rafmacalaba/datause-encoder-data huggingface dataset:
garian manufacturing firms to show that import competition has some positive effect on productivity growth, but it was more than offset by the effects of (successful) lobbying for credits by the affected firns. Market structure is found to have a significant effect on productivity changes - the study shows increased productivity for firms in sectors which experienced a large number of entries either through spin-offs from existing companies or through start-ups. A recent study of Russian manufacturing firms in the period 1992-94 (Earle and Estrin, 1997) found a positive correlation between import competition and several enterprise adjustment indicators (labor productivity, total layoffs, new product lines). At a more aggregate level, a study of Polish industries in 1991-93 (Falk et al, 1996) finds that total factor productivity growth was positively associated with import penetration ratios and negatively associated with sectoral concentration.

We use these stylized facts to construct control variables in the regression analysis. In particular, we use firm-specific information on access to financing and competitive pressures to purge the effect of the extemal environment on firm restructuring. This will likely alleviate the bias in our estimates of the effect of privatization and different privatization methods on restructuring.

# 3, The Surveys

To study the relation between modalities of privatization to insiders and enterprise restructuring, a stratified random survey of 92 manufacturing finrs in Georgia and 149 manufacturing firms in Moldova was conducted in the autumn of 1997. Most enterprises included in the study went through some type of privatization, even though in some cases the state still has partial ownership. The Questionnaire was designed by the author, following a pilot phase dunrng which a dozen enterprises were visited in each country. During these visits, detailed interviews with the general directors, financial directors, and marketing and sales directors of the companies were conducted to document the process of restructuring. The pilot survey also revealed that managers were sensitive to the confidentiality of the data. All questionnaires hence featured an introductory section explaining that the results of the survey would only be used in an aggregated form in cross-country studies on the development of the private sector in the forner Soviet Union. Managers were also presented with copies of an earlier study by the author (Pohl et al, 6
(expressed as percentage of total adults in the country).<sup>5</sup> The depth of credit information index ranges from 1 to 6 depending on how many of the following features are present in the credit information sharing schemes: (a) data on both firms and individuals are distributed, (b) both positive credit information (for example, outstanding loan amounts and pattern of on-time repayments) and negative information (for example, late payments and the number and amount of defaults and bankruptcies) are distributed, (c) data from retailers and utility companies as well as financial institutions are distributed; (d) more than 2 years of historical data are distributed; (e) data on loan amounts below 1% of income per capita are distributed; and (f) borrowers have the right to access their data in the largest credit registry or bureau in the economy. The private credit bureau coverage indicator reports the number of individuals and firms listed by a private credit bureau's database as a percentage of the adult population.

Table 5 shows that access to finance increases by 0.4 percentage points when a CB is introduced with a one percentage point higher coverage. We can glean the impact of CB coverage by looking at a change in coverage from the 25th percentile to the 75th percentile of countries (37.4 percent). The estimates in Table 5 indicate that such a change in coverage leads to an additional 15 percentage point increase in the probability of access to finance by firms. We find qualitatively similar effects of CB coverage on loan maturity. Similarly, a 1 point increase in the depth of credit information index<sup>6</sup> (this also corresponds with a move from 25th percentile of the index to the 75th percentile) increases the impact of CB reform on access to finance by 2 percentage points. Loans have a 1.3 percentage point lower interest and 1.5 months higher maturity when CB reform is introduced with a 1 point higher depth of credit information index.

> 5 We introduce these variables as country averages mainly because of data availability issues. The Doing Business database starts in 2005, whereas our enterprise survey sample dates back to 2002. In addition, our aim with the interaction is to explore the differential impact across countries.

> 6 The depth of credit information index ranges from 0 to 6 with each point corresponding to the existence of an additional measure of rules and practices affecting the coverage, scope and accessibility of credit information.

14
started exploring changes in the degree of firm openness and technological innovation and relating it with changes in the firm’s demand for skills. In general, these studies have found strong evidence of self-selection into the international activities so that the causal effect, when positive, is smaller than the cross-sectional estimates. For example, Doms and others (1997) and Pavcnik (2003) find no evidence of changes in technology adoption and global integration increasing the use of skilled labor. For Indonesia, Fernandes and Sundaram (2008) find opposite effects for export and import activities. In their sample, firms increasing their export intensity also increase the use of less skilled labor while firms increasing the imports of inputs tend to hire more skilled labor.

The increasing shares of educated workers in the workforce in the region and the rising industry skills premiums have been well documented in the literature by Di Gropello and Sakellariou (2008). Exploring household level data, they find robust evidence of increasing shares of skilled/educated workers in the long run across the region and of increasing skills wage premium. This simultaneous movement along quantities and prices is likely to be a consequence of an increase in the demand for skilled labor. In fact, if only the supply of skills had expanded, one would observe higher shares of skills but most likely a lower relative price. However, household level data have severe limitations to determine whether the demand for skilled workers has occurred within narrowly defined industries and whether it can be explained by openness and skill-biased technological change. This happens because household level data rarely contain information pertaining to openness, technology adoption or detailed sector of activity. Firm level data, in contrast, rarely contain information on the education of the workforce, except the more subjective occupational classification (skilled/unskilled indicators). Our paper relates closely to this recent international industrial organization literature exploring micro firm level data and emphasizing the importance of plant heterogeneity (e.g., Roberts and Tybout 1996). We explore a large cross-country firm level data set covering eight countries in East Asia to investigate whether the increased integration into global economic activities (captured by exports and FDI) on the one hand, and technological innovation on the other, has contributed to the increasing demand for skilled workers. The data set we explore is particularly suitable to study this topic. On the one hand, it collects information on the educational composition of the workforce 4
6 also confirm many of the findings of the cross-sectional study of Pitt and Khandker (1998) that only used 1991/92 data. The results using the 3 rounds of data also corroborate the poverty reduction effects of microcredit as found in Khandker (2005), who used the data of first two rounds (1991/92 and 1998/99).

# <u>5. Estimating dynamic effects of microcredit</u>

The estimates of credit effects might not be the same across years if timing of the borrowing matters; that is, if the credit received in the past affects behavior differently from the credit received recently. In other words, unlike the implied assumption in equation (3), parameters of the credit demand and other regressors may vary over time, allowing for differential credit impacts over time.<sup>21</sup> Credit effects may vary by time for different reasons. For example, during the initial years of membership, participants may choose conservative projects (or be influenced to by other members of the group) until they have demonstrated an ability to repay, focus more on accumulating assets, and thus cement the new insurance network, a result of group participation. As time goes by, they may have a larger cushion, allowing for some risk-taking behavior with new loans. Second, the unobserved local market conditions that influence a household’s demand for credit may change over time so as to exert a favorable impact on credit demand. Third, if the non-credit effects of program participation are important, and if the changes in attitudes they engender are functions of time spent in the group, the total effect of participation may decline over time. On the other hand, if the knowledge gained through self-employment experience is an important component of the returns to self-employment, credit effects may rise over time. Finally, returns may fall as the rents that accrue to early participants get competed away. It is worth investigating if there are differences in these

> 21 Similar time-varying effects of program participation (measured by a dummy variable) can be estimated. Khandker and Samad (2014) differentiated the effects of regular or continuous participation from the effects of irregular or discontinuous program participation. Table A6 shows the test of whether male and female credit can be combined.  The test shows that they cannot be combined for most of the outcomes considered here.

17
Table A7: Effect of ACASI on Choosing “Don’t know” or “Refuse to answer” in IPV Questions

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||=1 if don’t know|or refusal to _indi_|_vidual_ question in|following category:|All|
||Controlling<br>Behavior|Emotional<br>IPV|Physical<br>IPV|Sexual<br>IPV|questions<br>pooled|
|**Panel A. Malawi**||||||
|SI|0.011**|0.007*|0.011**|0.006**|0.012**|
||(0.006)|(0.003)|(0.005)|(0.003)|(0.006)|
|FTFI mean|0.017|0.009|0.015|0.007|0.017|
|Number of individuals|1,737|1,737|1,737|1,737|1,737|
|Observations|12,159|12,159|12,159|12,159|34,740|
|**Panel B. Liberia**||||||
|SI|0.007**|0.002|-0.001|0.003**|0.004|
||(0.003)|(0.002)|(0.003)|(0.001)|(0.003)|
|FTFI mean|0.005|0.004|0.007|0.001|0.006|
|Number of individuals|1,261|1,261|1,261|1,261|1,261|
|Observations|8,827|8,827|8,827|8,827|25,220|

Note: Regressions are at the respondent- _question_ level (violence is not aggregated into indexes). Regressions include question-level fixed effects and individual controls (including all variables in Table 1). Standard errors clustered at individual level in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively.

30
on the intercept term only marginally changes the estimated income mapping. Based on this pseudo-panel estimation, we check whether the income dynamics for the average individual in any of our cohorts give rise to a poverty trap and find they do not. Thus, while income mobility is low in Mexico, there is no evidence for a poverty trap in income. These results continue to hold once we allow for slope parameter heterogeneity across education groups, and when we estimate dynamics for full household income and expenditure using an alternative data set.

The remainder of the paper is organized as follows: Section 2 describes the econometric method used to estimate nonlinear income dynamics and detect poverty traps with panels and pseudo-panels; Section 3 describes the ENEU data; Section 4 provides the results; Section 5 examines the robustness of our results to slope-parameter heterogeneity and to the use of alternative measures of household resources than labor earnings; and Section 6 concludes.

# 2 Econometric Method

## 2.1 Panel Data without Measurement Error

We follow Jalan and Ravallion (2004) and Lokshin and Ravallion (2004) in allowing for non-linear income dynamics in the form of a cubic function of the lagged dependent variable, and in allowing for individual heterogeneity through the inclusion of unobserved individual effects, αi. We discuss below extension to higher-order polynomials. The data generating process for the true income Yi,t<sup>∗ofhouseholdiintimeperiodtisassumedtobe:</sup> If the true values of income are observed and the ui,t are serially uncorrelated, then equation (1) can be estimated via the GMM method of Arellano and Bond (1991). This is the method used by Jalan and Ravallion (2004), who difference (1) and then use Yi,t<sup>∗</sup> −2<sup>,</sup> ¡Yi,t<sup>∗</sup> −2¢2 and ¡Yi,t<sup>∗</sup> −2¢3 as instruments for ∆Yi,t∗ −1<sup>,</sup> 6
chauffeur. Less than 10 percent of Indians have a license. Thus, many would apply for a license either because they purchased a motorcycle or a car, or because they seek to be employed as a driver/chauffeur.

From Figure 2, identity cards are more accessible than other government provided goods and services. About 87 percent of respondents were able to apply for an Aadhaar card<sup>11</sup> and 75 percent were able to apply for a voter ID card. Relatively few people are denied these cards compared with services such as issuance of a driver’s license or registration of land or property. There are several reasons why ID cards are more accessible than other goods and services. Because they are provided by the federal government, ID cards are not affected by a state government’s lack of ability to deliver this service.<sup>12</sup> Furthermore, voter ID cards issued by the Election Commission of India are required to vote in national, state and local elections. Competing political parties have a strong incentive to ensure that their constituents have voter ID cards. As a result, political parties help citizens procure voter ID cards. The government's Aadhaar policy was launched in 2014 with the goal of providing all citizens with biometric identification cards. As of 2016, more than 92 percent of adults had an Aadhaar card.<sup>13</sup> Among state government provided goods and services, access is relatively higher for government run schools, where 48 percent of those in need could apply for access. By contrast, access is lower for issuance of driver’s licenses<sup>14</sup> (26 percent), registration of land or property (36 percent),

> 11 While here access to Aadhaar Card is used as a measure of state capacity; successful implementation of the Aadhaar card program, can itself lead to improvement in state capacity (see for eg. Muralidharan, Niehaus, Sukhtankar 2016: _“Aadhaar cards have improved the efficiency and governance of social programs such as the National Rural Employment Guarantee Scheme and the Social Security Pensions.”_ )

> 12 See Iyer (2010) and Banerjee and Iyer (2005) for some insights to variation in state capacity in India.

> 13 _The Times of India_ . 2016. “92% of India’s Adult Population Has Aadhaar Card - Times of India.”

> 14 The difficulty in procuring a driver’s license is consistent with Bertrand et al. (2007) who make the following observation (Page 1669): _“To summarize, there are two main tracks to procuring a driver’s license in Delhi. The formal track involves directly applying through the RTO and no bribery. Some of our results, however, suggest that this track might be fraught with extralegal hurdles. The informal channel, on the other hand, is operated by agents,_

12
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via logit model. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa) and log of MFI's total asset values.

# Panel C:  Regulatory <u>and supervisory capacity for microfinance (0-4)</u>

|_Microscope variable_|Bank dummy|Credit Union &<br>corp dummy|NGO dummy|NBFI dummy|Group A (Bank,<br>NBFI or Rural<br>Bank)dummy|Group B (NGO or<br>NBFI dummy)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory and supervisory capacity for microfinance (0-4)|-0.3461|-0.2492|0.1676|0.0332|-0.1006|0.2711|
||[0.281]|[0.326]|[0.467]|[0.873]|[0.626]|[0.185]|
|Observations|778|778|778|778|778|778|
|Number of countries|41|41|41|41|41|41|

# Panel D: Regulatory framework for deposit-taking <u>(0-4)</u>

|_Microscope variable_|Bank dummy|Credit Union &<br>corp dummy|NGO dummy|NBFI dummy|Group A (Bank,<br>NBFI or Rural<br>Bank)dummy|Group B (NGO or<br>NBFI dummy)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory framework for deposit-taking (0-4)|-0.2722|-0.1851|0.4202|-0.0905|-0.3355|0.2551|
||[0.354]|[0.619]|[0.214]|[0.745]|[0.251]|[0.331]|
|Observations|778|778|778|778|778|778|
|Number of countries|41|41|41|41|41|41|

# Panel E: Formation/operation of non-regulated microcredit institutions <u>(0-4)</u>

|_Microscope variable_<br>Bank dummy|Credit Union &<br>corp dummy|NGO dummy|NBFI dummy|Group A (Bank,<br>NBFI or Rural<br>Bank)dummy|Group B (NGO or<br>NBFI dummy)|
|---|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|(6)|
|Formation/operation of non-regulated microcredit institutions (0-4)<br>-0.383|-0.2897|0.5520**|-0.022|-0.2721|0.34|
|[0.130]|[0.539]|[0.044]|[0.905]|[0.208]|[0.250]|
|Observations<br>778|778|778|778|778|778|
|Number of countries<br>41|41|41|41|41|41|

55
Table B2: Descriptive Statistics 1999 and 2000

|_Panel A: O_|_utcomes_||||
|---|---|---|---|---|
|Variable|Mean|Sd|Min|Max|
|Share informal workers|60.25%|33.37%|0.00%|100.00%|
|Share informal and non-salaried workers|43.47%|29.60%|0.00%|100.00%|
|Share informal frms|84.15%|18.26%|0.01%|100.00%|
|Share informal residents|46.68%|10.58%|1.00%|91.97%|
|Share informal high-skilled residents|35.65%|7.47%|1.42%|76.99%|
|Share informal low-skilled residents|50.31%|10.47%|1.07%|93.01%|

|_Panel B: Treatme_<br>|_nt Variable_<br>|_s_<br>|||
|---|---|---|---|---|
|Variable|Mean|Sd|Min|Max|
|Euclidean Distance to new stations (meters)|11223.33|6625.81|411.89|32838.87|
|Walking Distance to new stations (minutes)|124.70|73.62|4.58|364.88|
|Dummy variable (dist<2463)|10.74%|30.97%|0.00%|100.00%|
|Dummy variable (minutes_≤_25)|10.00%|30.04%|0.00%|100.00%|

_Notes:_ This table reports summary statistic of the main variables. Panel A presents the statistics for the outcomes of interests: workers’ informality rates from the Economic Census in 1999 and residents’ informality rates from the Population Census in 2000. Panel B for the different definitions of the treatment group that includes: the euclidean distance, the network walking distance, a dummy variable whether the centroid of the ageb is within buffer zone of 2000 meters to the new stations, and a dummy variable whether the centroid of the ageb is within a 25 minutes walking range.

Table B3: Results: Census tract characteristics 1999 and 2000 vs. Treatment

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Outcome:|ln Income|High Skill Share|Occupation share|Informality Rates|
|_Ti_|-0.026***|-0.032***|-0.012***|0.026***|
||(0.009)|(0.008)|(0.002)|(0.007)|
|Observations|3,193|3,193|3,193|3,193|
|R-squared|0.299|0.205|0.332|0.125|
|Distance controls|X|X|X|X|
|State FE|X|X|X|X|

_Notes:_ This table reports the results of a regression relating census tract characteristics with a dummy variable whether the centroid of the census tract is within a 25 minutes walking range. The first column reports the results for the log of income, the second column for the share of high-skilled workers, and the third column for the informality rate. Standard errors are clustered at the census tract level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

9
- Huffman, W.E. 1977. “Allocative Efficiency: The Role of Human Capital.” _Quarterly Journal of Economics_ 91: 59–79.

- IFPRI and World Bank. 2010. “Innovations in Rural and Agriculture Finance: Focus 18.” Washington, DC.

- Institute of Development Studies. 2012. “Young People and Agriculture in Africa.” Special Issue, Volume 43, Issue 6.

- Jensen, P. 2007. “The Digital Provide: Information (Technology), Market Performance, and Welfare in the South Indian Fisheries Sector.” _The Quarterly Journal of Economics_ 122(3): 879-924.

- Kasanga, K. and N. Kotey. 2001. “Land management in Ghana: Building on tradition and modernity.” London: International Institute for Environment and Development.

- Kevane, M. 1996. “Agrarian structure and agricultural practice: Typology and application to Western Sudan.” _American Journal of Agricultural Economics_ 78 (1): 236-45.

- Kloeppinger-Todd, Renate, A. Nair, and A. Mulder. 2004. “Leasing : an underutilized tool in rural finance.” Agriculture and Rural Development (ARD) Discussion Paper No. 7. World Bank, Washington, DC.

- Lahiff, E. and G. Li. 2012. “Land Redistribution in South Africa: A Critical Review.” Working Paper for the “Land Administration and Reform in SSA” study. World Bank, Washington, DC.

- Larson, D., K. Otsuka, T. Matsumoto, and T. Kilic. 2012. **“** Should African Rural Development Strategies Depend on Smallholder Farms? An Exploration of the Inverse Productivity Hypothesis.” World Bank Policy Research Working Paper 6190. World Bank, Washington, DC.

- Losch, Bruno, Sandrine Fréguin-Gresh, and Eric Thomas White. 2012. _Structural Transformation and Rural Change Revisited: Challenges for Late Developing Countries in a Globalizing World._ African Development Forum series. Washington DC: World Bank.

- Maertens, M., B. Minten, and J. Swinnen. 2009. “Growth in High-Value Export Markets in SubSaharan Africa and its Development Implications.” LICOS Discussion Paper 245, Katholieke Universiteit Leuven.

- Minot, N. 2011. “Transmission of world food price changes to markets in sub-Saharan Africa.” IFPRI Discussion Paper No. 01059. International Food Policy Research Institute. Washington, DC.

- Morris, M., H. Binswanger-Mkhize, and D. Byerlee. 2009. _Awakening Africa's Sleeping Giant._ The World Bank: Washington, DC.

- Nagayets, Oksana. 2005. “Small Farms: Current Status and Key Trends.” Information brief prepared for the Future of Small Farms Research Workshop held at Wye College, June 26–29.

- Nin-Pratt, A., M. Johnson, and B. Yu. 2012. “Improved Performance of Agriculture in Africa South of the Sahara: Taking Off or Bouncing Back.” IFPRI Discussion Paper No. 01224. International Food Policy Research Institute. Washington, DC.

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