rafmacalaba/datause-encoder-data huggingface dataset:
from agriculture into a non-agricultural sector. Our sample hence consists of those individuals who are still in agriculture and those who have moved out of agriculture within the last year.

Estimating such a model is only possible because the PNAD contains a special section on employment histories. This section allows identifying those who move out of agriculture and, very important for our undertaking, the characteristics of the movers at the time of moving, eliminating thus a source of endogeneity often present in migration models. For all the movers _before_ they moved out of agriculture we know, for example, which type of land right they had if they were self-employed. To our knowledge, this information has not yet been exploited.

The estimated model combines the idea of the mover-stayer model from the migration literature<sup>16</sup> with the approach to modeling occupational dynamics applied in typical income generation microsimulations.<sup>17</sup> In the latter approach, bi- or multinomial choice models are estimated on the entire population. In our case, this would imply comparing the characteristics of those in agriculture with those in non-agricultural sectors. Instead, the mover-stayer model compares the characteristics of only the movers with those of the stayers. This appears to be more appropriate in the current setting, as our goal is to simulate the transition from agriculture to non-agriculture.

Let _move_ be a dichotomous variable that assumes a value of 1 if the individual has moved out of agriculture in the last year, and 0 if the individual has stayed in agriculture. As indicated by equation (23), an individual will move ( _move_ = 1) if the utility ( _U_ ) associated to this choice is higher than the utility of staying in agriculture.<sup>18</sup> Otherwise, the individual will stay in agriculture (24).

As indicated by equations (25) and (26), the utility of moving depends on a set of explanatory variables X and a random error term _ε_ . A linear relationship between utility and the explanatory variables is assumed. The subscripts _msh_ and _msnh_ refer to heads and

> 16 See for example Nakosteen and Zimmer (1980).

> 17 See for example Robilliard, Bourguignon, and Robinson (2001) and Bussolo and Lay (2003).

> 18 The utility of staying in agriculture is assumed to be 0. This is typical identifying assumption of the logit.

17
countries in the two methodology papers on synthetic panels (DLLM, 2014; Dang and Lanjouw, 2013), we believe these results are promising for the application of synthetic panel data to help us better understand welfare dynamics, particularly in the absence of true panel data.

# **3.2.3. Welfare Dynamics for India during 2004-2009**

We turn next to investigating the consumption dynamics for India, where the synthetic panel data are constructed using two cross sections of the National Sample Survey (NSS) in 2004 and 2009. Similar to the US, there is no single national poverty line for India. Thus we construct a population-weighted monthly national poverty line from those for urban and rural areas in the Tendulkar report (GOI, 2009), which is 483 rupees per capita in 2004 prices. This poverty line yields a national poverty rate of 38 percent in 2004/05, which is close to the rate of 37.2 percent in the cited report. All expenditures data in 2004 are then converted to a common scale using as deflators the ratios of this national poverty line and the state poverty lines (which also vary between urban and rural areas). We also convert all expenditure data in 2009 to 2004 prices, using as deflators the state poverty lines in the two years.

Given the national poverty line of 483 rupees per capita in 2004 prices, and using the second definition of the vulnerability line ( _P_<sup>_2_</sup> ), we estimate its range of vulnerability index as 15 percent to 43 percent. These higher values of the vulnerability index for India—compared to the corresponding ranges of 5 to slightly more than 20 percent for the US and Vietnam (Table 2)— suggest that households in India are more vulnerable to falling into poverty than the latter two countries. For illustration purpose, we then fix _P_<sup>_2_</sup> at 20 percent and use its corresponding monthly vulnerability line of 998 rupees per capita, which is somewhat higher than the higher 27
**<u>Table 7:  Labor allocation for fuelwood collection by women 10 years and older</u>**

||(1)Collect|(2)Hours|(3)Collect|(4)Hours|(5)Collect|(6)Hours|
|---|---|---|---|---|---|---|
||Ove|rall|Le|ss Biomass|M|ore Biomass|
|Age inyears|0.011***|0.009|0.008*|0.003|0.015**|0.017|
||(0.004)|(0.007)|(0.005)|(0.009)|(0.006)|(0.013)|
|Age Square|-0.0001***|-0.0001|-0.0001**|-0.0001|-0.0002**|-0.0002|
||(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0002)|
|Head Education: Snr.|||||||
|Prim or more|-0.053**|0.088*|-0.073**|0.110*|-0.012|0.009|
||(0.026)|(0.049)|(0.033)|(0.062)|(0.045)|(0.085)|
|HH Size|-0.014***|0.015|-0.021***|-0.003|0.004|0.038**|
||(0.005)|(0.010)|(0.006)|(0.012)|(0.009)|(0.016)|
|Real Value of Assets<br>'000|-0.002***|0.000|-0.003***|0.002|-0.001*|-0.001|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|
|Wet Season & Wet|||||||
|Area Interaction|0.009|0.042|0.003|0.056|0.026|-0.014|
||(0.021)|(0.044)|(0.024)|(0.051)|(0.041)|(0.088)|
|Dry Season and Dimba|||||||
|<br>Area Interaction|0.153**|-0.150|0.068|-0.105|0.312***|-0.251|
||(0.065)|(0.108)|(0.081)|(0.148)|(0.121)|(0.174)|
|Owned & uncultivated<br>last season|0.117***|0.021|0.129***|0.084|0.090***|-0.052|
||(0.021)|(0.040)|(0.031)|(0.055)|(0.030)|(0.061)|
|Total hectares of trees|0.259|-0.378|1.392**|-0.776|-0.124|0.229|
||(0.259)|(0.449)|(0.549)|(0.663)|(0.344)|(0.666)|
|Predicted Real Avg<br>Wage for Women|-0.001***|0.001|-0.001***|0.000|-0.001**|0.002*|
||(0.000)|(0.001)|(0.000)|(0.001)|(0.000)|(0.001)|
|Number of <5yr|||||||
|<br>children|0.076***|-0.075**|0.095***|-0.053|0.038|-0.085|
||(0.020)|(0.036)|(0.024)|(0.046)|(0.034)|(0.061)|
|Married for age 12+|0.175***|-0.074|0.201***|-0.049|0.147***|-0.176*|
||(0.031)|(0.063)|(0.038)|(0.080)|(0.053)|(0.106)|
|Rural Centre|-0.033|0.359***|0.020|0.278**|0.019|0.388***|
||(0.045)|(0.085)|(0.063)|(0.119)|(0.072)|(0.139)|
|Rural South|-0.073|0.410***|0.088|0.322***|-0.206***|0.489***|
||(0.045)|(0.083)|(0.065)|(0.125)|(0.066)|(0.137)|
|Wet Season Dummy|-0.105***|-0.001|-0.107***|-0.046|-0.120**|0.179|
||(0.032)|(0.064)|(0.040)|(0.079)|(0.058)|(0.122)|
|ADMARC Market|||||||
|Distance KM|0.0002|0.0011**|0.0003|0.0010**|-0.0020|0.0091***|
||(0.0003)|(0.0005)|(0.0003)|(0.0005)|(0.0015)|(0.0029)|
|Distance to Asphalt|||||||
|<br>Road KM|-0.0011***|0.0004|0.0005|-0.0017|-0.0018***|0.0014|
||(0.0004)|(0.0009)|(0.0005)|(0.0013)|(0.0007)|(0.0015)|
|Elevation|-0.00010**|-0.00004|0.00000|-0.00007|-0.00029***|0.00011|
||(0.00004)|(0.00008)|(0.00005)|(0.00011)|(0.00008)|(0.00016)|
|**Biomass (m3/ha)**|**0.001****|**-0.001 **|**0.009*****|**-0.018*****|**0.005*****|**-0.006*****|
||(0.001)|(0.001)|(0.003)|(0.005)|(0.001)|(0.002)|
|Pi f Mi Gi|||||||
|rce o aze ran<br>MK/Kg|0.002|-0.018|-0.011|-0.015|0.020*|-0.026|
||(0.006)|(0.013)|(0.008)|(0.016)|(0.011)|(0.023)|
|Recent illness/injury|0.023|-0.084*|0.035|-0.061|0.013|-0.135|
||(0.027)|(0.050)|(0.034)|(0.062)|(0.047)|(0.089)|
|Do you suffer from a<br>|||||||
|chronic illness|-0.035|-0.149**|-0.026|-0.188**|-0.047|-0.122|
||(0.040)|(0.075)|(0.048)|(0.088)|(0.072)|(0.144)|

42
Despite the progress that has been made, more work remains to be done in modeling minimum wages theoretically– in particular, enriching the model to include enough heterogeneity, most importantly, by placing some minimum wage workers in poor families and others in non-poor ones – and estimating the relevant parameters empirically. The studies reviewed by Cunningham (2007), Freeman (2007), and Svejnar (2007) offer guidance in this respect.

# **<u>Topics for Future Research</u>**

Based on this review as well as other investigations, I would highlight six priority research areas. Within each, I pose a number of questions, highlighting in **bold** one question in each category which in my view merits the highest priority:

# 1. Growth and labor market outcomes

- Using the indicators recommended in World Bank (2007a), **to what extent does macroeconomic growth improve labor market conditions?**

# **Through what channels?**

- How do the outcomes in one country compare with those in another? What explains the differences?

- How do changes in labor market outcomes for one population sub-group compare with those of another?

- There is a perception that economic growth has not necessarily translated into desirable labor outcomes. Is this perception right? Where? Under what circumstances?

- Growth in many countries is characterized as jobless. To what extent is it the case that growth is indeed jobless for the population as a whole? Or is it that growth has not been accompanied by growth in key sectors, e.g., the formal sector?

- Has the relationship between growth and labor market opportunities for the poor changed as the world has become more globalized?

# 2. Demand for labor

- **Why is the labor demand curve in a country positioned where it is?**

- Which components of the business environment are important influences on domestic and foreign investment?

60
The evaluation has several phases over several years designed to see who takes advantage of the scheme, how it affects their and their families’ economic decisions and outcomes, and how it impacts their communities and nation. The first step, which this paper describes, was a base-line survey conducted in late 2007 and early 2008, designed to record living standards prior to the introduction of the scheme, the knowledge and access that potential workers have to the scheme and the characteristics of those who seek to take it up. Respondents included workers who had been selected to go to New Zealand, those who aspired to do so but had not yet been selected and those who were not interested, along with their community leaders, recruitment agents and certain members of the Vanuatu government. The survey was conducted on three of Vanuatu’s islands – Efate (where the capital, Port Vila is located), Tanna, and Ambrym. Subsequent surveys of both the workers and their families will allow us to identify the scheme’s effects on the lives and prospects of the residents of Vanuatu in a formal and rigorous way.

The initial survey is useful mainly as a base-line for future analysis, but it also contains useful and interesting information in its own right. Ultimately the development impact of the program will depend in large part on who (and how many) of the NiVanuatu participate. Our baseline survey allows us to detect early on the characteristics of the participants, and to start to infer whether any differences between them and nonparticipants are due to the differences in the decision to apply for the program, or to the selection process among applicants. Part of this is to see how broadly and precisely knowledge of the scheme has spread and whether its workers are drawn from among the poorer or better-off sections of society. We find the main participants in the program to be males in their late 20s to early 40s, who are literate in English, do not have schooling

- 3 -
# **Acknowledgements**

This document was made possible by generous funding from UK Aid. The authors would like to thank Raka Banerjee, Flavio Bolliger, Klaus Deininger, Sam Desiere, Stein Holden, Talip Kilic, Gbemisola Oseni, Marco Tiberti, Abhilasha Vaid, Colin Williams, and Ismael Yacoubou Djima for their inputs during fieldwork implementation and preparation and review of this paper and companion guidebook (Carletto et al., 2016). Fieldwork for the methodological experiments was executed in partnership with the Central Statistical Agency of Ethiopia, the National Bureau of Statistics in Nigeria, and the Ministry of Agriculture and Natural Resources in Zanzibar, Tanzania. Our greatest appreciation goes to these partners for their dedication to the projects. This paper has large areas of overlap with a companion guidebook (Carletto et al., 2016), which serves as a practical guide for implementation of land area measurement in household surveys.

2
# **3.5 Sub-samples and characteristics of adjacent plots**

Our subsequent analysis will rely on three subsamples of borewells: First, we have the _estimation_ sample, consisting of 1646 observations from the districts of Mahbubnagar, Guntur, Kadapa, and West Godavari. As the name implies, this sample will be used to estimate the structural econometric model as described in the next section. Borewells from the remaining districts shall comprise two distinct _holdout_ samples reserved for model validation. Following Keane and Wolpin (2007), the choice of holdout districts is dictated by their outlier status with respect to the first and second moments of groundwater supply. The top two panels of Figure 2 show that the coefficients of variation of end-of-season flow (second moment) and pipe-widths (first moment) in the holdout districts are indeed well beyond the median values for the sample as a whole. In the case of Anantapur, the most arid district, groundwater uncertainty is extremely high and borewells have median pipe-width of only two inches. By contrast, in the much wetter and groundwater abundant East Godavari, median pipe-width is 4 inches, far above that of the other five districts, and CVs are also among the lowest.

Table 3: Characteristics of Adjacent Plots by Subsample

|||**Holdout **|**Samples**||
|---|---|---|---|---|
||**Estimation**|**Anantapur**|**E. Godavari**|**Total**|
|Mean number per _adjacency_|3.46|3.64|3.25|3.45|
|Mean area (acres)|3.16|2.59|3.20|3.04|
|% left fallow in _rabi_|11|40|2|14|
|% irrigated in _rabi_ by|||||
|_•_ reference borewell|34|16|46|33|
|of which, % irrigated under|||||
|-joint ownership|31|96|17|33|
|-land lease|7|2|4|6|
|-water sale|62|2|80|62|
|_•_ own borewell|48|43|41|46|
|_•_ other borewell|12|1|13|10|
|% owned by|||||
|_•_ brother|12|24|5|12|
|_•_ other relative|11|11|4|10|
|_•_ unrelated/same caste|45|31|57|45|
|_•_ unrelated/diferent caste|32|34|34|33|
|Number of plots|4992|1106|1195|7293|

Descriptive statistics on the nearly 7300 adjacent plots, shown in Table 3, provide insights into the nature of groundwater transfers and how they vary across subsamples. Overall, a 19
# 4 Conclusion

We started the paper with the presumption that distance would impose a cost on micro...nance development. However, how the cost would be transmitted to borrowers was an open question. The analysis conducted here suggests that the cost is born in part by borrowers who face higher interest rates, and more constraints and delays to obtain a loan, but is certainly also faced by marginal borrowers who are then excluded from the semi-formal credit market. The marginal borrower is moving higher up in the income distribution as distance increases. Moreover, we argue that adverse selection and moral hazard also plague the credit market, increasing transaction costs. Finally, we speculate that distance is also associated with the rarity of micro...nance institutions, putting providers of ...nance in a monopoly situation that acts as a monitoring tool for borrowers: reputation of creditworthiness is more important to preserve as distance increases.

We do not make any normative statement on whether the micro...nance sector is developed enough or not. The results shown in this paper bring back the tension that exists between outreach and ...nancial sustainability. If micro...nance institutions need to be sustainable, they need to manage their portfolio carefully. Distance will de facto impose a risk on their portfolio, so that they will need to screen the demand for ...nance accordingly. There is therefore an intrinsic contradiction between outreach and sustainability that is exacerbated by low population density, making distance an important parameter in this tradeo¤.

Our ...ndings also suggest that limiting outreach has important consequences, as distant clients are more likely to be traders, while producers (especially breeders) are more likely to be left out of the credit market. Beyond the e¢ ciency concern, we also raise the equity concern whereby the poorest of the poor might be more likely to be excluded as they live further away from economic centers, and are engaged in activities that would not be considered creditworthy for MFIs. Screening policies implemented by MFIs to maintain the quality of their portfolios will potentially hurt the poor (see also Paxton and Fruman, 1997). The tradeo¤ between sustainability and outreach therefore deserves more attention in academic research and policy discussions.

14
The data (Table 3) further suggest that manufacturing becomes a leading sector in towns in Zhejiang, whereas in other two provinces agriculture remains the leading sector. About 40 percent people employed in manufacturing sector in Zhejiang, whereas in Henan and Sichuan, there were only 12 percent and 14 percent employed in the manufacturing sector respectively. But, retail and hotel sector seem well developed in these two provinces. This could also have created more job opportunities. It is also interesting to note that the percentage of people employed in the local government institutions were higher in the less developed regions such as Henan and Sichuan than in the developed region of Zhejiang.<sup>3</sup> Table 3 Employment by industrial sector in towns in three provinces: 2000

|Province|1|2|3|4|5|6|7|8|9|
|---|---|---|---|---|---|---|---|---|---|
||||(|%)||||||
|Zhejiang|16.54|6.76|39.50|4.74|17.38|1.27|4.59|5.60|3.54|
|Henan|43.21|5.37|11.92|4.36|12.68|1.73|2.64|8.44|9.46|
|Sichuan|32.50|6.55|14.07|5.33|18.12|1.66|4.32|10.00|7.22|

Sources: calculated from Tabulation on the 2000 Population Census of the P. R. China, Table 4-1b, page 989-1042.

Note: 1, agriculture; 2, construction; 3, manufacturing; 4, transportation, and telecommunication; 5, retail and hotel; 6,finance; 7, services; 8, health, education, and research and development; 9. government institution.

The living conditions are also much better in towns in Zhejiang than in other two provinces. Table 4-1 presents the availability of tapped water and composition of fuel resources for cooking in households in towns in three provinces. Data show that in Zhejiang and Sichuan, majority of households in towns were equipped with tapped water, whereas in Henan only about half of town residents had access to the tapped water, which was lower than the national average of 70 percent.

Furthermore, most of households in towns in Henan (two-thirds) used coal for cooking, about twice as much as likely the national average (one-third). The percentage of using coal for cooking in Sichuan was slightly higher than the national average. In Zhejiang, gas became the major fuel source for cooking, which was about twice as much as likely the national average (41%). Nationwide, a little over 20% of households were using wood/grass as their cooking fuels in towns in China, and the level of using wood/grass as fuels for cooking were found to be lower in all three provinces examined in this paper.

> 3 Scholars have emphasized that at the local town level there was a “local state corporation” in parts of rural China, an entity which related local government closely with enterprises and people (Oi 1992, 1999).  This might explain why some of rural towns had higher share of population working in the local government institutions.

5
**11**

|||**La**|**Table**5<br>**bor Regulations**|||
|---|---|---|---|---|---|
|_Country_<br>____|_Payroll_<br>_Taxes'_<br>__________<br>_________|_Severance_<br>_Paymentsb_|_Employment_<br>_Growth_<br>**_(1992-1995)c_**|_Unemployment_<br>_Rate (1996)_|_Collective_<br>_Bargaining_|
|**Australia**|27.8|Low|1.0|9.0|Centralized|
|**Chile**|20.9|Low|2.3|6.3|Firm Level|
|**Japan**|22.9|None|0.6|2.5|Firm Level|
|**Malaysia**|24.3|Low|3.3|2.8|Firm Level|
|**New Zealand**|_11.5_|None|1.4|8.0|Firm Level|
|**United States**|20.1|None|1.8|_5.5_|Firm Level|
|**Argentina**|50<br>0 <sup>d</sup>|High|-0.7|17.2|Centralized|
|**France**|54.7|High|-0.4|11.6|Centralized|
|**Italy**|52.8|High|-1.7|10.2|Centralized|
|Spain|38.2|High|-1.6|22.4|Centralized|

**a** Payroll taxes are firm donations plus obligatory personal contributions. The values for France, Spain, Italy and Japan correspond to 1994, those for Malaysia to 1995, and those for Argentina and Chile to 1996.

b Severance payments based on OECD indexes.

Employment growth is measured as annual average percentage growth.

d Argentina amended its labor laws in 1996, and payroll taxes now average 41.0.

Source: Guasch (1997)
refinance their debts after the construction stage, there will be a growing need to tap other sources of finance, especially long-term finance.

The Chilean private pension system offers an attractive source of funds. These institutions could provide much needed long-term finance. However, pension funds are tightly regulated and in the initial stages of the programme these institutions were legally constrained from investing infrastructure projects. In particular, the lack of investment grade rating for bonds or other financial instruments issued by concession companies was an obstacle.

In order to facilitate investments from pension funds and insurance companies-as well as to expedite the participation of banks-legal changes to financial and infrastructure regulations were introduced between 1995 and 1996. These reforms, among other things, increased the lending allowances of commercial banks for infrastructure projects from 5% to 15% of capital and reserves. In addition, they opened the door for pension funds and insurance companies to invest in bonds without history. As a result of these reforms, a new long-term financial instrument-the Infrastructure Bond-was created.<sup>4 4</sup>

**Table 6.1 Infrastructure Bonds emissions**

|**Concession**|**Currency**|**Amount**|**Date of issuance**|**Rating'**|
|---|---|---|---|---|
|AMB<br>International<br>Airport|US$ denominated<br>bond|US$213 million|1999|AAA|
|Route 5, Talca-<br>Chillan|Domestic<br>denominated<br>inflation linked bond|US$ 185 million|1999|AAA|
|Route 5,<br>Santiago Los<br>Vilos|Domestic<br>denominated<br>inflation linked bond|US$ 230 million|pending approval<br>by financial<br>regulator|A|
|Nogales<br>Puchuncavi|Domestic<br>denominated<br>inflation<br>linked bond|US$ 12 million|Advance stage of<br>preparation||

Source: MOP ' Rating by Standard & Poor's.

To date, there have been two bond emissions, both in early 1999 (See Table 7.1). The consortium of the AMB International Airport in Santiago was successful in placing a dollar denominated bond in the North American market for US$213 million. These bonds were rated as AAA by Standard and Poor's. Another bond issue was successfully placed in the domestic market in February of this year. Pension funds were for the most part the purchasers of these domestic currency financial instruments. These bonds were also rated as AAA by Standard and Poor's.

> 4 See Ministry of Public Works and Ministry of Finance (1998).

44