rafmacalaba/datause-encoder-data huggingface dataset:
# **5. ANALYTICAL METHODOLOGY**

The data do not contain information on the specific communication patterns, selfperceptions on leadership status, or numbers of nominations from peers received by farmers selected for training. It is thus not feasible to develop measures of opinion leadership for these farmers along the lines of work by Chatman (1987) or Valente and Davis (1999). However, it is possible to rely on the fact that the selection of farmers by program officers and village leaders to participate in the intensive IPM training was related to their observed or presumed opinion leadership potential, and on the availability of a number of socio-economic indicators related to opinion leadership for every farmer in the sample. In support of this approach, it is noted that studies of FFS training programs indicated that trained farmers were expected to proactively disseminate the knowledge gained in the course of the training to other members of the community (van de Fliert, 1993, Winarto, 1994, Scarborough et al., 1997, p. 105, Simpson and Owens, 2002). Obviously, not all potentially strong opinion leaders were selected for training. Similarly, some people with low opinion leadership capacity were included in the training in some villages. But it is reasonable to expect that persons with higher opinion leadership capacity had a higher probability of being selected for training. While the capacity for opinion leadership is not observable to the researcher, the literature suggests that such capacity is related to observed or measurable characteristics of the farmers, such as education, pre-training pest management knowledge, and wealth. One can thus utilize a dichotomous choice model such as probit to estimate, based on a sample of program-trained and untrained farmers, the parameters associated with observable indicators associated with opinion leadership.

Where _P_ is the probability of being selected, _X_ is a vector of observed leadership characteristics, and β is a vector of parameters. The imputed value of the probability of selection 11
simple average of the national poverty lines for these fifteen countries was $1.25 in 2005 PPP terms and this then became the revised global poverty line.

Using this new poverty line and the 2005 PPPs, Chen and Ravallion (2010) found that the number of poor was much larger than had previously been thought. The $1.08 poverty line and 1993 PPPs suggested that 1.3 billion people were poor all over the world. However, the new $1.25 poverty line at 2005 PPPs resulted in an estimated count of 1.8 billion people who were poor in revised estimates for 1993. Thus, the adoption of the 2005 PPPs along with the revision of the global poverty line to $1.25 (as well as other new data), resulted in increasing the estimated number of poor in 1993 by 500 million.<sup>11</sup> This large revision in the estimated count of poor people coincided with a growing focus on the global poverty measures in the international development community, and brought about a vigorous debate on how best to measure global poverty. As one example, Deaton (2010) presents the regional profile of global poverty in 1993 when assessed using the 1985, 1993, and 2005 PPPs and the matching global poverty lines for each benchmark year. He makes a couple of points: First, there is substantial variation in both the regional profile and overall level of poverty depending on which PPP and poverty line are used. Second, the $1.01 and $1.08 poverty lines combined with their benchmark PPP data produced very similar global poverty counts for 1993. Deaton essentially argues that the $1.25 line and the 2005 PPPs resulted in a higher estimated poverty count primarily because of an upward revision of the value of the international poverty line in poor countries, rather than due to the effect of the 2005 PPPs.

A more general point that arises from Deaton’s critique – and which the 2011 revisions we are currently incorporating reinforce – is that global poverty estimates have generally been – and remain – very sensitive to the introduction of new PPP conversion factors. As the ICP changes the way in which it collects and combines price data from different countries in an attempt to construct the best possible multilateral price index, estimates of the relative costs of living across nations can change substantially. To the extent that PPPs are used to compare living standards in poverty measurement, these changes inevitably – and occasionally powerfully – affect poverty estimates.

> 11 The re-estimation of the poverty line to $1.25 in 2005 PPPs by Ravallion, Chen and Sangraula (2009) did not aim to maintain strict comparability with the previous $1.08 line at 1993 PPPs. Instead, it aimed to better reflect recent national poverty lines in some of the world’s poorest countries.

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World Bank in its World DeveloDment Regort 1987, p. 83. In the World Bank rankings, countries were classified in one of four categories: strongly outward oriented, moderately outward oriented, moderately inward oriented and strongly inward oriented. This ranking, with strongly outward oriented the top rank, was used in the Spearman correlations of Table 8.

The World Bank ranking by trade regime presumably took into consideration the real exchange rate policy of each country; there should thus be a negative correlation between trade regime and adjusted real exchange rate ranking. This correlation exists, but is rather small. Rankings by trade orientation are positively correlated with rankings by economic growth, reduced domestic inflation and current account success. The policies of financial deepening, low government expenditure ratio and outward trade orientation are significantly combined in countries experiencing above-average economic results on external and internal balance.

Contingency tables and correlation analysis provide no testing of causal relationships. In the present analysis, it is impossible to choose from among those positive policy influences to identify which (if not all) caused the relative success in structural adjustment. These results do establish, however, some stylized facts about the structural adjustment process that will be useful in future theoretical research and policy planning in this area.

These conclusions are based upon a complicated statistical technique. It is useful to compare the results with those of unadjusted (j,e, historical) rankings in terms of performance and policy. Spearman correlations based on such rankings are presented in Table 9.
Stotsky, Janet G., Lekha Chakraborty, and Piyush Gandhi. 2019. “Impact of Intergovernmental Fiscal Transfers on Gender in India: An Empirical Analysis.” Institute for Studies in Industrial Development, National Institute of Public Finance and Policy Working Papers, New Delhi.

Stotsky, Janet G., and Asad Zaman. 2016. “The Influence of Gender Budgeting in Indian States on Gender Inequality and Fiscal Spending.” IMF Working Papers, IMF, Washington, DC.

Thomas, Alastair, and Pierce O'Reilly. 2016. “The Impact of Tax and Benefit Systems on the Workforce Participation Incentives of Women.” OECD Taxation Working Papers, Organisation for Economic Cooperation and Development, Paris.

Valera, Harold Glenn A., Takashi Yamano, Ranjitha Puskur, Phoebe Ricarte, and Rohini Ram Mohan. 2018. “Women's Land Title and Empowerment: Evidence from India.” ADB Economics Working Paper Series, Asian Development Bank (ADB), Manila.

World Bank. 2012. _World Development Report 2012: Gender Equality and Development_ . Washington, DC: World Bank. http://hdl.handle.net/10986/4391.

Young, Claire F. L. 1995. “(In)Visible Inequalities: Women, Tax and Poverty.” _Ottawa Legal Review,_ 99– 127.

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Table 2: **NLS estimates of the country-specific risk-sharing parameters** Robust t-statistics in parentheses, using spatial-correlation consistent standard errors of Driscoll and Kraay (1998). The Wald test statistics at the bottom of the table are distributed as chi-square with 50 degrees of freedom.

|Income forecastingmodels|(1)<br>IID|(2)<br>AR(1)|(3)<br>FAPAR(1)|
|---|---|---|---|
|**Summary statistics of risk sharing estimates**||||
|Number of risk sharing parameters between 0 and 1|43|48|49|
|of which signifcantly positive|29|43|40|
|Number of risk sharing parameters greater than 1|0|0|0|
|of which signifcantly greater than 1|0|0|0|
|Number of risk sharing parameters smaller than 0|7|2|1|
|of which signifcantly smaller than 0|2|0|0|
|Median|0.22|0.52|0.53|
|Maximum|0.87|0.90|0.90|
|Minimum|-0.23|-0.03|-0.07|
|**Average estimates**||||
|All countries|0.26|0.49|0.50|
||(13.2)|(27.7)|(27.7)|
|Advanced countries|0.30|0.57|0.59|
||(10.0)|(23.3)|(23.9)|
|Developing countries|0.24|0.42|0.43|
||(9.6)|(18.2)|(17.2)|
|Wald test of difference in means (p-value)|0.00|0.00|0.00|
|Wald test of joint signifcance of all risk sharing parameters|0.00|0.00|0.00|

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# MANAGING POPULATION GROWTH AND THE ENVIRONMENT

153. Helping women develop economic strength and human capital produces social benefits by influencing the size and quality of the labor force, the direction of population trends, and the quality of the environment.

# Future Labor Force

154. Perhaps because of their generally greater responsibility for children, mothers in many societies have more impact on and may attach greater importance to children's welfare. The mother's education has more impact on child health and schooling than the father's does and in an area like South Asia women's employment in the labor force (and resulting higher income) actually improves children's survival rates. Market-based economic productivity of members of a family probably directly affects shares of family consumption, human capital formation, and overall welfare.

# Fertility Determinants

155. Family size reflects the frequency of childbearing and the length of time a woman is exposed to pregnancy (age at marriage). In more traditional societies, girls still marry young (sometimes essentially at menarche), but increased education and income are strongly associated with delayed marriage. Deliberate policies -- notably, legislation -- to delay marriage may also help, although the effect is difficult to predict. Recent declines in fertility in the developing world reflect gradual delays in marriage -- which may account for about one-third of the decline in fertility in the developing countries of Asia. Breastfeeding, which delays ovulation, also affects fertility. But family size still primarily reflects marital fertility and the couple's ability and desire to control it.

156. Demand for children and women's opportunities. Avoiding pregnancy requires a deliberate decision. Parents balance the advantages and disadvantages, as they see them, of available means of family planning against those of having another child. Parents tend to opt for smaller families when the opportunity cost of high fertility rises and the opportunity cost of family planning falls. Available evidence suggests that the opportunity cost to women has the most influence.

157. Poor parents tend to want large families to provide reliable help and protection around the home or farm, to increase family income, and to assure old age support (World Bank WDR 1980 and 1984; USAID 1976). If daughters "marry out" or face less promising earning prospects or if culture favors sons, parents may prefer sons. This preference may manifest itself as less care for daughters and higher mortality rates for girls (Schultz 1989; Pitt and Rosenzweig 1988; World Bank 1989b). Or, if daughters maintain ties to their parents, poor parents in rural areas may even "export" daughters to wore distant farming areas as a sort of portfolio diversification strategy.
<mark>TABLE 4. Correlation between Divorce and Shocks in Previous Period</mark>

||Divorce (t)|
|---|---|
|Croploss(t − 1)|−0.0033<br>(0.0045)|
|Business loss(t − 1)|0.0205<br>(0.0181)|
|Illness(t − 1)|−0.0061<br>(0.0071)|
|P value forjoint significance|0.55|
|N|5,796|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Dependent variable is divorce. Reference year is 2001. Estimates are from a household fixed-effects model pooling first and second follow-ups, controlled for child composition and time dummies. N is the number of households remaining in the sample by first follow-up. Standard errors, clustered at the municipality level, are in parentheses.

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negative impact of the share of workers with tertiary education ( _emp_ter_ ) on the supplier’s share of output going to foreign firms. A higher educational level of the general manager ( _man_educ_ ) also reduces FDI linkages. While our focus here is on the suppliers’ output share to foreign firms and not on FDI spillovers, our findings can be related to those by Sinani and Meyer (2004) who find that a larger share of human capital leads to negative FDI spillovers (see section 2.2), although the underlying mechanisms may be different. It may be possible that suppliers with highly educated managers supply a larger share of inputs to firms abroad, for instance, because they may have fewer language barriers. In the overall sample (column 9), however, only distance to the foreign firm (ln _dist_ ) shows a significant effect.<sup>4</sup>

**Table 11: The Effect of Suppliers’ Absorptive Capacity with Supplier Relationship of at Least Three Years on Output Share to Foreign Firms, OLS**

|Dependent variable:|_outpisc_|||||||||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_gapisc_|-3.1899<br>(0.341)||||||||-3.4042<br>(0.412)|
|_sophisc_|6.9340*<br>(0.055)||||||||6.8075<br>(0.102)|
|_emp_terisc_||-0.2337*|||||||-0.2822|
|||(0.067)|||||||(0.105)|
|_emp_secisc_|||0.1797||||||-0.0169|
||||(0.179)||||||(0.911)|
|ln_experisc_||||-2.2745|||||-0.0370|
|||||(0.709)|||||(0.996)|
|_man_educisc_|||||-14.2539**||||-13.6016|
||||||(0.048)||||(0.176)|
|_man_experisc_|||||5.5674||||10.0019|
||||||(0.469)||||(0.265)|
|ln_empisc_||||||-2.3064|||-4.5781|
|||||||(0.302)|||(0.200)|
|_exportisc_|||||||10.8120||7.7413|
||||||||(0.114)||(0.414)|
|ln_distisc_||||||||-3.7772**<br>(0.025)|-2.5183*<br>(0.097)|
|_constantisc_|49.5283**|67.6262***|50.7481***|65.6000**|95.1645***|66.2781***|56.0470***|68.6250***|113.8197***|
||(0.015)|(0.001)|(0.008)|(0.014)|(0.000)|(0.000)|(0.001)|(0.000)|(0.001)|
|Country-sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>R2|0.33|0.36|0.34|0.30|0.35|0.33|0.32|0.36|0.54|
|Observations|102|100|100|102|105|100|103|99|87|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p-values in parentheses). Note: All variables refer to FY 2012. All regressions control for country-sector fixed effects. Standard errors are robust to heteroscedasticity.

4 We also interact each absorptive capacity with a SSA dummy to test for location-specific effects. The SSA dummy takes the value of 1 if the supplier is located in Ghana, Kenya, and Mozambique, and 0 if the supplier is located in Chile and Vietnam. We find that the effects are more favorable if firms are located in SSA. A larger share of workers with secondary education has a more positive impact on FDI linkages in SSA compared to non-SSA countries, while a higher educational level of the general manager and larger distances between suppliers and multinationals both have a less negative effect (results available upon request).

25
among others, Neidhofer et al. (2018), Azam and Bhatt (2015), Emran and Shilpi (2015), Hertz et al. (2008), Thomas (1996)). Among the few contributions on income mobility, see Fan et al. (2020) on China. The recent analysis of intergenerational occupational linkages in developing countries includes Bossuroy and Cogneau (2013), Emran and Sun (2015) and Emran and Shilpi (2011).

The literature on intergenerational mobility in Indonesia is small, with only a handful of studies available. We are aware of three studies on Indonesia that can be broadly classified as dealing with intergenerational issues in education, and all of them study the effects of the same policy experiment: a large scale school construction program in the 1970s, originally studied by Duflo (2001).<sup>12</sup> Hertz and Jayasundera (2007) analyze the effects on IGRC in schooling for the children, and find that the exposure to new schools weakened the intergenerational persistence for men, but not for women. In a recent paper, Mazumder et al. (2019) focus on the long-term effects of the program on the second generation children, i.e., children of those who benefited from the school construction when they were children themselves. Since most of these second generation children have not completed their schooling, it is difficult to estimate IGRC in schooling attainment. They thus focus on the academic performance of the children which would be correlated with the final educational attainment of a child. They show that the children of the mothers exposed to the large scale school construction in the 1970s had significant gains in school examination scores. But they do not find any effects of the fathers. In a related paper, Akresh et al. (2018) study the effects of school construction on the socioeconomic well-being of the first generation directly exposed to the program and the intergenerational effects on school attainment using the SUSENAS 2016 cross section data.

# **(3) Rural vs. Urban: A Model of Intergenerational Educational Mobility**

We consider an economy consisting of two-person households with the parent and a child. The parent of child _i_ has education level _Ei_<sup>_p_(yearsofschooling).FollowingSolon</sup>

> 12The main impact of the program was on the cohorts who went to school in the early 1970s.

6
Table 3: VIIRS Nighttime Lights and Economic Growth at Quarterly Frequency

|||night|light grow|th (yoy)||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||AE|EM|LIDC|EMDE|All|
|GDP growth (yoy)|0.266|0.492**|0.710**|0.513***|0.467***|
||(0.249)|(0.216)|(0.321)|(0.140)|(0.138)|
|country fxed efects|Yes|Yes|Yes|Yes|Yes|
|date fxed efects|Yes|Yes|Yes|Yes|Yes|
|No. of observations|799|1676|482|2158|2957|
|No. of countries|33|61|23|84|117|
|Adjusted _R_<sup>2</sup>|0.217|0.381|0.464|0.353|0.286|

_Notes_ . This table presents the regression results of nighttime light growth on GDP growth for different country groups between 2013Q2 and 2020Q4. Mutually exclusive country groups include advanced economies (AEs), emerging markets and middle-income economies (EMs), and low-income developing countries (LIDCs). Emerging markets and developing economies (EMDEs) is the union of EMs and LIDCs. Throughout, standard errors are clustered by date and in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05, _∗∗∗ p <_ 0 _._ 01.

# **5 Elasticity between Nighttime Lights and Economic Activity**

The elasticity between nighttime lights and GDP is key to quantify the link between nighttime lights and economic activity. However, both nighttime lights and GDP are measured with errors, complicating the estimation of the elasticity. In this section, we provide a novel framework to identify this elasticity, employing varying measurement errors in nighttime lights across countries.

## **5.1 Identification of the Elasticity: A Novel Approach**

Let _yi,t_ and _zi,t_ be the measured GDP growth and night light growth of country _i_ in quarter _t_ , respectively. Both _yi,t_ and _zi,t_ are observable. In particular, _zi,t_ is the average of monthly nighttime lights that are in turn predicated on daily observations. While daily observations are not accessible in our data, the number of effective days used to construct monthly observations is known. Let _Ni,t_ be the number of effective daily observations of nighttime lights for country _i_ in quarter _t_ , and _zi,t,j_ be the nighttime light growth in day _j_ . Then the relationship between quarterly and daily observations follows: 12
Mozambique, Niger, and Burkina Faso, although the reports mention that it is significant. Little coherent analysis of this important item is possible unless all foreign inputs into the capital and recurrent budgets are itemized.

5.15 Local government financing of education. One-third of the reports mention local government financing of education. Here again, the large disparities in the treatment of the role of the local government in education are because of administrative or budgetary opacity. It is not always clear whether the local authorities are distinct revenue-generating and decisionmaking bodies with regard to education, or whether they are merely recipients and administrators of central govermnent pass-throughs. The role of local administrations should be clarified.

5.16 Other central government financing of education. Twenty percent of all reports cite extrabudgetary spending as a result of balanced budget requirements, off-budget earmarked funds, special foreign currency accounts, and so on. Eight reports briefly describe the role of government ministries other than the Ministry of Education in sector financing. Vocational training, building construction, and maintenance are functions that often fall outside the purview of the Ministry of Education. In addition to budgetary contributions from specific ministries, education may be funded from other public financing sources (see box V.1). Too narrow a focus in the sector financing analysis may lead to underestimation of the public resources allocated to the sector and to distortions **i;a** the assessment of how they are distributed.

5.17 In general, vocational training programs are small, and the exclusion of figures for them in a functional education budget, while distorting, is not critical. The same cannot be said for operations and main.enance costs. In many countries, it is difficult to determine what percentage of expenditures by the public works or other relevant ministry is functionally linked to education, and these amounts can be substantial. In Guatemala, most capital investment in education is financed by the Ministry of Public Works; in Peru, over half of all school construction is funded by the National Development Corporation.

5.18 In only a few countries does a functional budget appear to be readily available or calculable, so some reports have gone to great lengths to approx1mate these costs.2/ Such efforts are necessary because the relegation of these largely capital costs to the budgets of other ministries clearly distorts the relationship between capital and recurrent financing needs in the sector. Increased attention to the development of functional, rather than administrative, budgetary categories is suggested.

Z/Tanzania's PER (1988), for example, lists recurrent and development budgetary expenditures by administrative categories (central government ministries) and by crossministerial end-use categories (general administration, public safety, sanitation, education, and so on). These categories are only partially comparable, and the creation of a workable functional budget depends upon detailed knowledge of budgetary categories.

44