rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 5 (cont’d): Inefficiency Parameters from the Stochastic Frontier Translog Production Function</u>**

||||**Model**|||
|---|---|---|---|---|---|
||**No traits**|**Alltraits**|**Pre-selection **|**LARS**|**Stepwise**|
|Achievement Orientation||-0.626|-0.645|-0.558|-0.517|
|||(0.226)|(0.207)|(0.256)|(0.258)|
|Power Motivation||0.288|0.308|0.272||
|||(0.309)|(0.263)|(0.298)||
|Tenacity||-0.230|-0.229|-0.205||
|||(0.557)|(0.557)|(0.589)||
|Work Centrality/Passion||<br>-1.080**|<br>-1.105**|<br>-0.963**|-0.917**|
|||(0.026)|(0.020)|(0.029)|(0.025)|
|Internal Locus||0.255|0.216|||
|||(0.480)|(0.529)|||
|Optimism||-0.145||||
|||(0.721)||||
|Polychronicity||<br>-0.970***|-0.955***|-0.914***|-0.852***|
|||(0.001)|(0.001)|(0.001)|(0.001)|
|Organization||0.348|0.327|||
|||(0.348)|(0.370)|||
|Impulsiveness||0.528|0.526|||
|||(0.282)|(0.281)|||
|Digit Span||-0.297|-0.296|-0.271|-0.295*|
|||(0.116)|(0.116)|(0.131)|(0.069)|
|Weta Dummy|2.513***|2.081***|2.020***|1.995***|2.676***|
||(0.009)|(0.005)|(0.005)|(0.004)|(0.000)|
|Cultivator Age|0.003|-0.010|-0.010|||
||(0.927)|(0.616)|(0.625)|||
|Cultivator Education|-0.072|-0.032|-0.033|-0.030||
||(0.271)|(0.530)|(0.517)|(0.544)||
|Cult. Experience|0.016|0.022|0.022|0.017||
||(0.684)|(0.443)|(0.442)|(0.478)||
|Female Cultivator|1.425**|0.929*|0.919*|0.875*|0.890**|
||(0.035)|(0.064)|(0.066)|(0.065)|(0.038)|
|No. HH Members (log)|1.181**|0.909**|0.901**|0.888**|0.933**|
||(0.033)|(0.026)|(0.026)|(0.023)|(0.015)|
|Transplanting Used|-3.619***|-2.594***|-2.588***|-2.461***|-2.510***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|**Tests for joint significance**||||||
|<br>All Personality Traits||0.011**|0.006***|0.002***|0.001***|
|Standard Human Capital|0.655|0.795|0.823|0.689||
|Traits||||||
|Motivational Traits Only||0.032**|0.018**|0.024**|0.009***|
|<br>No. of Observations|1,778|1,778|1,778|1,778|1,778|
|/Usigma|1.502|1.163|1.160|1.134|1.122|
||(0.128)|(0.103)|(0.102)|(0.105)|(0.106)|
|/Vsigma|-2.791***|-2.777***|-2.778***|-2.790***|-2.802***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|

_Note:_ Indicators of seed type and parcel’s tenure status included throughout but not reported. Likelihood ratio p-values in parentheses (*** p<0.01, ** p<0.05, * p<0.1). Standard human capital traits are age, education and experience. Motivational traits are achievement motivation, power motivation, tenacity and work centrality/passion.

22
to the primary teaching job is either zero or positive. Effort devoted to second jobs moves in the opposite direction, while the effect on effort devoted to leisure is ambiguous.

# TABLE B.1.

# COMPARATIVE STATICS RESULTS: HOW A MARGINAL INCREASE IN SALARY AT THE PRIMARY

# TEACHING JOB AFFECTS EFFORT ALLOCATION

|EACHI|GOBFFECTSFFORT<br>if in the optimum<br> <br><br><br> <br><br>|LOCATION<br> <br><br>|<br><br>|
|---|---|---|---|
||E<sup>p</sup>= Emm<br>~~p~~<br>E<sup>s</sup>=0<br>E<sup>p</sup>> Emm<br>~~p~~<br>E<sup>s</sup>=0|E<sup>p</sup>= Emm<br>~~p~~<br>E<sup>s</sup>>0|E<sup>p</sup>> Emm<br>~~p~~<br>E<sup>s</sup>>0|
||<br> <br> <br> <br>Effect on effort on thepri<br> <br>|<br> <br>maryteaching<br>|<br> <br> job|
|Pro-socialpreferences|0<br>0|0|+|
|Gift exchange|0<br>+|0|+|
|Informalpressure|0<br>+|0|+|
||Effect on effort on the sec<br> <br>|ondary job||
|Pro-socialpreferences|0<br>0|-|-|
|Gift exchange|0<br>0|-|-|
|Informalpressure|0<br>0|-|-|
||Effect on effort devoted to<br> <br>|leisure||
|Pro-socialpreferences|0<br>0|+|+|
|Gift exchange|0<br>-|+|ambiguous|
|Informalpressure|0<br>-|+|ambiguous|

In the remainder of the appendix we derive the results presented in Table B.1. Note that all of the extensions of the standard model discussed in this appendix can be written in the general form: Table B.2 shows the partial derivatives of the function _V_ , depending on the model that is chosen. In all cases, effort on the primary teaching job contributes positively to utility, but the effect of salary varies depending on the model. The cross-partial derivative is positive, except in the model with pro-social preference, where it does not appear.

# TABLE B.2.

PARTIAL DERIVATIVES FOR _W_

||dV<br><br>|<sup>dV</sup><br>|dV<br>|
|---|---|---|---|
||dE<sup>p</sup><br>|dS|dE<sup>p</sup>dS|
|Pro-socialpreferences|+|0|0|
|Gift exchange|+|+|+|
|Informalpressure|+|-|+|
<mark>Martin, M., Billah, M., Siddiqui, T., Abrar, C., Black, R., & Kniveton, D. (2014). Climate-related migration in rural Bangladesh: a behavioral model.</mark> _<mark>Population and Environment</mark>_ <mark>,</mark> _<mark>36</mark>_ <mark>(1), 85-110.</mark> **<mark>[</mark> 1]** <mark>Martinez Flores, F., Milusheva, S., & Reichert, A. R. (2021). Climate anomalies and international migration: A disaggregated analysis for West Africa.</mark> _<mark>World Bank Policy Research Working Paper</mark>_ <mark>n.9664.</mark> **<mark>[</mark> 1]** <mark>Mastrorillo, M., Licker, R., Bohra-Mishra, P., Fagiolo, G., Estes, L. D., & Oppenheimer, M. (2016). The influence of climate variability on internal migration flows in South Africa.</mark> _<mark>Global Environmental Change</mark>_ <mark>,</mark> _<mark>39</mark>_ <mark>, 155-169.</mark> **<mark>[</mark> 1]** <mark>McCarthy, N. (2011). Understanding agricultural households' adaptation to climate change and implications for mitigation: land management and investment options.</mark> _<mark>Inc., Washington, DC, USA</mark>_ <mark>.</mark> **<mark>[</mark> 3]** <mark>McCarthy, N., Kilic, T., De La Fuente, A., & Brubaker, J. M. (2018). Shelter from the storm? householdlevel impacts of, and responses to, the 2015 floods in Malawi.</mark> _<mark>Economics of disasters and climate change</mark>_ <mark>,</mark> _<mark>2</mark>_ <mark>(3), 237-258.</mark> **<mark>[</mark> 3]** <mark>McKenzie, D and Yang D. (2012). Experimental Approaches in Migration Studies. In Carlos VargasSilva (ed.), Handbook of Research Methods in Migration, Edward Elgar Publishing, 249-269.</mark> **<mark>[</mark> 2]** <mark>McLeman, R., & Gemenne, F. (Eds.). (2018).</mark> _<mark>Routledge handbook of environmental displacement and migration</mark>_ <mark>. Routledge.</mark> **<mark>[</mark> 1]** <mark>McMichael, C., Dasgupta, S., Ayeb-Karlsson, S., & Kelman, I. (2020). A review of estimating population exposure to sea-level rise and the relevance for migration.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>15</mark>_ <mark>(12), 123005.</mark> **<mark>[</mark> 1]** <mark>McNamara, K. E., Bronen, R., Fernando, N., & Klepp, S. (2018). The complex decision-making of climate-induced relocation: Adaptation and loss and damage.</mark> _<mark>Climate Policy</mark>_ <mark>,</mark> _<mark>18</mark>_ <mark>(1), 111-117.</mark> **<mark>[</mark> 1]** Michler, J. D., Josephson, A., Kilic, T., & Murray, S. (2022). Privacy Protection, Measurement Error, and the Integration of Remote Sensing and Socioeconomic Survey Data. _arXiv preprint arXiv:2202.05220._ **[3]** <mark>Minet, J., Curnel, Y., Gobin, A., Goffart, J. P., Melard, F., Tychon, B., ... & Defourny, P. (2017).</mark> 46
Consequently, a buyer that acts according to altruistic reciprocity derives the optimal self-enforcing contract by maximizing his long term utility: Following the same steps as in section 2.1, I solve for the optimal contract in the presence of an altruistic buyer and a self-interested seller. A buyer who acts according to altruistic reciprocity offers the optimal stationary REDD contract defined in Proposition (3).

**Proposition 3.** _An altruistic principal offers an optimal stationary REDD contract y_<sup>_∗_</sup> = _⟨p_<sup>_∗_</sup> _, b_<sup>_∗_</sup> ( _q_<sup>_∗_</sup> ) _⟩ that implements conservation of the forest land_ _<u>q,</u> and satisfies IRCs_<sup>_m,DICC_</sup> _s_<sup>_m,_</sup> _IRCb_<sup>_aandDICC_</sup> _b_<sup>_a,whereIRC_</sup> _s_<sup>_mandDICC_</sup> _b_<sup>_abind.Thecompensationschemeisgivenby_</sup>

As expected, the contract is structured in the same way as in the presence of purely profit-maximizing parties because the seller’s preferences have not changed. She needs the same incentive structure to perform. As a consequence, regardless of the preferences the buyer may have (self-interested or altruistic), the optimal REDD contract has the same characteristics: a fixed payment no greater than zero and a performance payment that contains the entire payment including the cost of forest conservation and the value of the alternative economic activity for the seller. In other words, no matter how sympathetic the buyer is toward the seller, it never results in an upfront payment or a larger bonus.

Once again, the contract is self-enforcing if parties find cooperation to be the best strategy. Proposition (4) addresses the conditions for self-enforcement.

_a_<sup>_b_</sup> **Proposition 4.** _Let_ _<u>δ</u> >_ 0 _. Cooperation among an altruistic buyer and a self-interested seller under the optimal REDD contract is achievable ∀ δ ∈_ � _<u>δa</u>_<sup>_b_</sup> _,_ 1� _, where_ _<u>δa</u>_<sup>_b_</sup> = _V_ <u>(</u> _c_ (( _qq_ ))+ _−πu_ _~~−~~_ <u>)(</u> _a_ 1 _−bca_ ( _qb_ <u>))</u><sup>_._</sup> 17
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If the wage rate for child labor is inflexible downwards then a small increase in the stipend will have no effect on child labor - the extra time at school will come out of leisure.

However, it is hard to see what would generate downward inflexibility in child wages in this setting. There are no child labor unions to our knowledge. Adverse effects of low adult wages on nutritional status and (hence) productivity can yield downward wage inflexibility (under the well-known Efficiency Wage Hypothesis). However, while income pooling may not be complete within the household, it is plain that children do share in the family's total resources, in which case the link from children's own wages to their nutritional status will be weak.<sup>8</sup> Thus it would seem reasonable to assume that parents in this setting are free to determine how their children's time is allocated. In making that choice, let parents' utility be: _U= U(C,S,H;Z_ _(1)_ where the household's current consumption is _C, S_ is the child's school attendance, and H is the child's leisure. We assume that U is strictly quasi-concave in _C, S_ and _H._ We allow for heterogeneity by including a vector of exogenous household and local geographic variables Z. The child's total time available _(I)_ can be devoted to schooling, leisure (H), or wage labor (L): _S+H+L =T_ (2) In addition to income from child labor or the FFE stipend, the household obtains an income Y from other sources, which we assume to also be a function of Z. (The latter will include the parents' education and landholding.) So the budget constraint is: _C_ = _wL_ **_+_** _bS + Y(Z)_ (3) where w is the wage rate for child labor, and b is the monetary value of the food received under 8 A survey by the Bangladesh Bureau of Statistics in 1996 found that 83% of children in rural areas paid their earnings to their parents (BBS, 1996, Table 5.12, p. 54).

8
# **Appendix A:**

# **History and scope of the EU’s General System of Preferences**<sup>**20**</sup>

The general system of preferences (GSP) is a unilateral preference program legally embodied under the GATT/WTO framework under the so-called ‘enabling clause’ adopted in 1979, which allows developed countries to deviate from the MFN principle and give differential and more favourable treatment to imports from developing countries only. While the enabling clause is thus the WTO legal basis for the GSP, it is unilaterally up to the individual preference giving countries to decide which developing countries and products to include in their specific GSP schemes, subject to certain criteria.

The first EU GSP program was introduced in 1971 and have since then frequently been reviewed in terms of both country and product eligibility. Overall, three main waves of GSP can be identified, with first wave covering the period 1971-1994, the second 1995-2005 and the third 2006-2013.<sup>21</sup> In the initial period, 1971-1994, defining features of the GSP includes annual reviews of beneficiaries and product coverage, in addition to the use of quotas and ceilings on the specific products eligible for preferential access under the scheme. In the period 1995-2005, significant changes were introduced, including the replacement of previous quantitative limits on sensitive products by a modulated tariff system, under which the most sensitive products were given the less favourable tariff reductions.

The second element introduced with the 1995-GSP revision was a policy of graduation. The idea was to limit the preferences to the countries and sectors that needed them. The moment a sector in a country exported intensively to the EU, the sector would be considered sufficiently advanced to no longer receive the benefits of the GSP and the sector would graduate. The most advanced beneficiary countries meeting particular criteria could be removed completely from the list of beneficiary countries. Hong Kong (China), the Republic of Korea and Singapore were the first countries excluded in 1998.

The final innovation was the introduction of a number of special incentive arrangements. They were to be applied on the basis of an additional margin of preference granted to beneficiary countries complying with certain requirements related to labour standards and environmental norms, as well as to countries undertaking effective programmes to combat drug production and trafficking.

Finally, the special arrangement for the least developed countries (LDCs) known as the Everything But Arms (EBA) initiative was introduced and entered into force on 5 March 2001 and allowed free access for the poorest countries in the world for all products except arms and ammunition.

> 20 This description draws heavily on Section 1.2 of Thelle et al. (2015).

> 21 From the first of January 2014 a new GSP regime has come into place, but it lies mostly outside our period of analysis. Readers are referred to European Commission (2012) “The EU’s New Generalised Scheme of Preferences”.  For more details for a review of the most important changes introduced.

36
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

# **_Contract Manufacturers_**

Contract manufacturers make products for lead firms and sometimes provide design services as well. The popularity of contract manufacturing in the electronics industry is a direct result of value chain modularity, which enables a clear technical division of labor between design and manufacturing at multiple points in the value chain, most notably between the design and assembly of final products and the design and fabrication of integrated circuits, or ICs. At the product level, some lead firms still assemble products in their own factories, but the use of contract manufacturers has been a strong trend since the late 1980s. Production services alone— comprising component purchasing, circuit board assembly, final assembly, and testing—are referred to in the industry as electronics manufacturing services (EMS), and also known as original equipment manufacturing” (OEM) firms in Taiwan. Historically, the largest EMS contract manufacturing firms have been based the United States and Canada (see Table 5.); for example, Celestica was spun off from IBM in 1997. These firms tend to have global operations and produce for lead firms in most of product segments listed in Table 4. In recent years, Foxconn (Hon Hai), based in Taiwan but with very large production facilities in China, Vietnam, and the Czech Republic, has emerged as the industry’s largest player, in part on the basis of huge orders received from Apple for the production of the iPod and iPhone product lines. A number of firms based in Singapore have also risen in the EMS ranks, including Venture and Beyonics, ranked 7<sup>th</sup> and 12th in the world, respectively, in 2009.

**Table 5** Top-Five Electronics Contract Manufacturers Different Regions, 2009

|**Top-five contract manufacturers**|**Primary service**|**2009 revenue (US$, millions)**|
|---|---|---|
|**Taiwan, China**|||
|Foxconn/Hon Hai|EMS|44,065|
|Quanta Computer|ODM|23,265|
|Compal Electronics|ODM|19,424|
|Wistron|ODM|16,226|
|Inventec|ODM|12,349|
|North America|||
|Flextronics (U.S. & Singapore)|EMS|30,949|
|Jabil Circuit (U.S.|EMS|11,685|
|Celestica (Canada)|EMS|6,092|
|Sanmina-SCI (U.S.)|EMS|5,177|
|Benchmark Electronics (U.S.)|EMS|2,089|
|Other locations|||
|Venture (Singapore)|EMS|2.428|
|Elcoteq(Luxembourg)|EMS|2,090|
|SIIX (Japan)|EMS|1,360|
|Beyonics (Singapore)|EMS|1,120|
|Zollner Elektronik (Germany)|EMS|970|

_Source:_ Digitimes (Taiwan, China) and company annual reports. _Note:_ EMS = electronic manufacturing services; ODM = original design manufacturing (services).

Manufacturing plus product design services are known collectively as original design manufacturing (ODM) services. Nearly all large ODM contract manufacturers are based in 13
Correlates of NDVI proxy for agricultural production

# **Table A1**

_Panel A. NDVI on village_ _<u>proxies</u> of agricultural_ _<u>productivity</u>_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Crop suitability (log)|0.031***|||0.031***|
||(0.002)|||(0.002)|
|Irrigation (share)||0.022***||0.015***|
|||(0.002)||(0.002)|
|Consumption (log)|||0.050***|0.046***|
||||(0.002)|(0.002)|
|N|193276|193276|193276|193276|
|R2|0.53|0.53|0.54|0.54|

_Panel B. NDVI on district agricultural output_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Agricultural output|0.658***|0.660***|0.344***|0.245***|
||(0.027)|(0.027)|(0.043)|(0.042)|
|Fixed efects|State|State-Year|District|District, Year|
|N|2045|2045|2045|2045|
|R2|0.72|0.75|0.95|0.96|

_<u>∗ ∗∗ ∗∗∗</u> p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01 Notes: For validation purposes, our favored log-differenced NDVI agricultural production proxy is regressed on other likely correlates of yields. Panel A presents village level estimates of NDVI regressed on log crop suitability, share of village land irrigated, and log consumption per capita, all with district fixed effects. Panel B presents district-level regressions of NDVI on the value of agricultural output (log) for the years 2000-2006. See Data Appendix for details. The sample has been restricted to states from the primary specification, where states follow PMGSY population guidelines. Heteroskedasticity robust standard errors are reported below point estimates.
**<u>Table 4: Descriptive statistics by type of exit and entry</u>**

||**Period**|**All**|**Exits**|**Stayers**|**Entrants**|**Entran**|**ts who**|
|---|---|---|---|---|---|---|---|
|||||||**churn**|**stay**|
|Profit/ha (US$)|2001/11|27.7|-11.3|35.3|65.5|-2.7|94.1|
||2001/06|-2.4|-12.5|6.7|1.6|-15.7|7.4|
||2007/11|69.8|15.8|77.5|96.1|19.7|106.7|
|Output/ha (US$)|2001/11|250.4|134.3|294.3|354.0|214.0|412.7|
||2001/06|179.6|126.1|203.8|239.1|158.6|265.9|
||2007/11|367.1|232.7|393.9|415.2|246.1|438.7|
|Cost/ha (US$)|2001/11|222.7|145.5|259.0|288.5|216.7|318.6|
||2001/06|182.0|138.6|197.1|237.4|174.4|258.5|
||2007/11|297.3|216.9|316.4|319.1|226.4|332.0|
|Area at start (ha)|2001/11|1911|1829|2627|1688|1562|1741|
||2001/06|2036|1801|2363|1950|1606|2064|
||2007/11|2017|1488|2453|1445|1363|1457|
|End area|2001/11|1824|1396|2525|1973|1504|2170|
||2001/06|1856|1440|2203|2068|1485|2262|
||2007/11|2090|1359|2524|1689|1390|1730|
|Workers per 100 ha|2001/11|3.36|4.28|3.61|2.29|3.08|1.96|
||2001/06|4.14|4.34|4.34|3.45|3.75|3.35|
||2007/11|2.28|2.70|2.34|1.81|1.74|1.81|
|Wage per day|2001/11|4.70|2.68|6.32|6.13|4.52|6.80|
||2001/06|3.28|2.47|3.58|4.31|3.35|4.63|
||2007/11|7.26|4.94|8.18|7.04|6.24|7.15|
|No. of farms|2001/11|16724|7094|2898|6732|1991|4741|
||2001/06|12937|5364|4628|2945|738|2207|
||2007/11|9894|1962|5529|2403|293|2110|
|No. of obs|2001/11|92324|30281|31432|30611|6660|23951|
||2001/06|53095|17313|27613|8169|1857|6312|
||2007/11|39229|4589|27318|7322|655|6667|

_Source:_ Own computation from form 50SG 21
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restlicted with respect to federal-state interaction than at the state-local level, particularly wvhen states rmPre **ont** real difference in valuo and experiences (e.g. there are linguistic difference-). Even with respect to the Esimplem cas of purely local infrastructure, the possibility of building on indigenous infonmal organizations to provide local infrastructure in an efficient and qui.u ble E shimon my be much greater in a traditional rural commueity cnti in a dynamically changing metropolitan area.2o

# **Guidelines for Policy Design.**

Thc essential frmework for policy design with respect to the decentrazed provision of infastructure thus basically amounts to little more than the familar econor<sup>ic dictum "Get</sup> the Pr,ices RighLt" In terms perhaps more familiar in the public sector,<sup>this may be</sup> tanslated as 'Impose a Hard Budget Constaaint", i.e. face aU decision-makers<sup>at tlte margin</sup> X It is perhaps not entirely due to the terms of reference of the research project on which<sup>it is based that the</sup> stimatin boomr c by Octrom, Schroeder, and Wynd e (1993) deals solely with nni are ls.