rafmacalaba/datause-encoder-data huggingface dataset:
more accurate derivation of poverty indicators. However, as noted above, it assumes that intra-group distribution is constant. Put differently, the withingroup homogeneity assumption implies that the within-group rank ordering of households and individuals remains unchanged following any shock. As noted by Kanbur (1987) and Demery and Addison (1993) in a related context, the assumption that within-group distributions are unchanged and unaffected by policy shocks implies that workers are withdrawn from the sector of origin in a representative manner (leaving the distribution of income there unchanged) and that, as they move from one sector to another, they assume the income distribution characteristics of the sector of destination (in particular, the variance of income in that sector is assumed to apply to all new entrants).33 Thus, some workers may be poor not because of their personal characteristics but because of the economic circumstances that characterize their sector of employment. Whether the assumption of constant withingroup distributions is always warranted is not entirely clear; it represents therefore a potential weakness in this approach.

The procedure described above assumes that the modeler matches households as defined in the macro component of IMMPA and a household survey using information on the main source of income of household heads. An alternative treatment is also possible and depends on whether or not the household survey provides sufficient detail regarding the composition of income among individual members of each household; "light surveys" tend to concentrate on the household head, whereas more in-depth surveys provide richer information. To the extent that the information is detailed enough, and as long as each member of a household can be "allocated" to one of the six income groups identified earlier, growth rates of income and consumption can be applied separately to each individual income earner (as in Step 3 above). A more accurate measure of the change in each household's income can therefore be calculated and poverty and income indicators can be generated using either "individual" income earners or "composite" households. However, whether accounting for heterogeneity in the sources of income among individual household members makes a difference or not is generally case specific; it depends on both the characteristics of the intrahousehold distribution of income (which depends on each household's risk 33 It also implies that income transfers between households in any given group are ignored. In practice, intra-group income reallocation may be large in periods of hardship and may represent an important factor in understanding the poverty effects of adverse economic shocks.

55
|(1)<br>AlnGDPpc,|(2)<br>ADebt,|(3)<br>ABalance;|
|---|---|---|
|Disaster index;<br>-0.0687***<br>(0.0144)|0.0898***<br>(0.0196)|=-0.0296***<br>(0.00916)|
|Controls|||
|N<br>1382|1147|908|
|Standard errors in parentheses|||
|*op<0.10)**pi0.05;***p<0.01|||

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Table 5 Benchmark results

||After intervention Before intervention|
|---|---|
|Treatment|0.0353**0.0058|
||(2.44) (0.51)|
|# applications|0.0317***0.0178***|
||(4.16) (5.01)|
|Treatment * # applications|-0.0132 -0.0068|
||(1.49) (1.42)|
|R squared|0.1296 0.0751|
|N obs|1795 1791|

*** 1%, ** 5%, * 10% significance. Numbers in parentheses are absolute t values using robust standard errors. The sample consists of job seekers who were in the system more than 30 days at the time of taking the test and applied for less than 30 positions during the tracking period.

Table 6 Robustness

||After intervention Before intervention|
|---|---|
|Treatment|0.0408**0.0010|
||(2.29) (0.08)|
|# applications|0.0269***0.0170***|
||(3.57) (4.14)|
|Treatment * # applications|-0.0128 -0.0058|
||(1.43) (1.02)|
|R squared|0.1079 0.0670|
|N obs|1281 1278|

*** 1%, ** 5%, * 10% significance. Numbers in parentheses are absolute t values using robust standard errors. The sample consists of job seekers who were in the system more than 60 days at the time of taking the test and applied for less than 30 positions during the tracking period.

19
teachers who are posted to their school by the local community (about five percent of all teachers). If the local community can influence the assignment of teachers, then they are also capable to influence decisions such as firing and demotion. Our estimation results using each sample (not shown) provide very qualitatively similar results.

We also implemented other heterogeneity analyses including restricting the estimation sample to schools that exempted less than half of their students from paying tuition fees or adding village characteristics for more control variables.<sup>29</sup> The hypotheses behind these specifications are respectively that teachers at richer schools may exert more effort due to heightened monitoring power from students’ parents (see, e.g., World Bank, 2003) or that teacher effort can be influenced by their socio-economic environment and other economic opportunities that can compete for their time away from teaching. Estimation results (not shown) are, however, qualitatively similar.

# **VI. Further Investigation of After-School Tutoring**

Our school survey has a rich design which also offers details about whether teachers provide tutoring for pay or without pay, as well as the number of students that they tutor. We exploit these data for further analysis in this section. Given the rather complicated modeling (and execution) of a simultaneous equation framework if we are to combine this addition information with the working hours equation,<sup>30</sup> we now revert to using single-equation estimation methods.

Assuming our estimation sample is large enough, estimates would be consistent but less efficient than the related results in Table 2.

> 29 These village characteristics include the share of fathers with lower secondary education or higher in the village, whether the village has a school at the lower secondary or higher level, the availability of non-agricultural work, the average daily wages, and the share of poor households in the village.

> 30 For example, a good candidate for a joint estimation of tutoring with or without pay and work hours is the multinomial probit-OLS model with school random effects, which we leave for future research.

22
Policy Research Working Paper 7188

# **Abstract**

Parental involvement in matchmaking may distort the choice of spouse because parents are willing to substitute love for market and household production, which are more sharable between parents and their children. This paper finds supportive evidence in a survey of Chinese couples. In both rural and urban areas, parent matchmaking is associated with less marital harmony between the couple, more submissive wives, and a stronger belief in old age support for the son. In contrast, its association with couple income differs by rural and urban regions, perhaps because of differences in earning opportunities and in the enforcement of the onechild policy. Moreover, parent matchmaking is associated with more children for the couple and lower schooling for wives _only_ in rural areas. Thus, in places with a stronger need for old age support, parents tend to be involved in matchmaking and use it to select submissive daughters-inlaw to ensure old age support. The results render support to Becker, Murphy and Spenckuch (2015), who imply that parents would meddle with children’s preferences to ensure their commitment to providing old age support.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lxu1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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coefficients are remarkably stable except for the premium. Therefore, we accepted the hypothesis of premium endogeneity and proceeded to test for the hypothesis of inflation exogeneity given the endogeneity of the premium. The results shown in the lower panel of table 5 strongly support the exogeneity of inflation.

The regressions were estimated by TSLS in order to avoid the simultaneity bias. The set of instruments comprised the nominal growth of domestic credits, the lagged premium and the current and lagged values of the exogenous variables (all explanatory variables but the premium). The TSLS estimates are presented in tables 2-C, 2-D, 3-C, and 3-D. Tables 2-C and 2-D show the results obtained with the stock of migrant workers, with remittances defined in levels and per capita terms, respectively. Tables 3-C and 3-D present the results obtained with the stock of total migrant population. A comparison of the results obtained through OLS and TSLS reveals that the most significant change lies in the coefficient of the premium--the TSLS estimates are three times higher (in absolute value) than their OLS counterparts. It should be mentioned that very similar results were obtained when inflation was considered endogenous in additional (unreported) TSLS regressions. These results are consistent with the formal endogeneity tests reported before.17

# 4.4. Interpretation of the Results

In this section we select a representative equation and use it to derive some policy implications. First, despite the reasonable results obtained 17 Treating the stock of migtant workers as endogenous and enlarging further the set of instruments did not produce different results either, although a broader theoretical framework would imply treating both remittances and the stock of migrant workers as endogenous. The fact that most of the migration occurred before the start of the sample period may be the explanation for these results.
paper argues is that climate change affects not only the variability of precipitation and runoff, but also the interest of riparian states in international rivers to look for solutions to these phenomena by altering existing treaties and by signing new treaties among the basin riparian states.

Using a set of variables traditionally used in economic and international relations literature on international cooperation, we are also able to make some prescriptive suggestions as to how to increase cooperation in times of climate change: strengthen democracy and governance in the basin states and develop basin integration activities such as trade, stable diplomatic relations, and economic development in order to reduce economic power asymmetry and to increase basin harmonization. While there is not much new in this message, it comes with a quantitative demonstration and with the connotation of climate change impact on cooperation.

While our work provides a first attempt at looking into the relationship between climate change and treaty cooperation, it is certainly far from being complete. Additional analyses could benefit from inclusion of the treaty institutions, and especially those related to past water allocation regimes, as a possible response to increased water variability. We also plan on extrapolating the functions by introducing predicted values for precipitation and runoff into the time horizon for which Global Circulation Models (GCMs) calculate future precipitation and temperature as affected by future climate change. And finally, some of our present variables are still at the state level rather than at the basin level. The interaction between local, basin-level, and state-level variables (e.g., GDP, population) would add an important dimension to the analysis (Milner 1997).

25
# **2. Growth drivers in the business-as-usual baseline**

In this section we explore the assumptions that are required to calibrate the LTGM-PC to simulate Malaysia’s business-as-usual baseline over 2020-50. To create a business-as-usual baseline for the Malaysian economy in the long run, we need to calibrate the future paths of the growth drivers like investment, the growth of both TFP and human capital, as well as other key parameters. These assumptions are summarized in Table 2.1. We calibrate the LTGM-PC as the foundation of the analysis and add the LTGM-TFP extension and the LTGM-HC extension to simulate TFP growth and human capital growth, respectively. Additionally, short-to-medium term forecasts produced by the IMF in its Article IV report help us calibrate the future paths of public and private investment. We use population growth projections (by age cohort) from the UN for demographic trends until 2050. The remaining projections are determined by assuming that long-term trends in Malaysia remain constant and by performing some steady state calculations. The labor share of income is calibrated to 50%, which is close to its value from Penn World Tables version 9.0 (PWT 9).

**Table 2.1. Summary of Assumptions for the Malaysia Business-as-Usual Baseline**

|**Variable**|**Baseline**|**Source/Comments**|
|---|---|---|
|Labor Share|50.0%|Similar to the 2014 PWT 9 figure of 53%|
|Depreciation Rate(aggregate)|5.8%|PWT 9 figure for 2014|
|Capital-to-Output Ratio|2.25|At steadystate value|
|Human Capital Growth|0.6% 0.1%|Calculated usingthe LTGM Human Capital Extension|
|TFP Growth|0.9% 0.6%|Similar to the last 15-year average and the last 30-year<br>median usingPWT 9|
|Investment-to-GDP Ratio|24%|Based on projections of public and private-investment-<br>to-GDP|
|Public Investment-to-GDP Ratio|6.0%|Based on recent trend and IMFprojection upto 2023|
|Private Investment-to-GDP Ratio|18.0%|Based on recent trend and IMFprojection upto 2023|
|Population Growth|1.3% 0.4%|UN Populationprojections(via WB HDN)|
|GDP Growth 2020-50|4.5% 2.0%|4.6% is MTI forecast for 2020 and 4.8% is the IMF Oct.<br>2019 WEOprojections average for 2020-23.|
|Atlas GNI PC Level|US$ 10,460|World Bank WDI for 2018|

Investment-to-GDP is assumed to remain around 24% given recent trends and the IMF Article IV projections for the next few years. For the baseline, we calibrate public investment-to-output and private investment-to-output ratios ( and ) of 6 percent and 18 percent respectively (Figure 2.1). This is based on projections made by IMF (2019), which forecast that declines from 7 percent in 2019 to II<sup>GG</sup> /YY II<sup>PP</sup> /YY II<sup>GG</sup> /YY 8
# **4.3 Model predictions for import protection during other recessions**

In light of the evidence from the last section that IRR estimates changed for the Great Recession relative to 1988:Q1-2008:Q3, one last question we investigate is the ability of the model to predict new TTB import protection during _earlier_ cyclical downturns. Here we consider the question in the context of the United States. Our approach is to estimate the US model with data from 1988:Q12000:Q4 and to then use the estimated IRRs to predict out-of-sample TTBs for 2001:Q1-2007:Q4, given the realizations of aggregate variables during that period.<sup>34</sup> We continue to implement a basic model that also allows for trading partner-specific channels of aggregate fluctuations to bilateral real exchange rates and foreign real GDP growth to affect the formation of new TTBs. Nevertheless, the exercise can also be viewed as examining whether identification of the model’s parameters for a period that includes only one major US recession - i.e., the 1990-1991 downturn – can be used to predict trade policy activity alongside the subsequent domestic recession of 2001.

The estimated IRRs for the model for 1988:Q1-2000:Q4 are qualitatively similar to the full sample of IRR estimates for the period of 1988:Q1-2008:Q3 in Table 3. They continue to align with theoretical expectations although the IRR for the real exchange rate is smaller in magnitude and it is not precisely estimated in the limited sample.<sup>35</sup> Figure 5 illustrates the predicted amount of new TTBs over 2001:Q1-2007:Q4 using the US model estimates based on the 1988:Q1 – 2000:Q4 data and the realized macroeconomic data for this later period. We illustrate predictions in comparison to the actual US TTBs taking place during that period.

As Figure 5 illustrates, the model does a reasonable job at predicting the quantity of protection over several quarters. First, the model predicts a general increase in TTBs for the period of 2001:Q1-2003:Q2. Over that time period, the model predicts 989 total new TTBs would arise; the US initiated 1209 in practice. The increase in TTBs taking place during this period is related to the sharp appreciation of the US dollar and the rising unemployment rate. However, as Figure 1 again illustrates, note that the largest quarterly increase in the real exchange rate was in 2001:Q1, whereas

> 34 We estimate the same specification as that presented in Figure 4, i.e., Table 3, specification (2), in which the determinants are each defined at two lags.

> 35 We do not present the estimates in a table so as to conserve space. Nevertheless the estimated IRRs (and t- statistics) for the key determinants for the 1988:Q1-2000:Q4 period are: 1.01 (1.06) for the real exchange rate, 2.01 (3.42) for the change in unemployment, and 0.90 (2.91) for the foreign trading partner real GDP growth rate.

24
Policy Research Working Paper 7759

# **Abstract**

In rural societies of low- and middle-income countries, land is a major measure of wealth, a critical input in agricultural production, and a key variable for assessing agricultural performance and productivity. In the absence of cadastral information to refer to, measures of land plots have historically been taken with one of two approaches: traversing (accurate, but cumbersome), and farmers’ self-report (cheap, but marred by measurement error). Recently, the advent of cheap handheld GPS devices has held promise for balancing cost and precision. Guided by purposely collected primary data from Ethiopia, Nigeria, and Tanzania (Zanzibar), and with consideration for practical household survey implementation, the paper assesses the nature and magnitude of measurement error under different measurement methods and proposes a set of recommendations for plot area measurement. The results largely point to the support of GPS measurement, with simultaneous collection of farmer self-reported areas.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gcarletto@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 4: Estimated effects on dimensions of the composite 4P performance metric

||Summary<br>metric|Preparation|Presence|Pedagogy|Pupil<br>learning|
|---|---|---|---|---|---|
|_Model A: Direct efects on_|_ly_|||||
|Advertised P4P (_τA_)|-0.04<br>[-0.09, 0.01]<br>(0.11)|0.07<br>[-0.13, 0.32]<br>(0.40)|0.00<br>[-0.05, 0.07]<br>(0.93)|0.03<br>[-0.06, 0.10]<br>(0.42)|-0.02<br>[-0.08, 0.02]<br>(0.27)|
|Experienced P4P (_τE_)|0.23|0.02|0.08|0.10|0.09|
||[0.19, 0.28]<br>(0.00)|[-0.13, 0.16]<br>(0.84)|[0.02, 0.14]<br>(0.01)|[-0.00, 0.21]<br>(0.05)|[0.03, 0.15]<br>(0.00)|
|Experienced P4P _×_|0.03|0.07|-0.01|0.07|-0.00|
|Incumbent (_λE_)|[-0.01, 0.07]<br>(0.10)|[-0.03, 0.18]<br>(0.17)|[-0.06, 0.05]<br>(0.70)|[-0.01, 0.16]<br>(0.11)|[-0.04, 0.03]<br>(0.86)|
|_Model B: Interactions bet_|_ween advertised_|_and experienc_|_ed contracts_|||
|Advertised P4P (_τA_)|-0.03<br>[-0.12, 0.05]<br>(0.42)|0.16<br>[-0.11, 0.48]<br>(0.19)|-0.01<br>[-0.16, 0.17]<br>(0.86)|0.12<br>[-0.27, 0.55]<br>(0.44)|-0.01<br>[-0.12, 0.11]<br>(0.91)|
|Experienced P4P (_τE_)|0.22<br>[0.15, 0.29]<br>(0.00)|-0.00<br>[-0.26, 0.25]<br>(0.97)|0.08<br>[-0.01, 0.16]<br>(0.07)|0.17<br>[-0.05, 0.38]<br>(0.12)|0.08<br>[0.00, 0.16]<br>(0.04)|
|Advertised P4P _×_|-0.02|-0.11|0.02|-0.11|-0.03|
|Experienced P4P (_τAE_)|[-0.11, 0.07]<br>(0.65)|[-0.45, 0.23]<br>(0.53)|[-0.12, 0.16]<br>(0.69)|[-0.45, 0.24]<br>(0.53)|[-0.15, 0.08]<br>(0.64)|
|Experienced P4P _×_|0.05|0.09|-0.01|0.00|0.00|
|Incumbent (_λE_)|[-0.01, 0.10]<br>(0.07)|[-0.07, 0.26]<br>(0.27)|[-0.09, 0.07]<br>(0.82)|[-0.13, 0.14]<br>(0.96)|[-0.05, 0.06]<br>(0.90)|
|Observations|3996|2514|3455|2136|3049|
|FW recruit mean|0.49|0.65|0.89|1.98|0.48|
|(SD)|(0.22)|(0.49)|(0.31)|(0.57)|(0.27)|
|FW incumbent mean|0.37|0.50|0.87|2.05|0.45|
|(SD)|(0.24)|(0.50)|(0.33)|(0.49)|(0.28)|

Notes: For each estimated parameter, the table reports the point estimate, 95 percent confidence interval in brackets, and _p_ -value (or for FW means, standard deviations) in parentheses. Randomization inference is conducted on the associated _t_ statistic. All estimates are pooled across years, but outcomes are observed in the FW arm during the second year only. Outcomes are constructed at teacher-round-level as follows: _preparation_ is a binary indicator for existence of a lesson plan on a randomly chosen spot-check day; _presence_ is the fraction of spot-check days present at the start of the school day; _pedagogy_ is the classroom observation score, measured on a four-point scale; and _pupil learning_ is the Barlevy-Neal percentile rank. The _summary metric_ places 50 percent weight on learning and 50 percent on teacher inputs, and is measured in percentile ranks.

31