rafmacalaba/datause-encoder-data huggingface dataset:
# **What Drives the Sources of Pro-Poor Growth?**

I now turn to the question of what drives the various sources of pro-poor growth. In light of the results of the previous section that cross-country differences in the sensitivity of poverty to growth in average incomes are relatively unimportant, I focus primarily on the first and third sources of pro-poor growth: growth in average incomes, and changes in relative incomes. I measure growth in average incomes as the average annual growth rate over the spell of household average income or consumption. I use five different measures of changes in relative incomes. The first is simply the average annual change in the Gini index, for comparability with existing results on the determinants of changes in inequality. The next four measures are the discrete-time distribution components of the change in each of the four poverty measures I have been considering. Recall that, for infinitesmal changes, the distribution component of the change in the headcount measures the growth rate of incomes of those at the poverty line relative to average growth. For the poverty gap and the squared poverty gap, the distribution component measures a weighted average of relative growth rates of those below the poverty line, with the poverty gap giving most weight to those at the poverty line. For the Watts index, the distribution component measures the average growth rate of those below the poverty line relative to overall growth.

There are many limitations to this dataset which make it very difficult to use it to identify causal determinants of growth or change in relative incomes. The sample of countries is quite small, especially when we consider the long spells dataset where the determinants of longer-term growth and distributional change are more likely to be apparent. There is also substantial measurement error in the data on growth in survey means, and for measures of distributional change. While classical measurement error in these dependent variables will not necessarily lead to biases in coefficient estimates, it will inflate standard errors and reduce the significance of estimated coefficients. Because we have relatively few spells per country in the dataset consisting of all spells, and only one per country in the long spells dataset, we cannot meaningfully base identification on the within-country variation in the data. This raises the possibility that any partial correlations we uncover may be driven by unobserved country-specific characteristics excluded from the regressions. The small number of spells per country 20
# **I. Introduction**

During the twentieth century, one of the most important achievements for Colombian women was the remarkable increase in their labor force participation, especially during the second half of the century. Women’s participation increased from 18% in 1951 to 26% in 1978, 47% in 1992, 55% in 2000, and 60% in 2018.<sup>1</sup> The upward trend in female labor participation can be attributed to the remarkable advancements in education, fertility, sanitation, civil rights, and political engagement that women experienced during this period. The contribution of married women to the growth of labor participation was a key driving force behind this significant improvement. They seemed to be more responsive to economic cycles and to the legislation introduced in the first half of the twentieth century, which allowed married women to enter the workforce.

This paper contributes to identifying the main drivers of women's participation in the labor market since the second half of the 20th century, tracing the transformation of women's roles and opportunities. The changing nature of female labor force participation and its determinants highlights the importance of considering the historical moment and context in which these variables are examined. We present evidence that the effects of these variables have changed over time. To this end, we follow a two-step approach in which, first, we implement a principal component dimension reduction to summarize the whole set of variables (fertility, education, mortality, marriage, labor force participation, violence, female political participation, and some macroeconomic variables) with a few components. Second, we employ the Time-Varying Effect Modeling (TVEM) method to find the dynamic pattern evolution for the coefficients over time under different specifications on the female labor force participation determinants. We use annual aggregate data from 1960 to 2018.

The literature on female labor participation, drawing from both cross-country comparisons and individual country analyses, has produced valuable insights.<sup>2</sup> Among OECD countries, several studies have shed light on the determinants of female labor participation, with Cavalcanti and Tavares (2008) highlighting the impact of increased availability and lower prices of domestic appliances, Mishra and Smyth (2010) noting a negative correlation with fertility rates, and Gonzales, Chandra,

> 1 This trend was interrupted by the Covid-19 pandemic in 2020. Since the beginning of 2022, the participation of women started to recover. For more details, see, for example, García-Rojas, Herrera-Idárraga, Morales, Ramírez-Bustamante, and Tribín-Uribe (2020).

> 2 Another strand of the literature uses individual datasets from population surveys and censuses. See, for example, Blau and Kahn (2007), Fernández (2013), Gangl and Ziefle (2009), Gasparini and Marchionni (2015, 2017), Goldin (1988, 2006), Goldin and Mitchell (2017), Olivetti (2014).

2
**6.3 Summary of results**

Details of all the beta values calculated for this study can be found in Appendix A2. For each company, where possible, a five-year beta calculated over the period January 1990 to August 1995 is given in addition to a series of annual betas for the years 1990 to 1994. All are calculated using daily data and the index used in the calculation is given by the heading for each country. All estimates are asset betas (ie, corrected for the gearing level of the individual company) except where stated otherwise.6 Although individual companies are of interest, these results need to be summarised in some way so that overall trends may be identified. First, average beta values for the utility sectors in each country were calculated. These results are given in Table 6.2, while Table 6.3 summarises the corresponding regulatory regimes for each sector.

**Table 6.2: Average beta values by country and sector**

||**Electricity**|**Gas**|**Energy**|**Water**|**Telecoms**|
|---|---|---|---|---|---|
|UK|0.60|0.84|-|0.67|0.87|
|United States|0.30|0.20|0.25|0.29'|0.72 (AT&T)|
||||||0.52 (others)|
|Canada|-|-|0.25|-|0.31|
|Japan|0.43|-|-|-|0.62|
|Argentina|0.81**e**|_|_|-|0.86'|
|Chile|0.95e|_-||-|1.20e|
|Germany|-|-|0.66|-|-|
|Spain|0.43|0.81|-|0.57|-|
|Sweden|-|0.34|_|-|_0.50_|
|Australia|-|0.38|-|-|-|
|New Zealand|-|-|-|-|0.97|
|Other European|0.41|0.49|0.46|0.42|0.70|

_Key:_ t Monthly beta, **e** Equity (not asset) beta.

> **6** Accounting data on a comparable basis was taken from Datastream. This was not available for all of the countries in the study.

27
_COTE D’IVOIRE: Volatility, Shocks and Growth_

# **1. TRENDS AND DEVIATIONS FROM TREND**

**We first seek to distinguish between long run trends in certain time series, versus short run deviations from those trends.** Hoderick and PrescottF<sup>1</sup> F (HP) pioneered the use of “HP filtering” to “smooth” or “de-trend” time series, producing a less volatile time series, which arguably represents a localized moving central tendency for a time series. When a time series fluctuates upwards and grows precipitously to a peak, it will exceed the trend. If it falls into a trough, will move under trend. In this section, we first review some of the times series and trends in the levels. We then focus on the difference or deviations between times series and their trend. When treated in this fashion, it is easier to see when time series move above or below trend, and cyclical patterns in the time series become apparent.

## **Time Series of Key Indicators and Trends**

We look at some examples at three different kinds of variables: measure of output or productivity; trade price indices, and commodity data. In the units in which they are normally measured, they have quite different profiles over time. Trends lines can be readily fitted to each.

**Figure 1: Real Output per Capita Versus Trend in Cote d’Ivoire, 1960-2002**

<!-- Start of picture text --> Real Output GDP Per Capita Versus Trend in Cote d'Ivoire<br>In 1995 US dollars per capita, 1960-2002<br>1500<br>1400<br>GDP PC<br>1300 HP Trend<br>1200<br>1100<br>1000<br>900<br>800<br>700<br>600<br>500<br>Year<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000<br><!-- End of picture text --> 1 Hoderick & Prescott, 1997. Note that for many years an earlier working paper by the same authors circulated widely, and the technique is very familiar to macroeconomists who study business cycles
west coast and falling in the wet zone, Colombo city also faces hazards of high precipitation and floods regularly. Colombo district gets inundated during the south-west monsoon due to heavy rainfall and run-off of large volumes of water from the river catchment areas and canals.<sup>17</sup> Colombo is a harbor on the western coast of Sri Lanka and faces a warm and humid tropical climate with average temperature of 27.8°C and humidity ranging from 74% during the day and 85% during the nights as per data for the period of 1996-2012.<sup>18,19</sup> The city and surrounding areas fall under the Wet Zone in the south-western quadrant which receives rainfall throughout the year. Colombo district falls under the Kelani River basin which starts from the Sri Pada Mountains in central hills and flows down until it reaches the river mouth in Colombo city. This river basin gets a high amount of rainfall each year with the annual average of 3,000 millimeters (mm) and the river flow variations are between 800 m<sup>3</sup> /s and 1,500 m<sup>3</sup> /s.<sup>20</sup> The topography combined with high rainfall during monsoon causes frequent disastrous floods in the middle and lower reaches of the river. Flooding is particularly severe in the lower reaches starting from Hanwella to most downstream, i.e., Colombo city.<sup>21</sup> Besides the heavy rains in the river basin, the average annual rainfall over Colombo city has also been quite high at 2,273.52 mm over the period of 2001-2014 as per data obtained from the Department of Meteorology in Sri Lanka. However, there have been significant variations from year to year with the lowest average of 1,774.2 mm in 2011 and highest average rainfall of 3,674.12 mm in 2012. The annual rainfall recorded between 2001 and 2014 at the weather station in Colombo is shown in Figure 2 below. The annual rainfall was also quite high at 3,369.9 mm in 2010 when Colombo city experienced disastrous flooding.

> 17 NBRO (2010), Status of Disaster Management, National Building Research Organisation, Ministry of Disaster Management, Sri Lanka.

> 18 Central Bank of Sri Lanka (2006), Economic and Social Statistics of Sri Lanka 2006, Vol. XXVIII, November 2006. 19 Central Bank of Sri Lanka (2013), Economic and Social Statistics of Sri Lanka 2013, Vol. XXXV, April 2013. 20 Sugeeswara S. and Jayawardena A., (2010), Development of a flood forecasting model for Kalu river and Kelani river basins in Sri Lanka using radial basis function neural networks. Available at <u>www.icharm.pwri.go.jp/training/master/pubilication/pdf/2010/sugeeswara.pdf.</u>

> 21 _Ibid._

6
wards were identified.

The next step involved matching the bridge with survey data to obtain treatment assignments for each ward surveyed. The survey data included 119 unique wards, all of which were successfully matched using the ward name as the unique identifier. The final step involved linking the survey bridge with TRA data using the street location name as the unique identifier. This reduced the sample size from 3,585 wards to 119 wards and from 408,106 taxpayers to 40,180 taxpayers. Throughout this process, matches were consistently ensured to be within similar tax regions.

Balance tests were conducted to assess payment trends across treatment and control wards. The results indicate no statistically significant differences in the average payment amounts between treatment and control wards during the 3- and 5-month periods preceding the intervention (see Table 2).

Table 2: Balance Tests

||(1)|Control|(2)|Treatment|(1)-(2)|Pairwise t-test|
|---|---|---|---|---|---|---|
|Variable|N|Mean/(SE)|N|Mean/(SE)|N|Mean diference|
|pay_trend_|3<br>17,938|87,330.319<br>(2,834.644)|22,239|80,620.056<br>(5,541.215)|40,177|6,710.263|
|pay_trend_|5<br>17,938|1.27e+05|22,239|1.18e+05|40,177|8,179.031|
|||(3,450.451)||(5,742.526)|||

If the table includes missing values (.n, .o, .v etc.), see the Missing values section in the help file for the Stata command `iebaltab` for definitions of these values. Significance: ***=0.01, **=0.05, *=0.1. Full user input as written by user: [ `iebaltab pay_trend_3 pay_trend_5, grpvar(treatment) savetex("C:/Users/wb589463/OneDrive - WBG/World Bank/poverty/Taxes/replication package/admin/tables/balance.tex") replace]` The analysis was conducted at two levels: while randomization was implemented only at the ward level, the administrative data was not randomized. The primary objective was to assess whether the intervention had an impact on compliance among firms in the surveyed wards. Ideally, compliance behavior would have been observed directly through the survey. However, due to the inability to link administrative tax records to survey respondents, the next best option was to aggregate administrative data within the surveyed wards.

13
may have been some amount of untapped revenues for government during the boom, proxied by gap between these estimated rents values and collected revenues.<sup>10</sup> Figure 9. Cumulative natural resource rents and revenues per capita <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Resource rents Resource revenues<br>Thousand US$<br>12,787.9 9,924.0<br>7,090.6<br>42,852.5 12,851.2 3,150.9 1,396.2 838.8 796.8 676.6 656.1 200.2 189.0 101.3 67.2 41.8 39.9 28.3 27.2 17.3<br>Equatorial Guinea Gabon Angola Botswana Congo, Rep Nigeria Sudan Chad Sao Tome and Principe Cameroon Mauritania Zambia Guinea Cote d'Ivoire Niger Congo, Dem Rep Sierra Leone Liberia Mali Uganda<br><!-- End of picture text --> Notes: Sum of natural resource rents (orange bars) and natural resource revenues (blue bars) per person between 2004 and 2014 by country in thousand US dollars. Natural resource rents include rents from oil, natural gas, coal, and principal mineral commodities (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, and zinc), but exclude rents from other important commodities – such as diamonds and other valuable minerals – which could underestimate the real value of total natural resource rents. No resource rents data for Sao Tome and Principe. Source: World Bank staff estimates based on UNU-WIDER (2021) and The Changing Wealth of Nations 2021.

10 Two important caveats are necessary to interpret this finding. First, the measure of rents is likely subject to sizeable uncertainty and measurement error. It relies on measures of both total revenues – which typically uses on world prices rather than the actual price paid for a unit of the commodity – and an estimated measure of costs – which are held confidentially by the industry, and vary significant by project, by country, by geology, and even by the level of prices for the commodity (Toews and Naumov 2015). Second, the relationship between what the government ‘ought’ to collect, i.e. the rents value once a reasonable cost and return on capital is deducted, and what it actually collects is complicated by numerous factors. Taxes and contracts might be poorly configured or badly negotiated, either due to bad luck, significant information asymmetries, lack of relevant expertise, or deliberate (and potentially corrupt) actions by decision makers. Alternatively, government may be unable to recover more of the rents without deterring investors due to additional risk to capital, like political risks, associated with operating in particular context. This is also very hard to evaluate on a general basis.

17
get it in a timely manner. The rationing rate will depend in part on how effective the scheme‘s implementation is at quickly responding to demand.

How can we measure the true demand for work and hence the rationing rate? The administrative data indicate virtually no un-met demand for work on MGNREGS. According to the administrative data, 52.865 million households in India demanded work in 2009/10, and 99.4% (52.53 million) were provided work.<sup>8</sup> However, this is deceptive. What is called ―demand for work‖ in the administrative data is unlikely to reflect the true demand. Several studies have found that the work application process and the system for recording demand for work is not yet in place.<sup>9</sup> Further, state and local governments have an incentive not to report un-met demand given that this implies they should pay unemployment allowances. Also, some people will undoubtedly be deterred from formally obtaining job-cards, demanding work from the officials, or do not even know that they have the right to make such demands.<sup>10</sup> A better measure of demand for work is obtained by asking people directly in their homes and independently of the scheme. The data we use here comes from the 66<sup>th</sup> Round of the NSS for 2009/10 which included questions on participation and demand for work in MGNREGS that allow us to estimate demand and rationing rates across states. The survey was conducted between July 2009 and June 2010 in all states. The Employment-Unemployment Survey (―Schedule 10.0‖) included three questions on the program: (i) whether the household has a job card; (ii) whether it got work on the scheme during the last 365 days, for which responses were coded under three options: got work, sought but did not get work, and did not seek work in MGNREGS; and (iii) if the household got work, the number of days of work, and the mode of payment. In addition, the daily status block in Schedule 10 collected information on activities for all household members during the week preceding the survey, including number of days worked and wages received, if the respondent worked on MGNREGS public works (PW).

Note that in this paper we limit our definition of participation and rationing to whether households got work or did not get work. Unmet demand can also take the form of fewer days of work than desired. Many households who participated were no doubt rationed in that they would

> 8 Data is from the official Government of India website for MGNREGS (http:\\nrega.nic.in).

> 9 This is described in a number of the papers in Khera (2011).

> 10 In Dutta et al. (2012) we provide supportive evidence on this point for Bihar, based on our surveys in 2009/10.

5
<!-- Start of picture text --> 200 0.45%<br>150 0.40%<br>0.35%<br>100 0.30% a<br>D 50 0.25%<br>£ 9 0.20% 6<br>S = 0.15% §<br>Ss -50 0.10% 5<br>100 0.05%<br>0.00%<br>“0 -0.05%<br>-200 -0.10%<br>2010 2011 2012 2013 2014 2015 2016<br>gags ©FD from collection losses mam QFD from T&D losses<br>Gam QFD from underpridng =®=Total QFD as% of GDP<br>0.2 Full capital cost<br>= 0.16<br>3 Limi al cost<br>ee<br>2 0.12<br>vo<br>3<br>c<br>cal)<br>><br>= 0.08<br>cal}<br>i)<br>£<br>cal)<br>=><br><= 0.04<br>0<br>Industry and mining Commercial Government Residential<br><!-- End of picture text -->
also consider the potential impacts on poor households through induced changes in the wage rate for their net sales of unskilled labor. We ignore changes in the returns to skilled labor on the grounds that these returns make an extremely small contribution to the incomes of the poor. Our focus is on real price changes, and we ignore the potentially serious costs (Easterly and Fischer 2001; Ravallion and Datt 2002) imposed on the poor through the market disequilibria created by inflation. This omission seemed reasonable given that the average rate of inflation in developing countries<sup>2</sup> has been very moderate by historical standards until very recently. It averaged 5 percent in the period from 2000 to 2007 before rising to 7 percent by January 2007 and 8 percent in February 2008. However, to the extent that commodity price changes are contributing to inflation increases in developing countries, there may be some important additional costs—the Ravallion and Datt (2002, p389) estimate suggests that a rise in the inflation rate of three percentage points would increase the poverty headcount by 1.2 percentage points.

When we take into account impacts through changes in unskilled wage rates, we use national versions of the GTAP model to assess the short run impacts of changes in commodity prices on wage rates—essentially measuring the Stolper-Samuelson relationship between the prices of goods and the prices of factors. Given our short-run focus, we specify the standard specific-factors model of production, with capital fixed in each sector. Under our standard labor market closure, we assume that unskilled labor is mobile between agricultural and non-agricultural employment so that the benefits of an increase in demand for unskilled labor are shared between agricultural and non-agricultural workers. This assumption of mobility between agricultural and non-agricultural labor is supported by studies such as Bertrand and Squire’s (1980) classic analysis of rural-urban labor mobility in Thailand. To assess the robustness of our results to this assumption, we examine in Appendix B a second

> 2 Data from the World Bank’s Projections Group (DECPG) were kindly provided by Hans Timmer.

4
move to the location of the consumer (or vice versa). As the conventional definition of trade – where a product crosses the frontier – would miss out on the latter type of international transactions, the WTO has defined trade to span four modes of supply:

- _Mode 1 – Cross-border_ :  services supplied from the territory of one country into the territory of another.

- _Mode 2 – Consumption abroad_ :  services supplied in the territory of a country to the consumers of another.

- _Mode 3 – Commercial presence_ :  services supplied through any type of business or professional establishment of one country in the territory of another (i.e., FDI).

- _Mode 4 – Presence of natural persons_ :  services supplied by nationals of a country in the territory of another.

-

Data on services trade are notoriously weak. The primary source is the balance of payments, where measured flows correspond mainly to modes 1 and 2. IMF data reveal that service imports roughly tripled over the decade from 1994 to 2004.<sup>11</sup> For the 178 countries for which the IMF reports data the median value of imports of services in 1994 was some US$550 million. As of 2004, this had increased to US$1.5 billion. For the world’s largest services trader, the US, total reported imports rose from almost US$200 billion to US$340 billion.

Notwithstanding this growth in the absolute value of services trade, world service imports as a percentage of GDP rose slightly from 11 percent in 1994 to 12 percent of GDP in 2004 (Table 1). This masks declines in the services share in developing countries. The rising importance of service imports in the aggregate stems mainly from increases within the most developed countries (from 8 percent to 12.7 percent), driven by strong growth in producer and business services imports (whose ratio to GDP rose from 3.3 percent to 6.7 percent).

In parallel with the declining share of imports of non-travel, non-transport services in lower-income countries, there has been a structural shift away from travel (tourism) and transport services towards producer and business services (Figure 1). While the overall structure of trade has been relatively stable, some notable differences between countries at different stages of development can be observed. Travel was the 7