rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The question as to whether population growth contributes to poverty is longstanding in population and development studies. Yet one cannot find in the huge literature on that topic the most obvious calculations needed to measure the direct contribution of demographic change to the extent of poverty in the developing world. Poverty is routinely measured as the headcount index, given by the proportion of the population living in households with consumption or income below the poverty line. The headcount index falls when a poor person dies but it rises when a non-poor person dies ( _ceteris paribus_ ). Similarly, the poverty rate rises when a child is borne into a poor family, while it falls when a child is born into a non-poor family. Furthermore, recent research (reviewed later) points to significant socioeconomic differentials in mortality and fertility; in particular, both birth rates and death rates tend to be higher for the poor.

These observations beg the main question addressed by this paper: How much bearing do socioeconomic differentials in mortality and fertility have on the most widely used international measure of poverty for the developing world, namely the headcount index for $1 a day at Purchasing Power Parity, as given in Figure 1 (from Chen and Ravallion, 2004)? The paper measures the contribution of socioeconomic differentials in demographics against an explicit counterfactual, namely the situation when births and deaths are random, i.e., independent of poverty status. The simplest approach to measuring that counterfactual is to net out the differential demographic changes over a given time period and then recalculate the poverty measure at the end point. Comparing this counterfactual poverty measure to the actual value gives us an estimate of the contribution of the demographic changes during that period. By comparing those impacts to the actual changes one can assess the contribution to the evolution of measured poverty.

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## pandemic shock, and as a counterfactual. Then, we introduce the shock.

# **1.1. Economic Environment**

Time is discrete. There is a measure of agents _mt_ which changes over time. Each year _t_ , _bt_ = _b_ 0 _gb_<sup>_t_agents are born with age</sup><sup>_a_=0, where</sup><sup>_gb_is the growth factor in birth rates over</sup> time. Agents of age _a_ die with probability _δ_ ( _a_ ) , and proceed to age _a_ +1 with probability 1 _− δ_ ( _a_ ). We assume there is some ̄ _a_ such that _δ_ ( _a_ ) = 1 for _a ≥ a_ ̄.

An agent _i_ is born with schooling _sit_ = 0. With probability _πs_ (0), agents begin schooling at age _<u>a</u>_ <u>.</u> An agent _i_ currently in school with schooling _sit_ proceeds to the next level of schooling _sit_ + 1 with probability _πs_ ( _sit_ ), otherwise they leave school.

Agents not in school work with probability _πe_ ( _ait_ ) where _ait_ is age. The probability _πe_ ( _·_ ) will be less than one to capture the possibility of unemployment and nonparticipation. If they work, they generate earnings and accumulate experience. _πe_ ( _·_ ) depends on age because youth unemployment differs from average unemployment, and because agents typically do not enter the workforce until a certain age _aw_ .

Earnings _wit_ of a working agent _i_ at date _t_ satisfy ln _wit_ = _hit_ where _hit_ is their human capital measured in units of the return it generates. Let _rs_ ( _·_ ) be the return to education, and let _re_ ( _·_ ) be the return to experience. While in school, where _rs_ ( _sit_ ) is the return to schooling level _sit_ . When not in school, where _re_ ( _sit, pit_ ) is the return to experience, which may depend on schooling _sit_ and on potential experience _pit ≡ ait −_ max _{aw, sit}_ .

Finally, GDP per person _pcGDPt_ equals total earnings divided by the population: We model human capital as earnings potential. Returns are based on earnings data.<sup>14</sup>

> 14 It could be that _rs_ and _re_ reflect different amounts of human capital. Alternatively, it could be that agents earn the same quantity of raw human capital in different countries but it is rewarded differently. Either interpretation of equations (1) and (2) is consistent with our quantitative exercises.
expected to increase, the current level of inefficiency and awareness of the availability of the best practice technologies to improve energy efficiency.

The concept of MAC curves has been used in the literature for a long time in determining potential for GHG mitigation from various options in different economic sectors (production sectors, buildings, transportation sectors). Examples of early studies using MAC curves include UNEP (1992), Jackson (1991, 1995), Timilsina and Lefevre (1998), Timilsina et al. (2000). Apart from academia, private companies, such as McKinsey & Company (Mckinsey and Company, 2009, 2010, 2011), Bloomberg (Bloomberg, 2010); and international institutions, such as World Bank ( World Bank, 2009; 2010; 2012) have been widely using the concept the MAC curve to prioritize climate change mitigation options/technologies in various countries.

Although the literature on MAC curve is rich, there are still some methodological issues that need to be resolved. For example, should an MAC curve analysis compare potential GHG mitigation options in a static fashion as if all mitigation potential of an option can be exploited now, or should the MAC curve be based on analysis over a time period in which the GHG mitigation option, such as improvement of energy efficiency, is implemented gradually? The latter approach can be interpreted as a dynamic MAC curve analysis and it would be more realistic because it is impractical to assume that the full GHG mitigation potential of a measure is ready for exploitation in a short interval of time. Another issue is if the investment costs of GHG mitigation be the same when: (i) an energy inefficient device or process already installed in an existing facility be replaced with new energy efficient device or process and (ii) when consumers face a choice between energy efficient and inefficient device/process to install in a new facility (e.g., choice of roof and window insulations in a building to be built). Further, what assumptions are to be made regarding the penetration of a GHG mitigation measure in the baseline where no policy measures are introduced to incentivize the implementation of the measure?

Existing studies have used different approaches to address these issues. Most existing studies do not differentiate between the existing and new facilities while developing MAC curves and thus use the same opportunity costs in both cases. Therefore, MAC curves produced by various studies are not comparable and the use of the same opportunity costs is misleading. Moreover, existing studies use different assumptions on penetration rate of GHG mitigation measures in baseline as well as climate change mitigation scenarios. This also leads to different calculations of GHG mitigation potential of the same measure.

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Figure 5 2 <!-- Start of picture text --> Outstanding Bank Loans as Parcent of Total External Finance: 1990.1999 Time<br>Series<br>90 -<br>70 -- --<br>60 _<br>50 -<br>40-<br>30 -<br>20<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>--- #- Korea -- Malaysia Thailand , Indonesia -i Philippines<br>100<br>90 -<br>80 - _ _- -<br>70 -<br>60 -<br>50<br>20-<br>1990 1991 1992 1993 199.4 1995 1998 1997 1998 1999<br>1--Ctt a-. -Singapom U u_.<br><!-- End of picture text --> <!-- Start of picture text --> Note External finance is defined as the sum of outsmnding bank loans, outstanding corporate bonds issued<br>and equity market capitalization<br>Source Shirmi (2001 b Chart Id, 9)<br><!-- End of picture text --> 70
son of canada and the united states. Working Paper 3691, National Bureau of Economic Research.

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**Figure 3. Returns to Schooling, External (Real) Shock and Labor Market Rigidity**

<!-- Start of picture text --> Correlation Between the RTS and External Shock Against the<br>Standard Deviation (SD) of the RTS and the SD of the<br>SD of the External Shock<br>External Shock: Salaried Workers<br>1.00<br>0.050 0.80<br>0.045<br>0.040 0.60<br>0.035 0.40<br>0.030 0.20<br>0.025<br>0.020 0.00<br>0.015 -0.200.040 0.050 0.060 0.070 0.080 0.090 0.100 0.110 0.120<br>0.010 -0.40<br>0.005<br>0.000 -0.60<br>0.040 0.050 0.060 0.070 0.080 0.090 0.100 0.110 -0.80<br>Standard Deviation External Shock -1.00<br>Standard Deviation External Shock<br>Correlation Between the RTS and External Shock Against Correlation Between the RTS and External Shock Against<br>the Rigidity of Working Hours the Rigidity of Employing Workers<br>70.00 80.00<br>60.00 70.00<br>50.00 60.00<br>40.00 50.00<br>40.00<br>30.00<br>30.00<br>20.00<br>20.00<br>10.00<br>10.00<br>0.00<br>0.00<br>-1 -0.5 0 0.5 1<br>-1 -0.5 0 0.5 1<br>Correlation between RTS and External Shock<br>Correlation between RTS and External Shock<br>Source: Own calculations<br>SD of RTS External Shock<br>Correlation between the RTS and the<br>Index Index<br><!-- End of picture text --> 25
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10. The paper explores this commute-wage relationship by using data from Antananarivo, the capital of Madagascar. Despite the accelerating urbanization in Africa, currently, little evidence is available for policy makers in the region. The current paper contributes to adding new evidence to the literature. Understanding how individual households decide to commute to work is essential for urban planners and transit developers. Methodologically, the paper relies on an instrumental variable (IV) technique to deal with potential endogeneity associated with the commute decision, which is another contribution to the literature.

11. The rest of the paper is organized as follows: section II provides a brief overview of Antananarivo; section III develops our empirical strategy, and section IV explains our data; section V discusses the main results; and section VI examines the robustness of the results; and then, section VII concludes.

# **II. OVERVIEW OF URBAN TRANSPORTATION IN ANTANANARIVO**

12. As experienced in many African countries, Madagascar is currently experiencing very rapid urbanization. According to UN-Habitat (2016), the population of Antananarivo, the capital of Madagascar, more than doubled in the last decade, from 1.2 million in 1995 to 2.6 million in 2015. It is the fourth largest city in East and Southern Africa, following Dar es Salaam (5.1 million), Nairobi (3.9 million) and Addis Ababa (3.2 million) ( **Figure 1** ). Attracting more people and more businesses, the city population is projected to continue growing to 4.1 million by 2025.

13. Similarly to other large cities in Africa, Antananarivo is the growth center in Madagascar. The country’s GDP per capita was about US$401 in 2016, and the total population is about 25 million. Thus, about 10 percent of the total population lives in Greater Antananarivo. More than half of Malagasy establishments, which amount to about 120,000, are located in Antananarivo ( **Figure 2** ). Analamanga is a region surrounding Antananarivo and
**Table 5.** ITT effects on different knowledge outcomes.

||Overall k|nowledge|Principles|knowledge|How-to k|nowledge|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T1 (extension)|0.050***|0.036***|0.030*|0.020|0.068***|0.052***|
||(0.015)|(0.013)|(0.016)|(0.014)|(0.015)|(0.013)|
|p-value|0.001|0.006|0.062|0.152|0.000|0.000|
|T2 (extension + video)|0.082***|0.068***|0.063***|0.054***|0.091***|0.073***|
||(0.013)|(0.011)|(0.014)|(0.011)|(0.016)|(0.013)|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|
|Test T1 >= T2 (p-value)|0.014|0.008|0.018|0.007|0.102|0.103|
|Endline control mean|0.4|48|0.5|22|0.3|82|
|Additional controls|No|Yes|No|Yes|No|Yes|
|R-squared|0.034|0.219|0.012|0.155|0.048|0.221|
|Observations|2,334|2,334|2,334|2,334|2,334|2,334|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. Additional control variables are age, gender, education (in completed years), whether respondent had access to off-farm work or a nonfarm family business, whether hh adopted the ISFM quickwin package at baseline, whether hh has a cell phone and radio, number of times hh had contact with an extension agent, whether it has participated in agricultural training, number of local organizations involved, and walking distance to nearest farmer training center. Two region dummies for Oromia and Amhara included. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

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# **Table A20: Prevalence of Re-Assignments**

||**% of all**<br>**declarations**|**% of all**<br>**re-assignments**|**probability of**<br>**re-assignment**<br>**conditional on**<br>**initial state**|
|---|---|---|---|
|Declarations without initial excess interaction|**90.1%**|||
|Not re-assigned|84.1%|||
|Re-assigned to inspector without excess interaction (RNN)|5.7%|89.0%|6.3%|
|Re-assigned to inspector with excess interaction (RNE)|0.4%|5.9%|0.4%|
|Declarations with initial excess interaction|**9.9%**|||
|Not re-assigned|9.6%|||
|Re-assigned to inspector without excess interaction (REN)|0.3%|4.3%|2.8%|
|Re-assigned to inspector with excess interaction (REE)|0.0%|0.8%|0.5%|
|Any re-assignment|6.4%|||

_Notes:_ Re-assignment No Excess -> No Excess (RNN) are cases in which a declaration is taken from an inspector who did not act significantly excessively frequently with the broker handling the declaration to another inspector who did not interact significantly excessively frequently with the broker either. Re-assignment Excess -> No Excess (REN) are cases in which a declaration is taken away from an inspector who interacts significantly excessively frequently with the broker in question to one who was not. RNE and REE are defined analogously. Measures of excess interaction are calculated using calibration methods (see section 4 for details). The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

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to a competitive labor market model (Gera and Grenicr 1994). Rationales for **Lhc** efficiency wagc hypothesis are provided by, among others, Akerlof and Yellen (1986) and Katz (1986). They suggest that some firms might pay a wage that is higher than the competitive wage to reduce tumover costs and shirking (see Salop 1979), to increase work effort and loyalty of employees and to attract higher quality job applicants. The implication is that firms enjoy benefits from sharing rents. These benefits may be in the form of savings associated with lower quit rates. These savings may be as high as 1 to 2 percent of labor costs (Freeman and M 'doff 1984). Had the Guatemalan data set contained information on turnover (quits), an investigation of the relationship between turnover and wage premiums would help **to** determine whether there is presence of rents (negative relationship between turnover and industry differentials) or compensating differentials (no relationship between turnover and industry diffierentials). Empirical results by both Krueger and Summers (1988) and Gera and Grenier (1994) support the efficiency wages (rent-sharing) explanations.

Testing Lucas's assumption of **external effects of human** capital In this section I look at the impact of human capital on wages and attempt to test Lucas's assumption of the external effects of human capital. Lucas (1988) describes how an individual's human capital investment can lead to extrnal effects upon his co-workers by increasing their productivity and wages. Unlike most previous empirical investigations of the sources of growth (and most empirical tests of New Growth Theory), which were made using time-series and cross-section data, a two-stage microeconometric approach is used here, following Winter-Ebmer (1992).

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|**Table 8: Compari**<br>|**ng registry-bas**<br>|**ed with survey in**<br>|**formation**<br>||||
|---|---|---|---|---|---|---|
|Registry\Survey|Male indiv.|Female indiv.|Joint|No title|Total|% Type 2 errors|
|Male indiv.|79|27|30|49|185|57.30|
|Female indiv.|15|190|16|56|277|31.41|
|Joint|23|28|227|47|325|30.15|
|No title|45|63|46|762|916|16.81|
|Total|162|308|319|914|1,703||
|% Type 1 errors|51.23|38.31|28.84|16.63|||

_Note:_ For explanations, see text.

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The nature of the measurement is revealed by estimating equation (2) seven times, with village-averaged log total consumption from modules 1 to 7 as the measured dependent variable,

* _y_ and the village average of log consumption from the benchmark individual diary taken as the approximation to the true _y_ . We find that θ ˆ > ,0 λ ˆ < 1 in all seven regressions, with λ<sup>ˆ</sup> ranging between 0.42 and 0.66 and always statistically significantly less than one. The three survey designs with lower variance than in the benchmark design have the strongest degree of mean ˆ reverting error, with .042 > λ> .054. For these designs, the shrinkage due to the λ<sup>2</sup> term in the denominator of equation (5) outweighs the effect of adding the variance of the random noise ˆ term. Furthermore, finding λ < 1in all regressions implies that there is a negative correlation between the errors and the true values (as proxied by the village average of log consumption from the ‘gold standard’ design).

# **IV. Evidence from Food Engel Curve Regressions**

Consider a simplified version of the food Engel curve of Deaton and Paxson (1998), where the demographic composition and control variables are ignored to reduce clutter: The food share for household _i_ , _w f_ , _i_ depends on household total consumption,<sup>_x_</sup> _i_<sup>and</sup> household size,<sup>_n_</sup> _i_<sup>along with a random disturbance,</sup><sup>_u_</sup> _i_ . The data on log per capita consumption, ln( _x n_ ) _i_ are affected by measurement error in<sup>_x_</sup> _i_<sup>and since these errors occur</sup> through both food and non-food, the food share is also affected unless there are equi-proportional errors in both consumption components.<sup>9</sup> Without more structure on the nature of the measurement error, it is impossible to analytically sign the direction of bias in β<sup>ˆ</sup> and γ ˆ because it depends on the relative degree of measurement error in food and non-food.<sup>10</sup>

> 9 Beegle _et al._ (2012) show equi-proportional errors in food and non-food to be unlikely. Errors in household size affecting per capita consumption are also unlikely; just one module (usual month recall) had slightly significantly different household size than the benchmark module.

> 10 Equations (14) and (15) of Gibson and Kim (2007) show the direction of bias in each coefficient depends on the relative magnitude of several terms if the measurement error is allowed to have a general (potentially non-random) nature along the lines of what is described in equation (4) above. If

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