rafmacalaba/datause-encoder-data huggingface dataset:
Thus the connections of our indices to these properties are intriguing and may differ from those in advanced economies, and we quantify these relationships below.

# **3. The Evolution of Indian Specialization and Diversity**

This section describes the levels and trends in the specialization and diversity of Indian manufacturing. As we are the first to depict these patterns for India (and generally among the first studies conducted outside of an advanced economy<sup>4</sup> ), we devote extra attention to this section.

## _Levels, Trends and Persistence of Indian Specialization and Diversity_

Table 1a begins with descriptive statistics for our indices by survey. Panel A provides the specialization index, and Panel B provides the diversity index. Within each panel and survey year, we provide the mean, standard deviation, min, max, and median values across our 429 districts. The statistics are calculated after the winsorization noted earlier. In addition to these annual values, we calculate the average specialization and diversity for a district across the five periods. We also calculate the change in specialization and diversity for a district in relative terms using the formula of (AverageValue2000-2010 - AverageValue1989-1994)/ AverageValue19892010.<sup>The last two columns provide summary statistics for these metrics.5</sup> We focus our descriptive analysis on 2005 due to changes in industry codes between the 2005 and 2010 surveys that make 2010 less comparable to earlier periods (discussed further in Table 6). Looking at 2005, the average specialization value is 6.65, and the median value is 4.49. This suggests that, for the average Indian district, the maximum degree to which one industry’s employment exceeds its national share is around 600% (e.g., the industry constitutes 6% of the district’s employment relative to 1% nationally). There is a wide variance in this metric, with the standard deviation equal to the mean. The lowest winsorized level in 2005 is 2.1 (e.g., a 2% local 4 Duranton (2013b) considers specialization and diversity in Colombia.

> 5 The change formula follows Davis et al. (1996) by measuring changes relative to the average across periods. This approach limits issues related to mean reversion and reduces the impact of outliers that could result from abnormally low initial values. The value of the change metric is bounded between [-2, 2] and treats positive and negative adjustments symmetrically.

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# **Table OA8. Welfare Outcomes using Propensity Score Matching**

||Percent of output<br>sold|Log total household<br>income|Food security<br>PCA index|
|---|---|---|---|
||(1)|(2)|(3)|
|_Panel A: Full Baseline Sample_||||
|Beneficiary|0.277*|-0.381|0.425|
||_0.054_|_0.348_|_0.187_|
|Lottery|0.324**|0.300|0.459|
||_0.023_|_0.514_|_0.178_|
|Lottery x Beneficiary|-0.117|0.155|-0.400|
||_0.495_|_0.815_|_0.320_|
|R-squared|||0.013|
|Mean of dep. var. for NB in NL vil.|0.090|12.7|-0.737|
|P-val of B = NB in L villages|_0.105_|_0.702_|_0.928_|
|Model|Tobit|Tobit|OLS|
|Percentage of censured observations|0.64|0.03||
|Number of households|381|260|427|
|_Panel B: Matched Baseline Sample_||||
|Beneficiary|0.377**|-0.695|0.279|
||_0.019_|_0.201_|_0.596_|
|Lottery|0.415**|-0.213|0.431|
||_0.011_|_0.634_|_0.393_|
|Lottery x Beneficiary|-0.270|0.527|-0.286|
||_0.171_|_0.517_|_0.594_|
|R-squared|||0.008|
|Mean of dep. var. for NB in NL vil.|0.069|13.1|-0.641|
|P-val of B = NB in L villages|_0.310_|_0.813_|_0.979_|
|Model|Tobit|Tobit|OLS|
|Percentage of censured observations|0.622|0.033||
|Numberof households|299|212|330|

Note: This table reports the estimation of the following specification: _y ij = α ij + β 1 Beneficiary ij + β 2 Lottery j + β 3 Lottery j *Beneficiary ij + ε ij_ , where _i_ indexes the individual (household) and _j_ indexes the village. _Beneficiary ij_ is a dummy that takes value of 1 if the individual _i_ in village _j_ is a beneficiary of the NAIVS voucher program in 2010/11. _Lottery j_ is a dummy that takes value of 1 if beneficiaries in village _j_ were chosen using a lottery. The models presented here are not adjusted for any baseline covariates. Dependent variables are constructed base on questions asked on follow-up household survey. Data are restricted to baseline respondents in both panels. Panel A reports results using full baseline sample of households. In Panel B the sample is restricted to households in non-lottery who are similar in observable characteristics based on a nearest neighbor propensity score matching algorithm. The sample in Panel B also include households in lottery village who share the propensity score distribution support of households in no lottery villages. Column 1 reports a percentage of maize production that is sold. Column 2 reports on log of total household income, including non-agricultural activities. Dependent variable in Column 3 is constructed using the households reported worry for scarcity of food, number of meals per day, size of meals, and consumption of least preferred foods; higher values represent lower food security. P-values are computed using the t-asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008) and reported below the coefficients. Clustering was done at the village level. ***,**,* denote statistical significance at the 1, 5 and 10 percent level, respectively. See Appendix Table 1 for definition of variables.
# Openness and Technological Innovation in East Asia: Have They Increased the Demand for Skills?

Rita K. Almeida<sup>1</sup> The World Bank

**Keywords** : Demand for Skills, Foreign Direct Investment, Exports, Firm Level Data. **JEL Codes** : J23, J24, J31, O33.

> 1 I thank Emanuela Di Gropello, Ahmad Ahsan and two anonymous referees for several comments and Joy Nam for research assistance. This paper was prepared as a background study for a World Bank Regional Study on the Demand for Skills in East Asia, led by Emanuela Di Gropello. The paper benefited from the financial support of a World Bank Development Economics Research Support Grant. Address: The World Bank. 1818 H Street NW, Washington DC, 20433. E-mail: ralmeida@worldbank.org. The findings expressed in this paper are those of the author and do not necessarily represent the views of The World Bank.
gender, and ethnicity), and _α_ is a set of village fixed effects. Farmers with a phone may access price information more easily, but this will not be reflected in a higher producer price unless farmers can use this information for arbitrage. _β_ 1 may be biased upward if mobile phone ownership is correlated with unobserved factors, such as motivation or intrinsic marketing ability.

Table 9 shows the results for equation (18). For all commodities except peanuts, mobile phone ownership is not associated with a significantly higher price received (Panel A).<sup>28</sup> These findings are similar to Fafchamps and Minten (2012), who show that a mobile phone-based price information intervention in India is not associated with a higher producer price levels.<sup>29</sup>

# **_Mechanisms_**

To understand the mechanisms behind our market-level results, we look for direct evidence of information acquisition and arbitraging behavior among different market agents. Our model suggests that mobile phone coverage improved _traders’_ access to information on producer prices in different markets, thereby facilitating spatial arbitrage. At the same time, mobile phone coverage may improve _farmers_ ’ access to information, thereby allowing them to engage in spatial arbitrage.

Tack and Aker (2014) measure the impact of mobile phone coverage on traders’ behavior, primarily focusing on their search behavior. Using the trader-level dataset, they find that the duration of mobile phone coverage is associated with an increase in traders’ numbers of search markets and the number of market contacts, with statistically significant effects. Although they do not provide direct evidence of a change in traders’ arbitrage behavior, the results do not appear to be driven by traders’ selection into mobile phone markets, changes in the composition of traders, or increased collusion among traders.

Table 9 (Panel B) provides some suggestive evidence of the impact of mobile phone coverage on farmers’ behavior using a specification similar to equation (18). Although the coefficient estimates are biased, we find that mobile phone ownership is associated with an increase in farmers’ probability of searching for price information and that mobile phones became a more useful source of such information. Unlike the trader results, farmers with mobile phones do not increase the number of search markets or the 21
This higher coefficient found in Mozambique points towards higher returns of management practices in Mozambique, but can also be the result of omitted variables that are related to both management practices and firm performance (e.g., use of capital, adopting more efficient technologies). Similar results also hold when using a common scale of management practices across countries, instead of using a normalized score within each country (see Appendix Tables 1 and 2).

# **8 Conclusion and policy implications**

In this paper, we review the management and organizational practices, as well as training and education of workers and managers in Mozambique. We find that the private sector in Mozambique has significant gaps in firm capabilities. Firms with improved management practices and better skills perform better.

A few questions are important at this stage: is the association between firm capabilities and performance causal for Mozambique? Why are firms not investing in these capabilities? Are there structural issues in the economy limiting access to skills and others or are these specific to certain types of firms or individual-level decisions in the companies? Can these firm capabilities be improved in Mozambique? How? What would be the impact on productivity?

Mozambique’s private sector development work has recently combined attempts to improve the business environment, reduce the gap in infrastructure, improve the sustainability and reach of the financial sector, and develop sectorial – mostly agribusiness – work in targeted locations of economic development.

The findings from this study suggest that Mozambique should test and evaluate more intensive ways of increasing the within productivity in targeted firms and sectors. The importance of information constraints, lack of risk-sharing mechanisms to cover a possible low return from capabilities upgrading, coordination problems, and lack of available services to support that upgrading, should be taken into account when designing interventions in this space. Learning from 30
# **Table 1: Sources of risk**

Households were asked to list the most important, second most important and third most important sources of risk that they face. Responses were classified into the categories listed below. The 'weighted sum' percentage is the sum across all three categories where 1st, 2nd and 3rd most important reasons are given weights of 1, 2/3 and 1/3 respectively. Questions 5 and 6 from Part O of the survey: Risk <u>Response.</u>

## **What are the major sources of risk that you face?**

|||Frequency||weighted|
|---|---|---|---|---|
||1st reason|2nd reason|3rd reason|sum|
|Drought|925|69|9|49.9%|
|Crop Failure|31|521|200|22.8%|
|Crop Disease|51|320|149|16.1%|
|Dramatic drop in crop prices|6|35|142|3.9%|
|Unsuccessful Investment|4|28|48|2.0%|
|Loss of livestock/disease|7|27|12|1.5%|
|Price changes|1|8|38|1.0%|
|Illness|3|13|9|0.8%|
|Job Loss|6|10|5|0.7%|
|Sudden death of household member|7|6|3|0.6%|
|Other, specify|2|2|6|0.3%|
|Fires|5|0|0|0.3%|
|Flood|2|1|0|0.1%|
|Loss of land|0|2|0|0.1%|
|**Total**|**1050**|**1042**|**621**|100%|

# **3. Policy Design and Marketing**

The rainfall insurance product studied in this paper is designed to insure farmers in semi-arid tropical areas of India against deficient rainfall. It was developed by the general insurer ICICI Lombard, with technical assistance provided by the World Bank. ICICI Lombard partners with local financial institutions that market the product to farmers. In the Mahaboobnagar and Anantapur districts of Andhra Pradesh, where the product was piloted in 2003, and where our survey villages are located, this role is performed by BASIX, a microfinance institution.

Below, we describe the insurance contract design, focusing on 2004, the year of our survey evidence. Our discussion draws in part on World Bank (2005a) and Giné, Lilleor, Townsend and Vickery (2005). Cole and Tufano (2007) also present additional detail about the product background, and BASIX’s commercial incentives in marketing rainfall insurance policies.

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# IV. CONCLUSION

In their decisions to extend loans, banks are known to be concerned not only with the creditworthiness of the individual borrower, but also with broader issues relating to the stance of credit and interest rate policy, regulatory and supervisory practices, and market sentiments. These factors are often referred to as determining the state of the credit market, and their significance lies in their influence on banks' degree of conservatism and credit rationing. They thus affect the banks' loan supply ca.z-sions independently of the creditworthiness; i.e. networth of individual borrowers. Thus, in times of tight money, or in "bad times," borrowers that otherwise would have been considered eligible are likely to face a more stringent credit evaluation process or simply be denied loans. As a consequence, firms' investment plans may have to be postponed or cut back.

Motivated by this phonomenon, this paper has developed a simple model of firms' investment behavior in an economy with exogenous, time-varying borrowing constraints. A particular form of credit ceiling linked to the firm's net-worth and to the state of credit market is introduced. The model suffices to illustrate the main theoretica message of the papers It is the joint impact of both the volume of credit an;. its price, i.e. interest rate, which is relevant to firms' investment decisions. Thus, focusing on the impact of interest rate changes in assessing the role of credit policy on investment and growth is misleading to the extent that such changes are accompanied by changes in the volume of credit as well.

The model is then used to specify an equation relating aggregate private itvestmen **t** o aggregate output, and two credit market variables; real interest rate and aggregate credit. The equation was estimated, using annual data (1965-85) for a sample of five developing countries, i.e. Brazil, Colombia, India, Korea, and Turkey, and the joint significance of both interest rate and credit supply conditions are tested. The evidence strongly supports the joint influence of interest rates and credit volume on the behavior of private investment in these countries. This supports the theoretical conclusion of the paper that effective
- Acemoglu, D., Naidu, S., Restrepo, P., and Robinson, J. A. (2019). Democracy does cause growth. _Journal of Political Economy_ , 127(1):47–100.

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support rising aggregate real incomes in the long run.<sup>8</sup> Ultimately, the impacts of immigration on real incomes through innovation are an empirical issue.

Indeed, pessimistic claims about the impacts of foreign workers seem inconsistent with continued political support, arising from the high-technology sectors, in the United States for sustaining immigration of skilled workers and engineering and science students.<sup>9</sup> Thus, an essential motivation for our paper is to investigate whether this support is rooted in the dynamic innovation impacts of such foreign workers studying and residing in the United States.

# **2b. Foreign Graduate Students and Skilled Immigration**

The issue of international students and their contribution to host-country economies has been addressed only recently although students have been leaving their home countries for study abroad for nearly four thousand years (Cohen, 2001). Until World War II, a substantial proportion of international students studied in Europe, but this began to change after 1945. Most pronounced was the dramatic shift by Asian students since 1985 toward study in the United States.

To put the U.S. situation into context, note that annual flows of skilled immigrants rose by a factor of more than 30 in the period 1960-2000, while those of international students rose by a factor of 8.2.<sup>10</sup> An important impetus was the Hart-Cellar Immigration Act of 1965, which removed the National Origins quotas established by the Johnson-Reid

> 8 See also Chander and Thangavelu (2004), who show in a theoretical model that permitting high-skilled immigration plus offering education subsidies is sufficient to ensure new technology adoption. 9 See “The Politics of Immigration” above note 6.

> 10 Skilled immigrants are defined to include both those coming under H1-B1 visas and employment-based immigration.  Data sources include Freeman et al (2004); _Statistical Abstract of the United States_ (various years); Institute for International Education, _Open Doors_ , various years; and Department of Homeland Security, _U.S. Immigration Statistics_ .

5
trast, Fryer and Levitt (2013) report that controlling for age, number of siblings, and other environmental factors, the partial correlation between socioeconomic status and cognitive ability is small and statistically insignificant. A child born into a family in the highest socioeconomic quintile can expect to score only 0.02 standard deviations higher on a test of cognitive ability than an average child, while one born into a family in the lowest socioeconomic quintile can expect to score about 0.03 standard deviations lower. These are small in magnitude, and are not statistically significantly different effects.<sup>25</sup> There are a number of important recent contributions in the literature on developed countries which breaks the potential genetic correlation by exploiting natural experiments, but all of them (at least what is known to us) are concerned with estimating causal effects of parent’s education (for a survey see Black and Devereux (2011). Note that it is extremely difficult, if not impossible, to find credible natural experiments that can enable us to estimate the causal effect of four different socioeconomic status defined in the conceptual framework. Finding a natural experiment that mimics a randomized allocation of children into four different groups is most likely impossible. For example, consider the case of policy reforms that exogeneously change educational attainment of parents used in many recent studies to estimate causal effect of parental education (for example, Black et al. (2005)). If we use such an exogeneous shift as an instrument, it will give us reliable estimate of the effects of parental education alone. But it cannot identify the effects separately for two groups differentiated by farm or nonfarm occupation within a given education level which is a central focus of the paper. In the absence of any conventional sources of identification,

> population. Other concerns about the usefulness of twin based studies have been noted by many economists, starting with Griliches (1979). See, among others, Sandewall et al. (2014), Bound and Solon (1999).

> 25It is also important to reiterate Goldberger’s observation that even when the differences in academic performances are due to genetic factors, it does not imply that there are no appropriate policy responses (Goldberger (1979)). If academic performance of some children are adversely affected by genetically inherited poor eye sights, it does not necessarily mean immutable disadvantages, policies that ensure equal access to eye care and eye glasses may be sufficient to address this.

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differences we observe in Latin America. This is a part of inequality that can not be explained as a return to effort, or even as the result of random shocks and pure luck.

In this paper, we have sought to lay the foundation for the measurement of inequality of opportunity, relating it to the relevant economic theory. It would be interesting for future work to investigate at least two aspects of these findings: first, how do our comparisons of opportunity-deprivation and poverty profiles relate to more standard measures of intergenerational mobility? Second, do differences among countries in the _nature_ (e.g. in the opportunity share) – rather than merely in the _level_ – of inequality, affect social and political attitudes, the nature of redistribution systems, and the rate of economic growth?

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