rafmacalaba/datause-encoder-data huggingface dataset:
determinants of _SAH_ influence work effort and hence personal incomes. To help alleviate these concerns we estimate regressions in which personal income is dropped. We also test sensitivity to dropping the labor force activity variables, given the endogeneity concerns.

# **3. Setting and data**

Russia experienced a severe deterioration in overall health status in the 1990s — dubbed a “health crisis” by Field (1995). Male life expectancy fell from 65 years in 1988 to 59 years in 2001, while for women it fell from 74 years to 72 years (Heleniak, 2002).<sup>8</sup> This was in a period of overall economic contraction and rising inequality (Lokshin and Popkin, 1999), the latter stemming in large part from the diverse labor market outcomes of the period (Milanovic, 1999). There is also evidence of considerable “churning” in living standards, with both gainers and losers at any given level of living (Lokshin and Ravallion, 2000). There are many ways these economic changes could impinge on health. Deteriorating public health services, combined with reduced purchasing power over private health inputs, can be expected to take a toll on health status, and probably more so for the poor. Other channels include the psychosocial stresses of the transition from a time (prior to 1989) in which there was relatively little economic insecurity at the individual level to a period of much uncertainty. New opportunities emerged, but so did new stresses on individuals, who naturally had different abilities to respond to those opportunities (Gavrilova et al., 2002; Brainerd and Cutler, 2005).<sup>9</sup> Using a nationally representative survey for Russia in 1995, Cockerham (2000) found no statistically significant

> 8 Field (1995) reports a number of other signs of worsening health in the early 1990s.  Further evidence and (sometimes differing) interpretations of Russia’s “health crisis” can be found in Ellman (1994), DaVanzo and Adamson (1997) and Brainerd and Cutler (2005).

> 9 This has also been a theme of the literature on socio-economic inequalities in health; Wilkinson (1996) argues that inequality generates health-impairing stresses.  Also see the critical assessments of this view in Elstad (1998), Lynch et al., (2000) and Deaton (2003).

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goods are worth more in the hands of the original supplier in case of buyer’s default, firms selling (resp. buying) differentiated goods should offer (resp. receive) more trade credit (liquidation hypothesis).

Using a sample of small U.S. firms, Burkart _et al._ (2008) show that service firms as well as firms producing differentiated products grant more trade credit, while firms offering standardized goods offer less trade credit.<sup>8</sup> This evidence seems to provide support for the moral hazard hypothesis, but does not allow to clearly disentangle whether the driver of the results is the different diversion value of the goods or the different cost in switching to alternative suppliers. A direct investigation into the relevance of this last motivation is provided by McMillan and Woodruff (1999), who, based on survey data collected on a sample of Vietnamese firms,<sup>9</sup> show that customers lacking alternative suppliers receive more trade credit.

As regards the collateral hypothesis, Burkart _et al._ (2008) provide some limited evidence in support of it, as firms offering differentiated goods offer more trade credit and firms buying a larger proportion of differentiated goods receive more trade credit. Also Petersen and Rajan (1997) provide evidence in support of this hypothesis, but they test it using the fraction of the firm’s inventory not consisting of finished goods as a proxy for the liquidation advantage, based on the assumption that when the intermediate inputs are converted into finished goods, the supplier’s liquidation advantage is lost.

# **_3.2 The role of credit chains_**

One potential risk arising from the use of trade credit is that it may cause shocks to propagate in the economy. In a network where firms borrow from each other, a temporary shock to the liquidity of some firms may cause a chain reaction in which other firms also get in financial difficulties.<sup>10</sup> This has obvious implications for the production activity along supply chains: disruptions in production may arise if suppliers at any stage of the supply chain cannot finance shipment of their products to the next stage of the chain. The scale of the damage depends on the length of the credit-chain between constrained agents. In a recession such chains are longer because more firms suffer negative shocks to their flow of funds. However, the presence of firms with sufficient access to outside finance to absorb defaults without

> 8 Looking at the credit terms offered, the analysis seems to partly support the diversion hypothesis as firms in the service sector offer also better terms.

> 9 A distinctive feature of this economy is the absence of legal enforcement of contracts. The existence of inter-firm credit in such a type of environment is interpreted by the authors as evidence of relational mechanisms to be in place.

> 10 Besides transmitting shocks from customers to suppliers, trade credit may also cause shocks to propagate from suppliers to customers, if the former, facing liquidity problems, reduce their credit to the latter (Coricelli and Masten, 2004).

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fewer regulations in 2012 with respect to 2011. The firms that informalized comprise approximately 20 percent of the total in the data. The firms that formalized are those that complied with more regulations in 2012 than in 2011. The firms that formalized comprise approximately 13 percent of the total in the data. About a third of the firms changed their formality status within a single year—going toward greater formality or informality.

Figure 3: Transitions Summary, 2011–12 <!-- Start of picture text --> 68%<br>20%<br>13%<br>Informalize Unchanged Formalize<br><!-- End of picture text --> _Source:_ EMYPE 2012–13. _Note:_ The data are for fiscal years 2011 and 2012.

# _Business and Labor Formality: Transition Regressions_

We use data from fiscal years 2011 and 2012 to construct variables that indicate transitions in formality status between those years. For instance, we construct a variable that indicates that a firm turned from not complying to complying and a variable that indicates that a firm turned from complying to not complying with business regulations between 2011 and 2012, respectively. We define similar variables for labor formality.

Table 3 reports the results from linear probability models of these transition variables on enforcement indicators and firms’ characteristics. We ran these regressions to uncover transition patterns, controlling for several observable characteristics. The results suggest that the probability of moving from not complying with business formality in 2011 to complying in 2012 (that is, the probability of business formalization) is higher for incorporated businesses. We also find that the probability of moving from complying with business formality in 2011 to not complying in 2012 (that is, the probability of business informalization) is higher for 16
_Note:_ a. The RBI manages domestic borrowings for each of the 28 states through separate agreements with each. b. A provident fund is a retirement benefit scheme; employees contribute 12 percent of monthly wages and can withdraw funds on retirement or on reaching age 55. Contributions are an unfunded liability in the Public Account, but balances are available to the state (see Rao, Prasad, and Gupta 2001).

Figure 1 illustrates the changing financing pattern of states debt during the 1990s and 2000s. This mirrors three phases, with a decline in the center‘s loan intermediation and onlending (since 1998–99), an increase in the National Small Savings Fund (NSSF)<sup>18</sup> and small savings borrowings (from 1999–2000), and the move toward market-based financing since mid2000.

**Figure 1. Composition of financing pattern of state deficits (as of end-March)**

<!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>-20<br>Market Borrowings Loans from Centre<br>NSSF Small Savings, Provident Fund, etc.<br><!-- End of picture text --> Source: Reserve Bank of India, NSSF=National Small Savings Fund, described in Box 1.

Traditionally, loans from the center were the dominant source of funding for states. In keeping with the trend of financial sector liberalization, the center‘s loan intermediation role has been reduced since 1999–2000. The other notable change has been the rising share of NSSF and small saving loans (see box 1 for a description). The share of NSSF increased sharply to 69

> 18 The NSSF was established in April 1999; small savings collections are invested in central and state government special securities. At present, 80 percent of all small savings collections within a territory of state are invested in the same state securities. This adds to the debt of the state but is not controlled by the center. Moreover, the inflows are autonomous and depend on the spread between the small savings rate and the deposit rate. See ―Report of the Committee on the Comprehensive Review of the National Small Savings Fund,‖ June 2011. There is an inflexibility associated with NSSF borrowing, since these are based on availability and not necessarily on the requirement by the state to borrow, and are at higher interest costs and with an asymmetry toward the center (Thirteenth FC, p. 144).

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and outlook based on a long-range energy alternatives modeling tool. Petroleum Science. 2016:1-14.

[24] Dogan E, Seker F. Determinants of CO 2 emissions in the European Union: the role of renewable and non-renewable energy. Renew Energ. 2016;94:429-39.

[25] Jebli MB, Youssef SB, Ozturk I. Testing environmental Kuznets curve hypothesis: The role of renewable and non-renewable energy consumption and trade in OECD countries. Ecological Indicators. 2016;60:824-31.

[26] Wang S, Li G, Fang C. Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels. Renew Sust Energ Rev. 2017.

[27] Bacon RW, Bhattacharya S, Damania R, Kojima M, Lvovsky K. Growth and CO2 emissions: how do different countries fare. Environment Department Papers. 2007;113. [28] Kuznets S. Economic growth and income inequality. The American economic review. 1955:1-28.

[29] Sugiawan Y, Managi S. The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy. Energy Policy. 2016;98:187-98.

[30] Arellano M, Bond S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies. 1991;58:277-97.

[31] Arellano M, Bover O. Another look at the instrumental variable estimation of errorcomponents models. J Econometrics. 1995;68:29-51.

[32] Blundell R, Bond S. Initial conditions and moment restrictions in dynamic panel data models. J Econometrics. 1998;87:115-43.

[33] Sung B, Song W-Y, Park S-D. How foreign direct investment affects CO 2 emission levels in the Chinese manufacturing industry: Evidence from panel data. Economic Systems. 2018.

[34] World-bank. World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519. 2017. [35] IEA. International Energy Agency, CO2 Emissions from Fuel Combustion 2017. http://www.oecd-ilibrary.org/energy/co2-emissions-from-fuelcombustion_22199446;jsessionid=7bbqif75he1ft.x-oecd-live-03. 2017.

[36] IEA. International Energy Agency, Energy Balances of OECD and Non-OECD countries; Various Issues. https://www.oecd-ilibrary.org/energy/world-energybalances_25186442. 2017.

[37] Dong K, Sun R, Hochman G, Zeng X, Li H, Jiang H. Impact of natural gas consumption on CO2 emissions: Panel data evidence from China’s provinces. J Clea Prod. 2017;162:400-10.

[38] Lee C-C, Chiu Y-B, Sun C-H. The environmental Kuznets curve hypothesis for water pollution: Do regions matter? Energy Policy. 2010;38:12-23.

[39] Dinda S. Environmental Kuznets curve hypothesis: a survey. Ecol Econ. 2004;49:431-55.

[40] Ali W, Abdullah A, Azam M. Re-visiting the environmental Kuznets curve 27
**Table 1 (cont'd). Variables used in the construction of the composite Investment Climate Index (ICI)**

|**INPU**|**TS**|
|---|---|
|**COST**|**PERCEPTION**|
|1 Excess labor|1 Share of short term finance obtained by banks|
|2 Cost of finance: value of collateral required to obtain a||
|loan|2 Share of long term finance obtained by banks|
|3 Proximity to raw materials (share of inputs bought by||
|domestic sources)|3 Short term finance represented by trade finance|
|4 Proximity to domestic customers|4 Duration of loan|
|5 Share of firms using new technology|5 Perception on access to finance|
|6 Trade credit: share of sales sold on credit|6 Perception on labor regulations|
|7 Trade credit: share of inputs bought on credit|7 Perception on customs|
||8 Perception on availability of skills|

# **INSTITUTIONS**

|**COST**|**PERCEPTION**|
|---|---|
|1 Law & order: security cost|1 Perception of law & order: crime|
|2 Law & order: losses due to theft|2 Perception of corruption|
|3 Manager time spent dealing with regulations|3 Perception on licensing & permits|
|4 Days spent with officials to deal with regulations|4 Quality of adm.: consistent interpretation of rules|
|5 Tax evasion (% of sales not declared)|5 Perception of tax administration: rates|
|6 Days to obtain a telephone connection|6 Perception of tax administration: administration|
|7 Days to obtain a electric connection|7 Perception of functioning of judicial system|
|8 Days to obtain a construction permit<br>9 Bribes to "get things done"||
|10 Share of firms reporting officials request gifts<br>11 Share of firms reporting gifts requested to obtain a<br>power connection<br>12 <sup>Share of firms reporting gifts requested to obtain a</sup><br>telephone connection||
|13 Share of firms reporting gifts requested to obtain a<br>construction permit||
|14 Share of firms reporting gifts requested to obtain a<br>main operating license||
|15 Average time to reach a courtjudgment(weeks)||

We assume that both costs and perceptions drive investors’ decisions. However since we do not know the relative weight of each we assume they carry an equal weight in the construction of the index. Hence we keep them separated in the aggregation process by estimating sub-indices. Aggregating variables into sub-indices before building the composite indicator allows us to avoid the composite indicator to be driven by data availability. As a matter of fact, estimating sub-indices for each category and then aggregating them into the ICI ensures equal weighting among each of the clusters of data.

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Within a few short years following the opening of its borders to international migration in 1990, Albania witnessed a remarkable out-migration, primarily to Greece and Italy, with about onefifth of the country’s population estimated to have left (King and Vullnetari 2003; Carletto, Davis et al. 2006). Whereas most migration from Albania at the start of the 1990s was maledominated, a decade later women comprised 41 percent of Albanians in Italy and Greece (Vullnetari 2007). We use nationally representative survey data from Albania from 2005, including a unique retrospective module on migration of sons and daughters of respondents, to examine the pattern as well as mechanisms that underlie the gender and migration relationship. Our analysis describes the pattern of change in male and female migration over time and the role of human capital and wealth. We explicitly consider agency in migration decisions using timevarying data on demographic constraints and economic and health shocks at the household-level. These data enable us to evaluate how household-level factors differentially impact male and female migration and whether a change in this impact is discernible over time. Finally, data on both family and community networks by gender are used to examine whether networks are gender-neutral and whether the effects of networks vary with the increased routinization of migration.

# **2. Gender and migration: history and context**

The complexity of the international migration process as well as interdisciplinary nature of scholarship in this field has yet to lead to the formulation of a unified theoretical perspective on migration and gender (Donato, Gabaccia et al. 2006). The economic approach, begun in the neoclassical tradition and further developed by Todaro (1969; 1976), emphasized the expected gains to potential migrants and implications of policy programs that aim to reduce rural outmigration. Later developments in this field, coming under the title of the new economics of labor migration, have focused on the context and boundaries of decision-making and have pushed both economists and non-economists to consider the complex household level strategies underlying migration (see Stark 1991). This has meant increasing attention to risk and credit constraints, for example, as primary motivations underlying migration strategies (Taylor 1986; Rosenzweig and Stark 1989). Sociological and demographic theory-building has paid keen attention to the contributions of economists, and the underlying rational actor micro-level model has been adopted in many cases, but parallel theories and models have paid equally close attention to the 3
Beyond socioeconomic status, a key factor in receiving vouchers is having a relationship with key leaders in the community. Having a village chief, a VDC member or the TA as part of the household network considerably increases both the probability and the number of coupons received. This result stands in contrast with respect to the insignificant coefficients associated with having a member of parliament or an agricultural extension officer in the community, and indicates the importance of being locally, as opposed to regionally or nationally, well-connected in obtaining FISP coupons. Lastly, an important factor in obtaining a voucher appears to be the agricultural potential. As noted, those with larger landholdings tend to get more coupons, which could be partly due to having greater agricultural production potential. However, having higher values for the index of favorable soil conditions also significantly increases the probability of program participation.

# **7 CONCLUSIONS**

The Malawi FISP has received remarkable attention given its perceived success in improving food security in Malawi through increasing production. The FISP is a large program covering a substantial portion of the rural population in Malawi and represents the country’s primary strategy for expanding agriculture. It follows the perceived wisdom that decentralization of government services and programs offers several informational and cost advantages compared to more centrally run alternatives. Reaching specific subpopulations via targeting mechanisms can improve program performance while reducing costs. As both of these tactics have become increasingly popular, questions remain about how to effectively design a decentralized targeted program in order to maximize the informational and cost advantages while minimizing the potential for unwanted variation across locales—a particularly challenging undertaking in a very large program.

Using nationally-representative data, this paper systematically analyzes the decentralized targeting performance of the FISP during the 2009/10 agricultural season, and considers a range of approaches to defining resource poverty. The analysis includes both a standard targeting assessment focusing on the rates of participation and the transfer amounts among the eligible and non-eligible populations as well as national targeting performance decompositions that identify the relative contributions of inter-district, intra-district inter-community, and intra-district intracommunity targeting. A multivariate analysis of the correlates of program participation is also considered.

Even with the range of analysis, the results present a very clear and robust picture. The FISP does not exclusively target the poor in Malawi, and concentrates primarily on the middle of the income distribution. The failure to target the poor is apparent in the decision-making at all levels 22
**Table 13 Regression estimates of co-determinants of pension capital accumulation**

EQUATION 1 Ordinary Least Squares Estimation ****** **s*************.*********************.**** ******.*************.** ****s***.***** Dependent variable **in** ANUMEN 1508 observations used for estimation from 1 to 1508 **Regressor** Coefficient Standard Error T-Ratio[Prob] UNEMPTOT -2671.0 96.3553 -27.7201X.000] YRSTERT 1529.6 344.1266 4.4449X.0001 LASTEARN 4.9689 .10672 46.5608[.000] **CNT** 35612.1 1150.5 30.9523C.o00] R-Squared .73490 F-statistic **PF** 3,1504) 1389.8[.000] R-Bar-Squared .73437 _S.E._ of Regression 16219.8 Residual **Sum** of Squares 3.96E+ll Moan of Dependent Variable 72344.5 S.D. of Dependent Variable 31470.5 Maximum of Log-likelihood -16756.3 DW-statistic 1.9243

|EQUATION 2<br>Ordinary Least<br>***********************..***..***.**************.**|Squares Estimation<br>**************************.***-***.***|
|---|---|
|**Dependent**variable**is**ANUWOdEN||
|1687 observations used for estimation|from<br>1 to 1687|
|Regressor<br>Coefficient|Standard Error<br>T-Ratio[Prob]|
|UNE1BMPTOT<br>-2414.0|195.4153<br>-12.3530(.000]|
|YRSTERT<br>1198.6|296.0607<br>4.0486[.000l|
|LASTEARN<br>3.3226|.093010<br>35.7226[.000]|
|YRSCHILD<br>-146.6611|39.1864<br>-3.7427(.000]|
|C'NT<br>23107.2<br>*************-****************<br>***********R**************|1138.5<br>20.29641.0001<br>**-*---********-******************.**|
|R-Squared<br>.51585|F-statistic**P(**4,1682) 448.0376[.000]|
|R-Bar-Squared<br>.51470|S.E. of Regression<br>15884.7|
|**Residual Sum**ofSquares<br>4.24E+ll|Mean ot Dependent Variable<br>37921.8|
|S.D. of Dependent Variable<br>22602.0<br>DW-statistic<br>1.9703|Maximum of Log-likelihood<br>-18709.8|

These equations show, not surprisingly, that the total amount of pension capital accumulated is positively related to last full-time earnings and to years of tertiary education for both men and women, and negatively related to years of unemployment. For women, pension capital is also negatively related to the number of years with dependent children in the household. Last full-time earnings is the dominant variable in both equations, with an elasticity of .69 for men and .57 for women. The elasticity on UNEMPTOT for women is -.15, and on YRSCHILD it is - .10, so in terms of pension capital accrual having a child under 16 in the household has almost as much impact on women as a year of unemployment. The interruption of employment experienced by women who have children necessarily diminishes their chances of accumulating reasonable pension entitlements in a funded pension system.

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