rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 10: Force and Momentum - Bahrain** (percentage points)

<!-- Start of picture text --> 30<br>Momentum<br>Force<br>20<br>10<br>0<br>-10<br>-20<br>1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: Authors’ calculations using IMF GFS data

## **h. Summary**

This section has presented evidence that force can provide a useful signal of future expenditure episodes. The noise-signal framework of Kaminsky provides a simple method to track changes in force through time. Put simply, whenever force reaches a sufficiently high level, this provides a signal for policymakers and analysts to be alert for sharp changes in expenditures in the next two years. One particular benefit of this methodology is that it provides a probability of an expenditure episode in the next two years, conditional on the signal being issued today. This section of the paper provides support for the usefulness of force, not only for total expenses but also for some of the sub-components in providing an early warning system.

It is important to note that the evidence provided here is at the aggregate level. Across countries and time, force has been shown to provide a timely indicator of future expenditure episodes. However, for any one country at any one time, this information needs to be used in addition to other monitoring systems. Force is intended to be a useful additional tool in assessing the stability and vulnerability of expenditures at a detailed level.

# **4: Rigidity in Public Expenditures**

In this section we look at the classification of public expenditures by their “rigidity” as a useful way to qualify the growth and acceleration of expenditures. Knowing both which budget items are driving expenditure dynamics and also how discretionary these key components are can be invaluable to policy makers conducting fiscal policy. Specifically, caution is needed before considering positive expenditure force in spending that will be relatively more difficult to adjust in the future. In addition, the ease in which certain expenditures can be adjusted is important for considering policy options for fiscal unwinding. The
-25-

Figure 2: Distribution of income and social payments, 1992 <!-- Start of picture text --> IA<br>1 0 0 - ...--........-.----.......--.---------------------------------..........---------.--............................................. l. .<br>9080 l . . -----------<br>80 . . . . .. . . .. . _ ..... ........ ... ... .... . .. . . .. . .... . . .. . .. . ... . . .. . . . ........ .. ... .. .. ..<br>' 60__ l<br>70-<br>o 0 ... < .................................................. 40 s-_ :- .... .......... .. .--.---------- ............<br>S~~~~~~~~~~~~~~~~~~~~~<br>0 10 20 30 40 50 60 70 80 90 100<br>Percent of population<br>'Line of Total 'Child<br>equality income allowance<br>Unemploy * Pensions Others<br>. compen.<br><!-- End of picture text -->
groups, including married women and mothers, such as those by Castañeda (1981), Magnac (1992), Arango and Posada (2007), Amador, Bernal, and Peña (2013), González and Daza (2015), and Santamaría and Rojas (2001), have contributed unique perspectives. They have highlighted the positive influence of education on labor participation and the challenges posed by childcare responsibilities and young children.

This paper contributes to further our understanding of the drivers behind the behavior of female labor participation in Colombia for almost seventy years. To our knowledge, this is the first paper that uses historical aggregate data for an emerging economy, considering that the relationship between female labor participation and its determinants has changed over time. Moreover, Colombia could serve as an example for promoting the rapid economic empowerment of women through progressive reforms and advancements. The country could provide a reference point for countries currently struggling with low female labor force participation.

This study underscores the significance of allowing variables to evolve and recognizing the importance of historical context in shaping women's empowerment. It is important to acknowledge that history cannot be condensed into a single average, as it is through historical analysis that we gain insights into the dynamics and transformations of women's empowerment. The analysis reveals the significance of the demographic transition, educational opportunities, economic conditions, social norms, and key events such as the peace process on female labor participation over time. The findings emphasize the need for policies and interventions that address barriers, promote gender equality, and create inclusive labor markets. By understanding and addressing these drivers, society can foster greater gender equity and unlock the full potential of women as contributors to economic growth and development.

The remainder of the paper is organized as follows. Section II describes the data and the empirical strategy used in our analysis. Section III presents the principal component analysis results and the individual variables' effect on the female labor force participation rate in Colombia. Section IV concludes.

# **II. Empirical strategy and data**

# **A. Empirical strategy**

4
been left unaddressed. We focus on two questions in particular: first, _how many_ deflators should be used? A recurring scenario in practice is the availability of several price indexes with partial coverage – say, food and non-food – alongside an aggregate price index. The paper works out the implications of two alternative price adjustment strategies, namely adjusting the sub-components of household expenditure separately using multiple price indexes, versus using a single index to adjust the total. Second, what is the optimal _sequence_ of temporal and spatial price adjustments? When working with household survey data, the analyst typically wants to adjust for intra-survey inflation – usually with a price index that is available outside of the survey – as well as for spatial cost-of-living differences – often by computing a deflator based on survey information. We discuss the implications of temporally adjusting all expenditures before estimating spatial deflators, versus after.

The findings deliver a few clear-cut suggestions for applied work. While using multiple indexes (MI) to adjust the components of nominal household expenditure may seem like the most accurate way to make use of the available information on price variation, we find that whenever we are able to rank the two methods, the single index approach (SI) comes out on top. This seemingly counterintuitive result depends on the fact that our goal is not to adjust nominal expenditures for changes in purchasing power, but rather to derive measures of welfare that are consistently comparable across individuals. Regarding the sequencing of temporal and spatial deflation, starting with the adjustment of all elementary expenditures for intra-survey inflation ensures that any spatial price index estimated from the survey is based on accurate (non-distorted) unit values and budget shares.

The paper is organized as follows. Section 2 explains the notation used in the rest of the paper; section 3 discusses the implications of the use of multiple indexes or a single index to adjust household expenditures; section 4 constructs a benchmark to determine which approach is better; section 5 tackles the question of the sequence between temporal and price adjustments; section 6 provides an empirical illustration of the consequences of these different strategies on the estimated distribution of welfare, using the Islamic Republic of Iran’s 2019 Household Expenditures and Income Survey (HEIS); section 7 concludes, and offers a summary of the recommended deflation strategy.

# **2 Price indexes: General notation**

This section introduces the notation for dealing with price indexes, in a way convenient for the analysis carried out in the rest of the paper. We denote a generic price index by _I_ – one can think of it as a spatial or a temporal index, indifferently – and will assume that _I_ can be described by a set of 3
Disease Collaborative Network, 2020).<sup>23</sup> Appendix N presents a practictioner guide to the construction of our index.

# **4.1 A case study of South Africa**

We first illustrate the relevance of our indices with the case of South Africa. Figure 4 reports the evolution of life expectancy, poverty rate and _PALE_ 1 for South Africa from 1990 to 2019. From the perspective of poverty, the progress of South Africa is impressive, with poverty rates decreasing from 31% to 21% over the period. However, life expectancy shows a different pattern. Following the AIDS epidemic, life expectancy decreased from the mid 90s onwards, to revert back to the pre-AIDS levels after 2013. Thus, in 2007, poverty rates are low, at 19%, but life expectancy is also low, at only 53 years. How then do we compare South Africa in 2007 to South Africa in 1990 ? _PALE_ 1 indicates that deprivation is higher in 2007 than in was in 1990. Indeed, _PALE_ 1 is equal to 44 years in 1990 as opposed to 42 years in 2007. We discuss in Section 4.3 the sensitivity of the comparisons made under _PALEθ_ to the choice of _θ_ .

**Figure 4:** South Africa Evolution of _PALE_ 1 and Life Expectancy, 1990-2019

<!-- Start of picture text --> 80 70<br>70 60<br>60<br>50<br>50<br>40<br>40 %<br>30<br>30<br>20<br>20<br>10 10<br>0 0<br>Year<br>Headcount ratio (right axis)<br>PALE Life Expectancy<br>Years<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Reading: in 1990, life expectancy was 63 years, 31% of the population was living below the poverty line and poverty adjusted life expectancy was 44 years.

# **4.2 Life expectancy and poverty in the World, 1990-2019**

At the world level, Figure 5 presents the evolution of life expectancy, the headcount ratio and _PALEθ_ between 1990 and 2019. Throughout this period, life expectancy

> 23See Appendix M for the list of countries in the database as well as their descriptive statistics for the year 2019.

16
border regression design to address selection of local policies. To incorporate these identification elements, we use an IV method paired with a city-border design.

To address price endogeneity due to unobserved product attributes, we use two sets of IVs. The first (set of) IV is central subsidy by leveraging the discrete nature of the central subsidy that varies based on driving range cutoffs. In addition, the amount of subsidy changes from year to year as discussed above. The central subsidy offers a valid instrument as it is related to consumer price but unlikely to be correlated with unobserved product attributes for two reasons. First, we use time fixed effects to control for time-varying national common shocks that could be correlated with the year-to-year variation in subsidy. Second, the driving range of the vehicle is included in the regression so the identifying variation in subsidy is due to the discrete nature of the subsidy around the driving range cutoffs. The identifying assumption is that unobserved product attributes are unlikely to change discretely around the driving range cutoffs.

The second set of IVs for consumer price is constructed based on battery capacity (kWh). In particular, we interact battery capacity with supplier dummies. We distinguish battery suppliers as CATL, BYD, and the rest. CATL is by far the largest EV battery supplier in China with a market share of 41.3% and 50.6% in 2018 and 2019 while BYD is the second largest battery supplier with a market share of 20% and 17.3% in 2018 and 2019.<sup>30</sup> Battery is a major cost component for EVs and could account for about 20-60% of vehicle price. The battery cost has decreased dramatically over time with the (volume-weighted) cost reduced from $1,100/kWh in 2010 to less than $160 in 2019 due to technology improvement and the production scale.<sup>31</sup> By the virtue of their production scale and technology capability, CATL and BYD batteries may have cost advantages. The identification assumption is that battery capacity is not correlated with unobserved product attributes. The choice of battery capacity is likely to be dictated by the decisions on vehicle driving range and weight, which we control in our regressions.

To address the endogeneity of the availability of charging infrastructure (i.e., the number of charging ports) due to simultaneity, we use as an IV the lagged stock (i.e., cumulative sales) of EVs purchased by institutions (e.g., government agencies or taxi fleets) during all previous quarters. Our demand analysis focuses on individual purchases, which account for about 65% of total EV sales in the sample cities during our data period. The size of the EV stock by 30BYD started as a rechargeable battery manufacturer in 1995 and entered the automobile business in 2003. It is the largest EV producer by volume in China and supplies batteries to all BYD models. CATL was founded in 2011 and specializes in the manufacturing of lithium-ion batteries for electric vehicles and energy storage systems. It grew quickly to become the largest battery supplier for EVs in China.

> 31Source: `bloom.bg/2LiZSu5`

18
Table 6: Ancestry and the Transmission of Education

||Yea<br>|rs of Educa<br>|tion Child<br>|ren<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Years of Education Parents|0.338***|0.328***|0.309***|0.310***|
||(0.005)|(0.004)|(0.005)|(0.005)|
|Edu. Parents x Native||||-0.008<br>(0.014)|
|Native|-0.441***|-0.390***|-0.346***|-0.276*|
||(0.072)|(0.067)|(0.062)|(0.165)|
|Urban||0.717***|0.593***|0.593***|
|||(0.051)|(0.039)|(0.039)|
|Constant|8.221***|7.646***|8.162***|8.160***|
||(0.055)|(0.064)|(0.061)|(0.060)|
|Observations|733,020|733,020|733,020|733,020|
|R-squared|0.221|0.225|0.237|0.237|
|Department FE|N|N|Y|Y|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : Standard errors clustered at the department level in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

that if the transmission of education by ancestry would continue as observed in the two generations we study, the distribution of education of Natives and non-Natives would not converge to be the same — a large difference would persist.

15
<!-- Start of picture text --> (a) BRA<br>1000<br>800<br>600<br>400<br>200<br>0<br>−200<br>1970 1980 1990 2000 2010<br>(b) FJI<br>400<br>300<br>200<br>100<br>0<br>−100<br>1970 1980 1990 2000 2010<br>(c) TUR<br>800<br>600<br>400<br>200<br>0<br>−200<br>1970 1980 1990 2000 2010<br>(d) USA<br>3000<br>2000<br>1000<br>0<br>−1000<br>1970 1980 1990 2000 2010<br>(e) ZWE<br>40<br>20<br>0<br>−20<br>1970 1980 1990 2000 2010<br>4<br>3<br>2<br>Levels<br>1<br>4<br>3<br>2<br>Levels<br>1<br>4<br>3<br>2<br>Levels<br>1<br>4<br>3<br>2<br>Levels<br>1<br>4<br>3<br>2<br>Levels<br>1<br><!-- End of picture text --> Figure 6: MODWT scalogram for Output of (a) BRA, (b) FJI, (c) TUR, (d) USA, and (e) ZWE.

growth rate of the original series. Figure 7 provides the scalograms of the MODWT wavelet coefficients using the first differences of the GDP level series for the five selected 15
<!-- Start of picture text --> : 3.00 Poverty risk, by ethno-linguistic group<br>°<br>5 250<br>Ss<br>a<br>re)<br>2 2.00<br>g<br>$<br>2 1.50<br>a<br>2<br>10 —<br>£ __ —_ a<br><!-- End of picture text --> <!-- Start of picture text --> Qo<br>><br>2 0.50<br>=)<br>=z 0.00 ij & ij & € - i] i= wo o o 3 i=]<br>=q o 4 = E) z = = a 3 a e A=J<br>= is © 9 = F = 7 2 £ g = 2<br>= 5 (Cg & 6 * :<br>Mother-tongue 2- 6<br>fe)<br><!-- End of picture text -->
developed states, sibling correlations declined slightly for upper caste women but remained stable for lower caste women. But in developed states, women from both caste groups experienced substantial decrease in sibling correlations.

# **(5) Robustness Checks**

We check the sensitivity of our estimates in two ways. As mentioned before, as children become old, they tend to leave the parental household, because of marriage (especially for girls), jobs and higher education. If children who exit household are better educated, then it may bias the estimates of intergenerational correlations. For instance, if there is substantial and negative birth order effect on children‘s education, and marriage timing follows birth order, then our estimate of intergenerational correlations may be underestimated. On the other hand, if better education among women delays marriage, the bias would be in the opposite direction. To check out sensitivity of our estimates, we repeat our entire analysis for younger age cohorts [16 to 20 years]. The possibility of having children exiting household at this age cohort is much smaller than that for 16-27 year age cohort.

Table 9 reports the results for the full sample. For intergenerational correlations, we find no significant differences in the estimates for any of the sub-samples reported in Table 9 from those reported in Table 3 for any of the survey years. The changes in the sibling and intergenerational correlations between 1992/93 and 2006 implied by Table 9 are similar to those implied by Table 3. Consistent with Table 3, the estimates from Table 9 suggest large gender differences in sibling and intergenerational correlations. It also highlights the importance of parent‘s education and common neighborhood environment in explaining the sibling correlations. We omit the results from regional analysis for this younger age cohort to save space, but overall conclusions from our analysis based on 16-27 year age cohort hold true for 16-20 year age cohort too.

The estimates of sibling correlations for the age cohort [16 to 20 years] are larger in magnitudes both for men and women (see Table 9). This is consistent with the evidence in literature which finds higher sibling correlations among closely spaced children compared with widely spaced children (Bjorklund and Salvanes (2010)). Such higher correlations arise from the fact that for more widely spaced children, family background may change substantially over time.

The results presented above took the maximum of father and mother‘s education as the relevant metric of parental educational attainment. A reader might wonder if the conclusions reached earlier depend on this specific definition of parental education. To allay such concerns, we use the 23
# **References:**

Aroca, Patricio, Mariano Bosch and William F. Maloney. 2002. “Is NAFTA Polarizing México? or Existe También el Sur? Spatial Dimensions of Mexico’s Post-Liberalization Growth.” Mimeographed. Office of the Chief Economist for LCR, The World Bank Barro, Robert J. and Xavier Sala-i-Martin. 1992. “Regional Growth and Migration: A Japan-United States Comparison.” _Journal of the Japanese and International Economies_ 6: 312-46.

Ben-Akiva, M. and S.R. Lerman. 1985. _Discrete Choice Analysis. Theory and Application to Travel Demand._ MIT Press. Cambridge, MA, USA.

Beyer, H., P. Rojas and R. Vergara. 1999. “Trade Liberalization and Wage Inequality.” _Journal of Development Economics_ 59: 103-123.

Bickenbach, F. and E. Bode (2003) “Evaluating the Markov Property in Studies of Economic Convergence” _International Regional Science Review_ 16:363-392 Borjas, George. 2001. “Does Immigration Grease the Wheels of the Labor Market?.” _Brooking Panel on Economics Activity_ , March 29-30.

Borjas, George. 1994. “The Economics of Immigration.” _Journal of Economic Literature_ 32: 1667-717.

Cameron, Gavin, and John Muellbauer. 1998. “The Housing Market and Regional Commuting and Migration Choices.” Discussion Paper 1945. Center for Economic Policy Research. London, UK.

Chiquiar, Daniel and Gordon H. Hanson. 2003. “International Migration, Self-Selection, and the Distribution of Wages: Evidence from Mexico and the United States.” NBER working paper 9242. Cambridge, MA, USA.

Conover, Ted. 1987. _Coyotes: A Journey through the Secret World of America’s Illegal Aliens._ Vintage New York, NY, USA.

Cornelius, Wayne A. and Philip L. Martin. 1993. “The Uncertain Connection: Free Trade and Rural Mexican Migration to the United States.” _International Migration Review_ .27(3): 484-512.

Crane, Keith, Beth J. Asch, Joanna Z. Heilbrunn, and Danielle C. Cullinane. 1990. “The Effect of Employer Sanctions on the Flow of Undocumented Immigrants to the United States.” Urban Institute Report No. 90-8. Washington, DC, USA.

19
# **Table 1: Variable definitions and summary statistics**

This table provides a brief description of the variables and the summary statistics.

# **Panel A. variable definitions**

|Variable|Variable Description|
|---|---|
|_Privatized_|A dummy variable indicating the privatization of the firm:_Privatized_equals 1<br>if its state share falls below 50% or it exits from the database.|
|_Age_|The age of the city party secretary.|
|_College_|The dummy variable equals 1 if the city party secretary has college education<br>and 0 otherwise.|
|_Central Experience_|The dummy variable indicating the city party secretary having experience<br>serving in the central government.|
|_Provincial Experience_|The dummy variable indicating the city party secretary having experience<br>serving in the provincial government.|
|_Labor prod_|The labor productivity of the firm calculated as ln (sales/employment).|
|_Leverage_|The leverage ratio of the firm, calculated as total debt/total asset.|
|_GDP Per capita_|GDP per capita (of thousand RMB in the 1990 constant price) of the city.|
|_GDP Growth_|The real growth rate of GDP (%) of the city.|
|_Government activism_|The city-level average “_percent of the officials in the department who facilitate_<br>_the development of the company_” in city. Data is from the World Bank<br>Enterprises Survey for China (2005). We obtain the city average from firm-<br>level data.|
|_TFP_OP_|Total factor productivity (TFP) of the firm calculated using the Olley-Pakes<br>method (see Appendix C for details).|
|_TFP_OLS_|TFP of the firm calculated using the OLS method (see Appendix C for details).|
|_TFP_IN_|TFP of the firm calculated using the index number method (see Appendix C for<br>details).|
|_TFP_CS_|TFP of the firm calculated using the index function method, with the cost shares<br>generated from the ASIF data (see Appendix C for details).|

``` 30 ```