rafmacalaba/datause-encoder-data huggingface dataset:
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Aggregate changes in asset-based poverty follow from the relative gains or losses of the poor and vulnerable within specific sectors or groups as opposed to population shifts between these groups (Ravallion and Huppi, 1991, Sahn and Stifel, 2000). A methodology of decomposition of the change in asset-poverty can be stated as follows. Let us consider _P_ a poverty measure for two distributions at time _t_ and  , and two sectors _u_ (urban area) and _r_ (rural area), so that: The first two components are the within components: they show how asset-poverty in each of the residence areas (urban and rural) contribute to the aggregate change of assetpoverty between _t_ and  . The third component is the between component: it is the contribution of changes in the distribution of the population across two groups. The final component is a residual component that is a measure of correlation between population shifts and changes in asset-poverty within the groups.

Table 6 presents the decomposition of the change of the asset-based index headcount ratio between 1995 and 2005. This decomposition suggests that intra-rural effects account for most of the changes when the poverty line is chosen under the 80<sup>th</sup> percentile. Migration explains about 25% of the change and its contribution to the change generally declines when the poverty line gets higher. Finally, the contribution of declining asset-poverty in urban areas is low for low poverty lines and reaches nearly half of the change when the poverty line is fixed at the level of the 1995 80<sup>th</sup> percentile.

**Table** **<u>6. Decomposition of changes in asset-based poverty between 1995 and 2005</u>**

||Headco|unt|D|ecompositio|n|
|---|---|---|---|---|---|
|Poverty line||(Wit|hin)|(Between)|(Interaction)|
|(percentile in 1995)1995|2005|Change Urban|Rural|Migration|Crossed effect|
|20th<br>0.201|0.172|-0.028 0.016|-0.021|<br>-0.011|0.003|
|40th<br>0.400|0.339|-0.062 -0.002|-0.043|<br>-0.019|0.002|
|60th<br>0.600|0.513|-0.088 -0.011|-0.056|<br>-0.022|0.002|
|80th<br> 0.800|0.711|-0.089-0.041|-0.032|<br>-0.014|-0.002|
|Source:|Own co|mputations using DH|S 1995,|2005||

Other decompositions of the change in asset-poverty can be achieved by splitting the population into different groups of households according to education and gender of the head of household, and according to the presence of children under 5 years old in the household (see Table 7). It appears that the no or primary education group accounts for most of the change in asset-poverty, all the more so as lower asset-poverty line is chosen. The same statement can be made for households with male head or with under 5 children: households with these characteristics experienced a larger decrease in asset-poverty between 1995 and 2005. As a result of this analysis, we should emphasize that households with higher probability of being poor may have experienced a sharper decrease of assetpoverty over the last decade. This should thus be kept in mind when analyzing poverty in a more static manner.

12
# **Table II**

## **Timing of Major Patent Reforms**

This table provides information about the timing of reforms in the countries that strengthen their intellectual property rights and the number of reporting affiliates present in these countries at the beginning and end of the sample period.

|Country|Year of|Number o|f Affiliates in|
|---|---|---|---|
||Reform|BEA|database|
|||_1982_|_1999_|
|Argentina|1996|206|388|
|Brazil|1997|601|596|
|China|1993|11|522|
|Indonesia|1991|143|169|
|Japan|1995|704|928|
|South Korea|1987|93|241|
|Mexico|1991|702|927|
|The Philippines|1997|186|163|
|Spain|1986|362|569|
|Taiwan|1993|118|235|
|Thailand|1992|87|225|
|Turkey|1995|27|116|

45
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# **3 Survey Evidence on DTE Regulatory Governance in Practice**

A considerable amount of information has been collected on the degree to which DTE utility regulatory institutions conform to good practice.

The clear picture that emerges from this is that practice has been disappointing, particularly in areas such as transparency and predictability. Moreover, DTE governments have rarely been able to resist the temptation of interfering with regulatory processes and decisions they dislike – Botswana is an exception and there are others but they are unfortunately infrequent. In addition, there is sometimes evidence of collusion between governments and regulated incumbents to outflank or outwit regulatory agencies<sup>20</sup> .

Most of the evidence collected has been in the form of non-comparable – if highly informative – case studies. Much of the force of the 1994 Levy and Spiller paper came from its case studies and there have been many more since. The case studies and the general conclusions that can be drawn from them have been surveyed in Kessides (2004). The problem with such evidence is that it cannot be used for systematic econometric or comparative empirical analysis.

There are, however, now several studies of regulatory governance covering a number of sectors/regions/countries and based on a systematic survey approach that provides consistent data. In this section, we summarise the results of three studies:

- (i) The 1998 NERA report on six Asian developing countries as reported in Stern and Holder (1999);

- (ii) The Prayas Energy Group’s 2003 report on Indian State Electricity Commissions;

- (iii) The 2003 EBRD report by David Kennedy on energy regulators in CEE and CIS countries and similar studies.

Unfortunately, there does not seem to be a survey paper covering Latin America, although there are many case studies which suggest serious problems in making utility regulation effective<sup>21</sup> . For Africa, Brian Levy’s 2002 World Bank paper provides some useful related information.

## **3.1 The 1998 NERA Report on Six Asian Developing Countries**

In Section, 2.1 above, we drew attention to the early but increasing attention given to regulatory practice and “informal accountability” relative to formal, legal accountability. The key summary table of the 1998 NERA report allows us to demonstrate this clearly as the criteria can reasonably be divided into three formal

> 20

> 21

> See, for example, Estache’s 2002 discussion of Argentinian utilities privatization and Stern (2000) on CEE countries.

> See Noll (2000).   Kessides (2003) has many examples.

14
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a The bold (solid) line in the top panel represent the IV and OLS regression lines, respectively. The governance ratings are based on subjective assessments from a variety of sources and are subject to substantial margins of error, and in no way reflect the official view of the World Bank, its Executive Directors, or the countries they represent.

43
# **3.2 Empirical strategy**

We test the hypothesis that firms in industries with relatively lower entry costs will be most adversely affected by informal competition in economies where there exists a relatively onerous set of government rules and regulations and a government with the capacity to enforce these.

To confirm or reject the hypotheses presented above, we provide an econometric specification with a set of regulatory and law and enforcement variables to measure the relative level of regulatory obligations in an economy, and a government effectiveness variable to measure the capacity of government to enforce these rules. The description of the main business environment and the government effectiveness variable, their sources and the expected relation to the dependent variable can be found in Section 1.2 above. For the econometric specification, we use the regulatory indexes, from _Doing Business_ , and the government effectiveness index, from the _Worldwide Governance Indicators_ , to divide the countries in the data into four groups; good performers and bad performers with respect to the regulatory environment and within those two groups we divide further into good performers and bad performers with respect to government effectiveness.

The regulations examined are: tax rates, tax on profits, tax on labor, and taxes on other aspects of commercial operations not included in profits or labor. We also use the cost of starting a business and the minimum capital requirement to obtain an operating license. We use each of the rules and regulations and include them separately as independent variables in our econometric model.

8 In order to test our hypothesis we introduce in the linear probability model regression of with the same variable specification we introduced in Section 1.4 and add an interaction term between the business environment (level of regulation and government capacity) and financial dependency (Rajan-Zingales scale) as the proxy for the cost of entry; the higher the financial dependence the high the cost of entry. The econometric model appears as:

> The dependent variable ( _Y_ ) is a binary variable takes on the value one (1) if firm ( _i_ ) in industry ( _j_ ) in country ( _k_ ) considers competitors from the informal sector to be one of the top-three

> 8 Because we wanted a clear interpretation of the interaction terms, we did not use maximum likelihood methods used in discrete choice models to estimate the discrete, 0 or 1, dependent variable.
market survey used by Bruhn (2011) is an exception, and she finds that the increase in registered businesses was due to wage earners opening new businesses.

# **_Does making it easier to register a firm cause formalization of existing informal firms?_**

A follow-up study by Bruhn (2013) examines the impact of the business registration reform in Mexico on informal business owners in more detail. This paper takes into account that informal business owners are heterogeneous and some may be looking for a wage job instead of aiming to continue or grow their business. Bruhn splits up the group of informal business owners into those who have personal characteristics like formal business owners and those who have characteristics like wage earners and examines the impact of the reform separately for these groups. The results show that wage earner types are less likely to register their business due to the reform, but more likely to become wage workers since the reform created jobs. On the other hand, informal business owners with characteristics like formal business owner are more likely to register, but only in municipalities with high pre-reform constraints to formal entrepreneurship. The effects are relatively small though and most informal business owners remain informal even after the reform.

Some countries experience a large spike in firm registrations after the introduction of a business registration reform. For example, Mullainathan and Schnabl (2010) document that the number of firm registrations increased from 1,758 in the year before a municipal licensing reform in Lima, Peru, to 8,517 in the year after a reform. They conduct a survey to show that about 75 percent of the firms that registered in the year after the reform were previously informal and 25 percent were new firms. However, the majority of newly issued licenses were provisional, meaning that they had to be renewed every year. Many firms do not seem to have renewed the license after the year was over since the number of registrations dropped again to 3,500 in the second year after the reform, suggesting that the increase in formalization was temporary.

# **_The effect of providing information about how to register, or lowering the cost of formalizing, on formalization rates_**

Surveys of microenterprises typically show that many informal firms are not very well informed about either the process of formalizing, or the costs involved. They often overestimate the time and cost involved, particularly following efforts to simplify the registration process. For example, in Sri Lanka de Mel et al. (2013) report that only 17 percent of informal firm owners know the cost of registering. Most thought the process of registering would take over a month compared to one week or less in practice, 7
- 3 -

on distribution grounds, i.e. "making the poor even poorer". Efforts to narrow the VAT base and make it more distributionally acceptable normally give rise to problems of administrative complexity and, to the extent exemptions are used to grant relief, create numerous economic distortions that detract from economic efficiency. Moreover, in many developing countries more narrowly based commodity taxes are inappropriately levied on production rather than consumption.3! Production taxes may be easier to collect but they also lead to tax cascading which seriously distorts economic behavior and generates a haphazard pattern of tax incidence.

In a number of developing countries the challenge facing commodity taxation is to get taxes off of production and onto consumption that is linked to ability to pay and to do so in a way that is administratively feasible. The ET tax base offers one response to this challenge. First, as shown below, a wide range of social welfare functions would approve of its adoption in the C6te d'Ivoire for income distribution reasons and we suspect that the same result would hold in many other developing countries. Secondly, it is relatively easy to distinguish business from residential use of electricity and telephones; the only major difficulty occurs whenever households operate a business from out of their home. Mainly for this reason Cnossen (1978) argues that it is better to tax all usage of telephones. Thirdly, enforcement and collection of the tax is relatively inexpensive since the tax can be easily added on to commercial charges and services can be quickly turned off in the case of noncompliance. Fourth, because it applies to a nontradeable service, the ET tax does not have to grapple with the issue of taxing imports and domestic production in a fair 4assll#4/Welfare Dominance & the Design of Excise Taxation **in** the Coto D'Ivoire/WThirsk:ab/5/18/88
become urban areas. To the extent that rural (non-farm) economic growth may help create such re-classifications, as successful villages evolve into towns, this process may produce a downward bias in our estimates of the (absolute) elasticities of rural poverty to rural economic growth. The impact on the urban elasticities could go either way, depending on the circumstances of new urban areas relative to the old ones. We have no choice but to use the NSSO/Census classification. The rural and urban population estimates are also centered at the mid-points of the NSSO’s survey periods.

**_National accounts:_** We use private final consumption expenditure and net domestic product and its sectoral components from the National Accounts Statistics (NAS). To mesh the NAS data with the poverty data from the NSSO, we have linearly interpolated the annual national accounts data to the mid-point of the survey period for different rounds.

There has been a rising gap over time between NAS and NSS consumption aggregates.<sup>34</sup> From the point of view of the present discussion, it is notable that the NSS series does not fully reflect the large gains in mean consumption indicated by the NAS from the early 1990s onwards. The ratio of NSS-to-NAS consumption declined from about 70% in 1957 to 60% in 1991, and then steeply to 39% in 2011-12.

We do not know how much of the gap is due to errors in NAS consumption versus NSS survey methods. Until recently, the NSSO’s methods appear to have changed rather little over many decades. That is probably good news for comparability reasons, although it does raise questions about whether their methods are in accord with international best practice. However, it is notable that the MRP rounds of the NSS have helped close the gap between the NAS and NSS consumption aggregates.<sup>35</sup> Regressing the log difference of the NSS mean (  ln<sup></sup> _t_ ) on the log difference of NAS consumption per capita (  ln _Ct_ ) and the change in the dummy variable for MRP rounds, we get:  ln  _t =_ .0692  ln _Ct_  .0067  _MRPt_ (.0141) (.0021)

> greater than 5000, a density not less than 400 persons per sq. km. and three-fourths of the male workers engaged in non-agricultural pursuits. In the 2011 Census, 31.2% of the total population was classified as urban.

> 34 The gap is not only found in India, but it is larger there than most other countries (Ravallion, 2003). 35 The MMRP (modified-mixed reference period) aggregate introduced in the 2009-10 and 2011-12 rounds has further closed the gap.  The MMRP method which reduces the recall period for some food items to a 7-day recall (compared to a 30-day recall in the MRP) brings NSSO’s practices closer to the more common practice of a two week or less recall period for food items in other countries.

10
households than the benchmark. All other interaction terms are not significantly different from zero. As we have seen in this sub-section, while absolute poverty measures vary dramatically across the different modules, the partial correlations of poverty status with selected characteristics do not differ significantly and the relative rankings of households with those same characteristics are largely stable between modules.

# **5. Resource Implications**

The willingness to accept some degree of inaccuracy in consumption estimates from using a recall survey or a household diary compared with the personal diary is driven by the practical need to reduce the study costs and the burden to respondents of survey work. Our benchmark of frequently supervised individual diaries is often not a feasible option for field researchers, which leads to the question of recommendations for alternative approaches. Here we review the cost implications of different survey designs, focusing first on the cost in terms of time to administer a recall module and then on dollar expenditures for survey implementation in order to contrast recall with diary approaches.

Among the recall modules, the shorter lists in the subset and collapsed recall modules are assumed to significantly reduce the length of time to complete the module and this consideration in our experiences is often used as a justification to adopt a shorter consumption module. Figure 2 presents the average time to complete the recall modules. Respondents required an average of 50 minutes to complete the recall module that listed 58 foods for a 14-day reference period; for the 7- day full list module, the length of time to complete is just 1 minute shorter. The mean time savings is only 8 minutes (42 minutes total) when using the 11 broad food group headings (module 4) and 9 minutes (41 minutes total) when using the module 3 subset recall list with the 17 most important food items. While the interview time is shorter on average, the 8 or 9 minute savings in interview time of the shortened modules must be weighed against the overall lower mean consumption (especially for module 4) and truncated distribution relative to the longer list recall that these two survey modules generate. These results, of course, pertain to the study setting and in Tanzania households do not have a great deal of dietary diversity. In other settings with greater diversity, these estimated relative time costs may be less applicable.

The most demanding recall module, in terms of the average time taken by respondents, was by far the “usual” month consumption module. The module requires respondents to think about the number of months they consume the food and the typical consumption in those months. This is evidently a time-consuming computational task – taking 76 minutes on average. The previous
2 diversification -- is also analyzed to determine if Africa's failure to develop new export products contributed to the region's marginalization in world trade. In order to assess the importance of external markets conditions, statistics relating to OECD tariffs and nontariff barriers facing African exports are compiled and their trade restrictive effects assessed.

The study proceeds as follows. Section II discusses the nature of the trade performance indices used in the analysis and then applies these measures to United Nations statistics on African exports. The objective here is to determine how: (i) changes in Africa's ability to compete internationally, and (ii) how changes in the relative demand for the goods Africa exports affected the region's global trade shares. To put this analysis in proper perspective, Africa's trade performance indices are compared to those of other developing countries. Next, Section III employs a World Bank-UNCTAD database on OECD tariffs and nontariff trade barriers to determine how external market conditions affected African exports. Particular attention is give to the effects of the trade preferences Africa receives under the Lome Convention or the Generalized System of Preferences (GSP). Section IV then compiles data on African trade barriers with the objective of determining how their level and structure compare with those in developing countries whose export growth rates are well above average. The objective here is to determine whether Africa's own trade policies contributed to the regions poor trade performance. The study closes with an overall assessment of the findings and a discussion of their implications for policy purposes.

# II. Data and Methodological Issues

The empirical approach used in this study assumes a given country's export growth is the result of three separate factors, two of which relate to changes in demand and competitive conditions.' The influence of demand for traditional goods (defined as products exported in a specific base period) is 'A detailed description, and early application, of this procedure can be found in GATT (1966). The analysis also constituted a large part of Kravis (1970) classic analysis of the influence of trade on the 20th century growth of developing countries.