rafmacalaba/datause-encoder-data huggingface dataset:
The highest level of aggregation corresponds to the summary measure of product market regulation in the country.

# **Figure 1: The PMR indicator system**

<!-- Start of picture text --> Product market regulation<br>Inward-oriented policies (0.59)<br>Outward-oriented policies (0.41)<br>State control (0.49) entrepreneurship Barriers to (0.51) Barriers to trade and investment (1.0)<br>Public ownership (0.56) business operation Involvement in (0.44) administrative opacity Regulatory and burdens on startups (0.30)Administrative competition Barriers to (0.22) Explicit barriers to trade Other barriers (0.30)<br>(0.48) and investment (0.70)<br>enterprise sector Scope of public (0.30) Price controls (0.45) Licenses and permits Administrative burdens for corporation (0.36) barriers (0.45)Foreign ownership<br>system (0.55)<br>Size of public<br>Direct control over enterprise sector business (0.30) Use of command & control regulation (0.55) simplification of rules Communication and and procedures (0.45) sole proprietor firms (0.30)Administrative burdens for Antitrust exemptions Legal barriers (0.30) procedures (0.24)Discriminatory Regulatory barriers (1.0)<br>enterprises(0.40) 2 Sector specific administrative burdens (0.34) (0.70) Tariffs (0.31)<br>{regulation data} {regulation data} {regulation data} {regulation data} {regulation data} {regulation data} {regulation data}<br>Economic regulation<br>Administrative regulation<br><!-- End of picture text -->

- 1.The numbers in brackets indicate the weight given to each lower level indicator in the calculation of the higher level indicator immediately above it.

- The weights were derived by applying principal components analysis to the set of indicators in each of the main regulatory domains (state control, barriers to

- entrepreneurship, barriers to trade and investment, economic regulation and administrative regulation). The same approach was used to derive the weights used to calculate the indicators of inward and outward-oriented policies and the overall PMR indicator. The principal components analysis was based on

- the original 1998 data.

2. Two indicators from the 1998 version of the PMR indicators ('Special voting rights' and 'Control of public enterprise by legislative bodies') have been combined into this indicator.

_Source:_ Conway et al. (2005) 30
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24
from long-term loans,<sup>4</sup> if the changing effects of market conditions contribute to market saturation and village-level diseconomies, and whether market saturation and multiple program membership are causing adverse effects on household welfare.

The paper is organized as follows. Section 2 discusses the dynamics of microcredit using long panel data that were made available with a research grant from the World Bank, by re-surveying the households and communities in 1998/99 that were initially surveyed in Bangladesh during 1991/92 by the Bangladesh Institute of Development Studies. Section 3 discusses the pitfalls of panel data in terms of attrition and split-off. Section 4 presents the impact results using alternative household-level fixed-effects estimation methods and verifies whether estimation method matters. Section 5 addresses the hypothesis that the impact of credit varies over time, with the possibility that returns to microcredit programs decline over time. Section 6 addresses the possibility of market saturation in microcredit market in the sense that the returns to late entrants of microfinance borrowers are lower than those of their counterpart early entrants. Section 7 examines whether multiple program membership has any harmful effects on household welfare. Finally, the concluding section summarizes the results.

# <u>2. Dynamics of microcredit: A descriptive analysis</u>

The data used in this study were derived from a long panel survey over 20 years. The first round cross-sectional survey, conducted in 1991/92, was studied to determine the role of microfinance in the economic and social advancement of the poor. Carried out jointly by the World Bank and the Bangladesh Institute of Development Studies (BIDS), the survey covered 1,769 households randomly selected from 87 villages (72 program and 15 control villages) in 29 _upazilas_ (rural sub-

> 4 This is different from the effects of continued versus irregular participation as addressed in Khandker and Samad (2014) which observed that the effects of continuous participation are higher than those of irregular participation. The current paper examines the impact of cumulative borrowing (in taka) over time on outcomes such as income, expenditure and net worth.

4
# **4.3 Estimation Strategy**

Given the granular data on the empirical formal sector firm-to-firm network, we are able to obtain the majority of the model parameters directly from the data (see Table 3 for an overview). These include all entry probabilities _p_ ( _θ_ ) _∀ θ ∈_ Θ that a firm enters in a given sector, county, and size group as well as all interaction probabilities _p_ ( _θ, θ_<sup>_′_</sup> ) between all possible sector-county-size types. We use the cross-section from 2019, the last pre-COVID year of our panel, to obtain the _p_ ( _θ_ )s and _p_ ( _θ, θ_<sup>_′_</sup> )s.<sup>23</sup> The parameter we need to estimate is _r_ , the fraction of input links a firm obtains via undirected search independent of the network environment.

Table 3: Model parameters

|**Parameters**|**Description**|**Source**|**Proxy**|**Value**|
|---|---|---|---|---|
|_r_|Share of suppliers via random search|Estimated|-|0.45|
|_p_(_θ_)|Entry probability of type _θ_|Data|Share of frms observed as _θ_|(0,0.12]|
|_p_(_θ, θ_<sup>_′_</sup>)|Linking probability of _θ_ and _θ_<sup>_′_</sup>|Data|Share of links between _θ_ and _θ_<sup>_′_</sup>|(0,1]|
|_m_|Indegree|Data|Avg. number of suppliers|30|
|_t_|Number of entry periods|Data|Number of frms in admin data|56822|

First, we classify firms into types defined by unique sector-location-size combinations. Sectors refer to 13 aggregate sectors, namely, agriculture, mining, manufacturing, utilities, construction, transportation and logistics, wholesale, hospitality, retail, business services, non-market services, other services, and miscellaneous (incl. international organisations and non-classified firms). Locations are given by the county in which the firm is located. Within each sector and county we further group firms into large and small firms. We define small firms as firms in the bottom sales quartile within a sector-county group. By restricting ourselves to two size bins only, we avoid having too few observations in each firm-type bin and the matrix of linking probabilities becoming too sparse. For example, all firms in the top three sales quartiles of Nairobi’s manufacturing sector are classified as the same type.

Next, we compute the probability that a type exists for all types in Θ. We do so by dividing the number of formal firms of a sector-county-size type by the total number of formal firms in the economy. The interaction probabilities _p_ ( _θ, θ_<sup>_′_</sup> ) then represent the fraction of a sector-county-size type _θ_ ’s supplier relationships that it forms with type _θ_<sup>_′_</sup> . We compute the above probabilities for all possible combinations of types and use them to construct the matrix **B** . Moreover, we follow Jackson and Rogers (2007) and define _m_ as the average indegree (i.e. average number of suppliers) in the network. The variable _t_ , by definition, is equal to the number of firms observed in the data equal to 56,822.

> 23We exclude a small proportion of firms with zero suppliers in the data, as the model requires all entrants to form _m_ buying links.

21
<!-- Start of picture text --> g 1000 an 100% ww Urban<br>=<br>soo<br>rns 80% ©® ShareRural of population<br>relying on traditional<br>600 5 -- = s:hlCtC 60% biomass (right axis)<br>oO<br>pT<br>0 0%<br>India Other Sub- China Latin<br>Developing Saharan America<br>Asia* Africa<br><!-- End of picture text -->

*Includes developing Asian countries except India and China.
the relevance condition for a suitable instrument), the changes in political variables could still be correlated with unobserved local characteristics. This affects the voting behavior and thus the composition of political party in a given location, violating the exclusion restriction of the instruments. Hence, combining spatial difference with instrumental variable (IV) approach, one can allow for the identification of the effect of the local taxes as spatial differences now eliminates the unobserved site-specific factors that are correlated with the political variables used as IVs. Applying this strategy to the English manufacturing data, Duranton et al. (2011) identifies that local taxation has a negative and significant effect (an elasticity of -1.024) on employment growth but no effect on business entry. This finding is in contrast to the finding of the previous literature that depict a positive relationship between local taxes and employment, potentially suffering from biases resulting from one or more of the above mentioned problems.<sup>12</sup> Using triple difference (DDD) with IV, Hanson and Rohlin (2011) estimate the effect of Empowerment Zone (EZ) program on employment across different industries in the U.S. The federal government has begun offering a location-based set of incentives called the EZ program to firms willing to operate in and hire residents of parts of the designated inner city and rural areas. The main component of the EZ program is a wage tax credit, which is 20 percent of the first $15,000 in wages paid for a maximum credit of $3,000 per employee living and working in the designated EZ area. EZs are chosen from a group of applications made by state and local governments. Applications are accepted from areas where at least 20 percent of the population live in poverty and 6.3 percent are unemployed. Many of the nominees that do not receive an EZ designation become part of a less generous location based incentive program called Enterprise Communities (EC) that do not include the wage tax credit.

To estimate the effect of EZ tax incentive, the authors used EC areas as the best possible counterfactuals as EC areas were granted other benefits of EZ areas with the exception of wage tax credit. Their DDD strategy is to compare how the share of firms and employment in an industry changes between EZ (EC) areas and the larger city they are a part of, and test how the introduction of the EZ tax credit program changes this difference. This design reduces the

> 12 The positive relationship between tax rate and employment rather imply the reverse causality, i.e., the local authority tends to tax more the local businesses when they are doing better  or some establishments are larger than others for unobserved reasons and larger establishments happen to be located in higher tax jurisdictions. The reverse could be also possible that in good times local authority can afford to keep taxes low because of the lesser need of social welfare related expenditure.

26
- 21 -

worker for economic reasons. Some measures used by researchers combine both indices and expected costs of compliance (e.g., Botero _et al._ 2004; Doing Business).<sup>33</sup> Although there has been considerable work on measuring EPL, it is clear that indicators are imperfect. Most notably, the quantitative indicators do not take into account enforcement which obviously will have an effect on the real weight of job security rules (see Bertola, Boeri, and Cazes 2000). Second, while EPL indicators are essentially limited to the content of statutes, there are other ways in which some countries achieve similar goals—court interpretations, especially in common law countries, as well as through collective agreements.

The empirical literature on the impacts of EPL reflects the preponderance of evidence from OECD countries. Although increasing, there is less analysis pertaining to developing countries, with most of that research on Latin America. Living standards effects dominate the EPL research, with a limited number of studies on productivity impacts, and only indirect evidence on social cohesion effects.

# **_4.1  Impacts on living standards_**

Most of the relevant research examines the employment impacts of EPL. Much less attention is paid to any wage or income effects.

# _Employment_

Studies on the employment impact of EPL are divided between those finding no significant effect and those finding a modest negative effect, i.e., higher unemployment and/or lower employment. It should be noted that employment and unemployment impacts are not always the same. Where job security does have a negative impact on labor demand, employment will almost certainly decrease but unemployment may not be affected if the decreased labor demand leads to a reduced labor supply. Indeed, a number of studies have found a negative employment effect but no effect on unemployment (e.g., Heckman and Pagés 2000; Nickell 1997; Nickell and Layard 1999).

Not only are the findings on employment impacts mixed, but the results also can be characterized as fragile. Findings are often sensitive to model specification and the treatment of data (Glyn _et al._ 2003, Howell _et al._ 2007). Sometimes researchers in different studies of a common set of countries have found different employment impacts. For example, using crosssectional data for the 1980s and 1990s for Latin American and OECD countries, Heckman and Pagés (2000) found a negative employment effect of job security rules and then in a later study (2004) found no significant employment impact. Using similar data for many of the same countries, Micco and Pagés (2007) found a negative employment effect, especially in volatile sectors.

As was noted above, much of the analysis of EPL impacts has been carried out on OECD countries. There have been numerous studies designed to econometrically test the relationship

> 33 A third, but less used, approach is to categorize a country’s EPL according to qualitative data based on assessments by managers regarding the flexibility of hiring and firing arrangements (e.g., Di Tella and MacCulloch 2005; Feldmann 2009).
# **_Appendix 3:_** _Objective economic security outcomes: descriptive statistics and sample sizes_

||N|N|Mean|Mean|Mean|Differenc|
|---|---|---|---|---|---|---|
||Control|Treated|Total|Control|Treated|e p-value|
|Round 1 outcomes|**R**|**1 Benefici**|**ary Househ**|**olds(ECT-**|**1)**||
|Worked last week|480|89|0.522|0.506|0.607|0.082|
|Hours worked last week|480|89|21.098|19.907|25.528|0.013|
|Main source of income increased or stayed the same|475|87|0.336|0.314|0.460|0.008|
|Main source of income decreased or stopped|475|87|0.663|0.686|0.540|0.008|
|Other source of income increased or stayed same|303|89|0.349|0.284|0.573|0.000|
|Other source of income decreased or stopped|303|89|0.543|0.637|0.225|0.000|
|Inability to buy staple foods|492|55|0.117|0.116|0.128|0.803|
|Able to financiallysupport child's return to school|446|66|0.863|0.857|0.909|0.246|
|Round 2 outcomes|**R1 Benef**|**iciary (EC**<br>**exclu**|**T-1, R2 Ben**<br>**ded from C**|**eficiary ho**<br>**ontrol)**|**useholds**||
|Worked last week|623|78|0.603|0.588|0.731|0.015|
|Hours worked last week|623|78|25.471|24.416|33.898|0.003|
|Main source of income increased or stayed the same|606|76|0.683|0.690|0.632|0.304|
|Main source of income decreased or stopped|606|76|0.317|0.310|0.368|0.304|
|Other source of income increased or stayed same|450|67|0.603|0.625|0.463|0.011|
|<br>Other source of income decreased or stopped|450|67|0.265|0.256|0.329|0.208|
|<br>Inability to buy staple foods|566|76|0.081|0.077|0.105|0.41|
|Child identified as malnourished|214|24|0.080|0.080|0.084|0.947|
|Childgiven vitamin A supplementation|348|35|0.389|0.394|0.343|0.558|
|Round 2 outcomes|**R2 Benef**|**iciary (EC**<br>**exclu**|**T-2, R1 Ben**<br>**ded from C**|**eficiary ho**<br>**ontrol)**|**useholds**||
|Worked last week|623|40|0.594|0.588|0.700|0.161|
|Hours worked last week|623|40|25.185|24.416|37.175|0.004|
|Main source of income increased or stayed the same|606|38|0.688|0.690|0.658|0.681|
|Main source of income decreased or stopped|606|38|0.312|0.310|0.342|0.681|
|<br>Other source of income increased or stayed same|450|26|0.617|0.625|0.500|0.205|
|Other source of income decreased or stopped|450|26|0.265|0.256|0.423|0.06|
|Inability to buy staple foods|566|40|0.074|0.077|0.025|0.22|
|Child identified as malnourished|214|24|0.080|0.080|0.084|0.947|
|Childgiven vitamin A supplementation|348|38|0.388|0.394|0.342|0.537|
|Round 2 outcomes|**R1 and R**|**2 Beneficia**|**ry Househ**<br>**2)**|**olds (ECT-1**|**and ECT-**||
|Worked last week|623|118|0.609|0.588|0.721|0.007|
|Hours worked last week|623|118|24.416|24.416|35.008|0.000|
|Main source of income increased or stayed the same|606|114|0.682|0.690|0.640|0.299|
|<br>Main source of income decreased or stopped|606|114|0.318|0.310|0.359|0.299|
|<br>Other source of income increased or stayed same|450|93|0.599|0.625|0.473|0.007|
|Other source of income decreased or stopped|450|93|0.273|0.256|0.355|0.051|
|Inability to buy staple foods|566|116|0.078|0.077|0.077|0.996|
|_Source_: Authors’ calculations from CIMS (2020).|||||||

34
# **Appendix Table 3. Portfolio Investment International Positions: Equity vs. Debt**

||**Panel A: Intern**|**ational Investme**|**nt Positions/Wo**|**rld GDP**||
|---|---|---|---|---|---|
|||**Portfolio E**|**quity**|||
|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|North|North|12.2%|19.5%|10.3%|19.3%|
|North|South|1.3%|4.3%|1.7%|3.7%|
|South|North|0.5%|1.3%|0.7%|2.1%|
|South|South|0.1%|0.9%|0.6%|1.0%|

|||**Portfolio**|**Debt**|||
|---|---|---|---|---|---|
|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|North|North|13.8%|23.9%|20.8%|18.7%|
|North|South|1.0%|1.8%|1.3%|2.7%|
|South|North|0.5%|1.2%|1.0%|1.6%|
|South|South|0.2%|0.4%|0.3%|0.8%|

**Panel B. Regional Share of International Investment Positions**

|||**Portfolio E**|**quity**|||
|---|---|---|---|---|---|
|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|North|North|86.4%|75.0%|77.2%|73.8%|
|North|South|9.2%|16.4%|12.9%|14.3%|
|South|North|3.6%|4.9%|5.4%|8.0%|
|South|South|0.7%|3.6%|4.5%|3.9%|

|||**Portfolio**|**Debt**|||
|---|---|---|---|---|---|
|**Source**|**Destination**|**2001**|**2007**|**2008**|**2018**|
|North|North|88.9%|87.4%|88.9%|78.3%|
|North|South|6.6%|6.5%|5.5%|11.5%|
|South|North|3.4%|4.5%|4.2%|6.9%|
|South|South|1.2%|1.5%|1.3%|3.4%|

Note: This table shows, for each block, the year-end value of international portfolio investment positions distinguishing between equity and debt. Panel A shows the value of international investment positions scaled by world gross domestic product (GDP). For each investment type and year, world GDP includes the GDP of destination countries for which we have information for that year (all source countries with information for a given year also have information as destination countries). Within each investment type and year, world GDP is the same across blocks. Panel B shows the share of international investment positions. For each investment type, the sum of North-to-North, North-to-South, South-to-North, and South-to-South shares in a given year is 100 percent. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.