rafmacalaba/datause-encoder-data huggingface dataset:
factors may be at play as well when it comes to the poorer of the poor.

The results discussed so far are conditional on the growth rates being the same across the different transformational patterns. Yet, as highlighted in the introduction, the new economic geography emphasizes the critical importance of economic density and agglomeration economies in fostering growth (World Bank, 2009). As a result, metropolitization may well put countries on a much faster growth path, which could over time offset the less inclusive nature of its growth pattern in terms of poverty reduction. One simple test of this proposition would be to re-estimate equation (2) excluding GDP per capita growth (i.e. estimating equation (3)). By so doing, the total effect of the transformation from agriculture to rural nonfarm and metropolitan activities on poverty is estimated, including the indirect effects through changes in the aggregate income level.

The overall impact of rural nonfarm activities is negative and significant as before (Table 7, columns (1) and (2)), whereas the overall impact of the urban share remains insignificant (and substantially smaller). The coefficients on the (share weighted) change rate of the share of rural nonfarm activities are only slightly larger in absolute terms than the benchmark results in columns (1) and (2) of Table 6, suggesting that the effects of rural diversification on poverty reduction mainly work through the income distribution channel. The reduced form specifications, excluding growth, further suggest that the negative effects on poverty reduction from rising income inequality associated with metropolitization are not offset by the that larger growth agglomeration in mega cities may generate. Christiaensen and Todo (2013) explore this further by regressing income inequality and economic growth as such on the change rates in the middle and the metropoles controlling for a series of factors. They find that agglomeration in mega cities is on average associated with faster growth (as observed among the big city migrants from Kagera, Tanzania) and higher income inequality, while diversification into rural nonfarm and secondary town activities typically facilitates a more inclusive but slower, growth process.

22
|Title<br>of Project,<br>Country|Contraceptives<br>Donated|Company Subaidy|Donor Subsidy<br>for Services|Client Pays Fees|Policy andCetimates of<br>Costs/Revenuem or**Oudget**|
|---|---|---|---|---|---|
|OWLOMiNVITA/DUST|RJ -ASED<br>-<br>SmV<br>(co|mttd)||||
|India:<br>Alebic<br>Group<br>of Industries,<br>Sombay|No|Yes|No|No|Companyspent an average<br>ofaboutRa. 15.000 per<br>year on literature,<br>Incentives**and**other<br>lncdentals.<br>Personnel<br>**and***oe operating<br>costs<br>were not Included.|
|Philippines<br>Family<br>Plenalng<br>InHavailmn-<br>Philippinv<br>_CO._|Yes, by governmet|Yes.for<br>personnel_and_<br>medical expenses|No|No|Totalprogram cost to the<br>cmpany - 117,820(com-<br>traceptives<br>educational<br>aterial,<br>and training<br>provided free by the<br>government).<br>Staff<br>**costs**- 35000Iyear<br>(U Sl '<br>***pesos)**|
|torea:<br>KausRoTire<br>Industrial<br>Co.|No|Teo|No|Ho|1972 - mid-1978costs<br>57,330,000 won almost<br>702 of ItforIncentives|
|SriLanka,<br>Industrial<br>Sector<br>Program|Yee, byFPASL|Yes,for incentive<br>payments<br>and moti-<br>lvator<br>activities|No|No|Noinformation|
|Indonesia:<br>P.T. Imbritex|Te,byB10uH|<br><br>Teo|No|No|No Information|
|Bangladesh<br>Family<br>Planning<br>Services<br>for<br>Industrial<br>Workers|Yee, byMinistry<br>of Healthand<br>Population Control|No Inforomtion|Yes|No information|Projectcosts:<br>1972-78-<br>$30,426<br>1983-84 -<br>23,437<br>1987-88-<br>26,696|
|Turkey<br>Impact<br>of Trained<br>Female<br>Labor Union<br>Shop Stewardesses<br>on Moctivators<br>In<br>Family Planning<br>Services|No Information|Yea,forclinic<br>space|No|No Information|Project coet - $28S10|
|Rgypt:<br>TantaIndustrial<br>Family Planning<br>Project|Noinformation|Yee|Yes,for LOP|No Information|Project cost - $19,659<br>from Pathfinder|
This can be re-written as: Many studies use relative prices instead of value added, in which case the expression becomes: The relative price (or value added) of agricultural goods to non-agricultural goods can- change as a result of changes in the price (or value added) of agricultural, industrial, or home goods. Protection of industry will affect agricultural relative prices in three ways. The first is through the real exchange rate, i.e. the price of tradables relative to nontradables. Appreciation of the RER lowers both PA/PH and PI/PH, thus lowering PA/PNA. Secondly, industrial protection raises the domestic prices of industrial goods (PI), therefore lowering the relative price of agricultural goods. Thirdly, it lowers agriculture's value added by raising the cost of agricultural inputs.

Protectionist policies could theoretically result in higher PH, higher PI, lower VAA, or any permutation of the three, depending on what macroeconomic policies are followed. An increase in the price of non-agricultural goods, resulting from industrial protection using tariffs or other restrictions, will lower the relative price of agricultural products. Industrial protection may also result in higher prices of agricultural inputs such as fertilizer, which will reduce the value added of agriculture (Schiff and Valdes, 1992). Finally, an increase in the relative price of home goods (i.e. an appreciation of the real exchange rate) will adversely affect the relative price of agriculture to non-agriculture (since the value of a is usually significantly less than 1), thus reducing the value of the numerator more than the value of the denominator in equations (5) and (6).

Early discussions of the bias against agriculture as a result of industrialization policies pursued by developing countries is found in Diaz-Alejandro's (1970) study of Argentina, and Little, Scitovsky, and Scott's (1970) study of seven developing economies in the 1 950s and 1 960s. Diaz-Alejandro used relative prices to examine the impact of protectionist policies on agriculture, while Little, Scitovsky, and Scott examined effective protection through an analysis of value added. In the early 1980s, influenced by the work of Sjaastad (1980), the Trade Policy Program at the International Food Policy Research Institute (IFPRI) adapted and extended the "true protection" approach to analyze the issue 10
- 35 -

public expenditures (against 75 percent) (see Table 1.12). These proportions reveal the lesser degree of autonomy of LAC subnational governmetits. The average per capita revenue of subnational government (including transfers) is in fact twenty times lower than it is in industrial market economies (about US$100 against over US$2000, see Table 4.3a) while their average personal income is only seven times smaller (about US$1700 against US$12,000, see Table 3.7) One contradictory feature must also be noted: in the developed countries, the central government direct involvement in capital expenditures accounted for only 8.7 percent of their total expenditures during the 1975-1984 period while the corresponding share in LAC was two times as high (16.6 percent) (See Table 3.10b and Annex Tables 3.10c and 3.10d).

3.11 Are LAC Governments Welfare Minded? The budgets of central governments in developed countries show clearly that they are far more redistributive than those of LAC countries. In fact, as a proportion of GDP, transfers from the central to the other levels of government are three times higher in Part One countries (see Table 3.11a). During 1978-84, this type of outlays accounted Table 3.11a: INTERGOVERNMENTAL TRANSFERS AS PERCENTAGE OF GDP

||1975|119'6|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LACSAMPLE a/|||||||||||
||1.36|1.19|1.08|1.08|0.98|0.90|1.23|1..1|1.41|1.11|
|MARKET ECONOMIES a/|||||||||||
|_|3.51|3.59|3.55|3.51|3.41i|3.45|3.41|3.36|3.28|3.12|

**a/** Weighted averages.

Source: IMF, Government Finance Statistics Yearbook, various years.

about 13 percent of total expenditure in market economies (declining to 11 percent over the period) but averaged only 8 percent in LAC countries, fluctuating sharply in the last few years (Table 3.11b). The percentage of total expenditures devoted to subsidies and other transfers by the central governments amounted during the same period to about 56 percent in the average market economy and only 33 percent in LAC countries. Table 3.11c shows that expenditures in the social se2tors (Social Security, Welfare, Education, Health, Housing and Community Amenities) accounted for about 56 percent in the central
Table A.3: Sex ratio of first birth and changes in characteristics of FG vs FB families over time

|Dep Var: First child is female|(1)|(2)|
|---|---|---|
|_Post_|0.00324|0.00993|
||(0.00435)|(0.0147)|
|SC*_Post_||0.00749<br>(0.00802)|
|Educated (Mother)*_Post_||-0.00747<br>(0.00914)|
|Educated (Father)*_Post_||-0.00525<br>(0.00727)|
|Muslim*_Post_||-0.00946<br>(0.0175)|
|Hindu*_Post_||-0.00278<br>(0.0136)|
|Rural*_Post_||-0.000712<br>(0.00710)|
|High wealth (Top 60%)*_Post_||0.00417<br>(0.00603)|
|N|173,293|173,293|
|State FE|x|x|
|Households controls|x|x|
|Linear time trend|x|x|
|HH controls*Post||x|
|F-stat for joint signifcance of HH controls*Post||F(7,24) = 1.98|

NOTES: This table tests if ultrasound access affected the sex ratio at first birth. The outcome variable is an indicator for first birth being female. The sample is restricted to first births and we control for state fixed effects, state-specific linear time trends, and a set of socioeconomic controls (the same as that included in the main specification for health outcomes). In column (2), we control for a set of interactions between household controls with _Post_ (indicating _t ≥_ 1985). The table reports the F-statistic from a test for the joint-significance of the set of interaction terms in column (2). Standard errors in parentheses are clustered by state. Data: NFHS. *** 1%, ** 5%, * 10%.

51
Our model incorporates a statistically verified relationship between car speed (km/hr) and fuel use per km, and the U.S.E.P.A.’s formula for converting fuel to grams of CO2 per km. We use the model to compare the effects of: (1) a socially optimal toll per kilometer of car travel that internalizes the excess delays and excess fuel use caused by traffic congestion; (2) a toll on the excess delay externality only; and (3) an excise tax on the gasoline used by cars that raises the same revenue as the toll on excess delay only, but reduces fuel and emissions by about 1.6 times more. Having no data on average incomes by income group in Sao Paulo, we are limited to linear-in-income, trip-based indirect utility function. The value of time in commuting is made to increase by income group and is assumed proportional to the wage rate, while the sensitivity with respect to monetary cost decreases with income. Commuters’ choices are described by a trinomial logit model of choice between: (i) private car; (ii) bus and other public transit; (iii) and all non-motorized modes such as walking and bicycling. Under these assumptions, we calibrate the model so that the elasticities of demand for travel by car with respect to travel cost and travel time are reasonable, while the mode-specific constants of the logit model are set to replicate the modal shares observed in 2002.

Because congestion affects speed directly and fuel use indirectly, two congestion externalities arise. First, the marginal trip imposes a time delay on other travelers; and second, by slowing them down, it causes them to use more fuel per trip. We use the model to calculate the socially optimal congestion toll, internalizing both externalities. The optimal toll dramatically raises the monetary cost of a car trip by 5.82 times and cuts CO2 by 71%. A partial toll that internalizes only the time delay externality is also computed. We show that this partial toll is 59.2% of the optimal, achieves 75% of the fuel and CO2 reduction, 90% of the revenue and 86.4% of the welfare gains of the optimal toll, the remaining 13.6% of the welfare gains being due to the fuel externality. These results are subjected to extensive sensitivity testing by varying the congestion technology, the within income group idiosyncratic taste heterogeneity and a coefficient that is used to scale the values of time up or down. Ignoring only the most extreme values in these coefficients, the optimal congestion toll reduces CO2 by 60%-87%, and the partial toll internalizing only the delay externality reduces CO2 by 40%-74%. Aggregate welfare, the sum of consumer surpluses and toll revenue, improves significantly. The consumer surplus of higher incomes improves because they value highly the time savings, while that of lower incomes decreases.

4
Given some assumptions on , they show that GIR are unique and fully take account of correlations among different shocks.

The standard-deviation scaled GIR of the effect of a shock in the _i_<sup>_th_</sup> equation, _i_  ,12, 3,4 , at time _t_ on<sup>_Y_</sup> _t_  _n_<sup>,</sup><sup>_n_periods ahead, is represented by:</sup> ' where  _ii_ are the main diagonal elements of   _E_  _t_  _t_  for all _t_ , with    _ji_ , _j_ , _i_  4,3,2,1  a positive definite matrix,  _n_ is a coefficient matrix from (2), and<sup></sup> _i_<sup>is an</sup> 4  1 selection vector with unity as its _i_<sup>_th_</sup> element and zeros elsewhere. Cleary, the GIR in (3) does not depend on any lower triangular matrix, which defines the variables ordering in a Cholesky stile residual decomposition.

Kim (2009) argues against using GIR because they employ a set of extreme identifying assumptions. Economic inference based on GIR would be misleading, unless the covariance matrix is diagonal. In order to address those issues, we use a standard Cholesky identification scheme to compute orthogonalized impulse response and show that the main results also hold under a conventional ordering of the variables.

# **3. Results**

# **3.1 Data**

The model has five macroeconomic variables: (i) real output growth, (ii) inflation, (iii) interest rate (iv) nominal exchange rate, and (v) oil price. The first four are treated as endogenous while the last one is exogenous in the model. Real output growth ( is obtained directly from the Central Statistics Office and calculated on the basis of the GDP deflator. Inflation ( measures yearly variation in the Consumer Price Index and is also obtained from the Central Statistics Office. The interest rate ( ) used is the weighted average interbank rate published by the Bank of Mauritius, which is not a pure policy variable and should be understood as capturing the liquidity stance of the economy. Nominal exchange rate ( ) is obtained from the same source. We also use oil prices ( ) as an exogenous variable in the VAR. The data is quarterly and the sample runs from 2001Q1 to 2008Q4. Figure 1 display the time series.

**Figure 1 – Time series**
one. This accounts for about two thirds of the remaining gap, with the rest being explained by those with BRCs that have closed down or attrited.

The survey data confirm that treatment effects on other forms of registration are small. City council licenses are common, with 62 to 67 percent of the control group having one, but there is no significant difference across treatment groups. Receiving a business registration certificate is therefore not changing registration behavior on this other margin. Recall that the BRC is a prerequisite for being able to register for a tax-payer identification number. We see that only 4 to 6 percent of the control group gets a TPIN. In the first follow-up round we see statistically significant (at ten percent), but small, effects of the BRC treatment on the likelihood of reporting having a TPIN, but surprisingly no impact of the BRC+TPIN treatment. By the second round none of the treatments has a significant effect. This suggests that those who were assisted to get the TPIN were those few firms that were going to go and get tax registration anyway, and that, at most, the BRC helped speed up the process of tax registration for a few other firms that were otherwise going to register for taxes. It could also indicate that knowledge about tax registration process increased in the BRC+TPIN group and we are capturing a more accurate measure of tax registration in that group than in others.

Panel B of table 6 shows how these formalization results vary by gender. In contrast to the administrative data, we find female business owners to have significantly lower treatment effects on obtaining a BRC from all three treatments in the first follow-up round, and from two out of the three treatments in the second follow-up round. One part of the gap is explained by differences that already existed in the administrative data, even if not statistically significant for two of the treatment groups. Two thirds of the remaining gender difference in treatment effects, when compared with the administrative data, is explained by the higher rate of business closure among female-owned firms – there is a 5 percentage point difference between female-owned and male-owned enterprises among those in the treatment groups that had accepted the certificate. Firms not reporting in surveys on BRCs delivered with our assistance are more common for women and largely explains the rest of the gender gap. Differences between men and women in the control group are small and attenuate the effect, and there is no significant gap on attrition. Nevertheless, we still find sizeable and significant impacts of our treatments on the likelihood 21
of stickers of each child. It is standardized by subtracting its mean (among all participants) and dividing it by 10, which is the total number of stickers. We followed all of Cappelen et al.’s (2020) conventions so Figure 5 can be easily compared to the bottom panel of the paper’s Figure 2.

The figure indicates that the effects of the CHECC Preschool on social preferences documented in Cappelen et al. (2020) are mostly driven by girls. The figures on the right column show that preschool attendance increased the inequality aversion of girls. In contrast, the figures on the left column show that the inequality aversion of boys remained roughly the same. Appendix Table 10 shows regression results. The point estimates indicate that preschool girls implemented 36%-41% less inequality (depending on the specification) than control girls in the luck experiment. Combining the luck and merit experiments, we find that preschool girls implemented 35%-39% less inequality than control girls in the two experiments. Appendix Tables 11, 12, and 13 show that the controls are balanced both for the entire sample and if we break by gender.

There are at least three interesting take-aways from the comparison of the experiments in Chicago and in Rio. First, they provide some sort of external validity, indicating that the effects of early education on social preferences are not specific to a specific setting, program, or age group. Second, they indicate that the effects are long-lasting. Cappelen et al. (2020) find effects 3.5 year after the intervention started. We find effects 9 years after treatment children started attending daycare. Finally, they suggest that the aversion to inequality may apply not only to cases in which the decision-maker is an active participant but also to cases in which the decision-maker is not directly involved, as it was the case in Cappelen et al. (2020).

# **7. Aversion to Disadvantageous Inequality Predicts Field Behavior**

List et al. (2021) argue that one reason for studying children’s decision-making is that “children are active participants in their human capital production process”; their preferences and decisionmaking abilities may influence investments in their human capital with long term-consequences. In this section, we investigate this hypothesis by combining data from the incentivized experiments in Rio with administrative data on test scores. In particular, we study the relationship between the economic preference affected by daycare attendance – namely, aversion to disadvantageous 24
however, all our school districts were taken from income classes ranked 4 and 5, which created imbalance with Neuva Vizcaya division, where some of the school districts were ranked 3.

Second, schools were randomly sampled from the list of elementary schools in school year 2002/03 satisfying three criteria: (a) having a total enrollment larger than 120, (b) being mono-grade (having at least one class for each grade), and (c) being complete (offering grades 1 to 6). That is, schools had on average at least 20 students in each grade. In an intervention division, 15 schools were randomly sampled from the basic list satisfying the above criteria. Similarly, 10 schools were randomly sampled in a nonintervention division. In Antique (an intervention division), however, we decided to add 2 more schools since we found that 2 schools had been severely damaged in flash floods caused by a typhoon in 2006 (they were relocated and rebuilt on safer higher ground). Therefore, we have 17 schools in Antique. In Neuva Vizcaya, it was difficult to obtain student lists from one school at the early stage, so this school was omitted from our sample, but we increased the number of students from 2 large schools (to keep the number of sample students the same).

Third, we collected lists of students enrolled in grade 6 in school years 1999/2000, 2004/05, and 2005/06. School year 1999/2000 is a pre-intervention cohort, while both 2004/05 and 2005/06 are cohorts that were exposed to a school intervention if the students lived in TEEP divisions. The process required a few months in each division. We randomly sampled 15 students from school year 1999/2000 grade 6 (pre-TEEP cohort), while 20 students altogether were sampled from school years 2004/05 and 2005/06 grade 6 (TEEP cohort). The sampling disregarded gender and age. Delays in entering school and repetitions created variations in age even in the same cohort. Note that because the listed students are those who were enrolled at that time, some of our sample students might not have graduated from their elementary schools.

Table 5.1 shows the composition of our sample households and students. We have a total of 3,451 students in our sample. TEEP divisions and cohorts (school years 2004/05 and 2005/06) are oversampled. Among the TEEP divisions, Ifugao shows smaller numbers in each grade-6 sample year due to the decision to drop some unreliable and unverified information in the second qualification visit in the division.

# **_Household Survey and Student Tracking Survey_**

16