rafmacalaba/datause-encoder-data huggingface dataset:
providers–who share interdependent relationships while undertaking their tasks of delivering public health and nutrition services. In the first phase, data were gathered from villagelevel respondents–Gram Panchayat (GP) politicians, frontline health and nutrition workers (ASHAs, AWWs, and ANMs at health sub-centers), citizens and leaders of women’s Self Help Groups (SHGs), an important social institution in this context. In the second phase, the survey was implemented to block and district-level respondents.

# **3.1 Geography of the survey**

Budget and implementation constraints required us to select a sample of districts rather than covering all 38 districts of Bihar. At the same time, we needed a large sample to be representative of the diversity within the state, and allow us to capture some variation across districtlevel institutional characteristics. These constraints led us to determine 16 as the number of districts in which to undertake the survey. The purposive selection of _which_ 16 study districts, from among the 38 of Bihar is described in the sampling materials posted at the following website, along with survey questionnaires and all results currently available at this stage of our data analysis: https://sites.google.com/view/stutikhemani/public-health-systems-survey Within each of the 16 districts, 4 blocks were selected using a random number generator, after stratifying by proximity to the main railway line. Within each block, 4 Gram Panchayats (GPs) were selected using a random number generator. However, in one block each in the districts of Lakhisarai and Buxar, 3 GPs instead of 4 were selected because the sampling protocol required a sufficient number of replacement respondents to be available, and these districts only had 3 GPs fulfilling the replacement requirement (more details in section on Respondents below). This yields a sample of respondents drawn from 16 districts, 64 blocks from within those districts, and 254 Gram Panchayats (GPs) from within those blocks. The sampled districts and blocks are shown in Figure 2.

11
A recent comprehensive case study of the effects of Intel’s investment in Costa Rica by Larrain et. al. (2000) finds evidence that local suppliers benefited substantially from Intel’s investment. Similar evidence exists for other sectors and countries and such evidence is discussed in great detail in Moran (1998 and 2001). For example, in the electronics sector, Moran (2001) notes that in Malaysia, foreign investors helped their local subcontractors keep pace with modern technologies by assigning technicians to the suppliers' plants to help set up and supervise large-volume automated production and testing procedures. In a broader study, Batra and Tan (2002) use data from Malaysia’s manufacturing sector to study the effect of multinationals on inter-firm linkages and productivity growth during 1985-1995. Their results show that not only are foreign firms more involved in inter-firm linkages than domestic firms but also that such linkages are associated with technology transfer to local suppliers. Such technology transfers were found to have occurred through worker training and the transmission of knowledge that helped local suppliers improve the quality and timeliness of supply.<sup>13</sup> Javorcik (2004) examines backward linkages and technology spillovers using data from the Lithuanian manufacturing sector during the period 1996-2000. She finds that firm productivity is positively affected by a sector’s intensity of contacts with multinational customers but not by the presence of multinationals in the same industry. Thus, her results support vertical spillovers from FDI but not horizontal ones. Furthermore, she finds that vertical spillovers are realized only when the technological gap between domestic and foreign firms is moderate. Blalock (2001) uses a panel dataset from Indonesian manufacturing establishments to check for the same effects. He finds strong evidence of a positive impact of FDI on productivity growth of local suppliers showing that technology transfer from multinationals indeed takes place. He also plausibly suggests that since multinationals tend to source inputs that require simple technologies relative to the final products they produce, local firms that manufacture such intermediates may be in a better position to learn from multinationals than those that compete with them.

> 13 Pack and Saggi (2001) analyze the willingness of purchasers to provide such technology transfer given the potential sales to competitors of the original purchaser.

27
One of the main advantages of the data from the EAC-I is that plot size is measured by GPS. This avoids measurement errors that lead to biased results. We aggregate crop yields at the household level. We convert production to kilograms (kg) using World Bank conversion factors. Interestingly, our data indicate that crop yields vary according to the extent to which polygyny is practiced in the commune. Indeed, as shown in Table 1 crop yields is respectively 1263.1 kg/ha, 925.9 kg/ha, 892.7 kg/ha and 1027.4 kg/ha for the EA located in the first, second, third and the fourth quartiles of polygyny’s prevalence. This distribution of crop yields by quartile of polygyny suggests a negative association between polygyny and crop yields. The economic literature documents an inverse relationship between farm size and productivity in developing countries (Bevis and Barrett, 2020; Barrett, 1996; Henderson, 2015). The difference in crop yields between highly polygynous and less polygynous communes could thus be explained by the variation in farm size. We define farm size as the sum of the land area (in hectares) that is currently cultivated by a household.

# **3.3.1 Data and Measurement for Mechanisms’ Outcomes**

This sub-section presents the variables we use as the mechanism of our results. Descriptive results of these variables are reported in Table 2, and are shown by monogamous and polygynous households. For this table, the marriage type is constructed at the household level based on the EAC-I data.

# **_(i) Livestock Sales_**

We use the detailed information on livestock ownership and production, included in the EAC-I, to construct a binary livestock sale variable equal to 1 if the household has sold one or more of its livestock and 0 otherwise. Table 2 shows that nearly a quarter of households (24.1%) in Mali have traded livestock. In terms of comparison, polygynous households are slightly more likely to sell livestock (24.8%) than monogamous households (23.8%). Livestock sales contribute to family food security by providing financial resources that can be used directly to obtain food or to develop and improve the family’s production system (Schiere and Katere, 2001).

16
the fact that vanilla requires special conditions that may not apply to a farm’s entire land, however small.

Figure 3 Specialization of Madagascar's vanilla farms <!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>First crop income share<br>0-0.10.1-0.20.2-0.30.3-0.40.4-0.50.5-0.60.6-0.70.7-0.80.8-0.9 0.9-1<br>Frequency (percentage)<br><!-- End of picture text --> Table 2 shows descriptive statistics for vanilla farmers compared to the EPM’s average which are given in annex table A1. It can be seen that most individual characteristics are close to nationwide averages except for a higher proportion in the “medium remoteness” category and, more importantly, for incomes which stands 15% higher. Given the very low nationwide average, this is not much, though it implies that improvements in producer prices received by vanilla farmers could contribute to widen rural inequality rather than reduce it.

Table 2 Descriptive statistics, vanilla farming

|All, 1993-<br>2001|Vanilla|
|---|---|
|Sample size<br>9’504|578|
|Family size<br>5.12|5.08|
|HH head gender (proportion of HHs in category)||
|Male<br>0.85|0.87|
|Female<br>0.15|0.13|
|HH head education (proportion of HHs in category|)|
|None<br>0.31|0.25|
|Primary<br>0.54|0.58|
|Secondary<br>0.16|0.16|
|Cropland surface (ha)<br>1.51|2.72|
|Land tenure (proportion of HHs in category)||

10
32

**Table 2. Global Trade Reduction Indexes, Aggregate and Due to Production and Consumption Sides of the Economy, 2000-04 (percent)**

||**Aggre**<br>**TRI**|**gate**|**TRI,**<br>**production**<br>**component**|**TRI,**<br>**consumption**<br>**component**|
|---|---|---|---|---|
|**Grainsand tubers**||**10**|**7**|**12**|
|Rice||43|42|44|
|Wheat||4|2|7|
|Maize||3|-1|7|
|Cassava||10|10|9|
|Barley||4|3|5|
|Sorghum||6|3|9|
|Millet||-22|0|-43|
|Oat||9|15|3|
|**Oilseeds**||**3**|**2**|**4**|
|Soybean||6|2|10|
|Groundnut||-10|-6|-14|
|Palmoil||-3|0|-7|
|Rapeseed||12|13<br>|12|
|Sunflower||13|15|12|
|Sesame||32|39|26|
|**Tropical crops**||**11**|**10**|**13**|
|Sugar||55|52|58|
|Cotton||-4|-7|-1|
|Coconut||9|8|10|
|Coffee||2|0|4|
|Rubber||-3|-4|-1|
|Tea||17|12|21|
|Cocoa||33|35|31|
|**Livestock products**||**22**|**23**|**21**|
|Pigmeat||8|9|7|
|Milk||45|48|41|
|Beef||32|29|35|
|Poultry||18|16|21|
|Egg||7|5|9|
|Sheepmeat||20|19|21|
|Wool||0|0|0|
|**All of the above**|||||
|**28 commodities**||**12**|**11**|**13**|

Source: Authors’ calculations based on NRA and CTE estimates in Anderson and Valenzuela (2008).
returns to trend by 1984. The labor wedge worsens substantially and tracks output closely. The investment wedge also follows output closely until 1984; afterwards, output recovers but the investment wedge does not. The risk premium wedge, on the other hand, remains at until mid-1983; then it worsens slightly but recovers by the second quarter of 1984. The solid blue lines in Figure 4 displays Canadian data. Investment and labor co-move closely with output, although investment recovers more slowly than output does. Net exports, on the other hand, increase from 0 to over 4 percent and remain in surplus for several years.

Consider rst counterfactual economies with only one active wedge. The upper panel of Figure 4 shows the prediction of the models with just the eciency wedge and just the labor wedge. The model with the eciency wedge alone misses the drop in output, labor, and investment, and predicts a small decline in net exports. The model with just the labor wedge matches hours remarkably well and accounts for about two thirds of the drop in output. This model, however, misses investment and net exports. The model with just the investment wedge, shown in the lower panel of Figure 4, completely misses output and labor, but matches investment and net exports reasonably well. Finally, the model with just the risk premium wedge predicts an increase in output, an increase in labor, misses the behavior of investment, but matches net exports well.

Consider now models with all wedges but one. The top panel of Figure 5 displays the predictions of the models with no eciency wedge and with no labor wedge. The lower panel of that gure shows the models with no investment wedge and no risk premium wedge. The model with no eciency wedge matches labor, investment, and net exports closely, but predicts a somewhat smaller decline and slower recovery in output compared with those in the data. The model with no labor wedge completely misses output and labor, matches investment closely, and overstates the increase in net exports. The model with no investment wedge matches output and labor closely, although it predicts a somewhat faster recovery than that in the data. This model, on the other hand, misses the behavior of investment and net exports. Consider, nally, the model with no risk premium wedge. This model misses net 30
# **Table A3.  Regression analysis: linear probability model on correlates of upward mobility for the time period 1999 to 2004 and 2006 to 2011**

|||**Period 1999-20**|**04**||**Period 2006-20**|**11**|
|---|---|---|---|---|---|---|
||**Exiting**<br>**$2.5/day**|**Exiting**<br>**$5/day**|**Upward**<br>**mobility**|**Exiting**<br>**$2.5/day**|**Exiting**<br>**$5/day**|**Upward**<br>**mobility**|
|Female headed households|-0.0294|0.0273|0.00127|-0.0771|-0.0678**|-0.0358|
||(-1.26)|(1.78)|-0.06|(-1.53)|(-2.74)|(-1.42)|
|Age 25-29 (base)|||||||
|Age 30-34|0.0544|0.0200|0.0808*|0.0594|-0.0111|-0.00292|
||(1.42)|(1.02)|-2.36|(0.81)|(-0.23)|(-0.06)|
|Age 35-39|0.0914*|0.0316|0.111***|0.0842|0.064|0.0524|
||(2.55)|(1.67)|-3.44|(1.23)|-1.37|-1.11|
|Age 40-44|0.109**|0.0327|0.0966**|0.123|0.117*|0.0722|
||(2.93)|(1.63)|-2.92|(1.67)|-2.31|-1.49|
|Age 45-49|0.0897*|0.0359|0.0964**|-0.0294|0.0793|0.00708|
||(2.37)|(1.81)|-2.87|(-0.41)|-1.58|-0.15|
|Age 50-54|0.0892*|0.0341|0.0721|-0.175*|-0.0227|-0.118*|
||(2.09)|(1.45)|-1.93|(-2.41)|(-0.46)|(-2.52)|
|Age 55+|0.140|0.114*|0.225**|-0.153|-0.0474|-0.0971|
||(1.64)|(2.15)|-3.1|(-1.42)|(-0.75)|(-1.67)|
|Rural|-0.0111|-0.0149|-0.0234|-0.0845|-0.0438|-0.0870***|
||(-0.38)|(-0.85)|(-0.89)|(-1.46)|(-1.67)|(-3.43)|
|Household size|-0.0449***|<br>-0.0170*|-0.0352***|-0.0242|-0.0342***|-0.0170*|
||(-5.44)|(-2.42)|(-3.92)|(-1.78)|(-5.02)|(-2.13)|
|Household dependency rate<br>|||||||
|(children under 14)|-0.0296|-0.0428**|-0.0425|-0.0619|-0.0125|-0.0108|
||(-1.25)|(-2.98)|(-1.89)|(-1.64)|(-0.45)|(-0.44)|
|Household dependency rate (adults|||||||
|over 65)|-0.159***|-0.0613*|-0.184***|-0.719***|-0.104|-0.160**|
||(-3.57)|(-2.51)|(-4.43)|(-4.60)|(-1.28)|(-3.00)|
|_Secondary education (base)_|||||||
|Primary education|-0.166***|-0.0613***|-0.173***|-0.0823|0.0284|0.0523|
||(-5.96)|(-4.15)|(-6.39)|(-1.65)|-1.17|-1.93|
|Tertiary education|0.143***|0.139***|0.107***|0.267**|0.196***|0.0402|
||(3.53)|(5.17)|-3.42|(3.17)|-3.83|-1.01|
|_Household employment rate in:_|||||||
|Manufacturing|0.0801|0.0853**|0.0797|0.0313|0.201**|0.0694|

34
Table 2. Knowledge about the PEP Program Control Group Only

||Average<br>(1)|STD<br>(2)|Observations<br>(3)|
|---|---|---|---|
|_Panel A. People with correct knowledge about PEP_||||
|Knowledge about PEP [=1]|0.446|0.497|543|
|Knowledge of who can access the PEP [=1]|0.521|0.501|261|
|_Panel B. Demographic Characteristics of People with_|_knowledge_|_about PE_|_P_|
|Male [=1]|0.54|0.50|242|
|Age|48.12|15.24|242|
|Education: Primary school or less [=1]|0.08|0.28|242|
|Education: Secondary school [=1]|0.30|0.46|242|
|Education: Technician, university or more [=1]|0.62|0.49|242|
|Married or Cohabitating [=1]|0.52|0.50|242|
|Economic Strata: Low [=1]|0.43|0.50|242|
|Economic Strata: Medium [=1]|0.33|0.47|242|
|Economic Strata: High [=1]|0.23|0.42|242|
|Employed [=1]|0.89|0.31|151|
|Labor Contract [=1]|0.44|0.50|124|
|Student [=1]|0.13|0.34|242|
|Political Interest [=1]|0.86|0.34|242|
|Voted in 2019 Mayoral Elections [=1]|0.75|0.43|242|
|Voted in 2002 Presidential Elections [=1]|0.81|0.39|242|

# **VII. A.2 Once informed, do natives change their voting intentions?**

To evaluate the effects of the program, we estimate the following specification: where _i_ represents the individual and _Y_ denotes the primary outcomes of interest as preregistered. These outcomes include: (i) voting intentions; (ii) self-reported measures of social capital (positive reciprocity, negative reciprocity, altruism, and trust);<sup>18</sup> (iii) experimental measures of altruism toward migrants through a dictator game; and (iv) measures of political attitudes toward migrants. Finally, _X_ includes controls for the stratification 18All the questions have been validated in Colombia and taken from Gallup surveys as reported in Falk et al. (2018).

35
2004, the base year of the GTAP 7 Data Base. The bilateral migration data is based on a new migration data base by Özden et al. (2011) and the remittances data were also updated, using the IMF's balance of payments statistics on remittances and workers compensation.

Further adjustments are also made to the database in order to improve the distribution of unskilled foreign workers across sectors. Estimates of the number of unskilled foreign workers by industry were obtained for Malaysia, Korea, Singapore, and Thailand from Yean and Siang (2010); Hur (2010); Teng (2010); and Holumyong and Punpuing (2010) respectively, see Table 3. For other ASEAN countries – Indonesia, Philippines, and Vietnam – we use the average distribution of unskilled foreign workers by industry of the aforementioned countries as a proxy. For all other countries foreign and domestic workers are assumed to be allocated across sectors in the same proportions according to the underlying total data available in the GTAP Data Base. The choice to redistribute unskilled workers by industry for Indonesia, the Philippines and Vietnam, but not for the other economies, reflects the fact that Asia is the focus of this paper, and there is reason to believe that the sectoral relationships in the other Asian economies exist across all Asian economies. ASEAN thus tends to use unskilled foreign workers more intensely in agriculture and food processing; while Singapore and South Korea use unskilled foreign workers in food processing, light and heavy manufactures, construction and services, in some cases.

**Table 3: Distribution of unskilled foreign workers across sectors in ASEAN countries**

||**Manufacturing**|**Construction**|**Services**|**Agriculture**|
|---|---|---|---|---|
|**Malaysia**|35.12|14.79|24.74|25.35|
|**South Korea**|41.61|22.30|34.16|1.93|
|**Singapore**|25.00|26.00|49.00|0.00|
|**Thailand**|15.39|16.75|31.31|36.55|
|**Average (Indonesia, Philippines**<br>**and Vietnam)**|27.88|19.01|33.15|20.26|

Sources: Yean and Siang (2010); Hur (2010); Teng (2010); and Holumyong and Punpuing (2010)

# **3.2 Scenarios**

The model simulates the world economy from 2007 to 2050, under migration policies that allow labor to move freely within the EAP region in response to changes in real wages. These labor movements are in addition to, and the result of, the demographic and labor force 14
29

# **Reference&**

- Acharya, Sarthi and V.G. Panwalkar (1988): "The Maharashtra Employment Guarantee Scheme: Impacts on Male and Female Labour". Paper prepared for the Population Council Program on Women's Roles and Gender Differences.

- Basu, Kaushik (1981): "Food for Work Programmes: Beyond Roads that Get Washed Away", Economic and Political Weekly 16:37-40.

- Besley, Timothy and Stephen Coate (1990): "Workfare vs. Welfare: Incentive Arguments for Work Requirements in Poverty Alleviation Programs", mimeo, Woodrow Wilson School, Princeton University.

- Bhende, M.J., T.S. Walker, S.S. Lieberman and J.V. Venkatarpm (1990): "The EGS and the Poor: Evidence fr l Longitudinal Village Studies", Processed, International Crops Reseaich Institute for the Semi-Arid Tropics.

- Brown, R.L., J.Durbin and J.M. Evans (1975): "Techniques for Testing the Constancy of Regression Relations over Time", Journal of the Royal Statistical Society B 37:149-92.

- Dandekar, Kumudini and Manju Sathe (1980): "Employment Gudrantee Scheme and Food for Work Programme". Economic and Political Weekly 15 (April 12): 707-713.

- Dreze, Jean (1990): "Famine Prevention in India", in Jean Dreze and Amartya Sen (eds), The Political Economy of Hunger. Oxford: Oxford University Press, forthcoming.

- Dreze, Jean and Amartya Sen (1990): Hunger and Public Action. Oxford: Oxford University Press.

- Echeverri-Gent, John (1988): "Guaranteed Employment in an Indian State. The Maharashtra Experience". Asian Survey 28:1294-1310.

- Engle, Robert F. and C.W.J. Granger (1987): "Co-Integration and Error Correction: Representation, Estimation and Testing". Econometrica 55:251-276.

- Government of Maharashtra (GOM) (1984): Report of the Fact Finding Committee on Regional Imbalance in Maharashtra. Bombay: Planning Department, GOM.

(1987): Selected Indicators for Districts in Maharashtra and States of India 1984-85. Bombay: Directorate of Economics and Statistics, GOM.

(1989): Selected Indicators for Districts in Maharashtra and States of India 1986-87. Bombay: Directorate of Economics and Statistics, GOM.
69 the variation in the effective use of this input. The estimates sugest that the marginal product of the temporary labor is postive and that this easily adjustable input is not used to the point where its marginal product is zero. However, as we will show, the significance of this estimated coefficient drops in the industry-specific regressions with fewer degrees of freedom.

The estimated coefficient on the time trend is negative and significant, suggesting that, _ceci paribus,_ firms have tended to experience a _5-7_ percent decline in total factor productivity per year in the 1980s. While this finding is consistent with the view that the highly regulated labor market and a noncooperative system of industrial relations are having a negative productivity impact, many other factors undoubtedly contribute to this potentiaUly serious problem.

# 62 **IndustrySpecific** Poduction Function Estdmates

Tables 6A-6.13 report the IV and OLS estimates for the food industry, chemical and extraction industry, mechanical industry, textile, clothing and leather industry, and paper and wood industry. The principal advantage of these industry estimates is that they allow for inter-industry differences in technology and in the impact of various structural and policy factors. As such, they more accurately reflect the factors that determine valueadded in each industry. The main drawback of estimating the regressions by industry is that the number of observations per industry and the statistical significance of the estimated coefficients are naturally smaller in industry-specific runs than in the regressions based on the entire sample.

Because the number of firms in each industry is considerably less than the total number of firms in the overall (all industry) sample, we did not rely on the canonical correlation procedure to create instrumental variables for the industry-specific regressions; rather, the instruments used in these IV regressions were firm dummy variables interacted with tirne and with the value-added deflator. The predictive power of these instruments was quite high and, as can be seen from Tables 6.4-6.13, the OLS estimates are only marginally more significant than their IV counterparts.

With several exceptions, the industry estimates parallel the overall (sample-wide) findings discussed in section 6.1. The closest