rafmacalaba/datause-encoder-data huggingface dataset:
# Estimating the E¤ects of Credit Constraints on Productivity of Peruvian Agriculture4

or Maddala (1983) do not have ...xed e¤ects or time dummies but we use those here. Similar to the last equation, if the farmer i is credit constrained in period t; Wit = 1; then the productivity of the farm is where the ...xed e¤ect fi<sup>(1)</sup> and error term u<sup>(1)</sup> it are in general di¤erent from fi<sup>(0)</sup> and u<sup>(0)</sup> it in equation (1). Maddala and Nelson (1974) assume that the error terms in the selection equation and in the outcome equation are jointly normal. This implies that the error terms in the outcome equations do not have expectation zero conditional on the regressors. Therefore, Maddala and Nelson (1974) and Maddala (1983) subtract the inverse Mills ratio with a known coe¢ cient from the outcome equations. We also subtract the inverse Mills ratio from the outcome equations. However, since we do not assume that the error terms in the outcome equation are normally distributed we need to estimate the coe¢ cient of the inverse Mills ratio. In particular, we propose the following procedure. Let Qit denote the regressors of the selection equation of individual i in period t and suppose we observe N individuals for T periods. Our procedure allows for predetermined regressors (step 1A) or for exogenous regressors and correlated random e¤ects (step 1B).

Step 1A (Selection equation with predetermined regressors): Estimate a Probit model with predetermined regressors. Let (^�1; :::; ^�T ) denote the quasi maximum likelihood estimator, i.e.

Using (^�1; :::; ^�T ); calculate R<sup>^</sup> it = 1���(^(�^t<sup>0</sup> �<sup><u>Q</u></sup> t<sup><u>0Qitit)</u>)fori = 1; :::; Nandt = 1; :::; T:</sup> Step 1B (Selection equation with correlated random e¤ects): Estimate a Probit model with strictly exogenous regressors, constant slope coe¢ cients and correlated random effects. Let (^�;<sup>^</sup> 1; :::;<sup>^</sup> T ) denote the quasi maximum likelihood estimator, i.e.

T T (^�;<sup>^</sup> 1; :::;<sup>^</sup> T ) = arg max <u>PNi PT t</u><sup>ln[f�(�0Qit+</sup> <u>PTt=1</u> t<sup>0Qit)gWitf1��(�0Qit+</sup> <u>PTt=1</u> t<sup>0Qit)g1�Wit]:</sup> (4)
TABLE A6: Impact of text messages on individual ECD tests

||**ITT**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Obs**<br>(4)|
|---|---|---|---|---|
|**_Younger children (12 - 36 months)_**|||||
|ECD index (frst principal component)|-0.01|( 0.06)|0.87|774|
|Denver Personal-social|0.02|( 0.08)|0.82|790|
|Denver Language|0.04|( 0.05)|0.39|784|
|Denver Fine Motor|0.01|( 0.07)|0.87|781|
|Denver Gross Motor|-0.13|( 0.10)|0.17|779|
|**_Older children (37 - 84 months)_**|||||
|ECD index (frst principal component)|0.00|( 0.04)|0.95|1,711|
|Denver Personal-social|0.01|( 0.04)|0.81|1,757|
|Denver Language|0.06|( 0.04)|0.81|1,753|
|Denver Fine Motor|0.04|( 0.05)|0.37|1,746|
|Denver Gross Motor|-0.01|( 0.05)|0.80|1,742|
|Memory|-0.05|( 0.04)|0.21|1,927|
|TVIP (Vocabulary)|-0.03|( 0.04)|0.41|1,757|
|Self control|0.00|( 0.05)|0.93|1,713|

Each row corresponds to a separate estimation (equation 1). All outcome variables are standardized using the mean and standard deviation of control households. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variables for a set of households surveyed in 2016, as well as community fixed effects. Standard errors reported in parentheses. _∗_ p<0.1, _∗∗_ p<0.05, _∗∗∗_ p<0.01 54
Nationally representative data evidence migrants’ higher risk as well. While average HIV prevalence is 25 percent (second highest in the world), it is much higher for migrant‐dominated occupations: 38 percent of male miners and 58 percent and 38 percent of female textile and domestic workers, respectively, are HIV positive (see Figure 5). Two main reasons are typically offered as explanation. First, the total number of lifetime partners and the exposure period increase with age, and both mining and textile sectors tend to employ older men and women: there has been a lack of new hires of younger Basotho men in South African mines, and garment factories in Lesotho prefer hiring mature women in their 30s. Second, both sectors are highly localized, either in remote mines or in the industrial parks near Maseru and Maputsoe, and living away from one’s family is associated with higher rates of MCPs and other risky sexual behaviors.

_Figure 5. HIV Prevalence by Gender and Occupation (DHS 2014)_ <!-- Start of picture text --> A L L WO M E N T E X T I L E D O M E S T I C M E N M I N E R S<br>WORKERS WORKERS<br>58%<br>38% 38%<br>30%<br>25%<br>20%<br><!-- End of picture text --> We explored factors associated with higher risk of HIV infection among women employed in migrant‐ dominated sectors by replicating an earlier study on male migration to South Africa (Corno and de Walque, 2012). A simple regression of HIV status by occupational category shows a strong correlation for both textile workers and domestic workers: textile workers are 28 percentage points and domestic workers are 12 percentage points more likely to be HIV positive than the average Basotho woman (see Table 3). The relationship holds even when controlling for other factors, such as age, education status, and urban status, in a multivariate regression, albeit muted (see the methodological details and full results in Annex 3). While occupation remains a strong predictor of HIV status, women are more susceptible to HIV/AIDS with age, if they live in urban areas, or if they are divorced or separated.

42
beneficiaries (primarily non-users) could be substantial for the change in provision of this global public good. We however stress that this Delphi CV estimate is highly preliminary; it will need to be validated and calibrated by planned actual CV surveys in Europe.

We note also that the mean WTP estimate derived from this Delphi CV survey is close that found by Horton et al (2002, 2003), who asked random samples of households in the UK and Italy about their WTP to preserve rainforest areas in the Amazon. However, both of the preservation scenarios used in that study were different from those in our Delphi CV survey; the population covered was different; and that study was carried out more than 10 years prior to our Delphi Survey.

Our European Delphi CV survey indicates a European willingness to spend at least 8.4 million € annually to avoid any further forest loss in the Amazon rainforest. Drawing such a strong conclusion from a survey of 48 European environmental valuation experts may however be premature. The results need to be controlled, and corroborated, through population surveys in at least some of these European countries, which we intend to do later as part of the overall research program of which this study is part. Hopefully, our study may at least provide some useful information about the relative values across countries on the European continent, and how these co-vary with average national incomes.

The present European Delphi survey is also just a part of more comprehensive set of such surveys related to the global economic value of these preservation plans for the Amazon Rainforest. Delphi surveys (CV-based) are also being executed for experts from North America, Australia/New Zealand, and several countries in Asia; while random population surveys are planned for at least three European countries, Japan, and at least two countries in South America. We view the present study as a promising start of this research program.

25
Recording the Time Divide: A Comparative Study of Smartphone- and Recall-Based Approaches to Time Use Measurement Talip Kilic<sup>a</sup> , Gayatri Koolwal<sup>a</sup> , Wilbert Drazi Vundru<sup>a</sup> , Thomas Daum<sup>b</sup> , Hannes Buchwald<sup>c</sup> , Greg Seymour<sup>d</sup> , Peter Mvula<sup>e</sup> , Alister Munthali<sup>e</sup> , Monice Kachinjika<sup>e1</sup> JEL Codes: C83, J16, J22.

Keywords: Time Use, Employment, Unpaid Work, Recall Bias, Smartphone Time Use Diary, Survey Methodology, Household Surveys, Gender, Malawi, Sub-Saharan Africa.

> 1 Corresponding authors: Talip Kilic <u>(tkilic@worldbank.org) and Gayatri Koolwal (gkoolwal@worldbank.org).</u> a World Bank; b University of Gothenburg;<sup>c</sup> Independent Consultant;<sup>d</sup> International Food Policy Research Institute;<sup>e</sup> Palm Consulting. The Malawi Methodological Experiment on Time Use Measurement was implemented under the World Bank Living Standards Measurement Study (LSMS) program, in collaboration with the Measures for Advancing Gender Equality (MAGNET) initiative and PALM Consulting, the latter of whom implemented the fieldwork. The study benefited from funding from the International Fund for Agricultural Development, the World Bank Knowledge for Change Program, and the World Bank Umbrella Facility for Gender Equality, in partnership with the Bill and Melinda Gates Foundation. The authors are grateful to Andrey Prokhorenko (software programming) and Natalis Lorenz (illustrations) who were instrumental in designing the version of the _Time Tracker_ smartphone app that was used for this study; and Diego Zardetto for assistance with the sampling design. We thank Nancy Folbre and Hazel Malapit for valuable comments on the paper, as well as feedback from the participants at the 2023 International Food Policy Research Institute-RISE meeting, the Ninth International Conference of Agricultural Statistics (ICAS), the University of Maryland Conference on Measuring Time Use and Wellbeing, and the World Bank Development Data Group Let’s Talk Data Seminar.
Table 1: Summary statistics of key variables

|Variable|Mean|S.D.|Min|Max|Obs|
|---|---|---|---|---|---|
|**Panel A: City-level GDP, lig**<br>|**hts and over-re**<br>|**porting**<br>||||
|ln(GDP)|6.168|1.007|2.992|9.364|3642|
|ln(Lights)|1.257|1.086|-2.423|4.055|3642|
|∆ ln(GDP)|0.120|0.030|-0.151|0.315|3642|
|∆ ln(Lights)|0.074|0.211|-0.988|1.218|3642|
|∆ ln(Predicted GDP)|0.020|0.004|0.003|0.042|3642|
|Over-reporting (pp)<br>**Panel B: City party secretar**<br>|0.001<br>**ies’ characteris**<br>|1.377<br>**tics**<br>|-6.238<br>|7.221<br>|3642<br>|
|Female|0.042|0.201|0|1|1041|
|Minority|0.060|0.237|0|1|1041|
|YearsInOffice|3.879|1.771|0.252|10.67|1179|
|Age (assuming office)|50.61|3.762|39|60|1179|
|Native|0.070|0.254|0|1|1179|
|Bachelor|0.293|0.455|0|1|1179|
|Master|0.511|0.500|0|1|1179|
|Ph.D|0.135|0.342|0|1|1179|
|Connected to PPS|0.212|0.409|0|1|1179|
|CYL Exper.|0.287|0.448|0|1|1179|
|Prov Exper.|0.599|0.485|0|1|1179|
|Years in the Party|27.33|4.649|10|37|1179|
|(assuming office)||||||
|Rookie|0.830|0.375|0|1|1179|
|**Panel C: City-level socioeco**|**nomic indices**|||||
|Share of industry (%)|48.30|11.52|15.70|90.97|3642|
|Share of service (%)|35.56|7.864|8.580|69.88|3642|
|ln(Population)|5.816|0.671|2.771|7.122|3642|
|Target growth (%)|12.69|2.823|2.900|35|2895|

_Notes_ : This table shows summary statistics on the key variables used in the empirical analysis. The unit of observation used in the calculation of summary statistics for time-invariant demographic characteristics (i.e., gender and ethnicity) is the individual leader while that for time-varying variables is the city-leader spell. For variables that vary across years within the city-leader spell such as age and number of years as the party member, the summary statistics are calculated based on the year when the leader assumed office in the city.

39
among other measures.

Second, in view of the complex logistics, supervision requirements, and ultimately higher costs associated with crop cutting, the question is whether crop cutting can be implemented on a sub-sample basis and if so, the minimum share of plot observations that should be subject to crop cutting in a way that allows us to estimate an imputation model that provides reliable measures of imputed CC yields. We assessed the predictive accuracy of our preferred ML specification with different sizes of the training data set - experimenting with 10, 20, 25, 50, 67, and 75 percent of the crop cut measures, while leaving the remaining sample in each scenario to be the test sample. It appears (see top panel of Figure 8) that conducting crop cutting on 50 percent of the plot observations may be sufficient for deriving reliable imputed CC yields. After the 50 percent mark, the gains in accuracy appear to be marginal.

Third, a related question is whether it is necessary to randomly designate a share of plot observations for crop cutting in all primary sampling units or whether reliable predictions can be obtained by working with a subsample of primary sampling units and conducting crop cutting on all plots in the designated areas. Since ML algorithms can perform well out of sample, logistics and supervision of crop cutting can potentially be simplified by limiting our footprint to fewer areas in a given country. To answer this question, we create an alternative training data set composed only of crop cut measures obtained in one randomly chosen ERIVaS stratum – as opposed to working with one-third of plots randomly chosen across all strata. The test sample in this case are composed of plot observations from the remaining ERIVaS strata. The bar plot in the bottom left panel of Figure 8 shows that the choice of the stratum has an important effect on the accuracy of the ML measure. The choice of the first stratum as the source of the training data results in an the _R_<sup>2</sup> value of 0.24 from the bivariate regression of observed CC yield against imputed CC yield in the test sample. The comparable _R_<sup>2</sup> estimate is 0.04 while working with the fourth stratum as the source of the training data. The reason behind these differences in performance might be the fact that the first stratum was the most heterogeneous unit in terms of CC yields: the kernel density of CC yields is more spread out within the first stratum vis-`a-vis the rest of the strata (see bottom right panel on Figure 8).

So, in view of national surveys that may consider implementing crop cutting on a sub-sample basis and deriving imputed CC yields for the rest of the sample, the findings imply that implementing crop cutting in a way that is confined to a particular region may only result in reliable predictions if the chosen region is sufficiently heterogenous in terms of crop yields.

35
It is useful to analyze the issue of why firms said that MBAs were unavailable. Of the 25 firms that said that MBAs were unavailable, five said that MBAs with specific skills needed in their sector were unavailable, while 19 said that there were just too few local MBAs. Of these 19, five respondents were from Africa, and five were from the Middle East. This means that five out of 36 African respondents (14%) reported a shortage of MBA graduates; and five out of 23 Middle Eastern respondents (22%) found the same shortage. Though the sample size is quite limited, this could be an indication of a shortage not only of good programs, but of programs in general in these two regions.

By World Bank income category, 29% of respondents from Category I countries said that they did not hire MBAs because of a lack of availability.<sup>12</sup> This figure is significantly higher than the corresponding figure for other income categories, and implies that countries from the lowest income category have a significant shortage of qualified MBAs.

The previous research in this area is limited to the discussion in Pfeffer and Fong (2002), which focused only on the U.S. In their paper, Pfeffer and Fong found that consulting firms (such as Booz Allen and Hamilton and Boston Consulting Group) hired a significant number of non-MBAs, who learned the fundamentals of business after a few weeks of training (pg. 81). But there has been very little research on why firms would not want to hire MBAs.

> 12 World Bank income categories are based on the per capita incomes of countries.  The range of the income categories is I-V, with I being the lowest and V being the highest:  Countries in Category I have a per capita income of $746 or less;   those in Category II,  between $746 and $1435;  Category III,  between $1436 and $2975;  Category IV,  between $2976 and $5185;  Category V, over $5185.

11
5 Table 1 also shows the debt burdens as a percent of GNP and of exports (Table lb). During the 1978-82 period, the total debt burdens became more similar across categories. Since 1982, however, the burdens have become more different, mainly because of diffi:ent growth experiences. For manufacturing expotters and EMENA countries, debt burdens have increased only moderately since 1982. Manufacturing prices have held up relatively well and volumes have grown. For HICs, for oil and non-oil primary exporters, and for Sub-Saharan Africa, debt burdens have grown much faster than the ability to pay. Many countries devalued their currency in real terms, as part of the adjustment process, which increased their debt/GDP ratio.

Net transfers between creditors and debtors offer another perspective on the evolution of the debt problem and countries' efforts to adjust. Calculating net resource transfers from debt data yields a picture of where resources are going to and coming from externally. Net transfers from creditors would differ from the (negative) resource balance by items such as increased reserves, direct foreign investments, and unrequited transfers.1 Resource balances are considered separately later. Net transfers from a creditor would be disbursements minus repayments minus interest paid by the debtor. The estimates of net transfers in the following charts and tables are calculated from the World Debt Tables. The net transfers on commercial longterm debt (PPG and PNG) just sum the estimates of net transfers in the Debt Tables. For total commercial transfers we could not use this method, because there are no data on the net transfers for short-term debt and only incomplete 1 The resources balance equals exports minus imports of goods and nonfactor services. It is about the same as the non-interest current account, if interest payments are the bulk of factor services.
Figure 9 Return on Net Fixed Assets, 1960 **-** 90 Percent <!-- Start of picture text --> 60<br>50..... ...... 0<br>40 - - - - -<br>30 -------------------------------- -<br>10 -- CTC ENTEL-<br>0<br>-zo I I I I I I I I I I I I<br>-20<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990<br><!-- End of picture text --> Figure 10 Return on Net Worth <!-- Start of picture text --> Percent<br>60<br>40 , .,;<br>0~~~~~~~~~~~~~~~~~~~~~<br>-40 _--- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - %<br>-0<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990<br><!-- End of picture text -->
the body that processes claims in dumping cases. The Subdirección presents its findings to the Comité de Prácticas Comerciales (Trade Practices Committee) which makes a final recommendation. The Ministerio de Comercio, Industria y Turismo (Ministry of Trade, Industry, and Tourism) issues the overall decision corresponding to these recommendations. A more complete discussion (in Spanish) can be found at the Ministry website, http://www.mincomercio.gov.co/. The original source material for this data can be found in the dumping/subsidizing section of that website, particularly in the summary excel sheet provided there.

The Colombian spreadsheet contains the standard data items described in section 2, as well as a few additional data items described below.

**Table 3.5.1 : Additional Colombian Data Included in AD-COL-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-C**|**OL-Master :**||
|**U.**|**TERM_DATE**|Date of termination, usually instituted because the case was denied<br>initiation|
|**V.**|**P_AD_DUTY**|The preliminary rate|
|**W.**|**F_AD_DUTY**|The final rate|
|**X.**|**INIT_GP**|The resolution number of the initiation action|
|**Y.**|**P_GP**|The resolution number of the preliminary decision of dumping and<br>injury|
|**Z.**|**F_GP**|The resolution number of the final decision of dumping and injury|
|**AA.**|**TERM_GP**|The resolution number of the termination action|
|**AB.**|**REVOKE_GP**|The resolution number of the revocation action|
|**AC.**|**NOTES**|Comments|

# **In AD-COL-Products :**

# **D. NOTES** Comments

Because this data was compiled from a secondary source (the Ministry’s excel file), and not from the actual case files, some information is incomplete. It was unclear exactly when duties were imposed, so the P_AD_DATE and F_AD_DATE columns are largely missing. All other dates are those listed with 22
Table 8: MNCs and Employment by Country in Manufacturing, 2013~2019

||De<br>|pendent Variable: <br>|Logof Employm<br>|ent<br>|
|---|---|---|---|---|
||China<br>(1)|Vietnam<br>(2)|Japan<br>(3)|Other Asia<br>(4)|
|MNC_i ×_Post_t_|-0.0362***|-0.0617***|-0.0205|-0.0363**|
||(0.0120)|(0.0215)|(0.0284)|(0.0147)|
|Observations|36,060|29,365|27,829|31,755|
|R-squared|0.9538|0.9473|0.9493|0.9566|
||Europe|North America|Latin America|ROW|
||(5)|(6)|(7)|(8)|
|MNC_i ×_Post_t_|-0.0127|-0.0181|-0.0014|-0.0552|
||(0.0197)|(0.0128)|(0.0243)|(0.0586)|
|Observations|28,855|30,962|27,573|26,792|
|R-squared|0.9591|0.9588|0.9538|0.9488|
||_Q_1<br>|_Q_2<br>|_Q_3<br>|_Q_4<br>|
||(9)|(10)|(11)|(12)|
|MNC_i ×_Post_t_|-0.0608***|-0.0269**|-0.0201|-0.0067|
||(0.0173)|(0.0116)|(0.0132)|(0.0191)|
|Observations|29,864|31,149|29,669|29,348|
|R-squared|0.9368|0.9386|0.9537|0.9472|
|Fixed Effects:|||||
|Firm|Yes|Yes|Yes|Yes|
|Industry-Region-Year|Yes|Yes|Yes|Yes|

_Notes:_ MNC _i_ is an indicator variable that equals 1 if a firm _i_ has foreign affiliates in the years 2013~2016 and 0 otherwise. In each column, foreign affiliates are restricted to the specific country or wage group. For instance, in column (1), MNC _i_ refers to foreign affiliates in China. _Q_ 1~ _Q_ 4 in columns (9) through (12) refers to the quartile group of MNCs according to the level of (weighted) average wages in the countries where their foreign affiliates operate, respectively. Post _t_ is an indicator variable that equals 1 if the time _t_ is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

are exactly aligned with the employment adjustment channel through GVCs in Factory Asia. For the emerging Asian countries, China, Vietnam, and Other Asia, the coefficient estimates are -0.0362, -0.0617, and -0.0363, respectively, and they are all statistically significant at the 5% level. In stark contrast, for other countries/regions (Japan, Europe, North America, Latin America, and Rest of the World), all the coefficient estimates are statistically insignificant.<sup>32</sup> Alternatively, we look at whether the MNCs reduced their employment when their foreign affiliates operate in low-wage countries, regardless of the geographic location. To do so, we classify the Korean MNCs into four quartiles according to the level of (weighted) average wages in the countries where their foreign affiliates operate.<sup>33</sup> Each 32We provide event study analyses and dynamic treatment effects corresponding to Table 8 in Appendix Figures B.3, B.4, and B.5.

33The wages in each host country of foreign subsidiaries are measured by the average monthly earnings of employees in all industries (converted to 2017 US$ PPP; data source: ilostat.ilo.org/topics/wages). If 25