rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.2.4 Exploiting all Landsat Spectral Bands**

Next, the predictive power of the full set of spectral Landsat bands in the cross-section is explored. We regress a “kitchen sink” specification that includes all Landsat bands as explanatory variables and then compare the fit with the results from the regressions with the simple indices only. Band averages as well as other characteristics (such as the standard deviation, median, interquartile range) of the band distributions within a geographical unit are used as predictors. While the Landsat indices such as NDBI provide an easy to interpret measure of ground characteristics correlated with economic outcomes, a researcher who is interested in remote sensing economic activity needs to form a prior on which spectral signatures to use as predictors. In contrast, using all spectral Landsat bands as predictive variables allows for an agnostic and flexible (yet difficult to interpret) way of recovering the statistical relationship between measures of economic activity and remotely sensed data.

However, the large number of potential variables poses the risk of overfitting the data and of mistaking noise as a valuable signal. To alleviate this issue, two kinds of exercises are performed to guide the econometric approach: LASSO techniques (Tibshirani, 1996; Belloni et al., 2014) to restrict the variable space and cross-validation methods from the machine learning literature. The sample is divided into a training and a testing dataset to judge out-of-sample validity of the estimated parameters. The algorithm randomly attributes 70% of the sample to a training dataset, estimates the coefficients, and then examine the outof-sample fit for the remaining 30% testing dataset. This exercise is repeated 500 times to calculate the cross-validated R-squared as the average out-of-sample R-squared of these 500 draws.

Table 3 compares the regression results for the Landsat indices (column 1) with the ones of more flexible models. Column 2 first only uses the mean band values of each commune and then augments them with the standard deviation (column 3). Skewness of the distribution 16
0.7|0.7|0.7|0.7|
|Rural|8.3|7.8|8.1|8.2|8.3|8.5|8.0|8.4|8.2|8.3|

_Source:_ Authors’ calculations based on the GLSS7 and the CPI price data. _Note:_ F = Food; H = Housing; NF = Non-food; Variable weights are applied to the WCPD regression.

**Table 6. Regional poverty rates by spatial deflation methodologies**

||||Food||<br>F+|<br>NF||F+N|F+H||
|---|---|---|---|---|---|---|---|---|---|---|
||GLSS|price|CPI (item)|CPI(variety)|CPI(item)|CPI(variety)||CPI (variety)+|GLSS hous|ing|
||||||||Mdi|Mdi<br>WC|Hdi<br>PD +|Hdi|
||GEKS-F|WCPD|WCPD|WCPD|WCPD|WCPD|ean<br>paid rent|ean<br>imputed rent|eonc<br>paid rent|eonc<br>imputed rent|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Western|19.1|19.2|19.1|20.8|23.9|21.1|21.3|21.0|21.4|21.3|
|Central|17.8|16
# **5.  Educational differentials in migration**

The literature also emphasizes the importance of human capital as a precondition of migration (Schultz 1982; Todaro 1969). For example, one percent of national sampling survey conducted in 1995 shows that among all the migrants, 30.8 percent had received senior high and higher formal education, comparing to 11.3 percent for the same category of national average (Yang 1997). Zhao (1999) further found that better educated people in rural areas were not necessary to leave countryside to migrate to other provinces and likely to shift their jobs from agriculture to the non-agricultural sector in towns within own region. One of reasons lies in the fact that educated rural people regard migration as a loss of their social status.

Thus, it is unclear whether the development of towns attracts a large part of educated labor force, and whether development of towns attracts educated laborers from its own region who otherwise heads to other regions as suggested by Zhao. In Table 9, we represent the findings on educational attainment of those who migrated into towns, and of general population in towns in three provinces.

Table 9 Educational attainment of town in-migrants (6 years old or above) by migration status: 2000

|Province||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|---|
||||(|%)|||||
|Zhejiang|in-migrant|4.15|28.89|43.91|12.76|5.00|3.94|1.33|
||town pop|8.03|35.32|38.23|11.67|3.29|2.66|0.79|
|Henan|in-migrant|3.58|17.59|37.
that there are certain technological factors that are industry-specific.<sup>13</sup> The other explanatory variables are the same as those presented in Table 3, although the first six columns exclude industry dummies, but control for a sector‟s financial dependence, competitiveness and contestability, proxied by the Herfindahl index, the “natural” rate of entry and of employment turnover.

The results provide ample evidence that changing credit conditions were an important driver of firm exit during the crisis; firms operating in industries more dependent on external finance and with lower asset tangibility were ceteris paribus significantly more likely to exit during the crisis, both in absolute terms and relative to other periods as is evidenced between the significant interactions between the crisis dummy and these financial characteristics. These results hold using both productivity proxies. However, controlling for these financial characteristics does not eliminate the attenuation effect.

In addition, the protective impact of productivity in industries more dependent on external finance rose significantly (columns 2 and 4). By contrast, the crisis interactions between productivity and asset hardness as well as liquidity needs are not statistically significant (columns 2 and 4). Thus, it appears that firms in sectors that are more sensitive to changing credit conditions were hit harder but this does not account for the observed attenuation of the conditional correlation between productivity and survival.

In summary, firms more exposed to fluctuations in credit market conditions were hit harder by the crisis, yet these effects cannot fully account for the attenuation of the link between productivity and exit during the crisis. If anything, to the extent that the crisis had a differential impact on firms with different financial characteristics, it induced a cleansing effect amongst firms more exposed to the changing credit conditions.

> 13  The impact of liquidity needs proxied by the inventories to sales ratio (following Raddatz (2006)) do not rely on this assumption as this measures was constructed using the SI data.  Again, while not shown, the results with this measure give the same results.

25
the strength of buyer (supplier) linkages for each industry, a summation of regional (here district) industry firms weighted by the industry’s input (output) coefficient column (row) vector from the national inputoutput account is used: where,  _jk_ is the strength of the buyer (supplier) linkage, _wk_ is industry _k’_ s national input (output) co-efficient column (row) vector and _n jk_ is the total number of firms in industry _k_ in district _j_ in year _t_ . The measure examines local level inter-industry linkages based on national input-output accounts. The national I-O coefficient column vectors describe intermediate goods requirements for each industry, whilst the I-O coefficient row vectors describe final good sales for each industry. Assuming that local industries follow the national average in terms of their purchasing (selling) patterns of intermediate (final) goods, national level linkages can be imposed to the local level industry structure for examining whether district _j_ has a right mix of buyersupplier industries for industry _k_ . By multiplying the national I-O coefficient vector, which is time-invariant, for industry _k_ and the size of each sector in district _j_ , simple 8
36

**Table 1: Infant and under-5 mortality trends in Sub-Saharan African countries with a Demographic and Health Survey since 2005**

|||Infant mortalityrate|Under-5 m|ortalityrate|
|---|---|---|---|---|
|Country|DHS survey|per 1000<br>% decline|per 1000|% decline|
|||li bith<br>|li bith||
|||ve rs<br>peryear|ve rs|peryear|
|Benin|2001|89|160||
||2006|67<br>5.5%|125|4.8%|
|Ethiopia|2005|77|123||
||2011|59<br>4.3%|88|5.4%|
|Ghana|2003|64|111||
||2008|50<br>4.8%|80|6.3%|
|Guinea|1999|98|177||
||2005|91<br>1.2%|163|1.4%|
|Kenya|2003|77|115||
||2008|52<br>7.6%|74|8.4%|
|Lesotho|2004|91|113||
||2009|91<br>0.0%|117|-0.7%|
|Liberia|2007|71|110||
||2009|73<br>-1.4%|114|-1.8%|
|Madagascar|2003|58|94||
||2008|48<br>3.7%|72|5.2%|
|Malawi|2004|76|133||
||2010|66<br>2.3%|112|2.8%|
|Mali|2001|113
where the right-hand side is the ratio between the standard deviation and expected value of college payoffs, calculated for the student’s optimal effort, _e_<sup>_∗_</sup> _it_<sup>,chosenunderscenario</sup><sup>_p_.Given</sup> _e_<sup>_∗_</sup> _it_<sup>,therandomnessin</sup><sup>_V_ ̃</sup><sup>_coll_(</sup><sup>_·_)comesfromtheperformanceanddropoutshocks,</sup><sup>_z_and</sup><sup>_ddrop_</sup> respectively, which are associated with cumulative classes completed and therefore effort. Note that anticipated risk varies across students and programs, and over time.

Does effort lower anticipated risk? To investigate this matter, in principle we might think of using our simulated baseline data to regress anticipated risk on (optimal) effort, controlling for cumulative past performance. However, this regression would suffer from endogeneity because optimal effort would appear in both sides of the regression. Since _e_<sup>_∗_</sup> _it_<sup>isafunctionofthestate</sup> variables, _t, hit−_ 1 _, θi,_ and _yi_ , we instrument for effort using the state variables (see Appendix Table F1), and run the second-stage regression reported in Table 14. This regression indicates that, in our baseline model, effort indeed lowers anticipated risk. The elasticity of anticipated risk with respect to effort is negative and large at -2.34. Student effort is therefore highly effective at mitigating performance and dropout risk.

**Table 14: Anticipated Risk.**

||Dependent variable<br>ln(Anticipated risk)|
|---|---|
|ln(efort)|_−_2_._340<sup>_∗∗∗_</sup><br>(0_._089)
. Gender-Segmented Labor Markets and Trade Shocks.</mark> _<mark>arXiv preprint arXiv:2301.09252</mark>_ <mark>.</mark> <mark>Hamermesh, D. S. (1996).</mark> _<mark>Labor demand</mark>_ <mark>. Princeton University press.</mark> Hummels, D., Jørgensen, R., Munch, J., & Xiang, C. (2014). The Wage Effects of Offshoring: Evidence from Danish Matched Worker-Firm Data. _American Economic Review_ , Vol. 104, No. 6, pp. 1597-1629.

<mark>Jordà, Ò. (2005). Estimation and inference of impulse responses by local projections.</mark> _<mark>American economic review</mark>_ <mark>,</mark> _<mark>95</mark>_ <mark>(1), 161-182.</mark> <mark>Plagborg‐Møller, M., & Wolf, C. K. (2021). Local projections and VARs estimate the same impulse responses.</mark> _<mark>Econometrica</mark>_ <mark>,</mark> _<mark>89</mark>_ <mark>(2), 955-980.</mark> <mark>Robertson, R., Vergara Bahena, M. A., Kokas, D., & Lopez-Acevedo, G. (2021). International Trade and Labor Markets: Evidence from the Arab Republic of Egypt.</mark> 19
the distance to the mean in absolute terms, whereas spi-like anomalies directly relate the intensity of a hazard to its probability of occurrence.

Since there is little consensus around an optimal underlying variable for describing the RSQ, we test a range of usual inputs including precipitations, the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI) and Dry Matter Productivity (DMP). We acknowledge that other variables typically used for monitoring droughts and agricultural production could be employed and leave it to future work to test other indices such as the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Index, other vegetation indices, temperature-based indices or more elaborate direct measures of agricultural production and availability of pasture. That said, our results suggest that RSQ measurement choices matter less than the choice of heterogeneity structure (i.e. vulnerability factors) in the optimization of the model explaining power of the performance.

One final consideration for the calculation of RSQ indices is on the temporal dimension with the choice of time window ∆. First of all, in any given year _t_ , we account for differences in the onset of the rainy season that may exist across administrative units. For any geographic unit _a_ , we calculate monthly historical averages for our underlying variable _x_ and considering any time window width of _m_ months, ∆is defined such that it covers the _m_ consecutive months where _x_ is historically highest. Second of all, shorter time scales imply that conditions at the peak of the rainy season are the prime determinant of future food consumption whereas larger time scales consider that aggregate measures over the entire rainy season are more relevant to appraise agricultural campaigns. We do not favour one assumption over the other and simply test different time scales ranging from one to months.<sup>15</sup> Gridded precipitations are retrieved from the CHIRPS-2.0 product of the Climate Hazard Group. Monthly precipitations at a resolution of 0.05<sup>_◦_</sup> are available for the period 1981 to present.
**Figure 4. Marginal contribution to reduction in the Gini index, percentage points**

<!-- Start of picture text --> 55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>Pensions Means-tested transfers Non means-tested transfers Direct taxes SIC<br><!-- End of picture text --> Note: Incomes are equivalized using the (modified) OECD equivalence scale. Source: EUROMOD-2017 statistics and authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

The effect of the tax-benefit system on poverty reduction in Russia is moderate compared with the EU countries. The relative poverty rate, defined for comparability with the EU method as a share of population with equivalized disposable incomes below 60 percent of the median income, drops by a half after fiscal interventions. While this is a less-than-average performance for EU standards, it is still better than nine of 28 EU countries (Figure 5). Again, this effect is almost entirely driven by pensions, whereas in the EU other benefits (means-tested and non-means-tested) have typically a significant effect on poverty reduction.

**Figure 5. Marginal contribution to reduction in poverty, percentage points**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>Pensions Means-tested transfers Non means-tested transfers Direct taxes SIC Total<br><!-- End of picture text --> Note: Relative poverty is defined as the share of population with income per adult-equivalent below 60 percent of the median disposable income. Incomes are equivalized using the (modified) OECD equivalence scale. Source: EUROMOD-2017 statistics and authors’ calculations using RUSMOD for 2017 policy
if these questions are substituted for our morality proxy, they produce insignificant results (not reported in tables for space reasons).

# _E. Morality and Appropriation_

The theory in Section III is not the only possible explanation for the empirical links between morality (and the morality-ability correlation) with economic performance, shown in Tables 2-6. The theory’s relevance arguably would be more convincing supported if morality, and the morality-ability correlation, were also linked with lower levels of “appropriative” activities. It is difficult to measure the extent of “appropriation” in a reliable way at the country level, but in Table 7 we present some evidence from surveys of business executives, conducted by the World Economic Forum (2010) and available for 73 of the 74 countries in our main sample (Belarus is the missing country).

Surveys include about 100 respondents (and firms) per country. Samples are designed to be at least roughly representative by sector. Small firms are included, but larger firms and firms trading across borders are overrepresented. Survey responses for most questions are on a 1-7 ordinal scale, and the data made available by the WEF are country-level means of these ordinal scales, with higher values indicating “better” scores.

There are likely many important appropriative activities not addressed in the WEF’s questionnaire. Moreover, we do not attempt to identify and control for the numerous other variables that might influence the extent of “appropriation.” The regressions presented in Table 7 should therefore be interpreted as merely suggestive; they simply test whether or not certain appropriative activities seem to be larger problems in countries where morality is more prevalent among workers, and where morality is especially prevalent among workers with greater ability.

31
errors are in parentheses; ***, **, * denote statistical significance at 1%, 5% and 10% respectively Source: Estimates are based on Serbia LFS, 2008 and 2009 surveys

**Table 10. Gender Wage Gap at Quartiles, 2009** **<u>(Log of Monthly Wages)</u>**

||Q1|Q2|Q3|Q4|
|---|---|---|---|---|
||9.533***|10.003***|10.327***|10.800***|
|Male|(0.014)|(0.004)|(0.004)|(0.012)|
||9.523***|9.989***|10.326***|10.786***|
|Female|(0.014)|(0.005)|(0.005)|(0.015)|
|Wage|0.010|0.014**|0.001|0.014|
|Differential|(0.020)|(0.007)|(0.006)|(0.019)|
||**Threef**|**old Decompo**|**sition**||
||-0.009|-0.002|-0.020|0.023|
|Endowments|(0.026)|(0.008)|(0.013)|(0.023)|
||0.009|0.013|0.016*|0.047*|
|Coefficients|(0.024)|(0.008)|(0.009)|(0.023)|
||0.
parameters. In most cases researchers will not have data on the autocorrelation of the outcome of interest. It is hoped that the data provided in this paper for different outcomes from different surveys will therefore provide a starting point for researchers, and that more researchers will report these autocorrelations in future experiments.

**7.** _Can we just obtain multiple measures more cheaply by recall?_ A cheaper alternative to conducting multiple post-treatment or pre-treatment rounds is to ask multiple measurements in a single survey, with recall. While intuitively appealing, the value of this approach is likely to be limited in practice, since it is precisely the types of variables that are noisiest and hardest to measure in practice for which the data are typically less autocorrelated and for which multiple measures are of most value. For small informal firm owners who do not keep books, asking profits with recall over multiple months is difficult, while asking expenditure month by month or quarter by quarter with recall is likely to be highly inaccurate. Gibson and Kim (2010) show even with wage workers in the United States that retrospective recall is problematic, with workers underreporting transitory variation in earnings, creating non-classical measurement errors. However, such a strategy could be used in cases where good records are available, such as in experiments with larger firms which have accurate books or records.<sup>11</sup>

**8.** _To baseline or not?_ The power calculations above suggest that, in a number of cases, it can be optimal in terms of power to conduct a single post-treatment survey of size _n=K_ than to do a baseline and follow-up with _n=K/2_ . However, there are several other factors to consider in choosing whether or not to use a baseline. A baseline can be used to stratify the randomization on key variables, improving power and providing a basis for examination of treatment effect heterogeneity. It is often used to examine what determines take-up of some intervention. It can be useful for verification of
--|---|
||(a)|(b)|(c)|(d)|
|Lerner index|-0.434**|-0.387**|||
||[0.200]|[0.195]|||
|Concentration 3|||-0.000<br>[0.122]||
|Herfindahl index||||0.034|
|||||[0.138]|
|Log firm size|0.089***|0.089***|0.089***|0.089***|
||[0.003]|[0.004]|[0.003]|[0.003]|
|Manufacturing|0.019*|0.018*|0.020*|0.020**|
||[0.010]|[0.011]|[0.010]|[0.010]|
|Exporter|0.044***|0.042***|0.042***|0.044***|
||[0.008]|[0.012]|[0.008]|[0.008]|
|Foreign-owned|-0.074***|-0.086***|-0.076***|-0.075***|
||[0.012]|[0.013]|[0.012]|[0.011]|
|Government-owned|-0.198***|-0.232***|-0.197***|-0.200***|
||[0.022]|[0.037]|[0.023]
respondent who reports information on behalf of his spouse and/or young adult child living within the household.

In this paper, we show that alternative survey methods can address the underreporting of women’s and young adults’ labor market outcomes, and thereby measure more accurately underlying gender- and age-based employment gaps. To this end, we designed and implemented a survey experiment within the context of a household survey in El Salvador in order to estimate the impact of two alternative survey methods for the respondents’ reporting of employment and work, relative to the standard household survey approach. Our findings confirm that women report higher employment when they are given examples that enable them to recognize how they contribute to the working population. We also show that young men reported higher values of labor indicators compared to older men when self-reporting was enforced.

The analysis of different survey methods for labor measurement is particularly relevant in a context such as El Salvador where gender and age-based gaps in labor market outcomes are more pronounced compared to economically similar countries, and where informal work is prevalent (World Bank, 2023). For example, according to the 2022 Household and Multipurpose Survey (EHPM), in El Salvador the gender gap in employment was 30.6 percentage points, which has remained stable for the past 24 years, and youth experience 2.4 times more unemployment than adults (UN, 2023). Moreover, 66.8% of the employment in El Salvador occurs in the informal sector, which highlights why it is important for respondents to understand which activities can be classified as work when collecting and providing information. Furthermore, the share of proxy respondents who participated in the 2022 labor module of the household survey is high but even higher for young men (59.4% of 15 to 64-year-old individuals and 85.5% of young men were represented by proxy respondents).

To estimate each survey method’s causal effect on the respondents’ reporting of employment and work, we randomly distributed the 1,008 households and their