rafmacalaba/datause-encoder-data huggingface dataset:
Second, to the best of our knowledge, this paper is the first to investigate the impact of the anticipation or threat of polygamy on economic decisions of monogamous spouses. The only studies on the effects of polygamy on household economic behavior focus on polygamous households and do not study the impact of the risk of polygamy on nonpolygamous households. Yet, papers analyzing the economic impact of polygamy are scarce, with the exception of Dauphin (2013) and Dauphin and Fortin (2001), who focus on the effect of polygamy on the efficiency of agricultural households, or Grossbard-Shechtman (1986), who studies the impact of polygamy on fertility. At the macroeconomic level, the relationship between polygamy and savings has been explored in a theoretical model by Tertilt (2005). In the authors’ model, polygamy leads in particular to high bride prices and high fertility, which crowd out other investments. Descriptive evidence of the relationship between polygamy and savings at the microlevel is provided by De Laiglesia and Morrison (2008). Using household survey data from Ghana, Indonesia, and Côte d’Ivoire, the authors find that polygamous households have lower assets per capita than monogamous ones, but they do not account for self-selection into polygamy. Our paper tests another channel for the impact of polygamy on saving decisions based on the strategic behaviors of monogamous wives “at risk” of polygamy.

The article is structured as follows. Section II discusses evidence of noncooperative behaviors between spouses. In section III, we present our empirical model and identification strategy. The data are described in section IV. Results on the determinants of polygamy and on the impact of the risk on polygamy on savings are presented and discussed in section V. Finally, section VI concludes.

II. THE RISK OF POLYGAMY AND SAVINGS: DISCUSSION OF POTENTIAL CHANNELS Several works have shown that spouses in developing countries do not behave cooperatively, especially in the presence of asymmetries of information (Udry 1996; Ashraf 2009; Castilla and Walker 2013, among others), and that spouses may have conflicting views on the use of household income. For example, different preferences for consumption may lead to different preferences for savings, as modeled by Anderson and Baland (2002), who explain the higher participation of married women from a Kenyan slum to Rotative Savings and Credit Associations (ROSCAs) by a strategy aimed at preserving their savings from their husbands.

Polygamy is of particular interest since it is expected to exacerbate both issues: First, polygamy is a source of asymmetries of information between spouses. Second, it may give rise to opposite incentives to save for husbands and wives. As regards asymmetries of information first, there seems to be a consensus about the fact that men have the final say on whether to take a second wife (Madhavan and Bledsoe 2001). Anecdotal evidence even suggests that, in some instances, the first wife is told about the second marriage of her husband only after the ceremony. Husbands thus have private information on the prospect and date of arrival of a cowife in the household.

4
level is set at<sup>_p_</sup> _io_<sup>. The optimal tariff is given by the following expression which is</sup> derived from the FOC in (8): _o o o_ where _zi_ = _yi_ ( _pi_ /) _mi_ ( _pi_ ) is the equilibrium ratio of domestic output to imports and _eio_ = − _m_ ' _i_ ( _pio_ ) _pio_ / _mi_ ( _pio_ ) is the elasticity of import demand or of export supply.

The expression above states that only the industries where prices are below the reference point receive protection. The optimal tariff equates the marginal gain as a result of reducing sector-specific losses to the marginal loss in aggregate welfare that the tariff induces. As is standard, the tariff is increasing in the ratio of output to imports since the cost to the economy in terms of loss aversion is higher if the sector is large. In addition, it is greater for industries with a more elastic import demand (or export supply) since the welfare cost of the tariff is smaller if import demand is inelastic. There will always be a small positive tariff since _H’(p*)>0_ and _W’(p*)=0_ .

_Case 2b: The FOC is not satisfied (or there is an inflection point) in the region_ _pi_ * < _pi_ ≤ _p_ i_ : If the FOC is not satisfied, then we have _G_ ('<sup>_p_</sup> _io_ ) > 0 for the whole region. Alternatively, suppose the FOC is satisfied but the SOC is not, i.e. we are at an inflection _o o_ point and _G_ ('<sup>_p_</sup> _i_ ) = 0 & _G_ (''<sup>_p_</sup> _i_ ) = 0 . These imply, in either case, _H’(.)_ is larger than or equal to _W’(.)_ in absolute value at every point in the entire range. Therefore _G(.)_ is increasing, reaches its maximum at<sup>_p_</sup> i_ and we have a corner solution as depicted in Figure 3. In this case, loss aversion at<sup>_p_</sup> i_ is so large that the marginal gain from a reduction in loss aversion is always greater than the marginal loss in standard social _ _ welfare from protection, i.e. _H_ (' _p i_ ) + _W_ (' _p i_ ) > 0 . The government chooses trade policy so that domestic price is set at<sup>_p_</sup> i_ . Once the reference price is reached, however, loss aversion disappears. Any further increase in tariff simply reduces welfare and therefore never gets implemented.

14
|**Table 5: Population (in mill**<br>|**ions) reclas**<br>|**sified moving from**<br>|**per capita to root**<br>|**N rule**<br>|**- $1.90**<br>|
|---|---|---|---|---|---|
|Region|Not poor<br>under<br>both<br>rules|Poor by pc rule,<br>RECLASSIFIED<br>as not poor|Not poor by pc<br>rule,<br>RECLASSIFIED<br>as poor|Poor<br>under<br>both<br>rules|Population|
|(1)|(2)|(3)|(4)|(5)|(6)|
|Sub-Saharan Africa|571|68|43|363|1757|
|Middle East & North Africa|303|8|2|16|5865|
|Europe and Central Asia|483|1|1|5|329|
|**World**|**5048**|**135**|**135**|**547**|**640**|
|Other High Income|1012|0|1|7|1020|
|Latin America & Caribbean|555|2|6|20|491|
|South Asia|1521|49|66|120|583|
|East Asia & Pacific|602|6|15|18|1045|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: This table is an extension of Table 4 above, so these estimates are also for the 2017 reference year. These</u> results are based on the full sample of 162 countries covered in the paper.

34
Policy Research Working Paper Series

|Tile|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1306 CapRal Fows and Long-Term<br>Equilbrlum Real Exchange Rates<br>In Chile|Ibrahim A. Elbadawi<br>Raimundo Solo|June 1994|R. Marlin<br>39065|
|WPS1307 How Taxation Affects Foreign Direct<br>Investment (Country SpecificEvidenc|Joosung Jun<br>e)|June 1994|S. King-Watson<br>31047|
|WPS1 30B Ownership and Corporate Control in<br>Poland: Why State Firms Defied the<br>Odds|Brian Pinto<br>Sweder van Wijnbergen|June 1994|M. Kam-Cheong<br>39618|
|WPS1309 Is Demand for Polluting Goods<br>Manageable? An Econometric Study<br>of Car Ownership and Use in Mexico|Gunnar S. Eskeland<br>Tarhan N. Feyziogiu|June 1994|C. Jones<br>37699|
|WPS1310 China's Economic Reforms: Pointers<br>ror Other Economies in Transition|Justin Yifu Uin<br>Fang Cai<br>ZhouU|June 1994|C. Spooner<br>30464|
|WPS1311 The Supply Response to Exchange<br>Rate Reform in Sub-Saharan Africa<br>(Empirical Evidence)|Mustapha Rouis<br>Weshah Razzak<br>Carlos Molrinedo|June 1994|J. Schwartz<br>32250|
|WPS1312 The New Wave of Private Capital<br>Inflows: Push or Pull?|Eduardo Femandez-Arias|June 1994|R. Tutt<br>31047|
|WPS1313 New Estimates of Total Factor<br>Productivity Growth for Developing<br>and Industrial Countres|Vikram Nehru<br>Ashok Dhareshwar|June 1994|M. Coleridge-<br>Taylor<br>33704|
|WPS1314 The Significance of the 'Europe<br>AgreernentsW<br>for Central European<br>Industrial Exports|Barilomiej Kamninski|June 1994|M. Patenla<br>37947|
|WPS1315 Global Tradable Carbon Permits,<br>Participation Incentives, and<br>Transfers|Bjom Larsen<br>AnwarShah|June 1994|C. Jones<br>37754|
|WPS1316 Preserving the CFA Zone: Macroeco-<br>nomic Coordination After the<br>Devaluation|Shantayanan Devarajan<br>Michael Walton|June 1994|C. Jones<br>37699|
|WPS1317 Estimating the Efficiency Gains<br>of Debt Restructuring|Jeremy Bulow<br>Kenneth Rogoff<br>Ning S. Zhu|July 1994|FLVo<br>33722|
|WPS131B Exchange-Rate-Based Stabilization<br>in Argentina and Chile: A Fresh Look|Miguel A. Kiguel<br>Nissan Liviatan|July1994|M. Divino<br>33739|
poraneous differences between the treatment and control groups in the vicinity of the discontinuity. Formally, this requires that both lim _x↓x_<sup>_E_</sup> � _yi|xi_ = _<u>x</u>_ <u>�</u> and lim _x↑x_<sup>_E_</sup> � _yi|xi_ = _<u>x</u>_ <u>�</u> are continuous in the forcing variable _xi_ . If this assumption holds around the cutoff, then any discontinuity in outcomes observed at the cutoff can be attributed to the discontinuity induced by the treatment, in this case debt relief. As a test of this identifying assumption, Figure 4 plots the unconditional means of pre- program observables with accompanying local linear regressions to each side of the eligibility threshold. These graphical tests demonstrate that households in the vicinity of the program eligibility threshold are indeed similar along observable pre-program characteristics, and that there are no discontinuities in any of these variables in the neighborhood of the program threshold. Table D.3 in the Supplemental Appendix provides additional parametric tests for continuity and similarly demonstrates that there are no discontinuities in pre-program observables at the cutoff, indicating that the covariate continuity assumption is met.

The second identifying assumption is that the forcing variable, and therefore treatment status, is not subject to manipulation. Ex-ante manipulation of land status was highly unlikely for several reasons. First, the program was the first of its kind in India that made eligibility conditional on land pledged as collateral at the time the loan was disbursed, rather than the vintage or amount of outstanding debt. Second, the approved amount of agricultural loans is typically proportionate to the value of land pledged as collateral. This means that, in general, households have an incentive to over- rather than underreport their land to the bank, whereas households in the vicinity of the program cutoff would have had to manipulate their documented land downward to benefit from the program. _Third_ , several mechanisms were in place to assure faithful implementation and prevent the ex-post manipulation of land documentation. As a transparency measure, all bank branches were required to publicly post the land records and debt relief details of all eligible individuals. Banks themselves had multiple levels of internal audits and the central bank and local regulators performed further audits. Finally, I test for robustness to corruption concerns by auditing official land documents using a statewide electronic database of landholdings. Electronic land records are administered by a central authority and cannot be amended by local administrators. Detailed results of these land audits are reported in the Supplemental Appendix. All results are presented for the sample of all surveyed households, as well as a robustness sample of households with audited and matching land records for which land manipulation can be ruled out. In 13
# **_Does Economic Growth Enable Building Trade Facilitation Capacity?_** One assumes

that economic growth and efficiency in trade facilitation are positively related. The more developed a country is, the more resources it can devote to investing in trade facilitation capacity. By the same token, it is likely that the larger the economy, the higher is the rate of return on investment in improving trade facilitation. This positive relationship between growth and trade facilitation indicators is confirmed by the data in the 75 sample countries – the correlations between GDP per capita (adjusted by PPP) and port efficiency, customs regimes, regulatory policy, information technology infrastructures are respectively 0.78, 0.86, 0.68 and 0.81.

The new and candidate members of the EU are less developed than the EU15. Does their gap in economic development account in some way for the lagged progress in their trade facilitation capacity? Considering GDP per capita, is the development of trade facilitation in the new and candidate members of the EU what would be expected? Our analysis of the data suggests that the new and candidate EU member countries are in general weak performers in all four of the trade facilitation dimensions examined (figures 4a-d). Indeed, even in the context of their relatively low of level of economic development, their trade facilitation development is relatively low, in fact under the benchmark level. The only exception is Estonia, which performs stronger than the benchmark level in all four indicators. Compared with Hungary, a country of a similar economic indicator, Estonia is 40% more developed in port efficiency, 30% more developed in IT infrastructure and 20% more developed in regulatory policy.<sup>8</sup> In fact, the trade facilitation level of Estonia is even more developed than the average of EU15 in IT infrastructure. The strong growth in electronics and telecommunication sectors has likely contributed to the recent economic performance of Estonia – the real GDP growth rate was 4.8% in 2003, far above the regional average level.<sup>9</sup> Figure 4a shows that, besides Estonia, three countries (Latvia, Romania, and Turkey) perform above their benchmark levels in port efficiency. The other new and candidate EU member countries are lagging behind; in particular, Slovak Republic and Hungary exhibit a

> 8 Hungary and Estonia share the similar development level of customs regimes.

> 9 For the sake of comparison, the real GDP growth rate of Hungary was 2.8% in 2003 (data source: CIA Fact Book).

10
provide insight on how interventions that expand public budget or reduce information asymmetry (in line with our financial and nonfinancial rewards) may or may not redress electorally-driven inequities in resource allocation across different hamlets inside the GP.

For this purpose, we develop a theoretical framework where hamlets differ in the degree of accuracy with which their residents can observe politician quality, with _σs_ describing the level of “noise” in the realized signal among residents of hamlet _s_ . The candidates’ policy commitments are assumed to be binding, and the policy vector is defined by _Z_ = _{zs}_ , where _zs_ is the amount of public resource that they would allocate to each hamlet _s_ = _s_ 1 _, s_ 2 _, . . . , sS_ . The total GP budget is fixed at _G_ , and the set of feasible policy platforms is _Z_ = _{z ∈R_<sup>_S_</sup> +<sup>:Σ</sup><sup>_zs≤G}_.Thedetailed</sup> setup and derivations of the model is presented in Appendix C.

In this section, we highlight three key insights from this model. First, the inequities in equilibrium allocation across hamlets deviate from the utilitarian optimum. This inequity deviation exists as long as _σs_ 1 = _σs_ 2 for any hamlets _s_ 1 _, s_ 2. Second, a convergence in _σs_ s leads to a reduction in differences in the equilibrium resource allocation between hamlets, such that if _σs_ 1= _σs_ 2 for all hamlets _s_ 1 _, s_ 2, then _zs_<sup>_∗_</sup> 1<sup>=</sup><sup>_z_</sup> _s_<sup>_∗_</sup> 2<sup>=</sup><sup>_z_ ̄.Third,anincreaseinGPbudget,</sup><sup>_G,_willincreasetheequilib-</sup> rium resource amount allocated to hamlet s, _zs_<sup>_∗_, for all hamlets</sup><sup>_s_, but it will not change the relative</sup> resource inequities between hamlets _s_ 1 and _s_ 2, for all _s_ 1 _, s_ 2.

# **5 Data and empirical strategy**

## **5.1 Data sources**

The data that we use in our analysis come from multiple sources. First, baseline data was collected prior to the rollout of the second phase of the PVP project between December 2012 and January 2013. The baseline survey covered all 198 GPs in our sample and collected data on demographics and household characteristics of GP presidents through a GP president module; and hamlet-level information on new public investments in roads, streetlights, water, and sanitation. The latter also included data on the hamlet-wise provision of any new IAY grants—which are the most significant private transfers in terms of magnitude, during the current president’s tenure.

A second key data source used in this analysis is project monitoring data generated by the PVP project. These data included information on the identification of target beneficiaries, delivery of program benefits, and the implementation of key project processes related to participation, 13
Table 7 shows results of OLS models with the Y-variable expressed in rupees of the gross value of output (column 1) and also as the natural log of the rupees of the gross value of the output (column 2) in the Rabi season. We estimate the value of Rabi output from plot-level data of the total production of all crops grown by the farmers in the season and the median price realized for the crop. We value the entire production, including the portion saved for home consumption or use as seeds, at the price farmers received for the portion they sold in the market. 200 of the 757 farmers in our sample did not cultivate any Rabi crop in 2020-21. The gross value of crop output in the Rabi season for these farmers is zero. This significant clustering of the values of the Y variable around 0 is the reason we also estimate a Tobit model (column 3). Tobit is our preferred model here because the OLS results are likely to be biased and inconsistent.

**Table 7. OLS and Tobit Regression Estimates on the effects of JCLIS on Gross Value of Output in Rabi 2021**

|**Gross Value of Output in Rabi 2021**|**(1)**<br>**Log GVO**|**(2)**<br>**GVO (Rs.)**|**(3)**<br>**Tobit-GVO (Rs.)**|
|---|---|---|---|
|**JOHAR Farmer (vs non-beneficiary)**|1.275***|4,864*|9,627***|
||(0.430)|(2,747)|(3,507)|
|**Months of Operation of JOHAR System (**Base: <=18||||
|months**)**||||
|_>18 months_|0.320|-647.0|1,463|
||(0.547)|(4,148)|(4,757)|
|Total Land Holdings (Acre)|0.0684|990.0**|1,050**|
||(0.0438)|(423.8)|(490.1)|
|**Pump Access**(Base: No source)||||
|_Diesel (own)_|4.523***|12,606***|26,603***|
||(0.360)|(2,403)|(3,210)|
|_Diesel (rented)_|3.503***|10,068**|23,468***|
||(0.423)|(4,598)|(5,504)|
|_Electric_|4.607***|8,022***|23,088***|
||(0.315)|(1,946)|(2,906)|
|_Pond/Canal_|3.399***|4,362*|16,327***|
||(0.595)|(2,550)|(3,740)|
|JOHAR farmer used a supplementary source of|-0.417|-1,004|-3,632|
|irrigation|(0.349)|(4,335)|(4,402)|
|Household Size|0.187***|1,076**|1,631***|
||(0.0482)|(437.1)|(539.2)|
|At least one member migrates for work|-0.160|-2,099***|-2,408**|
||(0.119)|(758.6)|(978.3)|
|Type of JOHAR pump=Diesel (vs Solar)|0.228|-2,792|-2,682|
||(0.683)|(3,442)|(3,808)|
|The proportion of land that is upland|0.152|3,277|2,494|
||(0.447)|(3,232)|(4,433)|
|Used urea|-0.690**|2,240|-43.39|
||(0.341)|(1,601)|(2,273)|
|Used DAP|0.752|-1,083|3,286|

23
**Table 10 R&D Expenditures in  2001**

||**_Million $_**|**_$ per Employee in_**<br>**_R&D_**|
|---|---|---|
||_2001 _|_2001 _|
|FordMotorCo, USA|7,400|20,879|
|General Motors Corp., USA|6200|16,986|
|SiemensAG, Germany|6,028|12,455|
|Ericsson, Sweden|4,516|53,002|
|Finland|4,422.6|84,073|
|Denmark|3,604.2|101,100|
|**Latvia**|**81**|**8,719**|

_Source_ : OECD, 2002 ; Eurostat 2003 ; calculations by the authors

- Government R&D policy papers refer to five priority areas – ICT, electronics, material sciences, pharmaceutical/ biotechnology, and wood chemistry.  But actual government spending bears no relationship to stated government priorities. Instead, limited government financial resources are spread over 14 areas.  These 14 research fields were defined in the early 1990s and have not been revised since.  Moreover, for the past ten years, government R&D spending has been allocated in the same proportions among these 14 priority areas.  Scientists determine areas of research interest based on their scientific interests.  This bears only an accidental relationship, at best, to the needs of the economy in general or individual enterprises in particular.  This system is basically a social safety net for scientists.  But what began as a coping mechanism to keep the old R&D system intact at a time of wrenching economic change has become an obstacle to progress.

- As is well known in Latvia, there is very little private spending for R&D.  Indeed, Latvia ranks below all NMS’s in terms of the proportion of total R&D spending financed by the private sector.  At the same time, Government R&D spending is not designed either to catalyze private R&D spending or to allocate public resources to research areas and topics that might be of use to the private sector.

> budget of £219.2 million.  For details see, Colin Alexander, “Technology Transfer: The University of Oxford Model,” Presentation to the NATO Advanced Training Course, Moscow, September 30, 2003.

46
expenditure on non-wage operations and maintenance expenditure and transfers grew because the state could better utilize resources under central schemes.

The state government has four major sources of revenue – own tax and non-tax revenue, transfers from central taxes, and plan and non-plan grants received from the Government of India (GoI) (Figure 10).<sup>24</sup> The vast proportion of Tamil Nadu’s own tax revenue has come from a state sales tax (69 percent), a state excise tax (10 percent), stamp duties and registration fees (8 percent), and motor vehicle taxes (6 percent). With own tax revenue of 9.3 percent as a share of GSDP in 2003/04, Tamil Nadu’s revenue performance ranked high compared to other Indian states.

A major tax reform effort was aimed at improving the efficiency of the sales tax system which suffered from multiple layers of taxes and a complex rate structure, cascading, frequent and ad hoc changes, and extensive exemptions. The state has been preparing for a value-added tax (VAT) which is expected to resolve many problems associated with the sales tax system. The state has also reformed its stamp duty system. In an effort to reduce tax evasion, the government lowered the tax rate on property transactions, and improved its administration. On the non-tax side, the government’s efforts to increase cost recovery have had some success but have been limited due to political considerations.

# **Figure 9. Recurrent Expenditure Components (Cumulative Growth)**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Pension Growth Interest Growth Subsidy Growth Transfer Growth Salary Growth Non Wage O&M<br>Growth<br>1990/91-1997/98 1997/98-2000/01 2000/2001-2004/05<br>Percent<br><!-- End of picture text --> _Source_ : Government of Tamil Nadu Budget Documents.

> 24 Grants under the Fiscal Reform Facility of the GoI and certain technical up-gradation grants come as nonplan grants.

17