rafmacalaba/datause-encoder-data huggingface dataset:
to ensure monitoring of all births would be a financial and logistical challenge, and evidence from the Nawanshahr model in India suggests that sustaining such efforts is difficult.

Moreover, there is evidence to show that bans can be self-defeating in improving outcomes for women and girls. Bans can make access to legal reproductive health care services difficult, and they can push couples with strong son preference to use postnatal discrimination as a substitute. Having unwanted girls can also worsen the treatment of mothers in the household.

CCT programs do not seem like a viable alternative either. Evidence from India and China suggests that these programs are ridden with implementation challenges, deliver short-term gains at most, and do not change underlying gender preferences among parents.

# (3) Reform laws and institutional practices

Reform in areas such as family law, social security, workplace policies, educational polices, and political reservations can undermine some of the institutional underpinnings of son preference and bring about normative change. These reforms can potentially provide women with more bargaining power within the household, provide young adolescents with female role models, and encourage parents to invest in daughters. However, it must also be noted that legal reforms do not always bring about immediate shifts in gender norms.

# (4) Strengthen advocacy efforts, and employ a multi-sectoral approach to do so

The effect of legislative reform in bringing about attitudinal change can be strengthened through advocacy efforts. Advocacy, particularly mass-media advocacy, is one of the most cost-effective ways of changing gender-specific attitudes and behavior. Compared to the bans on sex selective technology, advocacy efforts (in combination with legal reform) offer more promise in terms of undermining son preference and producing a more permanent shift away from sex selection.

Advocacy efforts can be even more effective when a diverse set of partners (media groups, NGOs, religious leaders, local leaders, celebrities etc.) come together. Members of these groups can disseminate reports to raise national awareness, hold discussion sessions that force an examination of gender norms and sex selective practices, and generate greater visibility for female achievers. However, the framing of messages in advocacy efforts needs to be managed to ensure that they focus on promoting gender equity.

45
healthcare facilities which may reduce acute health impacts, but cannot be gleaned from this analysis (see below). Other useful controls include data on human capital.

Additionally, the interaction with other environmental stressors may be important to differentiate, especially if climate change and air quality vectors are not completely correlated in the future. Productivity impacts in hot areas may arise from ozone or carbon‐monoxide as well as temperature. Future analyses that can control for average ozone or carbon monoxide levels would help researchers understand the ways in which exposure to climate extremes and low air quality interact.

And finally, because the wealth scores are relative within countries, it is difficult to derive meaningful, policy‐relevant parameter estimates of the extent of exposure bias that is generalizable across countries. It is also impossible to ascertain the relative contribution of climate change to increasing heat stress in hot places versus reducing cold stress in cold places.

# _Other factors that may be important in ascertaining distributional welfare burdens_

# _Access to Physical Capital_

Poorer households are likely to have lower adaptive capacity for a variety of reasons. Even if they are aware of means to mitigate heat‐related impacts, they may be constrained from employing them on various margins. They are less likely to have regular access to electricity, which is vital when it comes to mitigating heat stress. Poorer individuals may also tend to live in more vulnerable housing (lower housing quality); and for example, in developed countries, live on the top floor of houses without centralized air conditioning. More generally, poorer households will be less able to smooth consumption in the face of income shocks that may arise from unusually hot summers.

Income and electricity rates have been documented as being the primary drivers of AC adoption (Biddle, 2008). This is a statement about the rate of change in adaptive technology uptake. The implication, then, is that poorer areas – which often also have low rates of electrification – will likely adapt more slowly to the onset of climate change‐related heat stress, exacerbating underlying inequalities in adaptive capacity. Given the geographic distribution of low‐income individuals (i.e. the fact that, on average, populations in already hot regions tend to be poorer), a further implication is that climate change may endogenously make lower‐income areas relatively less likely to adopt AC technology, exacerbating the rate of income inequality growth.

# _Demographic factors_

It is possible that demographic factors matter. Elderly and very young populations are on average more severely affected by temperature stress, which means that a given degree of warming may result in greater realized exposure for these individuals (Kovats and Hajat 2008; 34
33 higher income and lower price elasticity,<sup>Langhammer concludes that trade</sup> creation will outweigh trade diversion by<sup>a factor of 4. On that basis, the</sup> results in Table 3 would be reversed and the developing<sup>countries would be</sup> - gainers from EC-92 on all counts including diversification and investment. However it is difficult to give credit to<sup>these estimates as there is no</sup> explanation why the income elasticity should be<sup>so much higher than in other</sup> studies. In a wide ranging survey, Goldstein<sup>and Khan (1985) find income</sup> elasticities of 1-2.5 for different categories<sup>of manufactured imports.</sup> Moreover, the price elasticity has the wrong<sup>sign (implying that falling EC</sup> prices would divert trade towards the developing countries)<sup>and is statistically</sup> insignificant. A positive price elasticity might<sup>be appropriate if the existing</sup> national quotas are abolished instead of being<sup>translated into EC-wide quotas.</sup> Since that is a theoretical possibility, if extremely<sup>unlikely, one should</sup> perhaps note this dissenting estimate in passing.<sup>However Langhammer also</sup> acknowledges that the impacts of EC-92 on developing<sup>country manufactures is not</sup> likely to be dramatic and notes that EC<sup>imports of developing country</sup> manufactures have actually been falling in comparison<sup>to US imports of the same</sup> throughout the period of greater integration and liberalisation of the European markets (1968-85). Both of those observations support Table 3's estimates rather than the alternativ3s.

Davenport's (1990) analysis of EC-92 and developing<sup>country manufactures is</sup> another study which concludes that the effects<sup>on the developing countries will</sup> be relatively minor bf.cause there is a rough balance be.ween trade creation and trade diversion and because the changes within<sup>Europe will not be large enough</sup> to have a big impact (despite elasticities greater<sup>than unity). Most countries</sup> in his sample of 18 Asian or Latin American developing<sup>countries show small gains</sup> in certain industries (e.g., textiles),<sup>but those gains are substantially</sup> smaller than the average annual growth of 5%-7% experienced over the past decade. But much greater negative effects are to be expected<sup>from investment diversion</sup> and from either a tightening of the existing<sup>national import quotas/voluntary</sup>
(based on unobserved ability), then those left behind may appear to have higher wages, though they may not have been raised by emigration or the labor supply shock.<sup>14</sup>

# **Table 1**

Baseline Specification: Main Results (Coefficient estimates; standard errors in parentheses)

|||Ordinar|Dependent<br>yLeast Squ|Variable: Log<br>ares(OLS)|of MonthlyW<br>Hhld|ages<br>Quantile||
|---|---|---|---|---|---|---|---|
|||Male|Female|Urban|ouseos|Reression||
||All|<br>Sample|<br>Sample|<br>Sample|without<br>Migrants|g<br>Analysis<br>Q=0.25|OLS Cohort<br>Averages|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Ratio of emigrants to labor force|0.322***|0.405***|0.216***|0.245***|0.288***|0.189***|0.330**|
||(0.084)|(0.132)|(0.071)|(0.087)|(0.082)|(0.004)|(0.133)|
|Married|-0.038|0.114**|-0.141***|0.000|-0.028|-0.034***|0.072|
||(0.029)|(0.051)|(0.030)|(0.046)|(0.029)|(0.002)|(0.210)|
|Female|-0.245***|0.000|0.000|-0.338***|-0.249***|-0.209***|-0.168|
||(0.027)|(0.000)|(0.000)|(0.037)|(0.026)|(0.001)|(0.166)|
|Education (omitted: primary education)||||||||
|Secondary education|0.544***<br>(0.091)|0.488***<br>(0.131)|0.599***<br>(0.143)|0.241***<br>(0.051)|0.597***<br>(0.097)|0.498***<br>(0.013)|0.843***<br>(0.098)|
|Vocational education|0.858***<br>(0.087)|0.813***<br>(0.119)|0.886***<br>(0.144)|0.375***<br>(0.052)|0.916***<br>(0.093)|0.811***<br>(0.013)|1.241***<br>(0.105)|
|Higher education|1.499***|1.495***|1.479***|0.856***|1.539***|1.300***|1.953***|
||(0.089)|(0.124)|(0.145)|(0.048)|(0.095)|(0.013)|(0.125)|
|Experience|0.026***|0.035***|0.010*|0.036***|0.025***|0.013***|0.003|
||(0.006)|(0.010)|(0.005)|(0.006)|(0.006)|(0.000)|(0.014)|
|Experience-squared|-0.001*** <br>(0.000)|-0.001***<br>(0.000)|-0.000*<br>(0.000)|-0.001***<br>(0.000)|-0.001***<br>(0.000)|-0.000***<br>(0.000)|-0.000<br>(0.000)|
|Constant|5.819***|5.640***|5.822***|6.693***|5.814***|5.635***|6.100***|
||(0.144)|(0.209)|(0.157)|(0.082)|(0.142)|(0.013)|(0.159)|
|Number of observations|23,029|11,081|11,948|6,151|19,176|23,029|167|
|R-squared|0.178|0.171|0.183|0.102|0.173|...|0.739|

*** p<0.01, ** p<0.05, * p<0.1.

Note: The standard errors are adjusted for emigration cohort clustering.

> 14 We speculate that this is unlikely given anecdotal evidence that suggest that higher-ability individuals are more likely to find work abroad, given the costs, both monetary and non-monetary, of assimilating into foreign labor markets. In the literature on internal migration, this selectivity bias has been addressed directly using much richer databases that provide information on likely drivers of emigration, including childhood experiences (e.g., Vijverberg 1995).

10
# **<mark>Table 3A – Impact of FD Law on Bank Lending to Local Governments Given State Public Debt</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on bank lending to local governments of varying ex ante public indebtedness.</mark> The variable _Loans-Govts,m_ is the total bank lending (in logs) of state government _s_ in month _m_ . _Term Loan-Govts,m_ is the bank lending (in logs) in term loans of state government _s_ in month _m. Credit Line-Govts,m_ is the bank lending (in logs) from credit lines of state government _s_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the state-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. * ** *** <u>, , denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Loans-<br>|Govts,m<br>|Term Loa<br>|n-Govts,m<br>|Credit Line<br>|-Govts,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Postm|0.04*||0.10***||-3.19***||
||(0.02)||(0.02)||(0.55)||
|Postm*DebtStates,16Q1|-0.09**|-0.09**|-0.14***|-0.14***|-1.19|-1.19|
||(0.04)|(0.04)|(0.04)|(0.04)|(1.20)|(1.19)|
|Observations|1,080|1,080|1,080|1,080|1,080|1,080|
|R-squared|0.96|0.96|0.96|0.96|0.52|0.54|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|No|Yes|No|Yes|No|Yes|

48
17

- N. **Vijayaraghavan, K. (1988) "Assignment Report:** Analysis of **Nutrition Services--Lao People's Democratic Republic."** WHO.

0. "Third National Nutrition Survey of the Philippines 1987." (1989) Department of Sciences and Technology. Food and Nutrition Research Institute.

- P. Jamaica Living Conditions Survey, 1989, The World Bank.

- Q. Carlson, B.A. & T. Wardlaw (1990). "A Global, Regional and Country Assessment of Child Malnutrition." UNICEF Staff Working Paper No. 7.

- R. Child Anthropometry in Cote d'Ivoire: Estimates from Two Surveys, 1985 and 1986 (1990). Living Standards Measurement Study Working Paper No. 51. The World Bank.

- S. Pesquisa Nacional Sobre Sauda E Nutricao. Resultados Preliminares INAN, FIBGE, IPEA, March 1990 (Brazil).

- T. "Encuesta Nacional de Nutricion" (1989). Mexico. OF. INEGI and SSA.

- U. Amor, Jamie Sepulveda (1989). Estado Nutricional de los Prescholares y las Mujeres en Mexico: Resultados de una encuesta nacional probabilistica. Academia Nacional de Medicina.

- V. Report of the Child Nutrition Status Module. Bangladesh Household Expenditure Survey 1985-1986 (1987). Bangladesh Bureau of Statistics.

- W. Central Bureau of Statistics, Indonesia (1987). Status of Gizi Balita.

- X. Central Bureau of Statistics, Ministry of Finance and Planning, Nairobi, Kenva (1983). Republic of Kenva Third Rural Child Nutrition Survev, 1982.
# **Appendix 2: The vignettes** in the TLSMS

<u>Vignette 1:</u> Family A can only afford to eat meat on very special occasions. During the winter months, they are able to partially heat only one room of their home. They cannot afford for children to complete their secondary education because the children must work to help support the family. When the children are able to attend school, they must go in old clothing and worn shoes. There is not enough warm clothing for the family during cold months. The family does not own any farmland, only their household vegetable plot.

<u>Vignette 2:</u> Family B can afford to eat meat only once or twice a week. During winter months, they can heat several rooms, but not the entire house. They cannot afford for all their children to complete secondary education. Their clothing is sufficiently warm, but they own only simple garments. In addition to their household vegetable plot, they own a small plot of poor quality farmland that is distant from their home.

<u>Vignette 3:</u> Family C can afford to eat meat everyday. During the winter months, generally they are able to keep their home warm. They can afford for all their children to complete secondary education. They have sufficient clothing to keep warm in the winter. Their everyday clothing is simple, but they also have some fancy items for special occasions. In addition to their household vegetable plot, they have a larger plot of good quality farmland, not too distant from their home.

<u>Vignette 4:</u> Family D can afford to eat whichever foods they would like, including sweets and imported food. During the winter months, they have no problems with heating and are able to keep their entire house warm. They can afford for all of their children to complete their education, and then to continue at a local university. They are able to afford a variety of fancy traditional clothes and also imported brand clothing. The family owns property, including a good car. The family also has a large farm and acts as landlord to others in their area.

29
multilateral loans than bilateral based on the logic that multilateral loans are a greater part of the loan-level dataset sample and are disbursed more slowly.

# 5.2 Accounting for global shocks

financial cycle suggest that investment across countries may be subject to common global shocks (Miranda-Agrippino and Rey, 2022; Kose et al., 2009). While time fixed effects control for common shocks in the main results, as an alternative we include cross-sectional averages of public and private investment in our regressions. Based on guidance from Chudik et al. (2017) we include three lags of the cross-sectional averages. The multipliers presented in table 7 are consistent with the multiplier estimated in our main results, where time effect was used to account for common shocks. Specifically, we find a baseline estimate of 1.6, which rises to 2.3 after three years.

**Table 7:** The response of private investment to government investment, cross sectional dependence

||(1)<br>|(2)<br> <br>|(3)<br>Cumulative M<br>|(4)<br>ultipliers (hor<br>|(5)<br>izon h in years<br>|(6)<br>)<br>|
|---|---|---|---|---|---|---|
||Baseline|h=0|h=1|h=2|h=3|h=4|
|Change in government investment|1.558**|1.581**|2.237***|2.373***|2.300***|1.420|
||(0.623)|(0.658)|(0.863)|(0.782)|(0.815)|(1.092)|
|Observations|3,245|3,223|3,116|3,010|2,903|2,796|
|F-statistic|13.61|10.78|10.05|10.67|13.98|12.90|
|KP Underid stat|0.00115|0.00259|0.00359|0.00267|0.000847|0.00122|
|No. of countries|109|109|109|109|109|109|

_Notes_ : *** p <0.01, ** p <0.05, * p <0.1. Dependent variable is change in private investment to lagged GDP. Robust standard errors in parentheses are Driscroll-Kraay standard errors to control for the presence of error cross-sectional dependence á la Boehm (2020). All estimations include country and year fixed-effects and one year lag of the dependent variable. “KP” is Kleibergen and Paap underidentification test.

# 5.3 Exogeneity of the instrument

So far, we have estimated the effect of public investment on private investment using instrumental variable approach. Nonetheless, our results above rely on the assumption that our selected instrument satisfies the exclusion restriction for all endogeneity correlation between the error term and public investment. Importantly, the associated F-statistics for the IV are often marginally greater than the rule of thumb of 10 and in several cases smaller than 10. This suggests a weak instruments problem and challenges the assumption that our IV strictly satisfies the exclusion restrictions. At a more intuitive level, we are recycling an instrumental variable from Kraay (2014), where the instrument was used to identify the exogenous shocks in government spending. In our case, we are regressing private investment on public investment (a subcomponent of public spending) and are using 19
# **Table 1: Evolution of DAU Allocation**

||2001|2002|2003|2004|2005|2006|
|---|---|---|---|---|---|---|
|Existing Establishment Cost<sup>a</sup>|||||||
|- SDO/Inpres|80%||||||
|- Proportional Wage Bill||50%|45%|40%|40%||
|- 100% Wage Bill||||||50%|
|Formula-Based Allocation<sup>a</sup>|||||||
|- Lump Sum|1.5%|10%|5%|5%|5%||
|- Fiscal Gap Formula|18.5%<br>Ave (OSRt +<br>PBBt +|40%|50%|55%|55%|50%|
|Fiscal Capacity Formula|BPHTBt) * Ave<br>(IGRDP_SD +<br>IGRDP_nonSD<br>A +<br>IWorking_Age)|OSRi* +<br>STXi +<br>0.75*SDAi|OSRi* +<br>STXi +<br>0.75*SDAi|0.5*OSRi* +<br>STXi +<br>SDAi|0.5*OSRi* +<br>STXi +<br>SDAi|OSRi + STX<br>+ SDA<br>(0.3*IPOPi|
||(0.25*IPOPi<br>+0.25*IPOVG|(0.4*IPOPi<br>+0.1*IPOVG|(0.4*IPOPi<br>+0.1*IPOVG|(0.4*IPOPi<br>+0.1*IPOVG|(0.4*IPOPi<br>+0.1*IPOVG|+0.1*1/HDIi<br>+0.15*IARE|
|Expenditure Needs Formula<sup>b</sup>|APi+0.25*IAR<br>EAi+0.25*ICO<br>STRELi)<br>*Ave_Exp|APi+0.1*IA<br>REAi+0.4*I<br>COSTRELi)<br>*Ave_Exp|APi+0.1*IA<br>REAi+0.4*I<br>COSTRELi)<br>*Ave_Exp|APi+0.1*IA<br>REAi+0.4*I<br>COSTRELi)<br>*Ave_Exp|APi+0.1*IA<br>REAi+0.4*I<br>COSTRELi)<br>*Ave_Exp|Ai+0.3*ICO<br>STRELiCost<br>+0.15*IGRD<br>PPCi)*Ave_<br>Exp|
|Hold-harmless<br>- % of districts benefited|Yes|Yes|Yes|Yes|Yes|Yes|
|from HH|[...]|[...]|[...]|[...]|11%|3%|
|Amount<br>- Nominal Amount (in Bln|||||||
|Rp)<br>- % of Central|[...]|[...]|76,978|83,139|88,766|145,644|
|Expenditures|||||22%|23%|

Note: a = as share of Total DAU Pool (Province & Districts) b = OSR is imputed revenue based on a regression of actual OSR against regional income measured by GRDP PBB = shared property tax and BPHTB = shared change of property title tax income STX = Shared Tax Revenue and SDA = Shared Natural Resource Revenue IPOP = Population Index, IPOVGAP = Poverty Index, IAREA = Area Index, ICOSTREL = Cost Index, HDI = Human Development Index and IGRDPPC = GRDP Per Capita Index The Subscript t indicates total and the subscript i indicates the respective kab/kota. For more details see Annex 1 and Lewis (2002a) Source: Ministry of Finance data. See also Arze (2005).

The initial challenge for policy makers was to annually increase the prominence of a formula-based allocation relative to legacy allocations for localities, represented by expenditure on civil servant wages and the amount that localities had received in the previous year. Increasing the share of the formula meant that some localities were potentially bound to get less than the previous year. In a series of “hold-harmless” adjustments, those localities which would have received less than the previous year got 10
31

**Box 5 Average Applied Tariffs on Products Egypt Exports to OECD Markets**

Do tariffs on Egyptian exports to the OECD discriminate in any important way against particular industries or sectors. Table 12 suggested that the overall level of import duties was low in the EU and Japan (in the one to two percent rage) while United States duties were higher since this country does not extend GSP treatment to textiles, clothing and footwear. Are there specific sectors where Egyptian exports encounter significantly higher import tariffs. The statistics provided below show average "applied" (in the sense that they are the average of the GSP or MFN duty actually levied) tariffs on Egypt's exports of broad product groups.

|i_____________________________________|______________________<br>1992 OECD Imports|Aver|age Applied Tarif|f**(%)**|
|---|---|---|---|---|
|Product Group (SITC No.)|<br>from Egypt ($million)|EU|Japan|USA|
|All Items (0 to 9)|3,898.3|0.6|2.0|4.6|
|All Foods and Feeds (0+ 1 +22+4)|158.0|12.0|21.2|0.6|
|Food and Live Animals (0)|153.3|12.2|7.6|0.6|
|Beverages and Tobacco (1)|0.7|5.6|90.0|1.9|
|Animal and Vegetable Oils (4)|0.1|0.4|--|--|
|Agricultural Materials (2-22-27-28)|73.3|0.0|0.0|0.0|
|Ores & Nonferrous Metals (27 +28+68)|180.8|0.0|0.0|0.0|
|Mineral Fuels (3)|2,429.1|0.0|3.2|0.5|
|Manufactures (5 to 8-68)|1,020.1|0.0|3.0|8.8|
|Chemicals (5)|81.3|0.0|0.0|0.0|
|Textiles (65)|304.3|0.0|4.9|8.3|
|Transport & Machinery (7)|252.1|0.0|0.0|0.0|
|Clothing (84)|241.9|0.0|11.0|17.4|
|Miscellaneous Manufactures (89)|25.1|0.0|0.0|0.0|

# Source: World Bank-UNCTAD SMART Data Base

The tariff averages show different patterns of protection exist in Japan and the EU as opposed to the United States. Exclusion of textiles and clotliing from the United States GSP, scheme accounts for the relatively high tariffs (8.8 and 17.4 percent) on these goods, but outside these two sectors import duties average under two percent. In contrast, in the EU and Japan the highest duties are applied to agricultural products (clothing in Jap^ i is an exception) with tariffs of 90 percent facing Egypt's beverage and tobacco exports to Japan (mostly cured tobacco leaf).

At the line item level "peaks" are evident in the tariffs applied to Egyptian exports. The highest tariffs in the EU are 35 percent duties on various pastry products exported from Egypt while several jam and fruit preserve products face tariffs between 27 to 30 percent. In the United States the highest duties Egypt faces range between 33 to 35 percent on clothing products like sweaters and cotton under shirts.