rafmacalaba/datause-encoder-data huggingface dataset:
19 passing scores in grade 3 over those attending regular schools.<sup>99</sup> However, the effect is modest and suggests that merely increasing the time students are in school may have limited effects<sup>100</sup> without an increase in the time students are actually engaged in learning.

Overall, the instructional time received by students in the developing world is reduced appreciably due to various conditions and forces. While some of these factors are episodic in nature, others are structural with long-lasting effects.<sup>101</sup> Other factors come to play besides stark time use, such as the elaboration of concepts by students and the memorability of activities undertaken during the learning sessions. Nevertheless, cognitive research would predict that students elaborating concepts for a longer period would remember it better than students covering more concepts for shorter periods. Thus findings underscore the importance of effective time use in lower-income classrooms, especially through introducing more interactive instructional methods that enable students to “contemplate”, analyze or synthesize the subject matter. It is important to obtain detailed data about the incidence of all these factors and focus country dialogue maximizing the use of effective time.

> 99. Cerdan-Infantes and Vermeersch 2007). Full-time schools in Uruguay cost 60% more than regular schools and offer students extracurricular activities rather than extra instruction (OED 2005a). 100. Karweit 1985

101. Millot and Lane 2002
income is _za_ , who lives in a country where the median income is also equal to _za_ , is not considered to have sufficient income for social inclusion. Meanwhile, the set of admissible preferences _U_ corresponds to members of the parametric family specified by Equation (1) with parameter values _σ_ in the interval [0 _,_ 0 _._ 5]. The extent to which this set aligns with the actual heterogeneity of preferences in the global population remains an open empirical question, beyond the scope of the present study.

# **6.2 Empirical Values for the Weight of** _HHS_

We first provide some empirical insights on the hierarchical headcount ratio ( _HHS_ ). As seen in Equation (5), the hierarchical headcount ratio can be decomposed as the sum of the fraction of individuals who are absolutely income poor ( _HA_ ) and the fraction of individuals who are only-relatively income poor ( _HR_ ) multiplied by a weight _ω_ ( **y** ) _∈_ [0 _,_ 1].

Table 1 displays the median weight by country income group as defined by the World Bank. The results highlight two main points. First, we observe that for low- and middle-income countries, the median weight is close to 0.5 and there is little variation within each group, especially in the low-income and lower-middleincome groups. This means that the median poverty score of the only-relatively income poor individuals in low- and middle-income countries is approximately 0.5. More specifically, we observe that more than 60% of countries in our sample have a median endogenous weight between 0.4 and 0.55. Except in two cases, all these are low- or middle-income countries (see also Figure S.1 in online Appendix S4). This implies that for many countries in our sample, _HHS_ and the simpler index _HS_<sup>1</sup><sup>_/_2</sup> would yield similar poverty evaluations. Recall that _HS_<sup>1</sup><sup>_/_2</sup> is defined similarly to _HHS_ but with a fixed weight of 0.5 (see Section 2).

The fact that _ω_ ( **y** ) is close to 0.5 for many low- and middle-income countries can be explained intuitively. In those countries, the relative line is close to the absolute line. As a result, the density function of the income distribution is close to being constant in the small interval between lines. In other words, the distribution of individual incomes between _za_ and _z_ <u>(</u> _<u>y</u>_ <u>)</u> is close to being uniform. In such a ˆ _<u>y</u>_ <u>)</u> case, the average income for the only-relatively income poor is _y_<sup>_R_</sup> _≈_<sup>_za_</sup><sup><u>+</u></sup> 2<sup>_z_</sup><sup><u>(</u></sup> , which yields _ω_ ( **y** ) _≈_ 0 _._ 5 when _za ≈ z_ <u>(</u> _<u>y</u>_ <u>).</u> Second, we observe that the weight decreases as income grows along country groups. Indeed, the median weight among high-income countries is 0.31, considerably smaller than in the other groups. This implies that the incomes of 35
Table 1: Descriptive Statistics

||**Mean**|**SD**|**Min**|**Max**|**N**|
|---|---|---|---|---|---|
|**Panel A. Hazards**||||||
|Temperature (<sup>_◦_</sup>C)<br>|31.97|1.36|26.98|36.39|117,802|
|Precipitation (l/m<sup>2</sup>)<br>|6.19|4.45|0|41.15|117,843|
|_PM_2_._5 (_μ_g/m<sup>3</sup>)|19.57|6.91|4.19|48.45|117,843|
|**Panel B. Outcomes**||||||
|Diarrhea|0.16|0.37|0|1|29,946|
|Cough|0.26|0.43|0|1|29,956|
|**Panel C. Covariates and hou**|**sehold **|**charac**|**teristic**|**s**||
|Toilet|0.41|0.49|0|1|118,669|
|Refrigerator|0.06|0.25|0|1|118,644|
|Education: higher than primary|0.31|0.46|0|1|118,669|
|Household size|2.96|1.49|1|21|118,669|
|Urban|0.74|0.43|0|1|118,669|

_Notes:_ The average daily maximum temperature, and average precipitation and average pollution during the month leading to the interview are used. The sample has information from 4 DHS rounds for Cambodia: 2000, 2005, 2010, and 2014 Our dataset spans four DHS rounds conducted in Cambodia from 2000 to 2014, enabling a comprehensive exploration of the intricate relationships between these variables and health outcomes.

# **3 Empirical Strategy**

Our regression model investigates the relationship between environmental hazards and child health in Cambodia, focusing on diarrhea and cough incidence. To that end, we create temperature, precipitation, and pollution bins using each month’s average maximum temperature, average precipitation, and average pollution. This binned specification is standard in the environmental economics literature (as reviewed in Deschenes, 2014; Dell et al., 2014). A binned specification allows us to flexibly assess the hazard-morbidity link, accounting 7
post-secondary degree, differences between the two methods tend to be quite low regardless of activity type: the largest average discrepancy observed in the Bangladeshi data is 13 minutes (traveling or commuting), compared to 83 minutes in the Ugandan sample (farming, livestock, or fishing activities).

Next, we investigate potential sources of reporting bias that could impact differences in the time use estimates provided by the two methods. Reporting bias may arise for several reasons (see the discussion in the previous section). One of the most common forms is social desirability, which occurs when respondents attempt to (internally) edit their responses to the time use questions before they communicate them to enumerators due to the influence of factors, having to do with how respondents want to be perceived by enumerators and/or others in their community. We test for the presence of reporting bias a similar empirical framework and the same data as before. Our dependent variable is, again, the difference between respondents’ time diary and stylized question estimates. Models 2, 3, and 4, however, introduce additional sets of explanatory variables capturing three difference potential sources of reporting bias: respondent characteristics (sex, age, education, literacy); enumerator fixed-effects (i.e., dummy variables associated with each enumerator); and whether the respondent was interviewed alone or with other adults present.

Table 5 shows our results. Though not always consistent across countries, we find evidence of effects from respondent characteristics and enumerator fixed-effects, but not for whether the respondent was interviewed alone. Sex and education are statistically significantly related to differences between time diary and stylized question estimates for respondents in Uganda, though not in Bangladesh. Possible explanations for this pattern may stem from differences in the types of activities traditionally undertaken by women in the two countries, with women being more involved in a wider range of productive agricultural activities in Uganda and being less subject to barriers to mobility compared to Bangladesh, as well as the possibility that activities in monoculture rice cultivation (as in Bangladesh) may not vary much within the short span of time referred to in the interview. Enumerator fixed-effects, on the other hand, are jointly significant in both countries. This suggests that there may, indeed, be some correlation 18
# The Role of the Extractive Industries in the Mauritanian Econom <u>y</u>

## **MACROECONOMIC CONTEXT**

**In recent years Mauritania’s economy has enjoyed strong and sustained growth.** Grounded by an improving policy framework, GDP expanded by an average of over 6 percent annually over the last decade (2003-2013). GDP per capita<sup>1</sup> was USD1,106 in 2012. This solid overall growth rate was achieved in spite of the ongoing effects of the global financial and euro-zone crises, as well as a severe drought that struck the country in 2011. High iron ore prices and new gold and copper mines have led this growth, supported by fishing and construction, while a recovery in agriculture and the implementation of large infrastructure projects is expected to sustain the economy’s momentum through 2013.

**Mauritania’s fastest-growing economic sectors are fisheries, which expanded by 15 percent in 2012, and public investment, which grew by 23 percent as the government used rising mining revenues to finance infrastructure projects.** More than 50 percent of public investment currently focuses on energy and transportation, and recent net total private investment has also been robust, rising from an average of roughly 2 percent of GDP in 1999-2000 to an estimated 23 percent in 2012-2013<sup>2</sup> . Also gross fixed capital formation has increased remarkably in the same period, moving from roughly 15 percent of GDP to 40 percent of GDP. Iron ore prices have remained high by historical standards, and iron-ore production alone represents about 18 percent of GDP<sup>3</sup> , and other mining (non-iron) output represents approximately 7 percent of GDP. Early predictions for the oil sector proved excessively optimistic, and oil currently represents roughly 3 percent of GDP.  The medium-term growth outlook is very strong, being driven by substantial increases in iron-ore production and the development of the natural-gas sector. Agricultural production has rebounded following the 2011 drought thanks to abundant rainfall in 2012 and 2013. Mauritanian agriculture has been overwhelmingly rain-fed, making the sector highly vulnerable to climatic shocks. However, significant irrigation potential exists.  The fisheries sector corresponds to approximately 7 percent of GDP and is an important reservoir of jobs, with roughly 40,000 people employed<sup>4</sup> in the sector, though many of them are foreigners.

**Over the last two years the government’s available fiscal space has increased to near-record levels thanks to prudent macroeconomic policies, stronger tax administration and high world prices for iron ore and**

> 1 Current USD, source: WDI, World Bank 2 Source: WDI, World Bank 3 Source: Government, data refers to the year 2013

> 4 Source, Etude des aspets macro-economique pour des pecheries durables en Mauritanie, WB, 2008

1
Table 4A. Average monthly income of paid employees and self-employed by age group by age group and sex (current Shillings)

||Pa<br>Se<br>|id employee<br>x<br>|s<br>Total|S<br>S<br>|elf-employ <br>ex<br>|ed<br>Total|
|---|---|---|---|---|---|---|
||Male|Female||Male|Female||
|10-17|9339|7032|8360|11189|11046|11129|
|18-34|33767|32027|33181|41853|21019|31359|
|35-64|78470|60814|74567|58316|23059|42457|
|65+|37013|11294|34152|71163|13606|55527|
|Total|54423|38888|49954|48988|21335|36005|

Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.16 and 9.17.

Table 5A. Average monthly income of paid employees and self employed by age group and industry (current Shillings)

|||Paid em<br>Age G|ployees<br>roup|||Self-em<br>Age|ployed<br>Group||
|---|---|---|---|---|---|---|---|---|
||10-17|18-34|35-64|65+|10-17|18-34|35-64|65+|
|Agriculture/Forestry/Fishing|6447|14748|20796|18040|8258|19083|25223|16888|
|Mining&Quarry|41698|103503|46227||10821|18653|16874|7714|
|Manufacture|18318|43883|172574|20000|8771|36598|44780|13395|
|Electricity& Gas||84275|87128||||51482||
|Construction|29879|40018|61284|125955||61532|47055|43765|
|Trade|14247|25958|48332|38064|15408|40689|60673|183145|
|Transport|15000|59189|109341|36378|5571|46786|125660||
|Finance||91827|159447|||7714|264188||
|Personal service|7005|43822|77349|39866|12583|37297|60192|41769|
|Total|8360|33181|74567|34152|11129|31359|42457|55527|

Source: Author’s own elaboration based on the ILFS (NBS, 2002a). Tables 9.19 and 9.22.

38
Table 5: _Growth and initial stock market liquidity, 1976-1993_

|Dependant Variable|Value Traded<br>Ratio|Turnover Ratio|
|---|---|---|
|Real Per Capita GDP Growth|0.098***|0.027***|
||[0.003]|[0.006]|
|Adjusted R<sup>2</sup>|0.33|0.34|
|Real Per Capita Capital Stock Growth|0.093***<br>[0.005]|0.022**<br>[0.023]|
|Adjusted R<sup>2</sup>|0.38|0.35|
|ProductivityGrowth|0.075***|0.020**|
||[0.001]|[0.030]|
|Adjusted R<sup>2</sup>|0.21|0.21|
|[P-values in brackets]<br>Obser|vations = 42||

* significant at the 0.10 level, ** significant at the 0.05 level, * significant at the 0.01 level.

Value Traded Ratio = Value of domestic equity transactions on domestic stock exchanges divided by GDP Turnover Ratio = Value of domestic equity transactions on domestic stock exchanges divided by domestic market capitalization.

_Other explanatory variables included in each of the six regressions:_ log of initial income, log of initial secondary school enrollment, initial ratio of govemnement expenditures to GDP, initial inflation rate, initial black market exchange rate premium, initial ratio of commercial bank lending to private enterprises divided by GDP.

_Source: Levine and Zervos (1996)_
Fi ure 2a: Correlation in female activit for unor anized manufacturin b state <u>g y g g y</u> <!-- Start of picture text --> A. Female-owned firms vs. female employees<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Share of establishments in state that are female-owned<br>B. Female-owned firms vs. segmentation in female-<br>owned firms<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Share of establishments in state that are female-owned<br>C. Female-owned firms vs. segmentation in male-<br>owned firms<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Share of establishments in state that are female-owned<br>female<br>Share of employees in state that are<br>establishments<br>Share of female employees in female-owned<br>establishments<br>Share of male employees in male-owned<br><!-- End of picture text --> <!-- Start of picture text --> D. Female employee share vs. segmentation in female-<br>owned firms<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>Share of employees in state that are female<br>E. Female employee share vs. segmentation in male-<br>owned firms<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>Share of employees in state that are female<br>F. Segmentation in female-owned vs. male-owned<br>firms<br>100%<br>95%<br>90%<br>85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>40% 50% 60% 70% 80% 90% 100%<br>Share of female employees in female-owned establishments<br>establishments<br>Share of female employees in female-owned<br>establishments<br>Share of male employees in male-owned<br>establishments<br>Share of male employees in male-owned<br><!-- End of picture text -->
<!-- Start of picture text --> —)<br>Sie& fos:. . ry.,. . .<br>2 t t . 8 t 8<br>Do |e Ps e . 2<br>= { « i ! * . t] 8 *<br>3 i : . tr) . .<br>5° H j ! ! * ) .<br>=a2of |\t {i H °: ° . . .<br>goso | ! i .<br>{<br>:S ! ; | '°<br>ea |<br>i<br>2o|<br>1960 1970 1980 1990 2000 2010<br>year<br><!-- End of picture text -->
behavior found in this paper. Not only students may decide to not invest in education because of such debt aversion, but also, conditional on accepting a loan, they may change other behaviors due to their debt aversion, which may lead to unanticipated long-term consequences.

Finally, more broadly, it would be useful to see if the findings of this paper are also prevalent in other investment decisions agents make.

# **References**

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- Ashenfelter, O. and A. Krueger (1994). Estimates of the economic return to schooling from a new sample of twins. _The American Economic Review 84_ (5), 1157–1173.

- Becker, G. S. (1993). _Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education_ . Chicago, IL: University of Chicago Press.

- Card, D. (1999). The causal effect of education on earnings. _Handbook of labor economics 3_ , 1801–1863.

- Card, D. (2001). Estimating the returns to schooling: progress on some persistent econometric problems. _Econometrica 69_ (5), 1127–1160.

- Carneiro, P. and J. Heckman (2002). The evidence on credit constraints in postsecondary schooling*. _The Economic Journal 112_ (482), 705–734.

- Constantinides, G., R. Mehra, and J. Donaldson (2002). Junior can’t borrow: A new perspective on the equity premium puzzle. _Quaterly journal of economics_ (1), 269– 296.

- Eckel, C., C. Johnson, C. Montmarquette, and C. Rojas (2007). Debt aversion and the demand for loans for postsecondary education. _Public Finance Review 35_ (2), 233– 262.

- Field, E. (2009). Educational debt burden and career choice: Evidence from a financial aid experiment at NYU Law School. _American Economic Journal: Applied Economics 1_ (1), 1–21.

- Friedman, M. (1955). The role of government in public education. _Economics and the public interest_ , 123–153.

- Guiso, L., T. Jappelli, and D. Terlizzese (1996). Income risk, borrowing constraints, and portfolio choice. _The American Economic Review 86_ (1), 158–172.

- Mincer, J. (1974). _Schooling, Experience, and Earnings. Human Behavior & Social Institutions No. 2._ National Bureau of Economic Research, Inc., 261 Madison Ave., New York, New York 010016.

- Palacios-Huerta, I. (2003). An empirical analysis of the risk properties of human capital returns. _American Economic Review 93_ (3), 948–964.

14
available to farmers for any given price outcome. New and adaptive research into plant varieties that use nutrients more efficiently or systems that generate organic nutrients can help as well by allowing farmers to depend less on input markets.

The results also suggest that households with greater wealth, human capital and authority can exploit those assets to overcome hurdles that stand in the way of using fertilizers. This shows that households are resourceful in finding ways to compensate for market weaknesses. However, it also implies a self-enforcing link between low agricultural productivity and poverty, since low-asset households are less able to cope with market imperfections on their own. This in turn has consequences for incomes, food security and the sustainability of soils as the resource poor can remain trapped in an eroding livelihood strategy. It is therefore important to experiment with technological and economic solutions that target places where markets are incomplete.

# Bibliography

Abuja Declaration (2006): Abuja Declaration on Fertilizer for the African Green Revolution, issued at the Africa Fertilizer Summit: African Union Special Summit of the Heads of State and Government. Abuja, Nigeria, 13 June 2006.

- Adenikinju, A.F., and O. Olugboyega (1999): “Characteristics and Behavior of African Factor Markets and Market Institutions and their Consequences for Economic Growth,” CID Working Paper 31. Center for International Development at Harvard University. Harvard: Mass.

- Akiyama, T., J. Baffes, D.F. Larson, P. Varangis (2001): Commodity Market Reforms: Lessons of Two Decades. Washington: World Bank.

- Alem, Y., M. Bezabih, M. Kassie, and P. Zikhali (2010): "Does Fertilizer Use Respond to Rainfall Variability? Panel Data Evidence from Ethiopia," Agricultural Economics 41(2): 165-175.

Alene, A.D., V.M. Manyong, G. Omanya, H.D. Mignouna, M. Bokanga, and G. Odhiambo (2008): “Smallholder Market Participation under Transactions Costs: Maize Supply and Fertilizer Demand in Kenya”, Food Policy 33 (4): 318-328.

- Antle, J.M. (1983): “Sequential Decision Making In Production Models,” American Journal of Agricultural Economics 65(2): 282–290.

- Atwood, D. (1990): “Land Registration in Africa: The Impact on Agricultural Production,” World Development 18(5): 659-671.

- Bacha, D., G. Aboma, A. Gemeda, and H. D. Groote (2001): "The Determinants of Fertilizer and Manure Use in Maize Production in Western Oromiya, Ethiopia," Seventh Eastern and Southern Africa Regional Maize Conference Proceedings.

Banful, A.B. (2011): "Old Problems in the New Solutions? Politically Motivated Allocation of Program Benefits and the ‘New’ Fertilizer Subsidies." World Development 39(7): 1166-1176.

- Barrett C.B. (1996): “On Price Risk and the Inverse Farm Size-Productivity Relationship,” Journal of Development Economics 51(2): 193-215.

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22
10 regimes of Malaysia and Thailand could also be described as outward-focused and were shifted further in this direction by reforms during the decade. In contrast, however, Egypt, Nigeria, and Tunisia entered the 1980s with largely inward-focused trade regimes. The three countries attempted with unequal results to shift to export-led development as the decade progressed.

South Korea, Malaysia, and Thailand exhibited strong export-led growth from 1980 to 1986, consistent with their outward-focused trade regimes (Table 3). This growth appeared to accelerate as the decade progressed, softening somewhat in 1988. Manufactured goods accounted for most of South Korea's export growth, and were a major source of the growth in Malaysia and Thailand in 1987 and 1988. In both the iatter countries, manufactured goods rose sharply as a percent of total exports. Declining demand for oil slowed export growth in Egypt and Tunisia from 1980 to 1986, and in Nigeria, exports actually declined. Trade reforms led to the subsequent expansion of exports in all three countries with manufactured goods rising as a share of total exports.

Table 3

||Growth of Exports and M<br>19|anufactur<br>80-1988|ed Share of|Exports||
|---|---|---|---|---|---|
|Country|Growth Rate of Ex|ports|Manufac|ture Share (|Ratio)|
||1980-86<br>1987|1988|1980-86|1987|1988|
|Egypt|2.1<br>6.3|44.2|10.1|19.6|24.5|
|Malaysia|8.6<br>14.4|11.9|25.8|39.5|45.2|
|Nigeria|-8.5<br>-29.6|10.8|0.8|1.6|2.2|
|South Korea|11.6<br>21.6|13.1|91.2|92.4|93.3|
|Thailand|8.7<br>20.6|19.6|34.4|52.5|51.6|
|Tunisia|1.0<br>14.2|24.5|42.3|60.7|64.1|

Source: World Bank Movements in exchange rates are connected with shifts in trade regimes. The devaluation of exchange rates encourages exports and outward-focused development, while inflation of exchange rates is a sign of protectionist trade policies and inward-focused development. Egypt was the only country among the six whose exchange rate appreciated consistently over the period from 1980 to