rafmacalaba/datause-encoder-data huggingface dataset:
Table A6: Robustness check using the subsample with tenure being 3 years and less— Current salary

|||Log (curr|ent salary)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.477<sup>_∗∗∗_</sup>|-0.312<sup>_∗∗∗_</sup>|-0.328<sup>_∗∗∗_</sup>|-0.257<sup>_∗_</sup>|
||(0.169)|(0.119)|(0.113)|(0.143)|
|Social networks*tenure|0.055|0.085<sup>_∗_</sup>|0.100<sup>_∗∗_</sup>|0.063|
||(0.062)|(0.045)|(0.044)|(0.048)|
|Tenure in years|0.026|0.047|-0.001|0.004|
||(0.068)|(0.130)|(0.115)|(0.112)|
|Abilities||X|X|X|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.179|0.511|0.555|0.312|
|N|1745|1744|1744|1744|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

Table A7: Robustness check using the subsample with tenure being 3 years and less— Promotion

||||Dummy|for havin|g been pr|omoted|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.028|-0.021|-0.016|-0.015|-0.053|-0.075|-0.061|-0.069|
||(0.022)|(0.025)|(0.021)|(0.023)|(0.052)|(0.059)|(0.040)|(0.054)|
|Social networks*tenure|||||0.010|0.022|0.019<sup>_∗_</sup>|0.023|
||||||(0.014)|(0.016)|(0.011)|(0.016)|
|Tenure in years|0.002|0.001|0.015<sup>_∗∗_</sup>|0.016<sup>_∗_</sup>|-0.005|-0.022|-0.018|-0.035|
||(0.005)|(0.006)|(0.007)|(0.008)|(0.014)|(0.016)|(0.014)|(0.024)|
|Abilities||X|X|X||X|X|X|
|Abilities*tenure||||||X|X|X|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.016|0.034|0.045|0.072|0.017|0.050|0.054|0.087|
|N|1745|1744|1744|1744|1745|1744|1744|1744|

_Note_ . _Abilities_ indicates that the observable and unobservable abilities (years of schooling, age at hire, and father’s and mother’s completion of primary education, math and language z-scores and Big 5 index) and their interactions with tenure are included or not. The other controls included are geographical division fixed effects. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

43
# **On the Fungibility of Spending and Earnings - Evidence from Rural China and Tanzania**

Luc Christiaensen and Lei Pan<sup>1</sup>

**Key words:** marginal propensity to consume, mental accounts, safety nets, Tanzania, China

**JEL Classification:** D01, D12, D13, H31, O12

**Sector Board:** POV

> 1 Luc Christiaensen (lchristiaensen@worldbank.org) is a Senior Economist in the Chief Economist Office of the Africa Region of the World Bank. Lei Pan is an Assistant Professor in the Development Economics Department at Wageningen University. The authors would like to thank Erwin Bulte (Wageningen University), Erik Jonasson (Lund University), Will Martin (World Bank) and seminar participants at the UNU-WIDER conference on Behavioral Economics and Poverty, Helsinki, September 1-2, 2011 for insightful comments. Funding from JICA and DFID and collaboration with the Research on Poverty Analysis (REPOA) institute on the Tanzanian data collection is gratefully acknowledged, as is the assistance provided by the Chinese authorities of the World Bank’s Western Poverty Reduction Project in China (WPRP), Wang Pingping, Assistant Counsel, Department of Rural Surveys of the National Bureau of Statistics of China, and Sari Soderstrom, World Bank Task Manager of the WPRP in providing the data for the China case study. 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 or the governments they represent.
5 recipient countries<sup>2</sup> , empirical studies still lag behind and have focused mostly on growth, inequality and poverty, leaving issues of macroeconomic stability largely uninvestigated.

Relying on the idea that remittances could relax financial constraints and favor investment, few scholars have run cross-country growth regressions. The results are mixed: Faini (2002, 2004) finds a positive, but not very robust, relationship between growth and remittances, while Chami, Fullenkamp and Jahjah (2003) find the opposite; using instrumental variables to account for the potential endogeneity of remittances, IMF (2005) finds no statistically significant relationship. The mechanism through which remittances can positively affect growth can be better detected in micro-econometric studies based on household-level data. On the basis of recent and quite accurate evidence, surveyed by Lopez Cordova and Olmedo (2005a), the positive impact of remittances on education and entrepreneurship at the household-level is at this stage widely acknowledged<sup>3</sup> .

Remittances could instead finance basic consumption needs. Along these lines, crosscountry evidence by Adams and Page (2003), Adams (2004) and IMF (2005) shows that indeed remittances have a clear-cut poverty-reducing effect. Similar results are found by Esquivel and Huerta-Pineda (2005) and Lopez Cordova (2005) using a Mexican household survey and a crosssection of Mexican municipalities, respectively. In the same spirit, remittances help improve health conditions (Amuedo-Dorantes and Pozo, 2004a; Hildebrandt and McKenzie, 2004; Dureya et al., 2005; Lopez Cordova, 2005).

The first attempt to link remittances and volatility appears in IMF (2005), which finds lower volatility of aggregate output, consumption and investment in countries with larger remittance inflows. Amuedo-Dorantes and Pozo (2004b) find that remittances can cause an appreciation of the real exchange rate; as a result of this loss of price competitiveness, export flows might decrease (Lopez Cordova and Olmedo, 2005b).

> 2 Docquier and Rapoport (2005) provide a comprehensive and clear survey of such studies.

> 3 McCormick and Wahba (2001) on entrepreneurship in Egypt; Dustmann and Kirchkamp (2002) on entrepreneurship in Turkey; Cox Edwards and Ureta (2003) on education in El Salvador; Hanson and Woodruff (2003), Lopez Cordova (2005) and McKenzie and Rapoport (2005) on education in Mexico; Woodruff and Zenteno (2004) on entrepreneurship in Mexico; Adams (2005) finds a positive effect on education and real estate investment in Guatemala; Yang (2005) on education and entrepreneurship in the Philippines.
discuss potential mechanisms in Section 5, and we present a range of robustness checks in section 6. Section 7 provides concluding remarks.

# **2. Data**

# **2.1 Data Sources and Descriptions**

To understand the potential win or loss in demand for various services and potential reallocation of economic activities, we carefully choose Google search terms that can capture demand for major economic activities. The choice of sectors is informed by the relative importance of the sector to employment and GDP.<sup>3</sup> We also focus on services for which representative Google search terms that capture demand for a particular service can be found. For example, the manufacturing sector is a major employer, but finding representative search terms has been difficult, and thus not included in this study. Therefore, we focus on six service categories within the broader services sector: (i) hotels and restaurants, (ii) transport, (iii) tourism, (iv) retail trade, (v) ICT, and (vi) delivery services.

The Google search data provide both historical and near real-time data on search intensity for a given term or topic going back to 2004. Google makes these search data publicly available by transforming them into search intensity or popularity index, which measure the relative popularity of a search term in a specific geographic area and over a specific period. Google applies a two-layer normalization to calculate the popularity index for a given search term. First, it calculates the ratio of the total volume of search for a given search term to all searches in a geographic region and over a given period. Then, the highest ratio is normalized to be 100, and the remaining ratios are scaled relative to this maximum data point to obtain the search index. Because of these normalizations, the index always assumes a number between 0 and 100. This normalization across time facilitates a comparison of search intensity of a given term across time. Besides capturing spatio-temporal variation, the data are available at a granular level, such as district and city. These time-series data are available, on a weekly basis, for the period starting January 1, 2004. We downloaded these data on May 1, 2020, for 182 countries.<sup>4</sup>

> 3 For instance, the travel and tourism sectors were estimated to account for more than 10 percent of global GDP (travel bookings alone amounting to 1.6 trillion USD) in 2017, and the aggregate retail trade revenue of the top 250 retailers around the world was estimated at 4.4 trillion USD (Deloitte, 2018a, 2018b).

> 4 <u>https://support.google.com/trends/answer/4365533?hl=en. We used R Google Trends Application Program</u> Interface (API) gtrendsR to access the platform (https://cran.r-project.org/web/packages/gtrendsR/gtrendsR.pdf).

6
# **Minimum Wages and Labor Supply in an Emerging Market: The Case of Mauritius**

Zaakhir Asmal<sup>*1</sup> Haroon Bhorat<sup>*</sup> Ravi Kanbur<sup>**</sup> Marco Ranzani<sup>***</sup> Pierella Paci<sup>***</sup>

## JEL: J23; J31; J38.

Keywords: Minimum Wages; Employment; Hours of Work; Mauritius.

> 1 *: Development Policy Research Unit, School of Economics, University of Cape Town. **: Cornell University ***: World Bank. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
most of the observables. Indeed, the individual fixed-effects capture all time invariant individual characteristics such as ability, education, stamina, as well as several stable household characteristics and environmental factors (e.g. attitude towards refugees or IDPs in the local community). Furthermore, the time fixed-effects controlled for events specific to a certain time period, such as weather shocks or military events.

**<u>Table 6: Plan to go back (Y/N) - Only refugees and IDP - LPM with FE</u>**

||(1)<br>All|(2)<br>All|(3)<br>Head and<br>Spouse|(4)<br>Head and<br>Spouse|
|---|---|---|---|---|
|Employed|-0.082<sup>***</sup>|-0.080<sup>***</sup>|-0.075<sup>***</sup>|-0.071<sup>***</sup>|
||(0.018)|(0.018)|(0.019)|(0.018)|
|Safe during day|-0.085|-0.099|-0.091|-0.109|
||(0.153)|(0.177)|(0.178)<br>|(0.212)|
|Safe at night|0.140|0.144|0.155<sup>*</sup>|0.159|
||(0.085)|(0.091)|(0.091)|(0.097)|
|Own a weapon|0.299<sup>***</sup>|0.301<sup>***</sup>|0.225<sup>**</sup>|0.225<sup>**</sup>|
||(0.084)|(0.084)|(0.107)|(0.107)|
|Improvement Mali crisis|-0.076<sup>***</sup>|-0.075<sup>***</sup>|-0.068<sup>***</sup>|-0.069<sup>***</sup>|
||(0.014)|(0.014)|(0.014)|(0.014)|
|Refugee||0.409<sup>***</sup>||0.417<sup>***</sup>|
|||(0.040)||(0.040)|
|Constant|0.382<sup>***</sup>|0.126|0.389<sup>**</sup>|0.116|
||(0.125)|(0.141)|(0.160)|(0.183)|
|Time dummies|Yes|Yes|Yes|Yes|
|Observations|3272|3254|2489|2479|
|Within R^2|0.07912|0.08002|0.07254|0.07409|

Standard errors in parentheses. SE clustered at the individual level.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 Source: LDPS 2014-15 22
**Table 1: Summary Statistics**

||<br>Males|Females|
|---|---|---|
|_Baseline Characteristics_|||
|age|33.94|36.01|
|education (years)|11.18|9.35|
|married|0.446|0.506|
|number of children|1.54|2.31|
|total adults age 18 ‐ 65 in household|5.60|6.14|
|employed|0.718|0.657|
|conditional on being employed...|||
|self employed|0.482|0.697|
|usual hours of work|51.10|49.42|
|usual weekly earnings (cedis)|162.36|94.98|
|_Outcomes during the High‐Frequency Survey_|||
|full sample|||
|missed work due to own unexpected illness last week|0.020|0.024|
|missed work due to caretaking illness last week|0.000|0.013|
|missed work due to unexpected illness (own or caretaking) last week|0.020|0.037|
|ever missed a week of work due to own unexpected illness|0.109|0.134|
|ever missed a week of work due to caretaking|0.000|0.030|
|ever missed a week of work due to unexpected illness (own or care)|0.109|0.163|
|conditional on being employed at baseline...|||
|missed work due to own unexpected illness last week|0.025|0.031|
|missed work due to caretaking illness last week|0.000|0.020|
|missed work due to unexpected illness (own or caretaking) last week|0.025|0.051|
|ever missed a week of work due to own unexpected illness|0.136|0.162|
|ever missed a week of work due to caretaking|0.000|0.046|
|ever missed a week of work due to unexpected illness (own or care)|0.136|0.207|
|worked in the past week (full sample)|0.643|0.556|
|worked in the past week (conditional on employment at baseline)|0.850|0.809|
|conditional on working last week...|||
|days of work|5.27|5.30|
|hours of work|47.47|45.32|
|earnings (cedis)|167.72|107.447|
|Number of individuals|266|367|
|Number of observations|2,433|3,345|

_Notes: employed = 1 in the baseline if respondent reports that s/he has ʺstable work done for pay or gain and expect to continue doing it for next three monthsʺ OR ʺany work at all that you do for pay or gain and that you do regularlyʺ? Ill the past week = 1 if a respondent reported missing work for an entire week in which he or she had planned to work and the reported reason was injury/illness. The 2013 exchange rate was 0.42 cedis to 1 US dollar._
2016). Given the rising prevalence of e-filing, it is increasingly important to understand its impact.

In this paper, we examine the impact of e-filing adoption using data from small and medium sized businesses in Dushanbe, the capital city of Tajikistan. Since e-filing was available to all firms, we implement an encouragement design whereby we provide a randomly selected group of firms in-depth training on e-filing as well as logistical help in completing the different steps involved in the e-filing registration process. Ninety-three percent of firms in this group adopt e-filing, compared with 60 percent in the comparison group. We use this difference to estimate the impact of e-filing adoption on firms.<sup>3</sup> E-filing may improve tax administration in multiple ways. First, online submission may reduce tax compliance costs by removing the need for time-consuming, anxiety-inducing visits to the tax office, freeing up resources for productive activities. Second, by reducing the frequency of in-person interactions between taxpayers and tax officials, e-filing may reduce collusion between the two parties to reduce tax payments. Third, by removing the need for a tax official to verify submission of tax declarations, e-filing may also protect taxpayers from tax officials holding them up and extorting them. In addition, e-filing may produce system-wide efficiency gains by removing the need for physical collection of forms and data entry. Lastly, by making tax information submitted by taxpayers immediately available electronically, e-filing may increase the ease of data analysis for monitoring.

On the other hand, it is possible that e-filing may not deliver these expected benefits or may even lead to worse outcomes, especially in low income settings with limited information technology coverage and low capacity in other aspects of tax administration. For example, certain taxpayers may experience significant costs of adoption rendering e- filing inaccessible to them (Yilmaz and Coolidge, 2013). For small businesses with limited 3 Given the firm-level design, we are unable to examine system-wide efficiency impacts of e-filing.

3
# **APPENDIX B: Consistency checks of the information on the employeremployee data set**

We have performed a number of consistency checks on the information provided by “Quadros de Pessoal” to guarantee the accuracy of the data used.

- _Elimination of invalid or duplicated worker identification codes in a given year:_ According to the Ministry of Employment valid worker ID codes have 6 to 10 digits. In each year, observations with codes with less than 6 or more than 10 digits were not considered. Observations with duplicated identification codes were also eliminated. These restrictions led to dropping an average 8.93 percent and 4.51 percent of the observations in the original yearly data sets, respectively.

- _Consistency check of data for each worker across years:_ We have merged the yearly information and identified inconsistencies when gender, date of birth or year of hiring (for the same firm) were reported changing, or the highest schooling level was reported decreasing (or was missing) over time for the same worker.

- _Correcting missing values when reported data for the rest of the period was absolutely consistent_ by assigning the reported value for the remaining period. This changed 0 percent,<sup>17</sup> 2.25 percent, 0.71 percent and 0.23 percent of the observations in the initial panel for gender, age, schooling and firm tenure, respectively.

- _Correcting inconsistent data across years_ by taking information reported over half of the times as correct.<sup>18</sup> Inconsistent values on gender were replaced by the value reported over half of the cases the worker has been observed, provided that the year of birth in that observation is the same as that reported in more than 50 percent of the cases for that worker. Similar procedures have been implemented for year of birth and schooling. This affected 0.87 percent, 1.77 percent, 6.65 percent and 1.68 percent of the initial panel for gender, year of birth, schooling and firm tenure respectively. The whole information on a worker has been dropped when inconsistencies persisted after this correction. This restriction led to dropping 8.4 percent, 1.08 percent and 6.28 percent of the observations for gender, year of birth or schooling, respectively.

- _Dropping workers with remaining missing data on gender, age or schooling:_ 0 percent, 0.15 percent, 0.99 percent due to missing data on gender, age and schooling, respectively. The checked panel included 22,686,298 worker-year observations and 3,525,485 workers.

> 17Two observations.

> 18Note that this is a more demanding criterion than simply using the modal value as replacement.

22
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# **Annex 4. Elasticity estimates of demand of subsidized fuel products**

Very few attempts have been made for developing economies to estimate the responsiveness of demand for subsidized fuel products to those products own price, prices of substitute products, and to national income (Epsey 1998 surveys the literature). The elasticities underlying the discussion in Section 3 above are estimated in both one- and two-stage error correction frameworks. The table below reports the model specifications and coefficients. The cumulative impacts are based on impulse-response exercises using the estimated coefficients. These coefficient estimates were corroborated through simple dynamic models. The coefficients were estimated using monthly aggregate data of Indonesian consumption and prices of various fuel products. The income variable is interpolated aggregate GDP (measurement issues precluded use of gross national income, while monthly income indicators are not available for Indonesia).

The models estimated are based on the following form, comprised of a long-run component and a shortrun component:

## **<u>Estimates of subsidized fuel product elasticities</u>**

|_Dependent variable: change in_||||
|---|---|---|---|
|_product consumption of_|_Gasoline_|_Diesel_|_Kerosene_|
|Lagged change in (log) product|0.01|-0.17|-0.04|
|consumption:|_0.11_|_0.10_|_0.10_|
|Product rice (lo):|-0.26|-0.81|-0.16|
|p g|_0.06_|_0.30_|_0.02_|
|Change in (log) product price:|-0.35<br>_0.09_|--<br>_--_|-0.06<br>_0.07_|
|Cumulative price elasticity:|-0.25|-0.78|-0.13|
|Price of a substitute product (log):|0.11<br>_0.07_|--<br>_--_|--<br>_--_|
|Change in substitute (log) product|0.11|0.44|--|
|price:|_0.08_|_0.21_|_--_|
|Substitute product:|Unregulated<br>petrol prices|Industrial<br>kerosene|--|
|Cumulative cross-price elasticity:|0.12|SR: 0.4 ; LR: 0|<br>--|
|Income (log):|1.77<br>_0.16_|1.27<br>_1.73_|--<br>_--_|
|Change in (log) income:|--<br>_--_|--<br>_--_|0.78<br>_1.03_|
|Cumulative income elasticity:|1.90|1.35|SR: 0.8 ; LR: 0|
|Speed of adjustment:|-0.86<br>_0.14_|-0.47<br>_0.10_|-0.82<br>_0.13_|
|Adjusted R2|0.50|0.31|0.45|
|Durban-Watson|2.07|2.00|2.04|

Note: Standard errors reported in italics below coefficients. Intercepts not reported. Source: World Bank staff calculations, using data collected through CEIC.

The levels of the quantify of subsidized fuel consumed, and the price of subsidized fuel, the market price of fuel and income levels are all I(1), and the respective cointegrating relationships pass the usual tests (Johansen test for cointegration; detailed results are available from the authors on request).

28
Table A1.3. Standard and Poor's DRI/McGraw-Hill (cont.): Country Risk Service Secondary Risk Events

|Risk Event|Defintion<br>DOMESTIC POLITICAL RISKS|
|---|---|
|19 Military Coup Risk|A military coup d'etat (or a series of such events) that reduces the GDPorowthrate by 2%|
|20 Major InsurgencylRebelli|on<br>An increase in scope or intensity of one or more insurgencies/rebellions<br>that reduces the GDP<br>growth rate by 3% during any 12-month period.|
|21 Political Terrorism|An increase in scope or intensity of terrorism that reduces the GDP growth rate by 1% dunng<br>any 12-month Deriod.|
|22 Political Assassination|A political assassination (or a series of such events) that reduces the GDP growth rate by 1%<br>during any 12-month period.|
|23 Civil War|An increase in scope or intensity of one or more civil wars that reduces the GDP growth rate<br>by 4% during anv 12-month Period.|
|24 Major Urban Riot|An increase in scope, intensity, or frequency of rioting that reduces the GDP growth rate by<br>1% during any 12-month period.|
|25 Labor Strike and Unrest|An increase in scope, intensity, or frequency of labor strikes/turmoil that reduces the GDP<br>growth rate bv 1% during any 12-month period.|
|26 Kidnapping of Foreigners|An increase in scope, intensity, or frequency of kidnapping of foreigners that reduces the GDP<br>arowth rate by 1% during any 12-month period.|
|27 Govemment Instability|An increase in govemment personnel tumover rate at senior levels that reduces the GDP<br>qrowth rate by 2% during anY 12-month period.|
|28 Govemment<br>Ineffectiveness|A decline in govemment personnel quality at any level that reduces the GDP growth rate by<br>1% during any 12-month period.|
|29 Institutional Failure|A deterioration of govemment capacity to cope with national problems as a result of<br>institutional rigidity or gridlock that reduces the GDP growth rate by 1% during any 12-month<br>period.|
||EXTERNAL POLITICAL RISKS|
|30 Military Mobilization/Sma<br>Inter-State War|ll An increase in scope or intensity of an inter-state military conflict that reduces the GDP<br>growth rate by 2-5% during any 12-month period.|
|31 Major Inter-State War|An increase in scope or intensity of a military conflict that reduces the GDP growth rate by<br>more than 5% during any 12-month period.|
|Other measures included:|<br>Economic Sanction Risk. Trade Conflict Risk<br>ECONOMIC RISKS|
|Other measures included:|Interest Rate (Real increase), Consumer Price Inflation, Real Estate Price Decline<br>(Commercial)|

34