rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4: Ph**|**ysicians in Africa: 2004**<br>Physicians per 1000<br>population|
|---|---|
|Botswana|0.398|
|Congo,DR|0.107|
|Lesotho|0.049|
|Malawi|0.022|
|Mozambique|0.027|
|South Africa|0.770|
|Swaziland|0.158|
|Tanzania|0.123|
|**Zambia**|**0.116**|
|Zimbabwe<br>Source: WDI, World Ban<br>for Tanzania.|0.161<br>k. Figure for Lesotho is for 2003 and 2002|

In terms of the educational infrastructure for healthcare, Zambia currently has only one medical school, three nursing schools, and three technical colleges graduating doctors, nurses, and laboratory technicians, and pharmacists respectively. In 2004, these schools produced only 49 doctors, 540 nurses, 20 pharmacists and 38 laboratory technicians which sum to 693 healthcare professionals. Data provided by the United Nations on the number of students graduating in healthcare & welfare per capita show that Zambia ranks 8<sup>th</sup> from bottom in a sample of 101 countries in the world and same in the set of 27 African countries (see the next graph). These graduation rates are not only low by international standards but also inadequate to sustain acceptable standards of healthcare. For example, to meet the basic WHO recommendations on staff-population ratios (1:5000 for doctors and 1:700 for nurses) Zambia would require an additional 1,654 doctors and 10,636 nurses which equal about 34 and 20 times the respective annual graduation rates (MoH, 2005).

Zambia’s performance in generating skilled professionals has improved significantly over the last two decades. However, the country still remains an average performer by African standards and is not equipped to meet its own needs.

16
Inequality Database (WIID) (Azzarri and Signorelli, 2020; Cappelli _et al._ , 2021; Paglialunga _et al._ , 2022).<sup>7</sup> Still, a major limitation with household surveys is that such surveys are often not frequently collected, particularly in poorer countries due to resources and logistical constraints. A survey by Beegle _et al._ (2016) indicates that just slightly more than half (i.e., 27) of the 48 countries in Sub-Saharan Africa had two or more comparable household surveys for the period between 1990 and 2012. Dang _et al._ (2019) find a 10 percent increase in a country’s household consumption is associated with almost one-third (i.e., 0.3) more surveys, indicating the paradoxical fact that poorer countries with a stronger need for poverty reduction also face a more demanding challenge of poverty measurement given their smaller numbers of surveys.

Furthermore, many studies exploring the impacts of natural disasters on poverty and inequality traditionally rely on household surveys that collected data on extreme events (Datt and Hoogeveen, 2003; Khandker, 2007; Bui _et al._ , 2014; Arouri _et al._ , 2015; Karim, 2018). While these surveys, usually based on respondents’ recollections, provide direct insights into how households are affected by and respond to disasters, the accuracy of such data can be subject to recall bias and other inherent errors associated with self-reported information. In addition, many (older) surveys do not provide GIS information that accurately geo-locates households. Consequently, recent studies have increasingly turned to other sources of data that offer the geographical and temporal granularity needed to provide better and timely analysis of climate change and natural disasters that we discuss below.

 Are panel data often used?

The analysis of climate change and natural disasters’ effects on poverty and inequality has evolved in tandem with advancements in data collection and processing. Early studies often

> 7 For more details of the PIP database, see: https://pip.worldbank.org/home.

12
nights. Similarly, the November-December peak coincides with the migration of moths from the coastal cotton growing areas of Andhra Pradesh, being helped by the "November winds", blowing from the North-East to East (refer to Maps 1 and 2)2. Recent evidence from resistance monitoring studies (Armes et al.,1991) provides further support for this pattern of migration. Thus, it is found that the resistance of Heliothis to synthetic pyrethroids goes down between July-August and is probably due to a dilution of resistance arising from a mixing of susceptible moths migrating from the Maharashtra areas. Further, it is found that by November-December, resistance at ICRISAT mirrors that of the Guntur area. Clearly, this is most likely the result of the migration of moths from Guntur, inland with the "November winds".

The patterns of Heliothis moth migration mentioned above have important implications for a pest control strategy. We shall fill in details later but at this point it is important to realise that a pest control strategy (in the framework of sustainable agricultural development) cannot be successful if it addresses only one crop or even one geographically contiguous area. It has to go beyond and consider the problem in an integrated farming systems framework (Dover, 1985, Dixon, 1989).

One qualifying remark before we address other issues. At present there is little information on aspects of migration such as (i) the proportion of any emerging population of Heliothis that may 2 In Map 2, the conical shaded area is termed a "back track" and refers to the migration path for moths, deduced on the basis of moth catches at ICRISAT and on the prevailing wind patterns.

37
Since discrimination depends to a great extent on societal values and attitudes, many surveys are attempting to measure these aspects inter alia, in relation to labor market. The World Values Surveys, the Life in Transition Surveys, Latino Barometer and the Afro Barometer are examples of surveys that capture cross-country attitudes. Some governments have taken the initiative to field their own surveys showing their commitment to making a dent in discrimination. The Scottish Social Attitudes Survey conducted since 2002, examines views on whether different groups of people are equally suitable to a particular employment role (that of a primary school teacher) and compares responses to this question with views about these same groups marrying into a person's family. It also explores gender stereotyping in attitudes to parental leave, attitudes to older people working and perceived labour market competition from particular groups. The aim of this survey is specifically to provide inputs into policy to address discrimination. Other smaller studies have looked at perceptions of discrimination, and tried to “get into the minds” of hiring managers and disadvantaged workers. For instance, Jodhka and Newman (2007) interviewed human resource managers on the hiring practices of their firms, and found that caste-based stereotypes color the hiring process such that both very low caste and very high caste candidates are disadvantaged.

Ultimately, measurement depends on the outcome of interest. Where does discrimination play out? Studies of the labor market have historically looked at hiring practices, wages and promotion. In economies with large informal labor markets and concentration of workers in self-employment, these categories capture only some arenas of potential discrimination. Others are mobility across occupations, access to successful entrepreneurship, to credit and other markets, including to timely and relevant information. For still others, notably women in countries with low female labor force participation, the barrier is not wages or occupation; it is the very entry into the labor market. In order to consider multiple labor market outcomes, some researchers have used a hierarchy of employment types and looked at group-based access to different types of employment (see Das, 2002; Lanjouw and Shariff 2002; Barooah 2010).

11
Table A.2.1 Studies on advanced economies (continued)

|Authors|Questions|Economies (sample<br>period and frequency)|Methodology|Results|
|---|---|---|---|---|
|Galati,<br>Poelhekke, an<br>Zhou (2011)|d<br>Did the global financial<br>crisis affect inflation<br>expectations?|Euro Area, United<br>Kingdom, and United<br>States (2004-09, daily)|Test for anchoring by regression<br>of changes in inflation<br>expectations on macroeconomic<br>news|<br>There is some evidence that market-based inflation expectations<br>in the United Kingdom and the United States became more<br>sensitive to macroeconomic news during the global financial<br>crisis.|
|Garcia and<br>Werner (2018|)<br>Are Euro Area inflation<br>expectations well<br>anchored?|Euro Area, Germany,<br>Italy, and Spain (2005-<br>17, daily)|Test for anchoring by regression<br>of changes in inflation<br>expectations on macroeconomic<br>news|<br>The anchoring of inflation expectations has weakened in the<br>Euro Area since 2013.|
|Grishchenko,<br>Mouabbi, and<br>Renne (2017)|<br> <br>Are long-term inflation<br>expectations well<br>anchored?|Euro Area and United<br>States (1999:1-2016:2)|Estimation of inflation<br>expectations and inflation<br>uncertainty by dynamic latent<br>factor model with stochastic<br>volatility|Inflation anchoring improved in the United States, while mild<br>de-anchoring occurred in the Euro Area.|
|Gürkaynak,<br>Levin, and<br>Swanson<br>(2010)|Does inflation targeting<br>anchor long-term<br>inflation expectations?|Sweden, United<br>Kingdom, and United<br>States (1990s to early<br>2000s, daily)|Test for anchoring by regression<br>of changes in inflation<br>expectations on macroeconomic<br>news|<br>The response of market-based inflation expectations to<br>macroeconomic news is larger in the United States than in<br>Sweden and the United Kingdom. Inflation targeting affects<br>long-term inflation expectations.|
|IMF (2016)|1. Have inflation<br>expectations become<br>increasingly sensitive to<br>inflation outturns in<br>recent years?2. How do<br>monetary policy<br>frameworks affect the<br>degree of anchoring of<br>inflation expectations?|24 advanced economies<br>and 20 EMDEs<br>(1990s-2016)|1. Estimation of a hybrid New<br>Keynesian Phillips curve<br>(regression of actual inflation on<br>inflation expectations, past<br>inflation, and other control<br>variables)2. Test for anchoring<br>by regression of changes in<br>inflation expectations on<br>macroeconomic news, using a<br>time-varying parameter model|<br>1. The coefficient on lagged inflation started declining in the<br>mid-1990s, but this trend reversed in the aftermath of the Great<br>Recession, and the coefficient is now similar to that in the early<br>1990s.<br>2. The sensitivity of inflation expectations to macro-economic<br>news is inversely correlated with standard measures of central<br>bank independence and trans-parency; expectations become<br>better anchored when countries adopt an inflation targeting<br>regime. Estimations that allow for time-varying coefficients<br>indicate that, while expectations are better anchored in<br>advanced economies, anchoring has improved in both advanced<br>economies and EMDEs.|

29
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44
# **XOXX** INTRODUCTION

Flgure 15 Assumed Total Fertiliy Trend, Less Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 40 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>3.9<br>3.1<br>3.6 -20<br>3 4<br>3.3<br>3.2 -I9<br>3.1 -UN<br>30 #<br>2.9 1p1e<br>20<br>2.3<br>2 4<br>2.3 I4<br>IM9S- 1F95 19130 MOS MIDID05 2eos213 5 W<br><!-- End of picture text --> <!-- Start of picture text --> FIgure 17 Assumed Uffe Expectancy Trend, -<br>IX<br>Less Developed Countries, and U.N.<br>and Previous World Bank Trends<br>06IVI0S<br>69<br>66<br>65<br>04<br>63<br>UN.<br>02<br>60<br>59 I I<br>1985.90 i9W 95-I2000 2099-M W5-10 2010-15 2015.20 202-:<br><!-- End of picture text --> Figure 16 Assumed Total Fertility Trend, More Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 2.U8 -<br>2.5 -VIOh<br>~~~~~~~~~~~~~~~~2 04<br>1-<br>1.94<br>195 195 199.205 200-05 2010 2010.15 2015-20 20:<br><!-- End of picture text -->

> Figure 18 Assumed Life Expectancy sX 0/6ffi Trend, More Developed Countries, and U.N. and Previous World Bank Trends

<!-- Start of picture text --> U N<br>38<br>75-<br>3<br>1151 99.5 i995-20 200D3-5 MID-1 2010-15 2015-20 2O012.2'<br><!-- End of picture text -->
as mental distress or attitudes toward physical punishment, where the survey method can impact both. For instance, relative to online methods, phone-based surveys can bias the population group toward those who are more likely to answer the phone and at the same time modify the likelihood of reporting a sensitive issue. We explore this in our context and find suggestive evidence that the survey method does not affect the composition of respondents, at least when comparing an important outcome such as the likelihood of reporting a high level of stress.

Finally, we also measure the quality of the reporting of sensitive outcomes, such as stress and physical punishment of children, by the type of remote survey method. Specifically, we test if responses concerning this sensitive information are consistent by type of survey method. We find no differences in the selfreports of stress and perception of the effectiveness of physical punishment across survey waves by survey mode. This result adds to a growing literature implementing different survey methods within face-to-face interviews.

Our paper contributes to three strands of literature. First, we contribute to the growing literature on methodology and best practices for designing and conducting phone-based surveys in low- and middleincome countries, where such surveys are quite new. For example, the works of Himelein and Kastelic (2021); L’Engle et al. (2018) on sampling; Greenleaf et al. (2020); Henderson and Rosenbaum (2020); Lau et al. (2019) on survey mode; Ballivian et al. (2015); Dabalen et al. (2016); Gibson et al. (2019); Lau and di Tada (2018); Leo et al. (2015); Ozler<sup> ̈</sup> et al. (2021) on survey cost, non-response, attrition, and use of incentives; Glazerman et al. (2020) on questionnaire design; Demombynes et al. (2013); Brubaker et al. (2021) on sample representativeness; Abay et al. (2021) on survey fatigue; and more recently, the documentation of general practices for the design of high-frequency phone surveys in the context of a pandemic (Gourlay et al., 2021; Etang and Himelein, 2020; Himelein and Kastelic, 2021; Feng et al., 2018; Morse et al., 2016). In our setting, we show that the main finding—higher response rates for phone-based surveys relative to message-based ones—holds during peak stress periods in a hard-to-reach group. Our design also allow for a direct comparison of survey modes without confounding the results with each of the above mentioned dimensions. Moreover, our data allows us to present evidence on two potential mechanisms behind this difference; that is, amenability - or refusal to respond - and contactability - in our case, due to connectivity 5
cheaply are now the imported good. In this setting, workers performing routine tasks should be most vulnerable to offshore opportunities available in low wage countries, such as China or India.

An assumption common to all of these frameworks is that labor is completely mobile across sectors at no cost, and that as a result, wages will be equalized across sectors. We will explore this assumption in our empirical work. In particular, we will examine the impact of trade and offshoring on the movement of labor across sectors and occupations. We will then examine the consequences for wages of switching sectors or occupations or both. Since occupations are associated with the acquisition of specific skills or human capital, it is unlikely a priori that movement from one occupation to another is as costless as movement across industries; this intuition is supported by recent research by Kambourov and Manovskii (2009).

# B. Empirical Framework

Our identification strategy is to regress log wages of worker _i_ in industry _j_ in period _t_ ( _Wijt_ ) on lagged measures of exposure to offshoring and international trade ( _Git_ -1 _j_ ) using a panel from 1983 to 2002, first at the industry level and subsequently at the industry-occupation level. We use lagged measures of exposure to offshoring and trade for two reasons. First, since offshoring requires time to implement and wage adjustment is not instantaneous, it is unlikely that the causal effect of offshoring on wages will play out within a single calendar year. Second, within a given year, offshoring, trade exposure, and wages are likely to be affected by simultaneous shocks, which we sidestep by lagging right-hand-side variables by one year _._ We use four measures of exposure to offshoring and international trade ( _Gjt_ -1which we refer to as “globalization”): offshoring to lowincome affiliate locations, offshoring to high-income affiliate locations, export shares, and import penetration. To allow for the possibility that offshoring to low-income locations might have
penalize wives and create disincentives to work for pay if the tax rate for the second earner, typically a woman, is set at a higher rate than it would be if she was a single filer (Apps and Rees, 2005; Grown and Valodia, 2010).

In the sections that follow, we describe the data, harmonization process, and methodology used for the analysis. Then, we draw upon time use data to construct harmonized measures of time allocation across productive and non-productive activities to answer the four research questions. We use data from 19 countries: Albania, Argentina, Bangladesh, Colombia, Ecuador, Ghana, Guatemala, Iraq, Mexico, Moldova, Mongolia, Pakistan, Peru, South Africa, Timor-Leste, Turkey, Uruguay, the United States, and the West Bank and Gaza. We then discuss the potential role of social norms, labor market regulations, and other factors in determining time use decisions. We conclude by summarizing the findings and discussing the main implications for research and policy making.

# **2. Data and methodology**

Depending on the study, the collection of time use data may have the objective of sampling not only across the population, but also across hours of the day, days of the week, and seasons of the year. Some studies collect information on who the respondent was with during the activity, where the respondent was, and whether the respondent was carrying out another activity in addition to the primary task reported. Time use studies often collect information on respondents’ demographic and socioeconomic characteristics.

The choice of method for measuring time use is generally determined by the analytical purpose of the study. Time use data can be collected by direct observation, by self-reporting or by interview. Each instrument and data collection mode has its advantages and disadvantages relative to the reliability of the data obtained, effect on response rate and cost (UNDESA, 2004).

The most widely used method for collecting time use data in large-scale surveys is the _time diary._ This approach enables respondents to report all activities undertaken over a specific period of time, usually one day, and the beginning and ending time for each activity. The information is usually reported in 10, 15 or 30-minute intervals or may be left open for the respondent to report. Studies are generally conducted for a random sample of households and they are sometimes further randomized across days of the week, so that each randomly selected respondent is assigned to a designated day. This method makes the entire sample of diaries representative across days of the week. Activities are then typically classified into broad groups and then into more specific groups. Time diaries are perceived to be cost-effective to implement and to provide accurate estimates of how people spend their time. The time diary method does have limitations. One of the drawbacks of this approach is ‘over-kill’, as it runs the risk that respondents will confuse between simultaneous activities (e.g. taking care of children while cooking). This method of data collection 6
9 Table 1

|Endogeneous V|ariables||Number of<br>Variables|
|---|---|---|---|
|**Cvi**|=|Unit costs|n|
|Ki||Sectoral capital stocks|n|
|Ld|5|Sectoral employment|n|
|Vji||Composite intermediate purchases|n2|
|VDji||Domestic intermediate purchases|n2|
|VMji||Imported intermediate purchases|n(n-l)|
|XDi|=|Gross output of sector i|n|
|Di|=|Supply for domestic sales|n|
|Ek|=|Supply for export sales|m|
|PXk|=|Unit revenue of traded goods|m|
|PDi|=|Unit price of domestically sold goods|n|
|PCij|=|Unit price of composite intermediates|n'|
|PNi|=|value-added price of sector i|n|
|PEk,PWEk|=|Domestic and border price of exports|m|
|PMk(PIWMk)|=|Domestic (border) price of imports of<br>sector k|2m|
|RENTCk|=|Rents on imports subject to quotas|2m|
|VTDi,VTMk||Total domestic and import intermediate<br>demands|2m+n|
|CMk,CDi|=|Consumer demand for imports and<br>domestically produced goods|m+n|
|GR,Y,ER|=|Government revenue from tariff<br>collection, disposable income net<br>of transfers and real exchange rate|3|
|W,R|=|Wage, rental rates<br>TOTAL<br>3n<sup>2 </sup>+n(n|2<br>-l)+9n+7m+5|

Note: Number of endogenous variables varies according to model closure. (See text).