rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Weather Variables & Transformations

|**_Panel A_**_: Rainfall_||
|---|---|
|Daily rainfall|In millimeters|
|Mean|The frst moment of the daily rainfall distribution for the growing<br>season<sup>_†_</sup>|
|Median|The median daily rainfall for the growing season<sup>_†_</sup>|
|Variance|The second moment of the daily rainfall distribution for the grow-<br>ing season<sup>_†_</sup>|
|Skew|The third moment of the daily rainfall distribution for the growing<br>season<sup>_†_</sup>|
|Total|Cumulative daily rainfall for the growing season<sup>_†_</sup>|
|Deviations in total rainfall|The z-score for cumulative daily rainfall for the growing season<sup>_†_</sup>|
|Scaled deviations in total rainfall|The z-score for cumulative daily rainfall for the growing season<sup>_†_</sup>|
|Rainfall days|The number of days with at least 1 mm of rain for the growing<br>season<sup>_†_</sup>|
|Deviation in rainfall days|The number of days with rain for the growing season minus the<br>long run average<sup>_∗_</sup>|
|No rain days|The number of days with less than 1 mm of rain for the growing<br>season<sup>_†_</sup>|
|Deviation in no rain days|The number of days without rain for the growing season minus<br>the long run average<sup>_∗_</sup>|
|Share of rainy days|The percent of growing season days with rain<sup>_†_</sup>|
|Deviation in share of rainy days|The percent of growing season days with rain minus the long run<br>average<sup>_† ∗_</sup>|
|Intra-season dry spells|The maximum length of time (measured in days) without rain<br>during the growing season<sup>_†_</sup>|
|**_Panel B_**_: Temperature_||
|Daily average temperature|In Celsius|
|Daily maximum temperature|In Celsius|
|Mean|The frst moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Median|The median daily temperature for the growing season<sup>_†_</sup>|
|Variance|The second moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Skew|The third moment of the daily temperature distribution for the<br>growing season<sup>_†_</sup>|
|Growing degree days (GDD)|The number of days within bound temperature for the growing<br>season, following Ritchie and NeSmith (1991)<sup>_†_</sup>|
|Deviation in GDD|GDD for the growing season minus the long run average<sup>_† ∗_</sup>|
|Scaled deviation in GDD|The z-score for GDD|
|Maximum temperature|The average maximum daily temperature|

_Note_ : The table presents definitions for included weather variables and transformations from weather sources defined in Table 1. _†_ Growing season determined for each country following FAO crop calendar (see Table A1). _∗_ For variables when “long run” is referenced, long run is defined as the entire length of the weather data set. While each weather source has a different start date, to ensure blinding all data sets were shortened to 1983, which is the latest start date of the data sources.

60
In Panel A of Table 8, the first column highlights the results from the propensity score matching technique. The estimated impact of the Covid-19 anticipation measure is a significant reduction in household expenditure by 4.3% (i.e., [e<sup>-0.044</sup> -1] ×100), on average, ceteris paribus. In addition, the effect on household asset counts negatively changed by a magnitude of 0.4 units. However, the measure used for anticipation of Covid-19 lockdown is linked with a 14% (i.e., [e<sup>-0.151</sup> -1] ×100) reduction in the monetary value of assets. There is evidence of a reduced number of types of livestock owned, but the magnitude of change is low, and the number of livestock owned shows no significant change. The anticipation of Covid-19 accounted for an increase in household objective poverty by 4.4 percentage points, on average. Similarly, subjective poverty measures also estimate a reduction in welfare as the proportion of households self-reported to be living well and subjectively rich reduced by a significant 4.2 and 6.6 percentage points, respectively.

Panel C of Table 8 shows the Covid-19 anticipation effect on household and individual labor market outcomes. The results depict a reduction in the share of working-age individuals within a household by 2.8 percentage points, on average. In addition, the probability of employment reduced by 5.4 percentage points, with a slight increase in working hours per day of 0.29 hours for the employed, on average. In addition, the likelihood of employment in agriculture increased by 4.5 percentage points in anticipation of the Covid-19 lockdown while likelihood of employment in Trade and Service sector reduced by 1.8 and 3.4 percentage points, respectively.

**Table 8: ATT Estimates of Covid Anticipation on Household Welfare and Labor Market Outcomes**

||Propensity<br>Score<br>Matching|Inverse<br>Probability<br>Weighting|Nearest<br>Neighbor<br>Matching|
|---|---|---|---|
|**Panel A: Household Indicators**||||
|Log expenditure per capita|-0.044***|-0.046***|-0.052***|
||(0.008)|(0.006)|(0.008)|
|Number of different types of asset owned|-0.377***|-0.361***|-0.401***|
||(0.061)|(0.044)|(0.058)|
|Total number of assets owned (count)|-0.5340***|-0.540***|-0.539***|
||(0.113)|(0.080)|(0.104)|
|Log value of assets|-0.151***|-0.159***|-0.175***|
||(0.024)|(0.018)|(0.022)|
|Number of different types of livestock owned|-0.095***|-0.082***|-0.085***|

33
and changing patterns of migration and a brief history of forestry management practices in Nepal. In Section 3 we develop a model of a village economy that captures the key aspects of the interaction between migration, remittances and forests. In Section 4, we describe the data and in Section 5 we outline the estimation method and discuss some key results. We conclude with an outline of the next steps in Section 6.

# **2 The Context**

## **2.1 Background**

With an average per capita GDP of about $440 ($1,180 in purchasing power parity terms), Nepal is the poorest country in South Asia and one of the poorest in the world. About 30 percent of the Nepali population was poor in 2004, 46 percent of the adult population illiterate, and almost half the children under five years are malnourished. During the period of liberalization in the mid1980s and early 1990s, the economy grew at about 5 percent per year. The impact of this relatively high rate of economic growth on improvements in living standards was dampened by the country’s high population and urban-centered growth, limited access to basic services, poor governance, and persistent political instability. Economic growth slowed in the early 2000s due to a global economic slowdown that diminished export markets, and the escalation of a violent Maoist conflict that took a heavy toll on the economy. Although the conflict eventually came to an end in 2006, the overall pace of economic growth has not been as robust as it was in the early 1990s.

The Nepali economy is overwhelmingly rural and agriculture comprises 40 percent of the GDP. A Labor Force Survey completed by the Central Bureau of Statistics in 2008 found that 74 percent of the working age population was engaged in agriculture. However, unlike neighboring India that witnessed substantial growth of the rural economy, particularly the agriculture sector, during its Green Revolution, rural incomes in Nepal were by and large stagnant between 1965 and 1995. The initial challenges of a difficult geography have never been overcome adequately as progress on the extension of roads and other essential infrastructure to remote areas has been very slow. Input and technology costs are high, coverage of irrigation facilities is low, access to markets is limited, and as a result agriculture largely remains for subsistence.

What has been remarkable in the villages of Nepal since the middle of 1990s is the sharp increase in remittance income that rural households have received from family members working in urban areas within the country as well as numerous destinations abroad. Migration itself is not a new phenomenon in Nepal and even in recent years, there has been only but a modest increase in the absolute number of migrants. What is new is that there has been a significant diversification in terms of destinations of these migrants. Until the early 1990s migrants predominantly went to India in search of seasonal agricultural work as well as other service sector jobs. In the mid-1990s, the wave 6
**Figure 19. Predicted Probability of Moving from Employment to Unemployment (E-U) and from Employment to Out of the Labor Force (E-O) Between 2001 and 2002, by Age and Education in 2001**

<!-- Start of picture text --> EU<br>0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>15-19 20-24 25-34 35-44 45-54 55-ret ret-64 65<br>age<br>Higher Higher02 Secondary Basic<br>EO<br>0.15<br>0.10<br>0.05<br>0.00<br>15-19 20-24 25-34 35-44 45-54 55-ret ret-64 65<br>age<br>Higher Higher02 Secondary Basic<br><!-- End of picture text --> _Notes:_ Gender, ethnicity, employment status, occupation, economic activity and residence are controlled (see Appendix Table 19). Higher02: graduates of higher education institutions in 2002. Postsecondary vocational education merged with general secondary. “Ret” stands for retirement age. _Source:_ Calculations based on LFS 2002.

**Gender and ethnicity are not important determinants of the probability of becoming unemployed.** However, Appendix Figure 13 shows that females and non-Latvians were significantly more likely to leave the labor force during the 2001-2002 period. With respect to labor force exit, both effects are stronger for youth and the elderly. The difference in probabilities of exiting the labor force is about 0.03 between the sexes and 0.02 between the Latvians and non-Latvians. Note, however, that the 2002 Latvian Labor Force Survey documents that exit from employment to nonparticipation is linked to discouragement in only 7 percent of cases.

**Regional effects.** Appendix Figure 14 compares the probability of making transitions to unemployment across regions. For prime-age workers in the Riga region, Zemgale, and Kurzeme, the predicted probability of becoming unemployed between 2001 and 2002 is about 0.03.  In Latgale and Vidzeme this probability varies from 0.040 to 0.045. It is worth noting that, while higher rates of transitions to unemployment are observed in Latgale than in Riga or Zemgale, the difference in unemployment

26
# **Appendix**

# **Estimating mortality: A comparison between the sample based approach and the reconstruction approach**

This paper uses the sample based approach to estimate the impact of the mortality from the Khmer Rouge period on the current Cambodian population structure. To the best of my knowledge, it is the first to do so with a nationally representative sample of women interviewed about the birth, survival and deaths of all their siblings. However, this sample, interviewed more than 20 years after the mortality crisis is by definition a sample of survivors and, as such, as already explained in section II, likely to substantially underestimate mortality, since families in which all siblings died will not be included in the counts and, similarly, families which experienced a large proportion of casualties are less likely to be included in the sample. Families with a large migration rate are also less likely to be counted.

This appendix attempts to quantify the degree of underestimation of the mortality crisis by comparing death counts obtained using the death probabilities calculated from our sample of survivors with excess mortality estimates obtained using the reconstruction method. The most careful reconstruction exercise has been realized by Heuveline (1998 (a), 1998 (b), 2001 (a), 2001 (b)). He estimated, as baseline, the 1970 population of Cambodia by projecting the data from the 1962 census. He estimated the 1980 population by projecting backward data from 1993 electoral lists. Using “normal” mortality parameters and estimates of migratory flows, he then projected forward his 1970 estimate into 1980 and backward his 1980 estimate back to 1970. From both projections, the residual between the projection under “normal” parameters and the actual estimate can be computed. The average of the forward and the backward projection residuals is then presented as the number of excess deaths.

The results of the comparison are presented in table A1. For six birth cohorts born between 1940 and 1969 for which the sample based approach could provide reliable estimates of the death probabilities, the initial 1970 population for males and females is taken from Heuveline (2001(b)) and reported in columns 1 and 2. Notice therefore that, in this exercise, the sample and the reconstruction approaches start from the same baseline and so differences between estimates cannot come from this source. In columns 3 and 4, death probability during the 1970-74 period by gender and birth cohorts are taken from my estimates in table 1. The multiplication of columns 1 with 3 and 2 with 4 respectively yields the number of deaths, while a simple subtraction from columns 1 and 2 yields the 14
sample of children who switched teachers were not randomly assigned, selective matching might bias our estimate. A second interesting possibility was the role of uncertainty in teaching inputs on household investments. The model shows that greater uncertainty in teaching inputs leads to an ex ante response among households through greater precautionary spending. Faced with greater uncertainty in teaching inputs, the movers would have higher precautionary spending and thus be less susceptible to ex post shocks.

We would have liked to directly test which of these mechanisms is responsible for the difference in estimates. Do households really undertake precautionary schooling investments? That is, do parents of movers spend more time or money with their children than those of non-movers? Unfortunately, our data on household inputs does not allow us to investigate this directly. Although we surveyed households matched to these schools (see Das and others 2004a), these were all rural households and our sample of non-movers is too small to draw any meaningful inferences.

If we believe that households play an important role in determining educational outcomes of their children, this paper suggests a direction for future work. Using a model allowing for household responses to teacher and school inputs allows for richer insights than standard production function approaches. The impact of current year shocks on learning achievement depends on other sources of uncertainty (non time-varying attributes of the teacher in this paper) and this has important implications for future evaluation work.<sup>17</sup> Currently there is little research on the link between household and school inputs, and none on the precautionary motive discussed in this paper. Evidence either way would be helpful.

> 17 Suppose that an experiment were designed to study the effect of absenteeism on learning achievement. The ”treatment on the treated” estimator will represent the average effect, averaged across children who changed and remained with the same teacher. This paper suggests that the external validity of the experiment may be compromised due to this important source of heterogeneity–an empirical implication is to try and capture information on this and other changes that have occurred during the year of the experiment.

22
# Research Components of World Bank Edutation Projects, since 1982.

|L|IIIIiA|rea<br>Countryand<br>Loan/Credit<br>|<br>Number|Approved|<br>Research Components<br>|Current Status of Studies, including<br>ICompleted Docuomets/Data|
|---|---|---|---|---|
|ASIA<br>ASIA<br>ASIA|Indonesia<br>LN2355-IND<br>Indonesia LN 2547-IND<br>IndonesiaLN2992-lND|1984<br>1985<br>1989|OMii)efficiencyoperation<br>of schools and<br>colleges;<br>(iv) effects of educationrl<br>restructuring<br>on<br>teacher training (para. 3.10 p.38).<br>Establish monitoring system: Numbers of<br>teachers with training<br>, knowledge of teachers<br>use of training by teachers, effects on student<br>achievement,<br>effects on placement of teachers.<br>(3.21, 43)<br>Second Nonform_l Education.<br>Studies in:<br>(i)<br>mechanisms for local financing<br>of nonformal<br>education;<br>(ii)<br>national<br>system to test literacy<br>and<br>skill acquisition;<br>(iii) economic and finoncial analyses of<br>village-based<br>nonformtl education programs;<br>(iv)<br>study of volunteer<br>incentive<br>patterns;<br>(v) development<br>ofBPKBs, SKBs,andBPNs<br>training<br>centers.<br>Sundry evaluation<br>conponents(pera. 2.28 p.16).<br>Second tkniversity<br>Development. No research<br>component.<br>Tree Crops Husmn Resource.<br>No research components|,<br> <br>No information<br>sipplied<br>|
|ASIA|Indonesia LN 2102-IND|1982|Second Textbook. Establish database to monitor<br>textbook use, quality and effectiveness<br>(3.12<br>p.14)<br>(ii) Evaluate how changes in quality and cost|The following studies were founid:<br>ODec.ntralized<br>book distribution<br>using regional<br>centers" (1985) (doc. 8 355.112)<br>"Curriculum improvement<br>and textbook development|

40
Kim, H. (1999) “Was Credit Channel a Key Monetary Transmission Mechanism Following the Recent Financial Crisis in the Republic of Korea?” _World Bank Policy Research Working Paper_ 2103.

Laffont J-J., Garcia R. (1977) “Disequilibrium Econometrics for Business Loans'', _Econometrica_ , 45, No. 5, July, pp. 1187-1204.

Laffont J-J., Monfort A. (1979) “Disequilibrium Econometrics in Dynamic Models'', _Journal of Econometrics_ , 11, No. 2-3, October-December, pp. 353-361.

Laroque G., Salanie B. (1995) “Macroeconometric Disequilibrium Models" , in _Handbook of Applied Econometrics_ , vol. 1, Blackwell, Mass., pp. 391-414.

Laroque G., Salanie B. (1997) “Normal Estimators for Cointegrating Relationships", _Economics Letters_ , 55, No. 2, August, pp. 185-189.

Maddala G.S., Nelson F.D. (1974) "Maximum Likelihood Methods for Models of Markets in Disequilibrium", _Econometrica,_ 42, No. 6, November, pp. 1013-1030.

Pazarabasioglu, C. (1997) “A Credit Crunch: Finland in the Aftermath of the Banking Crisis”, _IMF Staff Papers_ , Vol. 44, No. 3, pp. 315-327.

Quandt R. (1988) _The Econometrics of Disequilibrium_ , Blackwell.

Sealey, C (1979) “Credit Rationning in the Commercial Loan Market. Estimates of a Structural Model under Conditions of Disequilibrium”, _Journal of Finance_ , Vol. 34, pp. 689-702.

Zellner, (1962) “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias,” _Journal of the American Statistical Association_ , vol. 57, pp. 348–368.

26
Policy Research Working Paper 10867

# **Abstract**

Accurate poverty measurement relies on household consumption data, but such data are often inadequate, outdated, or display inconsistencies over time in poorer countries. To address these data challenges, this paper employs survey-to-survey imputation to produce estimates for several poverty indicators, including headcount poverty, extreme poverty, poverty gap, near-poverty rates, as well as mean consumption levels and the entire consumption distribution. Analysis of 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam yields encouraging results.

Adding household utility expenditures or food expenditures to basic imputation models with household-level demographic, employment, and asset variables could improve the probability of imputation accuracy by 0.1 to 0.4. Adding predictors from geospatial data could further increase imputation accuracy. The analysis also shows that a larger time - interval between surveys is associated with a lower probabil ity of predicting some poverty indicators, and that a better imputation model goodness-of-fit (R<sup>2</sup> ) does not necessarily help. The results offer cost-saving inputs for future survey design.

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://www.worldbank.org/prwp. The authors may be contacted at hdang@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
for by _MAXf,j_ . Specifically, we estimate: where _αj_ and _αf_ are establishment and function effects.The estimates, reported in column 1 of Table 4, reveal that while _MAXf,j_ and _MOSTf,j_ are positively correlated within establishments, _MAXf,j_ explains only 34% of the variance in _MOSTf,j_ . This indicates that _MAXf,j_ is not a sufficient statistic for _MOSTf,j_ .

To explore why this is the case, we regress _MOSTf,j_ on _NUMf,j_ and compare this estimate with that of regressing _MAXf,j_ on _NUMf,j_ in specification (4). Specifically, we estimate The estimates, reported in column 3 of Table 4, imply that a 1-unit increase in _NUMf,j_ is associated with an increase in _MOSTf,j_ by just 0.25, suggesting minimal impact of new technology adoption on the most widely used technology. This contrasts with the 0.84 estimate for _MAXf,j_ , showing that, the extension of the use of existing technologies in a business function and the adoption of new technologies are distinct technology upgrading processes and they are driven by different forces.

**Drivers of the MAX-MOST Gap.** A first step towards understanding the nature of the MAX-MOST gaps is to study whether they are transitory or permanent. Sluggishness in the extension of the use of new technologies could cause a transitory MAX-MOST gap.<sup>21</sup> Alternatively, the gap could reflect persistent factors that induce establishments to underutilize the most sophisticated technologies available in a function.

We consider the subsample of functions where establishments have adopted top-tier technologies, marked in bold in the grids presented in subsection A.1. For these technologies, FAT collects information on the year of adoption in the business function. We then divide this sample into two groups: those where _MAXf,j_ = _MOSTf,j_ and those where _MAXf,j > MOSTf,j_ . For each group, we examine the distribution of the years since adopting the top-tier technology in the function.Figure 7 shows that the distributions are similar, indicating that time is not a significant factor in closing the MAX-MOST gap, and that this gap is persistent.

We continue exploring the nature of the MAX-MOST gaps by considering three potential

> 21As in vintage capital models where establishments slowly replace obsolete technologies embodied in old capital as it depreciates (e.g., Benhabib and Rustichini, 1991).

22