rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 8c Figure 8d<br>Adequacy status in Nepal (2011) Adequacy status in Nepal (2011)<br>By wealth<br>49<br>44 42<br>23 24 23<br>14 14 14 12 14 15<br>1 1 3 3<br>2 2<br>Resource-poor Resource-rich<br>None Food only None Food only<br>Care only Environmental health only Care only Environmental health only<br>Two of three adequacies All three adequacies Two of three adequacies All three adequacies<br>Source: Author estimates. Source: Author estimates.<br>100<br>100<br>80<br>80<br>60<br>60<br>40 40<br>20 20<br>0<br>Percentage of children who meet criteria 0 Percentage of children who meet criteria<br><!-- End of picture text --> Overall, this descriptive analysis reveals that (i) there are only a few children adequate in all three clusters and (ii) many children are adequate in none or in only one of the underlying clusters. With the exception of Bolivia and Bangladesh (IFPRI), more than one quarter of the children do not have adequate access to any of the three pillars (Figure 9). Bolivia, with the caveat that the adequate environment does not measure sanitation, has the highest percentage (13 percent) of young children with access to adequate levels in all three clusters. The other countries included in the study have less than five percent of the children meeting the three adequacies with Cambodia and Ethiopia having the fewest, with less than half a percent of young children having access to all three adequacies. Between 6% (Ethiopia) and 39% (Bolivia) of children are adequate in two of the three adequacies. When only one adequacy is met, in most of our study countries it is care. The only exception is Indonesia where a child who only meets one of the adequacy categories is more likely to be adequate in food than in care or in environment and health. However, in Indonesia the food adequacy measure is based on household level information on food consumption and not child specific information on what kinds of foods the child consumed.

35
suppliers). However, the part of domestic demand that is for imports faces exogenous world prices – and Liberia is viewed as a small country in world markets without any impact on import or export prices. Domestic demanders decide on import and domestic shares based on the relative prices of commodities from these two sources. Similarly, domestic suppliers (the activities) decide on the shares for exports and domestic supplies based on the relative prices received in these two markets.<sup>9</sup> <u>Factor markets</u> reach balance between demand and supply via wage (or rent) adjustments. Across all factors, the factor demand curves are downward-sloping, reflecting the responses of production activities to changes in factor wages. On the supply side of the labor market, unemployment is endogenous – the model includes a wage curve (a supply curve) that is upward-sloping until full employment is reached, at which point it becomes vertical (see Figure 2.2; its supply curve assumes a minimum unemployment rate of 5%). Unemployment is defined more broadly than in official statistics to include un- and under-employment. In the simulations, a broad definition of unemployment increases the scope for the existing labor force to generate a larger (smaller) amount of effective labor if the incentives to work were to improve (deteriorate) without any change in the labor-force participation rate; typically, this seems realistic. Over time, the labor force grows due to demography. For non-labor factors, the supply curves are vertical in any single year (the supply is fixed) but switch over time as supplies change (see next point).

The above discussion refers to the functioning of a model economy in a single year. In MAMS, growth over time is endogenous. The economy grows due to accumulation of capital (determined by investment and depreciation), labor (determined by demography), and other factors (following exogenous growth trends), as well as because of improvements in total factor productivity (TFP). Apart from an exogenous component, TFP depends on the levels of government capital stocks.<sup>10</sup> The disaggregation of MAMS varies widely across different applications depending on data availability and the kinds of questions the model is called upon to analyze. For the Liberian application, the database is disaggregated into some 60 accounts (for details, see Appendix Table A2.1), indicative of the aspects of Liberia’s economy that the model considers. Most importantly, the database includes 22 production sectors as well as two commodities without 9 Many individual production activities do not respond to changes in relative prices for exports and domestic sales as their output only has one destination, either exported in full or sold domestically in full. By the same token, domestic demanders do not have a choice between imports and domestic output for commodities that only have one source. Such structural features reduce the flexibility of Liberia’s economy.

10 In Appendix 2 (on the MAMS database), we discuss our treatment of the links between productivity and government capital stocks.

4
than the BREGS wage rate. There may be lags in the adjustment process, but we would expect to see casual wages catching up to BREGS wages.

There are a priori reasons to doubt whether BREGS is putting upward pressure on wages in the casual labor market. The guarantee is only conditional, up to 100 days, and in practice this is confined largely to the lean season, when there is less likelihood of a spillover effect on agricultural wages.<sup>23</sup> But, probably more importantly, as is shown in Dutta et al. (2013) for Bihar (and in Dutta et al., 2012, using NSS data for all-India), there is substantial unmet demand for work on the scheme even among those with less than 100 days of work. With so much rationing, it does not seem plausible that the scheme is driving up other wages in Bihar. The option value of BREGS in wage bargaining in the casual labor market depends critically on employers believing that the scheme is available. That might not require a strict guarantee of employment under BREGS; the scheme might still help workers bargain up their non-BREGS wages as long as there is a reasonably good chance of obtaining BREGS work. However, it is hard to believe that this would be the case with the degree of rationing in BREGS jobs that we observe in our data.

Our comparisons above of the average wage gaps in R1 and R2 suggest that market wages started catching up with PW wages in R2, consistent with labor market tightening, possibly _due_ to the scheme. On the other hand, non-PW wages do not respond in a predictable pattern to changes in the BREGS stipulated wage. In R1, other (non-PW) wages did not rise after the BREGS wage was increased; and in R2, non-PW wages actually fell even as the BREGS wage was raised during the reporting period. (Note that these are nominal wage rates. So the trend increase in wages reflects in part inflation.) Wage trends do show that the gap between non-PW and PW wages closed, though more for men than for women, even though it was women who began in R1 with a larger gap between the two wages.

Could tightening of the agricultural labor market have come instead from expanding nonfarm opportunities other than BREGS? Table 4 reports the count of person days in all types of non-PW operations from the Bihar sample of the 2009/10 NSS. We see substitution between casual labor (on someone else’s farm) and own-farm work between sub-rounds 2 and 3, while the total amount of agricultural work remained roughly constant. What increased between these two

> 23 Zimmerman (2012) finds evidence of wage gains for women when MGNREGS work is provided in the main agricultural season, but not for men.

16
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38
(World Bank 2018). In 2016, there were 138,301 higher education students, of which 88,559, or 64 percent, paid for the cost of their education. The other 36 percent were fully subsidized by the government. There is a high degree of cost recovery in Azerbaijan. On average, students spent 1,950 _manat_ in 2019. Student admission quotas are tightly controlled by the government. These quotas have resulted in limiting access to tertiary education for young people; the quota-toapplicant ratio has been declining since the mid-1990s. As a result, a smaller proportion of the young generation has graduated from universities and technical vocational colleges than that of their parents (World Bank n.d.). The cost for private tutoring, which is essential to prepare students for the university entrance examinations, is equivalent to an average of 30 to 50 percent of per capita income for households in the bottom three quintiles. This makes it unlikely for students from poorer families to be among the highest scoring students who qualify for the merit-based tuition-free places. Second, tuition fees for public universities are prohibitively high for poor people, amounting to 20 to 160 percent of the per capita income of the bottom quintile; few poor students who did not qualify for funding and who have lower grades are unable to afford tuition. Private universities are even more costly (World Bank 2011).

Public funding of higher education is relatively low in Azerbaijan. In 2018, public education spending accounted for only 3 percent of GDP. Only 10 percent of the total education budget goes towards tertiary education. Higher education institutions receive per capita student payments and for some universities infrastructure costs (World Bank 2018). The direct resource cost of schooling appears in Table 4. The nominal cost for tertiary education is calculated based on the fact that 64 percent of students pay tuition, at 1,800 manat, while the rest are covered by a public subsidy, or 1,700 manat, for an average cost of 1,764 manat. Figure A1 in the Annex shows the distribution 15
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26
opportunities than Upper Egypt and even closer to Lower Egypt in some cases.

**Table 5. HOIs in Four Egyptian regions, 2000 and 2009**

|**_Oortunit_**|||**_200_**<br>|**_0_**<br>|**_l_**|||**_200_**<br>|**_9_**<br>|**_l_**|
|---|---|---|---|---|---|---|---|---|---|---|
|**_ppy_**|**_Metropolitan_**|**_Lower Egypt_**|**_Upper Egypt_**|**_Frontier_**<br>**_Governorates_**|**_Inter-regiona_**<br>**_opportunity_**<br>**_a (max) *_**|**_gp_**<br>**_Metropolitan_**|**_Lower Egypt_**|**_Upper Egypt_**|**_Frontier_**<br>**_Governorates_**|**_Inter-regiona_**<br>**_opportunity_**<br>**_gap (max) *_**|
|Complete primary education|93|87|76|92|_17_|85|89|82|87|_7_|
|Complete secondary education|76|64|53|69|_23_|67|67|58|67|_9_|
|School attendance, 9-15|---|---|---|---|_0_|90|92|86|82|_10_|
|Water|98|80|69|94|_29_|97|87|88|83|_14_|
|Sanitation|97|35|7|30|_90_|88|33|16|36|_72_|
|Lighting energy source|100|99|96|92|_8_|99|100|99|86|_14_|
|Cooking energy source|98|89|53|93|_45_|99|99|97|93|_7_|
|Non-overcrowding, 0-5|57|57|38|58|_20_|64|72|45|64|_27_|
|Telephone|44|12|9|41|_35_|88|73|63|88|_25_|
|Assisted birth delivery|87|66|53|52|_35_|93|88|75|63|_30_|
|Post-natal care, 0-5|25|17|18|10|_15_|40|24|30|11|_29_|
|Prenatal care, 0-4|79|57|51|36|_43_|90|77|74|64|_26_|
|Immunization vaccines, 0-4|81|89|86|73|_16_|85|85|81|80|_5_|
|Non-wasting, 0-4|85|89|86|76|_13_|67|79|72|71|_12_|
|<br>Non-stunting, 2-17|78|70|64|51|_27_|74|64|74|53|_21_|
|<br>Non-underweight,0-17|86|81|76|68|_18_|89|87|80|72|_17_|
|_Aggregate HOIs by group_|||||||||||
|IOHa9-15-Housing Services|95|43|17|56|78|95|75|64|79|32|
|IOHa0-4-Housing Services|94|64|28|65|66|97|88|74|86|23|
|IOHa0-4-Nutrition & ECD|19|17|9|11|10|18|16|10|10|8|

_Notes_ : (*) The gap is computed as the difference between the maximum and the minimum HOI for each opportunity. _Source_ : HIECS 2000 and 2009 surveys, EDHS 2000 and 2008, and authors’ calculations.

_Improvements across Regions Benefit Upper Egypt in Housing Services, but Not in Nutrition_ . Figures 4 and 5 show aggregate HOIs for housing services (9-15) and nutrition & ECD (0-4), by region in 2000 and 2009. The fact that all points in Figure 4 are above the 45 degree line indicates that all four regions had better aggregate HOIs in 2009 than in 2000. Nevertheless, regions that made larger progress were those further from the 45-degree line, the Upper and Lower Egypt regions. Although for those two regions the aggregate HOIs advanced at a much faster pace (45 and 32 points, respectively), Frontier Governorates made moderate progress by 23 points, and the Metropolitan regions remained unchanged.<sup>26</sup> In contrast, the regional dynamic of aggregate opportunities in nutrition & ECD 0-4 (Figure 5) show the stagnation of opportunities for early childhood across all regions, except Upper Egypt that only “advanced” by one point. Thus, expanding opportunities for early childhood remain a crucial challenge across all Egyptian regions.

> 26 The pattern of progress across regions was nearly identical for the aggregate HOI of Housing Services for children 0-4 years of age.

16
characteristics _ηi_ with both nightlights _yit_ , and gold production _mikt_ to obtain the model The presence of serial correlation ( _α_ = 0) mechanically introduces correlation between the lagged difference ∆ _yi.,t−_ 1and the error term ∆ _uit_ . A well-known remedy to this is to instrument the first-differenced equation (11) with past levels of the outcome variables. Furthermore, gold production might be endogenenous to current local shocks. With sufficient serial correlation in gold production, we can also instrument current production with its past values. We report estimates of the models (10) and (11) in Table 5. In columns (1) to (3) we present OLS, fixed effects, and first-difference estimates of the static model (when _α_ = 0). We only find a systematic association of nightlights with mining output in the immediate proximity of mines. Reassuringly, the point estimates of this effect in columns (1) and (3) are almost identical to those reported in the top row of Figure 15. This suggests that how we periodize does not affect our estimates. In column (4) we report a basic AR(1) specification of nightlights without any covariates. The autocorrelation coefficient is close to a random walk; but it might be biased upward if there is serial correlation in the unobservables. We therefore experiment with different lags of nightlight as instruments, and find that a lag of 4 years seems to be sufficient to reject serial correlation in the ∆ _uit_ .<sup>14</sup> In column (6) we add our mining covariates, and we instrument for the difference in mine production ∆ _mikt_ with the second lag ∆ _mik,t−_ 2.<sup>15</sup> We find that nightlights respond to mines at locations in immediate proximity of mines. We also find a statistically significant and positive effect at 40-50 km distance.

The bottom line is that the nightlight data partly confirm and partly contradict our earlier findings. At the cross-section level, gold mining is associated with more economic activity in the immediate vicinity of a mine, but these effects do not spill over to adjacent localities. A similar finding arises when we look at localities with expanding gold output, and this finding is robust to dynamic panel instrumentation. But, contrary to Figures 13 and 14, we do not find

> 14The appropriate test is to test for autocorrelation of second order in the differenced error term, because differencing introduces mechanical autocorrelation of first order (Arellano and Bond 1991). We cannot reject second order autocorrelation for lags smaller than order four.

> 15Since the evolution of gold mines is non-monotonic (see Figure 1) the lagged difference of gold production is a much more informative instruments than the lagged level. If gold mining is endogenous to contemporaneous shocks, then the second lag provides the first valid instrument.

24