scope, and Performance of Government</mark> <mark>H44 - Publicly Provided Goods: Mixed Markets</mark> <mark>L52 - Industrial Policy; Sectoral Planning Methods</mark> <mark>L67 - Industry Studies: Manufacturing - Other Consumer Nondurables: Clothing, Textiles,</mark> <mark>Shoes, and Leather</mark> <mark>O14 - Industrialization; Manufacturing and Service Industries; Choice of Technology</mark> O25 - Industrial Policy

## **Keywords**

<mark>industrial parks, India, industry, competitive industries, textile, clusters, competitiveness,</mark> private sector development, implementation, special economic zones, industrial policy

## **Sector Board**

Financial and Private Sector Development

> * Yannick Saleman is a Private Sector Development Consultant with the Competitive Industries Global Practice of the World Bank Group, based in New Delhi (ysaleman@worldbank.org).

> † Luke Jordan is a Private Sector Development Specialist with the Competitive Industries Global Practice of the World Bank Group, based in New Delhi (ljordan@worldbank.org).
<!-- Start of picture text --> 50%<br>40%<br>a 30% c<br>(a) p<br>ae 20% , ;<br>| aera<br>10% Neth<br>0%<br>1980 1985 1990 1995 2000 2005<br>—o— Asia ex. China —*— China —e— Developing —*— Industrial<br><!-- End of picture text -->
# 5.3. Years of life lost

The impact of a reduction in the consumption of processed and ultra-processed foods on long-term income is derived from the reduction in years of life lost (YLL) because of mortality. Due to data limitation explained in section 4.2, the impact is estimated for the joint taxation on processed and ultra-processed foods.

The goal of this analysis is not to estimate the years of life lost related to dietary risks, as this is calculated using existing Global Burden of Disease estimates. Rather, we are interested in estimating the extent of the increase in income resulting from the reduction in YLL due to a lower dietary risk across households at the various levels of the consumption distribution. Potential gains in years of life lost and consequent increased income are determined based on the weight of processed and ultra-processed foods in households’ consumption in a given decile, and the price elasticity by decile. Income is calculated using household income per capita.

Table 7 shows the results in terms of income gains from reduction in years of life lost related to dietary risks. Every decile has income gains from increased expected income as a result of increased life expectancy, meaning that all values are positive. Notably, lower deciles have higher income gains, with the bottom consumption decile increase being almost 9 times that of the top consumption decile.

The pattern of income gains being higher for lower consumption deciles indicates that a tax on processed and ultra-processed foods also presents a progressive pattern when we analyze this specific impact. The progressiveness of taxation is due to higher elasticity for deciles at the bottom of the consumption distribution, as the other possibility for higher income gains would be a relative consumption of processed and ultra-processed foods among lower deciles in comparison to higher deciles, which is not the case, as shown in Table 2.

_Table 7_ – Income gains from reduction in years of life lost related to dietary risks

||**Processed and**<br>|**ultra-processed foo**<br>|**ds**<br>|
|---|---|---|---|
||Low|Medium|Upper|
|**1**|3.84%|3.85%|3.86%|
|**2**|2.45%|2.45%|2.46%|
|**3**|1.90%|1.90%|1.91%|
|**4**|1.62%|1.62%|1.63%|
|**5**|1.29%|1.30%|1.30%|
|**6**|1.10%|1.10%|1.10%|
|**7**|0.91%|0.91%|0.92%|
|**8**|0.74%|0.74%|0.75%|
|**9**|0.67%|0.68%|0.68%|
|**10**|0.43%|0.44%|0.44%|

Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019. Estimates assume a price shock of 20%.

Figure 4 further illustrates the progressive nature of income gains resulting from reduced years of life lost following the implementation of a taxation on processed and ultra-processed foods. The income gains decline across all consumption deciles, but the most significant drop occurs between the first and second deciles, at the lower end of the distribution. Notably, the first decile demonstrates a 50% higher income gain compared to the second decile. This finding highlights that individuals residing in households with minimal consumption stand to benefit the most from this measure.

18
9 average expenditures (income) of the poor fall short of the poverty line.<sup>10</sup> The table therefore also reports the poverty gap index, which measures in percentage terms how far the average expenditures (income) of the poor fall short of the poverty line. For instance, a poverty gap of 10 percent means that the average poor person’s expenditures (income) are 90 percent of the poverty line.

The remaining data for this study are defined as follows.

In the literature income is often found to be an important determinant of remittances. In micro-economic studies, income is typically measured in terms of both the migrant’s income abroad and the income of his family back home (e.g. Faini, 1994; Lucas and Stark, 1985); in macro-economic studies, this variable is usually measured by the GDP income of the labor-sending country (e.g. Glytsos, 1997). While microeconomic studies tend to find that family income back home has a positive and significant effect on the level of remittances sent home (Lucas and Stark, 1985; Cox, Eser and Jimenez, 1998), in macro-economic studies the impact of this variable is often unclear. To clarify the impact of income on the determination of remittances, this study includes data on the per capita GDP income (and its square) in the labor-sending country.<sup>11</sup> The distribution of income in the labor-sending country may also affect the flow of remittances. This study therefore includes data on the Gini coefficient, which is normalized by household size.

The next two variables in this study – percentage of population under age 14 and real interest rate in origin country – provide another way for examining the determinants of remittances.<sup>12</sup> Since both countries and families tend to be needier when they have a
are also related to changes in labor market power, the estimates of _γ_ 1 would be biased. Similarly, reserving a market partly or fully to MSMEs may reflect the intention by the government to favor these types of firms.

In order to address these endogeneity concerns, we proceed in three steps. First, we rule out that the changes to the NIL are systematically related to pre-reform trends in observables that may also affect wage markdowns. To that end, we regress the product market average growth rate of key economic variables in the pre-reform period (20082010) on a dummy taking value 1 if the product market was reformed in 2011. We focus on variables with a plausible impact on the markdown, including output, capital, employment, wages, along with the markdown itself and the share of foreign capital, which is an important determinant of the exposure to NIL. If product market trends within a broader 2-digits industry were related to subsequent reforming activity, we would expect a probit model to estimate a significant coefficient. The results are shown in Online Appendix Table A2, which rules out evidence of pre-trends in these variables. Even if the regulatory changes are not random, this check reassures of the absence of a clear selection of regulatory changes on key observable variables.

The second way in which we deal with the endogeneity concerns is by including a series of controls that should capture many of the factors generating the alleged endogeneity bias. Specifically, the inclusion of the 2-digit industry-year effects in Equation (6) should absorb the impact of a substantial part of the lobbying activity that may be behind the regulatory changes. That is because lobbying is typically organized around sectoral business associations, which tend to be defined according to a similar classification (e.g. textile, garments, processed food, etc.). In addition, we include year effects interacted with base-year product market characteristics that may influence regulatory changes. In particular we use six such characteristics: i) the product average import tariff rate; ii) a dummy equal to one if a product market is subject to at least one non-tariff measure;<sup>25</sup>

> 25To control for the impact of non-tariff measures, we aggregate HS 10 digit product-level information at the 5-digit industry-level using concordance tables provided by Indonesia Bureau of Statistics. We construct a dummy variable equal to 1 if a 5-digit industry has at least one non-tariff measure, and zero otherwise. The NTM and tariff data comes from Calì et al. (2021).

17
correlation between the weighted and unweighted aggregate indicators is that by and large, all of our individual data sources tend to agree quite strongly with each other in their assessments of governance.

In fairness, we do think that K suggests a number of reasonable alternative weighting schemes. Motivated by his suggestions, we here consider an alternative weighting scheme that organizes data sources by type and constructs an aggregate indicator weighting each _type_ of indicators equally. We consider four groups: (1) crosscountry surveys of firms and individuals, (2) commercial expert assessments, (3) expert assessments produced by NGOs, and (4) expert assessments produced by multilateral organizations. We simply average our indicators within each group, and then construct an aggregate indicator based on the four groups alone. We find that the correlation of the resulting indicators with our benchmark indicators is again very high, averaging 0.95 for five of the six indicators in 2005. The only exception to this pattern is for our Regulatory Quality measure, which has a correlation of only 0.44 with the benchmark measure. However, a closer look at the data reveals that this is entirely due to one data source, the Business Environment and Enterprise Performance Survey (BEEPS) carried out the World Bank in transition economies. This source alone among the very many we use, for this particular indicator alone, gives quite different assessments from other types of data sources. If we drop the BEEPS from the exercise, we find that our revised indicator based on four groups of data has a correlation of 0.96 with the benchmark indicator.<sup>14</sup> In fact, this example highlights the risks of relying exclusively on one or another individual data source, which may provide quite idiosyncratic assessments of governance, and points to the usefulness of considering a wide range of data sources, especially as summarized in an aggregate indicator.

To sum up, this critique and the previous one deal with the tricky issue of whether individual data sources on governance do in fact provide fully independent estimates of governance. We have argued that this is in general a difficult problem to

> changes over time in governance.  On average, the standard error of our equally-weighted indicators is about 10 percent higher than in our benchmark indicators.

> 14 We note in passing that while in general we think that firm survey data is extremely useful, it can also at times be anomalous, particularly in highly non-market or undemocratic countries.  It is striking for example that on the BEEPS survey of firms, Belarus and Uzbekistan rank 4th and 6th _best_ out of 27 countries on questions about corruption, while a commercial risk rating agency (DRI) ranks these countries 23rd and dead last, respectively.

22
- 31 -

|T2016|0.237|(0.102) **|0.255|(0.103) **|0.253 (0.103) **|
|---|---|---|---|---|---|
|Constant|0.469|(0.125)***|0.216|(0.158)|0.230 (0.163)|
|Obs.|474||474||474|
|R-squared|0.440||0.430||0.430|
|F-stat.|25.69||30.77||27.85|

The dependent variable is the log of the number of employees. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

# **VI. CONCLUSIONS**

Rural access is among the most important infrastructure constraints in rural Africa. Despite a relatively large number of earlier studies in this area, there are still divergent views on the results chains generated by rural access improvement. The paper examines the heterogeneous impacts of road accessibility on agriculture and non-agricultural growth. It is found that it is important to distinguish between different types of road to understand these heterogeneous effects.

Using the comprehensive household and other data from Ethiopia, the paper examined the impacts of corridor improvement and feeder road rehabilitation. It was found that crop production is increased by both major and feeder road accessibility. Significant synergy between them was also found. When investigating further into this effect, there are actually two different impacts: Farmers’ access to input markets, especially fertilizer, was improved mainly by major corridor improvement. On the other hand, output market access was improved by feeder road improvement, though this impact seems to be relatively weak. These findings may make sense given the current Ethiopian context. The timely distribution of fertilizer relies on the main road network, because many agricultural inputs are imported from abroad. Farmers may sell extra produce in their neighboring markets, but the market participation is generally still limited.

It is also found that there is no significant impact of accessibility on household income from livestock. By contrast, the households’ non-agricultural income is increased by improved