rafmacalaba/data-use-mentions-extended huggingface dataset:
)|(0.0003)|
|Observations|888,813|888,813|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6354|0.7742|0.3913|0.6318|0.7659|0.3889|
|Kleibergen-Paap rk Wald-F-stat||||105.254|105.254|105.254|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the baseline estimation sample. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
where _α_ 1 _< α_ 2 are the unknown cutoff points. The estimation of the parameters _α_ and _β_ is performed using maximum likelihood (Wooldridge, 2010).

# **4 Data**

We use data from Comoros - Enquˆete int ́egrale aupr`es des m ́enages (EIM 2004), Ghana - Ghana Living Standards Survey (GLSS 2012/13), Guinea - Enquˆete Int ́egr ́ee de Base pour l’Evaluation de la Pauvret ́e (EIBEP 2002/03), Madagascar - Enquˆete permanente aupr`es des m ́enages (EPM-2005), Malawi - Malawi Integrated Household Survey (IHS3 2010/11), Nigeria - Nigeria General Household Survey (GHS 2010/2011), Rwanda - Integrated Household Living Conditions Survey (EICV 1999/2000), Tanzania - Tanzania National Panel Survey (NPS 2009/2010), and Uganda - Uganda National Household Survey (UNHS 2005/06). The description of the sample composition in each country is presented in table 1. All the surveys are the latest representative datasets available that collect information on both children and their parent’s education regardless of whether parents are alive or, if alive, live in the same household. All the surveys have been conducted from 2000 onwards, and most have been conducted after 2005. The analysis is restricted to those individuals ages between 20 and 69, which corresponds to the year of birth going back as far as 1935 for Comoros, 1944 for Ghana, 1933 for Guinea, 1936 for Madagascar, 1942 for Malawi and Nigeria, 1931 for Rwanda, 1941 for Tanzania, and 1937 for Uganda.<sup>6</sup> After data cleaning, the total sample size ranges from 32,730 in Ghana to 6,778 in Tanzania; a total of more than 145,000 adult children (ages between 20-69) are represented in the study. The data in all the countries are organized into five-year birth cohorts based on the children’s years of
risk of income losses due to health shocks. However, health insurance often fails to cover informal sector and, even among formal sector, the coverage can be very low due to low enrollment (Wagstaff, 2009). Thus little is known about the extent to which health insurance reduces vulnerability to the health shocks.

# **3. Data and Institutional Background**

# <u>Data</u>

This study uses a nationally representative Yemen Household Budget Survey (HBS) conducted in 2006. In examining health risks by work status, the data should contain information on both health and labor market outcomes. The wealth of information on health as well as individual demographic and socio-economic characteristics, labor market indicators, and household living conditions, income, and expenditure enable to analyze the health implications of informality.<sup>12</sup> The Yemen HBS collected information on 13,121 households and 98,845 individuals. Among 56,600 adults aged fifteen or above, 20,888 individuals are working as either wage employed or selfemployed.<sup>13</sup> For all workers, the data contain information on job duration for the current occupation, the total number of jobs held, and an indicator of agriculture sector. In addition to this, wage workers are asked about social insurance coverage, benefits such as paid leaves, and earnings and working hours are included. For the self-employed, the information on the size of enterprises and rough measures of revenue and costs are collected.

> 12 A Labor Force Survey includes detailed information on labor market indicators including employment status and sector, job search efforts, and earnings, but often lacks information on health. On the other hand, Demographic Health Survey has detailed information on health, but misses labor market indicators.

> 13 Among those not working, it is unclear whether they are still in the job search process.  Thus, defining an economically active population using a standard definition is not possible here. The majority of non-working adults are
# **4 Effect of Changes in Minimum Wages on Labor Supply**

## **4.1 Identification strategy**

The methodology we employ here is to use individual-level pooled cross-sectional data (for the years 2004 to 2014), to estimate how changes in minimum wages have affected the employment of workers and number of hours worked holding constant other factors that might affect wages. The approach is similar to a number of approaches in the literature including most notably that of Gindling and Terrell (2007), who estimated employment effects for a similarly complex system of minimum wages in Costa Rica.

We estimate an equation of the following form: EEiiii � αα � ββββββMMMMiiii � XXiiii γγ � δδVVVVzzii � DDss � DDii � εεiiii ... (1) Where EEiiii = 1 if individual i is employed in the covered sector at time t EEiiii = 0 if individual i is an own-account worker, an employer or unemployed worker who has worked before at time t.

XXiiii = individual characteristics (gender, experience, education, and interactions).

- ββββMMMMiiii = logarithm of real hourly minimum wage that applies to individual i at time

- t.<sup>9</sup>

DDDD , DDDD = sets of dummies for industry/occupation and year VVVVzzii = value added of sector z at time t.

The coefficient ββ is an estimate of the impact on employment in the covered sector of changes in the minimum wage.<sup>10</sup> The minimum wage is assigned to each worker based on their RO category. We were unable to assign specific wages based on job title and years of experience. We have taken the minimum wage for each RO category to be the lowest stipulated wage in each RO for that category of RO workers.

> 9 Minimum wages are set in either hourly, daily or monthly terms. Where they are not in hourly terms, they have been standardized to a real hourly minimum wage using 8 hours as the reference number of hours for a workday and 168
# **Section 5. Conclusion**

This paper, to our knowledge, is the first to try and characterize the dynamics of cropland using a global georeferenced dataset combined with both geographic characteristics and national-level economic and institutional contexts. Our two-stage approach allows us to disentangle the purely geographic effects at the granular local level from the national level contexts. Beyond the expected role played by local geographic characteristics (i.e., droughts driving land degradation) or access to markets (with proximity to cities stimulating conversion to cropland), a key contribution of this study is to show that national economic contexts and national institutions matter. The pressure on land resources as proxied by a low amount of cropland per capita can accelerate cropland loss. Similarly, weak land governance and economic inequality can also accelerate cropland loss, with both channels possibly percolating through agricultural practices that facilitate land degradation. Interestingly, we find that although geographic characteristics explain movements in and out of cropland, national-level variables are less able to explain movements into cropland. This could be due to the specific role of government policies in putting land into cultivation, which could not be captured with globally available data.

Our paper focuses on the extensive margin, i.e., on the expansion or shrinkage of land put into cultivation. Other adjustments could nevertheless occur through the intensive margin, i.e., with the intensification of cultivation. Although data has become increasingly available to measure “greenness” or “biomass” (e.g., using the Normalized Vegetation Index or the Net Primary Product), such measures do not necessarily capture well cropland productivity and do not differentiate between vegetation and cropland, making it difficult to use them as proxies for cultivation intensity. A study of cultivation intensity would instead require a comprehensive dataset ideally accounting for crop type, the frequency of cultivation, and cultivation schedules that are very likely to vary across locations, which is presently unavailable at the global scale.

Even though this study shows that the global cropland mass is not undergoing
sup> This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition.<sup>5</sup> The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste.

# **4. Identification strategy and data description**

> 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored.

> 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007.

11
# **Table 2 (Continued)**

# <u>Notes:</u>

- (1) The sample is comprised of individuals 18 and older, and of those involved in agriculture. The estimates are weighted by the response weight. The agricultural parcels underlying the (individual-level) indicator definitions are those that are associated with the reference rainy season.

- (2) The rights-related variables are defined irrespective of the reported need to obtain consent/permission from anyone – a topic that the IHPS collected additional information on.

- (3) SDG owner is equal to 1 1 if the individual is a documented owner of any parcel or has the rights to sell or bequeath any parcel, and 0 otherwise.

- (4) Standard deviations in brackets. Adjusted Wald tests for equality of means were also conducted across T1 and T5, within the samples of women/men. Statistically significant differences (p<0.05) are in bold; all except two tests within the sample of non-HH headed women (exclusive economic ownership and SDG owner) were statistically significant at p<0.01.

- ***=p<0.01, **=p<0.05, *=p<0.10.

**Figure 2.  Exclusive Reported and Economic Ownership Among Self-Reporting Female Household Heads, by Survey Approach (T1/IHS4 vs T5/IHPS) and Age**

# <u>Notes:</u>

- (1) The sample is comprised of self-reporting female heads 18 and older, involved in agriculture.

- (2) The median age for female household heads was 46 years, and 20 percent of this sample was between 60 and 75 years.

- (3) The agricultural parcels underlying the (individual-level) indicator definitions are those that are associated with the reference rainy season.

Figure 3 looks more closely at how gender gaps between men and non-household head women vary by age. Individual interviews raise women’s exclusive reported
16 rates of poverty reduction across rural and urban areas. The simulated rates of poverty reduction are determined by both the growth rate and the distribution of consumption within each of the sectors.

# **5.2 Simulation Using National Accounts Household Consumption Data**

Results from a simulation applying growth rates for household consumption per capita (shown in the last column of Table 2) are displayed in Appendix Table A3 and Figure 5. While the overall pattern is unchanged, applying these growth rates instead of those for GDP per capita implies that rather than dropping fairly steadily since the mid-90s, poverty dropped steeply 1996-98 and has since remained flat. This is due to the fact that, according to the national accounts data, household consumption as a fraction of GDP grew during the mid-90s and then fell at the end of the decade, offsetting the effect of rapid growth.

# **Figure 5**

<!-- Start of picture text --> Simulated changes in poverty<br>using hhold exp per capita growth rates from national accounts<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->
_UBSA_ <br>_TAX_RA_|<br>_E0:_49 [2<br>_VE:_63 [2<br>_VE:_61 [<br>59 [25 ];<br>_TE_30[12|<br>0];<br>6];<br>25 ];<br> <br>]|
|Estimation sample|Ta|ble 3A, a|ll colum|ns;|T|able 5, a|ll column|s;|Ta|ble 6, col|umns [4|]-[6];|
||Tab|le 4,col|umns[2]|-[5]|T|able 6,c|olumns[1|]-[3]|Ta|ble 7,col|s.[1a], [|1b], [1c]|

_Notes_ : Countries covered: Eastern & Southern Europe – see Notes to Table 3A; Western & Northern Europe – see Notes to Table 5; Europe (27 countries) – EU-27 (except for Cyprus, Luxembourg, and Malta), Norway, Russia, and Ukraine.<sup>a</sup> Lagged 1 year.<sup>b</sup> Lagged 1 quarter. _Sources:_ Calculation with data described in Table A1.

55
longer‐term lower taxes, location, and local market access. Whether this expressed preference translates into improved zone performance in terms of exports, employment and economic transformation, however, is untested – indeed, it runs against most of the outcomes based findings in the FDI literature. Finally, in a study covering India, Bangladesh, and Sri Lanka, Aggarwal (2005) finds that infrastructure, good governance, and the overall national investment climate contribute to the success of economic zones. In a separate, recently completed paper (see Farole, 2011), we conduct a cross‐country investigation of the determinants of economic zone performance and find that among the key factors are market size and access, national competitiveness, and the zone‐level investment climate. In this paper, we focus specifically on the latter issue.

# **5. Data and methodology**

Original surveys and case study research was carried out during the second half of 2009 in six African countries (Ghana, Kenya, Lesotho, Nigeria, Senegal, and Tanzania) as well as two countries each in Latin America (Dominican Republic and Honduras) and Asia (Bangladesh and Vietnam). In addition, some data on zone performance are compiled from other sources including national zone authorities, key informant interviews, and established databases such as UNCTAD’s FDI database, UN COMTRADE, and World Development Indicators. The surveys provide a profile of the nature of investment in the zones and the expectations of investors, and then explore critical issues that may determine the degree of success of zones programs. Details of the survey methodology are provided in the appendix.

On some variables, the results from the zones surveys are compared with the conditions offered by the national (non‐SEZ) economies using weighted averages from the World Bank’s Enterprise Surveys. Although Enterprise Surveys use stratified samples to depict an accurate portrait of the business environment in the host economies, they were conducted in different years in the ten countries of study<sup>7</
|
|Gradient Boosting|-0.03|CD WD Night Call Skew, IPC WD Day Text Skew, IT WD Night<br>Call Min, IT WD Night Call Max, IT WE Night Call Max|
|_Panel C: Predicting asse_|_t index (conti_|_nuous)_|
|Linear Regression|-0.06|IPC Text Min, IPC WD Text Min, IPC WD Day Text Min, BOC<br>WD Text Min, % Initiated Conversations WD|
|LASSO Regression|0.00|Active Days WE Day, Active Days WD, Active Days WE, Active<br>Days, Active Days WD Day|
|Random Forest|0.00|IT Night Call Skew, IPC Text Min, IT WE Day Call Median, IT<br>WE Call Median, Entropy Contacts WE Night Call|
|Gradient Boosting|-0.02|IT Text Median, Entropy Antennas WE, Entropy Antennas WD<br>Night, Entropy Contacts WE Night Call, IT Recharges Night Min|
|_Panel D: Predicting CW_|_R group (cont_|_inuous)_|
|Linear Regression|0.01|PPI Night Text, IT Recharges Day Skew, IPC WE Call Min,<br>Active Days WE Night, IT Recharges WD Day Skew|
|LASSO Regression|0.05|PPI Night Text, Active Days WE Day, Active Days WE Night,<br>IT Recharges WD Day Skew, IT Recharges Day Skew|
|Random Forest|0.04|# Contacts WE Day Call, Entropy Contacts WD Night Call, IPC<br>Night Call Kurtosis, # Contacts WE Call, IT Call Kurtosis|
|Gradient Boosting|0.03|IT Call Kurtosis, IT Recharges Day Skew, # Contacts WE Day<br>Call, IT Recharges Day Kurtosis, IPC WD Night Call Kurtosis|

_Notes_ : Machine learning results for predicting: (A) Below-poverty-line status, using consumption data and based on Afghanistan
<mark>and education expenditures, for example) with large effects on future well-being through</mark> higher economic growth.

- CEQ Assessments cannot measure the redistributive role of pensions in an intertemporal framework, which is important to accurately estimate the true redistributive effects of pensions.

- The CEQ Assessment framework ignores behavioral responses that taxes and transfers trigger and which may imply important trade-offs in terms of efficiency, effectiveness, and sustainability of the fiscal redistribution compact.

- d) **In-kind benefits from free government education and health services are valued at the average cost of provision.** This does not provide an estimate of the value of these services, nor of the change in purchasing power due to accessing these services. Given that in-kind services cannot be “eaten” or traded, a CEQ analysis does not typically attempt to treat in-kind benefits as additional income in cash terms.

- e) **A CEQ Assessment is based on household survey data** , which typically underrepresent wealthier households.

- Adapted from Lustig <u>(2022).</u>

25
positions where neither the source nor destination report data. If both sides report, we use the two observations for validation purposes, as explained in the appendix.

The final data sets for the four investment types cover up to 239 countries and jurisdictions for the period 2001–18. Appendix table 1 lists every country by region and indicates for each one whether it appears as source and/or destination for the different investment types.<sup>3</sup> The data on loans and deposits cover 210 source countries and 210 destination countries. The data on portfolio equity and debt cover 92 source countries and 238 destination countries. The data on FDI cover 123 source countries and 239 destination countries. The country-to-region data on international reserves cover 182 source countries.

To show that our bilateral data provide good coverage of cross-border investment overall, we compare Lane and Milesi-Ferretti’s (2001, 2007) global IIP aggregates (summing across all countries) with our aggregated bilateral data (summing across all country pairs) on a yearly basis (figure 1). For portfolio investment, FDI, and international reserves, our data closely match Lane and MilesiFerretti’s aggregates. For loans and deposits, our data also match Lane and Milesi-Ferretti’s aggregates until 2007, and display the same trend thereafter, although our values are smaller. Part of the gap could be due to the fact that “other investment” in the IIP, which we use as a proxy for bank loans and deposits, covers more than just the loans and deposits included in our bilateral data.<sup>4</sup> Moreover, a greater number of South countries started to report aggregate IIP data than bilateral loans and deposits to the BIS over time. Thus, the larger aggregates in Lane and Milesi-Ferretti’s data might point to even greater financial integration of the South.

> 3 In appendix table 1, we consider a country to be a source (destination) of international investment if there are nonmissing data for at least one year
from Dasgupta and Kanbur (2007): Conditional on household income, as the supply of religious and social goods increases, perceived household decile increases. A precise test is made difficult by the lack of fine-grained geographic data on the supply of group-specific public goods. We utilize data on household expenditures on social and religious obligations. In the CPHS, spending on “religious obligations” includes the expenditure by a household during a month towards religious ceremonies, donations to places of worship, payments to religious leaders, and contributions towards religious events. Spending on “social obligations” refers to expenditures on religious ceremonies, gifts, weddings, funerals, social causes and events, and contributions to creation of local public conveniences. We sum all household expenditures on social and religious obligations in a district and use this as a proxy for the supply of social and religious goods in the district. Approximately half the sample across economic strata reports expenditures on social and religious obligations, and the amount of spending increases with household income (see Section F of the supplementary materials).

Using Equation (2), we explore whether district-level spending on these goods is correlated with perceived household income decile. Here, i ii<sup>refers to the perceived</sup> income decile of household _i_ in district _j_ in state _k_ . i refers to the district-level mean PPT household expenditure on social and religious obligations in district TT _j_ in state _k,_ with 1 being the quantity of interest. Our estimate of 1 is conditional on i , household _i_ ’s own spending ππ ii on religious and social goods, as well asππO i , the household’s decile based on income data. HH ii Standard errors i are clustered at the district level. To ensure that all districts are weighed OO ii equally in our analysis, each observation (i.e., household) is weighted by the inverse of the ηη district sample size in the CPHS. This guards against the possibility that our estimates are driven by districts with a larger sample
|-0.030|
|||(0.047)|(0.047)|(0.047)|(0.041)|(0.040)|
|Herfndhal index(s)(t-1)|||0.044|0.045|0.022|0.024|
||||(0.049)|(0.049)|(0.059)|(0.059)|
|Age(t-1)||||-0.004|-0.016|-0.011|
|||||(0.028)|(0.033)|(0.034)|
|Capital intensity(t-1)||||0.013**|0.014**|0.015*|
|||||(0.006)|(0.007)|(0.007)|
|Imported inputs_>_0|||||0.333***|0.405***|
||||||(0.028)|(0.041)|
|Firm fxed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fxed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|14,680|14,680|14,680|14,680|14,680|14,680|
|R-squared|0.036|0.036|0.036|0.036|0.093|0.094|

_Notes_ : The dependent variable is a dummy for firm _i_ having positive imports of capital goods in year _t_ . Output tariff(s)(t-1) are MFN applied tariffs from WITS-WB dataset at the 3 digit industry level and input and capital goods tariffs are constructed separately using these output tariffs and India 1993 inputoutput matrix. Importer