rafmacalaba/datause-encoder-data huggingface dataset:
suppliers ( _inp_dom_ ), (ii) the percentage of domestic workers in the firm’s total workforce ( _emp_dom_ ), (iii) the percentage of sales to the domestic market ( _market_ ), and (iv) the likelihood of supplier assistance ( _assist_ ). While foreign investor characteristics refer to FY 2012, we don’t know when supplier assistance took place. However, it is relatively safe to assume that major foreign characteristics remained constant over time.

Table 6 shows the descriptive statistics. Each line represents a foreign investor characteristic, _FC_ , using different thresholds, while columns 1 to 4 refer to our four measures of FDI spillover potential. Each panel in a column is estimated as a separate regression.

The share of foreign ownership ( _own_ ) matters for the FDI spillover potential. Multinationals with a foreign ownership share of at least 50 and less than 100 percent source more inputs locally compared to other firms, and this effect is even slightly higher for firms with full foreign ownership (column 1). However, we don’t find any effects on alternative measures of FDI spillover potential.

A multinational’s presence in the host country ( _age_fdi_ ) is negatively associated with the share of domestically sourced inputs if the firm has been in the country for at least 20 years (column 1), but positively related with the percentage of domestic workers (column 2). A presence in the host country of at least 10 but less than 20 years is also positive related with the probability to offer supplier assistance (column 4).

If a foreign firm has a moderate technology gap ( _tech_ ) to the leading domestic competitor in the same sector, it is more likely to offer supplier assistance (column 4).

The region of origin ( _origin_ ) also matters for the FDI spillover potential. Interestingly, foreign firms with the largest investor from SSA are more likely to assist domestic suppliers compared to other firms (column 4). In addition, they sell a higher share of their output to the local market (column 3). Firms with their largest foreign investor from Asia (including South Asia) also sell a significantly larger share of output to the local market, but offer significantly less assistance to their domestic suppliers (columns 3 and 4).

In a next step, we evaluate whether the FDI motive influences the extent of FDI linkages. As could be expected, market-seeking FDI ( _motive_market_ ) is positively correlated with the share of sales to the host country (column 3). It is also positive correlated with the probability of supplier assistance (column 4). However, firms where market-seeking FDI is moderate make significantly less use of local workers (column 2).

18
- 55 -

dW/d4 - (1/D 4)( (dFp/dZ][dFp/dM2] - [dFp/dZ1[dFF/da (1/D4)( (2a,Pf(n)u'(Z)+6mu"(Z)j[2BtnX + 2(1-qg)B,nWn] - [2(1-a1)(1-a)8,nPf(n)][-Wnu'(Z)I )

> - (1/D4)( 2(l-)2)B,n2 W)((lfa1)Pf(n)u'(Z)+5uu"(Z) ) > 0.

- - dW/dB, (1/D4)( [dFF/dZ](dFp/dB,] (dFp/dZ][dFF/dBiJ ) - (1/D4)( [2a,Pf(n)u'(Z)+6muu(Z)][-2(1-a2)nX] ) - 0. Excess demand is always zero.

dW/dI6 is zero since S2 does not occur in neither Fp nor FF.

dW/dP - (1/D4) ( [dFF/dZ][dFp/dP] - [dFp/dZ][dFF/dP] )

- (1/D4)( [2atPf(n)u'(Z)+6mu"(Z)](2(1-4)Bln(Zf(n)-I)] - [2(1-al)(1-a2)8,nPf(n)J[a,Zf(n)u'(Z)4a,f(n)5wI

- (1/D4 )( 2(1-aa)Bln)(2a,Pf(n)u'(Z)(Zf(n)-I)+8mu"(Z)(Zf(n)-I)

- (1-a,)Pf(n)a,Zf(n)u'(Z)-(l-a 1<sup>)Pf(n)a</sup> 1<sup>f(n)6w</sup> ) - (1/D 4)( 2(1-2)B 1 n)( a,Pf(n)u'(Z)[(1+a 1<sup>)Zf(n)-2I]</sup>

+ 6mu"(Z)(Zf(n)-I) - (l-a,)PZ(n)a,f(n)6W] A sufficient condition for dW/dP being positive is that (14*,)Zf(n)-21 > 0.

- dW/dn - (1/D 4 )( [dFF/dZ][dFp/dn] - [dFp/dZ][dFF/dn] )

- (1/D4)( (2a¶Pf(n)u'(Z)+6mu"(Z)][-2(1-2)B¶n(PZf'(n)-(l-a,)W]

- [2(1-ai)(1-a2)B,nPf(n)][(a,PZf'(n)-a4W)u'(Z)+a,Pf(n)Sw1 )

dW/dn has undetermined sign.

- dW/dUm - (l/D 4 )( [dFF/dZ][dFp/dUmj - [dFp/dZ][dFF/dUM )

- (1/D4<sup>)(-[2(1-a,)(l-ak)B,nPf(n)](-u'(Z))</sup> )

- - (1/D4)( 2(l-a,)(l-a2<sup>)BjnPf(n)u'(Z)</sup> ) > 0.
**-3-**

# I. STATISTICAL TECHNIQUES

4. The technique ior examining whether two time series have a stationary relationship over the long-run, i.e., move together, is based on the integration and cointegraLion test procedures. _Tesss_ for cointegration provide evidence of a stationary relationship betweer. two series. However, before testing for cointegration, the two series should be tested to see if they have the same intertemporaral properties, i.e., they are integrated of the same order.

5. The dynamic properLy of a time series can be described by how often it needs to be differenced to achieve time-invariant linear propeLcies and provide a stationary process. A series that has at least invariant mean and variance and whose autocorrelation has "short memory" is called I(O), denoting "integrated of order zero". A series which needs to be differenced n times to become I(O), i.e., become stationary, is said to be integrated of order n, denoted as I(n). A large number of economic variables need to be differenced once to become stationary which makes them I(1), i.e., they have a unit root.

6. There are substantial differences between I(O) and I(1) series. An I(O) series possesses the following characteristics: (i) its variance is finite; (ii) a shock has only a temporary effect; and (iii) the expected length of time between crossings of its mean is finite. For an I(1) series (i) its variance goes to infinity as t goes to infinity; (ii) a shock has a permanent effect; and (iii) the expected time between crossings of its mean is indefinite.
- [43] Hunt, Jennifer. 1992. “The impact of the 1962 repatriates from Algeria on the French labor market.” _Industrial and Labor Relations Review_ 45(3): 556-572.

- [44] ILO. 1982. “Resolution concerning statistics of the economically active population, employment, unemployment and underemployment.” Thirteenth International Conference of Labour Statisticians.

- [45] Jedwab, R ́emi and Alexander Moradi. 2012. “Transportation infrastructure and development in Ghana.” Working Paper.

- [46] Lewer, Joshua J. and Hendrik Van den Berg. 2008. “A Gravity Model of Immigration.” _Economics Letters_ 99(1): 164-167.

- [47] Lim ̃ao, Nuno and Anthony J. Venables. 2001. “Infrastructure, geographical disadvantage, transport costs, and trade.” _World Bank Economic Review_ 15(3): 451-479.

- [48] Longhi, Simonetta, Peter Nijkamp and Jacques Poot. 2005. “A meta-analytic assessment of the effect of immigration on wages.” _Journal of Economic Surveys_ 19(3): 451-477.

- [49] Lucas, Robert E.B. 1997. “Internal migration in developing countries.” _Handbook of Population and Family Economics_ 13: 721-798.

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- [51] McIntosh, Molly Fifer. 2008. “Measuring the labor market impacts of Hurricane Katrina migration: evidence from Houston, Texas.” _American Economic Review_ 98(2): 54-57.

40
for the EU in 1997, 5.5 percent for the US in 1998, and 5.8 percent for India in 2011 (Bown 2013a, Figure 1a).<sup>11</sup> Interestingly, Table 1 also indicates that many of the significant users of TTBs by 2011 had been non-users of these particular policy instruments as of the late 1980s. For many emerging economies in particular, only since their periods of unilateral, preferential, and/or multilateral liberalization of the 1990s has there been a significant increase in the use of the TTB instruments. One important question, of course, is the nature of the relationship between these forms of liberalization – not only with respect to each other (as highlighted by research described in the previous section), but also through changes to other timevarying protection policies, and in particular through non-tariff instruments such as TTBs.

An additional way to motivate the importance of how applied TTBs relate to PTAs is to consider case studies of individual countries. Turkey, for example, went from being a non-user of TTBs as of the late 1980s to becoming, by some measures, the trading system’s second most active user of TTBs by 2011, trailing only India – e.g., see Table 1, columns (2) and (6). Furthermore, during this same period, Turkey went from having control over its entire set of trade policy instruments, to an outcome whereby many of its trade policy instruments were constrained via a voluntary preferential trade agreement (Karacaovali 2011). In particular, Turkey formed a customs union with the European Union in the mid1990s; as such, Turkey then implemented bilateral free trade with EU member countries over most goods imports (applied preferential tariffs of zero toward imports from EU members), and it adopted and implemented the European Union’s applied MFN tariff toward most goods that it imports from third countries. The TTB policies of antidumping, safeguards, and countervailing duties became the few remaining trade policy instruments at the Turkish government’s disposal and discretion.

Figure 1 provides information on the share of Turkey’s imports each year that are subject to some imposed TTB, split into whether the imports derive from its PTA partners versus PTA non-partners for Turkey’s PTAs in effect that year. Turkey’s PTA partners include members of the European Union, of

> 11 The economy-specific chapters provided in Bown (2011b) provide additional detail on these and other major Group of 20 economies through the period ending in 2009.

11
- Calderon, C., A. Chong and R. Valdes. (2004). “Labor Market Regulations and Income Inequality: Evidence for a Panel of Countries.” WP514, IADB.

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- Chant, S. (1991) Women and Survival in Mexican Cities (Manchester University Press: Manchester)

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- Cortez Reis, M. And A. R. Vianna Crespo. (2004). “The Impact of Brazilian Labor

- Cunningham, W. (2000) “Breadwinner versus Caregiver.” In Elizabeth Katz and Maria Correia (eds.) The Economics of Gender in Mexico:  Work, Family, State, and Market (World Bank: Washington, DC).

- Cunningham, W. (2001) Sectoral Allocation by Gender of Latin American Workers over the Liberalization Period of the 1990s, Policy Research Working Paper 2742 (World Bank: Washington, DC).

- Cunningham, W. (unpublished) “Mexican Female Small Firm Ownership:  Motivations, Returns, and Gender,” LCSPG/World Bank.

- Cunningham, W. and Carlos Ramos Gomez (2004) The Home as the Factory Floor:  Employment and Remuneration of Home-based Workers.  Policy Research Working Paper No. 3295 (World Bank:  Washington, DC).

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- Cunningham, W. and W. Maloney (2003) “Heterogeneity among Mexico’s Microenterprises:  An Application of Factor and Cluster Analysis” Economic Development and Cultural Change (50:1), 131-156.

- Cunningham, W., D. Neumark and L. Siga. (2004). "The Effects of Minimum Wages in Brazil on the Distribution of Family Incomes:1996-2001." World Bank, Washington, D.C.

- De Ferranti, D., G. E. Perry, I. S. Gill, and L. Servén. (2000). "Securing Our Future in a Global Economy." Latin American and Caribbean Studies Series. World Bank, Washington, D.C.

- De Ferranti, D., et al. (2003). Closing the Gap in Education and Technology. Washington, DC: The World Bank.

- Elmeskov, J., J.P. Martin and S. Scarpetta. Forthcoming.  "Key Lessons for Labour Market Reforms: Evidence from OECD Countries' Experience". Swedish Economic Policy Review

- Fajnzylber, P and W.F. Maloney (forthcoming), “Labor Reform and Trade Elasticities in Latin America.” Journal of International Economics.

- Feenstra, R. C. and G. Hanson. (1996). "Globalization, Outsourcing, and Wage Inequality." Working Paper No. 5424.. NBER. Cambridge, MA.

28
questionnaire on 29 business practices and later delivers a dense 6-page report with recommendations for changes.

We randomly assigned firms to a control group that received the 6-page report and to four treatment groups. The treatment groups received the 6-page report plus one of four versions of an information sheet that provides information about business practices in a more succinct and compelling format. Adding to the content of the 6-page report, the basic version of the information sheet benchmarks business practices relative to other firms in the same sector. Further, it lists five simple actions the firm can take to adopt good business practices and invites the firm to register for a free training session. The other three versions of the information sheet additionally include: (i) a motivating sentence, stating that firms who adopt fewer business practices are more likely to close based on SEBRAE data, (ii) pre-enrollment in the free training session, or (iii) both features (i) and (ii).

To measure the effect of receiving the information sheet on take-up of business services, we used administrative data from SEBRAE-PR on the services they provided to firms in our sample during the 18 months following the intervention. About 28 percent of firms who received an information sheet used any SEBRAE service during the first few months after the intervention, compared to 21 percent of firms in the control group. However, this statistically significant difference disappears over time, as the control group caught up to the treatment group: 18 months after the intervention about 49 percent of firms in both the treatment and control group had used a SEBRAE-PR service. The short-run effect on take-up of SEBRAE-PR services is largest in the treatment group that received the most intense information treatment (including both the motivating sentence and pre-enrollment in a free training session).

The effect on take-up is mainly driven by relatively light touch services, meaning that firms either went to a SEBRAE-PR service center and received materials on best business practices, or accessed material through SEBRAE’s website. Receiving the information sheet increased participation in group training sessions or one-on-one consulting only for firms who implemented below the median number of business practices at baseline, possibly because these firms realized they needed more hands-on help with improving their business practices.

Given the relatively small absolute effect on take-up in the short-run (7 percentage points) and the lack of a medium-run effect, we do not expect the information sheet to affect business performance through take-up of SEBRAE services. However, we implemented a follow-up survey about one year after the intervention to check whether firms obtained business training from other sources or whether they improved business practices on their own. We do not find any effects on either the use of services from other sources or on a business practices index. Accordingly, the follow-up survey data shows no 3
# **Table C4.1: Balance tests for matching**

||Me<br>|an<br>|||
|---|---|---|---|---|
||Treated|Control|t-stat|p>|t||
|Pa|nel A: Ba|lance|||
|Log age|2.28|2.16|1.65|0.09|
|Foreign ownership|12.77|11.34|0.69|0.49|
|State ownership|5.70|4.71|0.59|0.55|
|Importer|0.56|0.56|-0.13|0.90|
|Exporter|0.39|0.41|-0.51|0.61|
|Panel B:|Balance (|Alternativ|e)||
|Log age|2.37|2.36|0.07|0.95|
|Foreign ownership|13.37|11.71|0.77|0.44|
|State ownership|5.97|5.66|0.17|0.86|
|Importer|0.57|0.55|0.55|0.59|
|Exporter|0.40|0.42|-0.43|0.66|

_Note_ : This table presents balance tests for propensity score matching, using the 5 nearest neighbors, with replacement. Suharto family firms are matched with non-connected firms, based on the following variables: logarithm of firm age, foreign and state ownership, indicators for being an exporter and importer, industry and year fixed effects; and the sample is restricted to 19931996. In panel A the matching is done on all firms, while in panel B only on firms that exist since 1993. After obtaining the frequency (weight) with which the observation is used as a match, we compute the average weight score for each matched firm and estimate weighted specifications. The results on the two matched samples are presented in table 3, column 7 in the main text and table C4.2 in the online Appendix.

63
adult will fail to survive into old age and so not produce any output in that phase of life, but rather that he or she will not enjoy her claim on the common pot in old age, whose value is _ρ ·_ **p** _t_ +1 **x** _t_ +1. Young adults in period 1 face the risk of losing 5.270 in period 2, which in relation to the VSL implies a ratio of 3.66 to one. Their counterparts in period 2 face the possible loss of 8.535 in period 3, which implies a ratio of 9.67 to one. All in all, these ratios for period 1 do seem to be rather on the low side, which suggests that the benefits stemming from the posited reductions in mortality may be somewhat higher than those implicitly reported in Tables 2 and 3.

# **5 Conclusions**

Providing backward rural areas with all-weather roads promotes not only production and trade in what can be called the ‘commercial’ sphere of life, but also the formation of human capital and health in the ‘non-commercial’ one. The main object of this paper has been to develop a method to value their respective contributions to the total benefit generated by rural roads. For given that obtaining independent estimates of the benefits, defined as willingness to pay, in the commercial sphere of production and trade is relatively straightforward, the respective shares of the ‘commercial’ and ’non-commercial’ spheres in the total benefits, as derived in the unified OLG-framework employed here, then yield an estimate of the willingness to pay in the non-commercial sphere that is anchored to the independent estimate of that in the commercial sphere. Based on survey evidence from a backward region of Orissa in east India and Bangladesh, as well as elements of more usual forms of calibration, the model yields a ratio of commercial to non-commercial benefits of about two-to-one in the first generation, falling to three-to-four in the second. This is broadly consistent with the valuations expressed by respondents in the Orissa survey, who ranked the latter benefits at least on a par with the former.

24
# **1. Introduction**

A sharp increase in the number of work migrants and in the amount of remittances they send to their countries of origin has fueled the recent debate on the costs and benefits of international migration for the sending communities (United Nations 2002). Remittances are considered to be one of the main factors through which migration affects economic growth. Most microeconomic studies of migration and remittances focus on their role in reducing poverty and economic inequality. At the same time, the impact of remittances and migration on the economic behavior of household members ‘left behind’ has received relatively little attention among economists (Kanaiaupuni 2000).

Most research on this issue comes from sociological and demographic literature which indicates that the time women spend working on home farms is increasing, due, at least in part, to the migration of males (Deere and Leon 1987, Crummet 1987). Among the few economists that focus on the labor market outcomes of members of households that have sent migrants, Itzigsohn (1995) assesses the effect of migrant remittances on income and labor market participation of members of low-income urban households in the Caribbean Basin. Rodriguez and Tiongson (2001) analyze the effect migrants have on the labor force participation of non-migrants in the Philippines. Sadiqi and Ennaji (2004) study the impact of male migration from Morocco to Europe on the women left behind. Amuedo-Dorantes and Pozo (2006) investigate how migration and migrant remittances affect the employment status and hours of work of individuals living in the sending households in Mexico. The relation between remittances, labor supply, and school attendance in El Salvador is examined by Acosta (2006). Cabegin (2006) examines the effect of overseas work-related migration on the market participation and labor supply behavior of migrants’ spouses left behind in the Philippines. Kim (2007) studies the impact of remittances on labor supply in Jamaica. The common finding of all these studies is that migration and remittances result in a decline in the labor force participation of household members left behind, in particular, of females.

The objective of this paper is to examine the extent to which male migration affects the labor force participation of prime age women in Nepal. This question is of 2
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# Tamil Nadu

<!-- Start of picture text --> Figure 15a:<br>Figure 15b:<br>Tamil Nadu Tamil Nadu<br>Predicted Revenue Growth All growth except Predicted Revenues<br>vs. Actual Revenue Growth and Stamp Tax<br>70 70<br>50 50<br>30 taxrevgrow 30 prop val grow th<br>stamprategrow1<br>10 10 real gdp grow th<br>predictedrevgrowth<br>-10 -10<br>-30 -30<br>1992-93 1994-95 1996-97 1998-99 2000-01 1992-93 1994-95 1996-97 1998-99 2000-01<br><!-- End of picture text -->

# Figure 16a:

# West Bengal

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