rafmacalaba/datause-encoder-data huggingface dataset:
egories of households, up to the end of the simulation horizon (say, T periods) .

- Step **3.** Apply these growth rates separately to the per capita (disposable) income and consumption expenditure for each household in the survey. This gives a new vector of absolute income and consumption levels for each group, for periods 1,...T.

- Step 4. Calculate poverty and income distribution indicators, using the new absolute nominal levels of income and consumption for each individual and each group, and after updating the initial rural and urban poverty lines, using the prices indexes generated by the structural component of the model, to reflect changes in the price of the consumption basket and purchasing power of income.

- Step **5.** Using the rates of growth of employment generated by the structural component of the model, adjust the relative weights of each household group in the urban sector (as given in the survey) and calculate the reweighted poverty and distributional indicators for that sector as a whole.<sup>20</sup>

- Step 6. Compare the post-shock poverty and income distribution indicators with the baseline values to assess the impact of the shock on the poor and the degree of inequality for periods 1, ... T.

The household survey that I use to perform the policy experiments reported below is an artificial survey, constructed as follows. First, a sample of 5,000 observations was produced, with the share of each household group corresponding exactly to that in the structural component of the model.<sup>2</sup> ' Each observation was considered to represent one household. Second, using a random number generator and a log-normal distribution, values for disposable income and consumption expenditure were drawn for each household. As parameters for each group, the initial values for average disposable income and

> 20The same reweighting procedure would need to be applied to the rural sector if there was more than one category of households there.

2 These shares are 28.2 percent of workers in the rural sector, 45.3 percent of workers in the informal urban sector, 13.7 percent of unskilled workers in the formal urban sector, 9.9 percent of skilled workers in the formal urban sector, and 3 percent of capitalists and rentiers.

33
Tonga by driving on it with a GPS receiver turned on. An existing base map of the coastline was obtained but no metadata were available to show the projection used. Even after choosing the most likely coordinate system for the base map, UTM1S for the UTM zone that Tonga is located in, the road and the coastline were misaligned since it appeared that on one side of the island the road that the author had driven on was in the ocean.

A more general issue is that in many developing countries there is not a lot of off-theshelf geographical information available at the resolution level needed to merge it with village or community level data. Information is more often available at a coarser scale, making it difficult to link household locations to local level geographic features. Even when information is available at high resolutions, it may not always match up with the household survey due to these differences in coordinate systems. It is therefore important for countries to have a spatial data infrastructure (SDI) which coordinates different collection activities so that different geographic datasets can be matched together.<sup>18</sup>

# **Road Network Problems**

Practical difficulties are increased when constructing a road network for measuring either distances or traveling times. The algorithm used in a GIS for calculating shortest distance requires good alignment between the lines and junctions of the digitized road network. For example, if digitized road segments at a junction do not line up, the algorithm will back-track and seek a path elsewhere. These problems may be especially apparent when roads have been digitized for another reason, such as cartographic display, so once again metadata about the origin and purpose of geographic layers used in conjunction with GPS data is very useful. It is also sensible to budget considerable research assistance time to clean a roads dataset since misalignment problems may be common. For example, the road network underlying the service areas for money transfer facilities in Figure 1 took more than one week to clean. The digitized roads had been obtained from an earlier cartographic project rather than being digitized with a GPS when the research team was in the field. So even though it looked like a digital road network it was in fact based on polygons (the basic units in GIS are points, lines and polygons)

> 18 The Global Spatial Data Infrastructure Association provides a codebook of how an SDI can be built. See <u>www.gsdi.org</u> [accessed February 21, 2007].

- 21 -
We then examine how female LFP in the year 2004 is affected by the predicted value of various public goods, generated from the first stage regression. We are able to confirm that the length of exposure to women PRs does influence access to a range of public goods, including primary health centers, banks, paved roads and safe drinking water. (Please refer to the Appendix for the results of this first stage regression.) As seen in Table 5, our second stage analysis finds that access to the first three public goods – primary health centers, paved roads, and banks – has a significant influence on women’s LFP in the year 2004. This is true with respect to overall female LFP as well as in specific categories of self-employment.<sup>10</sup> While more work is needed to unpack the mechanisms underlying the findings reported here, these findings are very encouraging in terms of one type of indirect channel through which women leaders have affected women’s LFP.<sup>11</sup>

# **5. Conclusion**

Women’s earning power is well recognized to be an important aspect of their bargaining power within and outside the household. However, labor market participation of women may be curtailed in societies where women have traditionally held second-class status – due to both supply and demand side constraints. Typical policy initiatives to encourage women’s LFP tend to be in the form of those that expand job opportunities for women. This paper explores whether political empowerment of women could be a novel policy tool to enhance labor force participation by mitigating demand-side constraints. Understanding a woman’s LFP decision at the micro level requires a keen appreciation for the various factors, social, economic, logistical and psychological, that influence it. Women in public office may be in a unique position to appreciate these multi-dimensional barriers – and ways to mitigate them, either through active efforts or by serving as catalysts for change.

We study this link between political and economic empowerment in the context of mandated political representation for women reform in India in 1993, taking advantage of cross-district and state level variation in the length of exposure to different elements of this reform (due to varying

> 10 We have used the closest year to the latest available census data on public goods (2001) for which household level data on labor force participation is available, i.e. 2004.

> 11 While there is an effect of one particular public good (the primary health center) on men’s LFP as employers in self-owned enterprises, there is little impact of the other public goods, or in any other form of LFP.

16
26

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(Figure 5.A). For example, Burundi imposes controls on the price of coffee while Benin imposes controls on cashew nuts. Contrary to some EMDEs, advanced economies do not impose price controls on exportable commodities.

# **Figure 5. Price controls on exported goods and financial products**

<!-- Start of picture text --> A. Share of countries with price controls on B. Number and percentage of Chart on interest<br>export goods rate ceilings<br>Percent of economies Percent of economies<br>26<br>SAR 3 (38%)<br>MENA 6 (29%)<br>24<br>EAP 7 (44%)<br>ECA 7 (23%)<br>22<br>LAC 14 (48%)<br>SSA 24 (50%)<br>20<br>LICs Other EMDEs 0 5 10 15 20 25 30<br><!-- End of picture text --> Source: World Bank; World Integrated Trade Solution (WITS); World Trade Organization A. 2017 data. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication. Countries that rely heavily on a single export defined as a country in which exports of one or more 4-digit HS category represents 10 percent or more of its total exports in 2017. Chart shows the share of all LICs and other EMDEs that relying heavily on a single export whose price is subject to price controls. Sample includes 12 low-income countries and 61 other EMDEs.

B. Replication of Figure 1 of Maimbo and Gallegos 2014.

While not covered in the price control data set, the financial sector is also often a target of price controls. Around 60 EMDEs have imposed ceilings on interest rates (Figure 5.B). These measures are often motivated by a desire to provide targeted support to strategic industries or to shield consumers from financial exploitation. For example, in the case of Zambia, controls were implemented from 2012 to 2015 to reduce the perceived risk of over indebtedness and broaden access to credit (Maimbo and Gallegos 2014).

# **3. Reforms of price control regimes**

Starting in the 1980s, several EMDEs reduced the scope of price controls, opting instead to strengthen their competition policies and regulation (WTO 2000-2019). In some cases, the liberalization of prices was supported and encouraged by policy lending programs and debt relief efforts in highly indebted poor countries (HIPC). The removal of controls often become more feasible following an easing of the conditions that led to their imposition. For example, after 2011, as food prices declined from cyclical highs, some countries eliminated controls. EMDEs such as Mexico, Rwanda, and Côte d’Ivoire took advantage of the sharp decline in oil prices in 2014-16 to reduce petroleum subsidies (Baffes et al. 2018; Stocker et al. 2015).

7
from village to that market, where the distance is measured by the travel time.<sup>1</sup> Market access, according to the theoretical model, would positively in fl uence a farmer’s propensity to sell at the market. The theoretical model also shows that in addition to a direct impact, wealth level can moderate or accentuate the impact of the market access index on a farmer’s place of sale decision.

The empirical speci fi cation derived from the theoretical model is estimated using data from a unique survey of farmers, traders, markets and villages in Tamil Nadu, India conducted during 2005.<sup>2</sup> The survey utilizes a design which allows us to map farmers to the nearest wholesale market. The survey collected detailed information on infrastructure and facilities available at this market, which are then utilized to de fi ne an index of market facilities. This index of market facilities is then combined with distance from the market to the villages where farmers reside to derive the market access index. The regression results con fi rm that an improvement in market facilities implied by an increase in the market access index leads to an increase in the farmer’s propensity to sell at the market. The impact of the market access index also depends on the wealth of the farmers. The estimated coe ffi cient of the interaction of wealth and market access index suggests that wealth reduces a farmer’s costs of accessing market facilities more than it increases his/her opportunity cost of leisure. This means that wealthy farmers are perhaps able to avail of cheaper modes of transportation and to reduce waiting time and costs in utilizing the market facilities.

We also combine the farmers’ place of sale choice with their total sales revenue and simulate the impact of a 20 percent improvement in the market facilities. The simulation

> 1 As will be evident from the analysis presented in the following sections, the empirical results are robust to alternative de fi nition of the market facility index.

> 2 Tamil Nadu, located in southern India, is the fi fth largest state in the country, with a population of about 62 million people and gross domestic product per capita of $588 in 2003/04 (World Bank, 2005a). Although agriculture accounted for 15.3% of total state gross domestic product in 2001, the sector employed 49.5% of the labor force (World Bank, 2005b).

3
According to the World Food Programme, Afghanistan is among the world’s most vulnerable countries in terms of absorbing food and fuel price shocks; such countries have consistently high levels of food insecurity, are heavily dependent on food and fuel imports, and have large populations of poor people who spend significant shares of their income on food (Sanogo, 2009). Also, mountainous terrain and poor infrastructure, coupled with weak governance, insecurity and corruption, have limited the government’s ability to manage its food distribution and supply networks.

International prices of food commodities increased substantially in 2007 and rapidly in early 2008, peaking around May-July 2008. During this period, Afghanistan experienced several shocks that led to a disruption of its food supply network, causing prices to soar throughout the country. Due to drought and early snow melt, the 2008 wheat harvest of 1.5 million metric tons was the worst since the 2000 harvest (Persaud, 2010). (The harvest period typically falls in the summer months: May-August.) The price impact of the large shortfall in wheat production was magnified by export bans in Pakistan and rising international food prices. In February 2008 the Afghan government eliminated import tariffs on wheat and wheat flour (tariffs had been set at 2.5%), but due to export bans in Pakistan, Iran and Kazakhstan, there was little effect on prices. Between fall 2007 and summer 2008, the prices of domestic wheat and wheat flour increased by over 100%. Figure 1 displays the retail price (Afghani per kilogram) of wheat in four major markets from January 2006 to December 2009.

Total inflation was largely driven by the surge in food prices. Figure 2 presents the consumer price indices (CPI) for food and non-food items in urban areas from 2006 to 2008 constructed by the Government of Afghanistan.<sup>19</sup> During the NRVA survey timeframe (August 2007-September 2008), the urban food CPI increased by nearly 60%, while the nonfood CPI increased by only 10%. Our calculations using the NRVA district price survey data similarly indicate a 60% weighted average increase in food prices in urban areas during this period (with the overall increase at 40%).

> 19 The consumer prices indices were constructed by the Afghan Central Statistics Organization, based on data from six urban areas of Afghanistan.

7
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It is unclear to what extent the high rates of informality and turnover among youths are voluntary or the result of deeper structural factors (Perry et al., 2007). In this paper, we test whether a temporary wage incentive can encourage Mexican secondary school graduates to obtain and stay in formal employment. We first present stylized facts that motivate our intervention. Entry-level hourly wages are 9%-17% lower for young labor market entrants in the formal than in the informal sector. This pattern is in line with evidence from other LMICs showing that youths with primary and secondary education earn higher hourly wages working informally (Shehu and Nilsson, 2014). However, while informal wages stay relatively constant, formal wages in our study sample grow by about 25% (35%) over the first six (twelve) months, driven by substantial training and on-the-job skill gains. Low entry-level wages in formal jobs matter given that they are lower than the reservation wages for about one-third of school leavers. To address the initial (wage) disincentive of formal work, we design an employment incentive that pays workers the equivalent of about 20% of the average formal entry level wage for up to six months for holding a formal job. These temporary incentives may lead to long-run employment gains as they help youths to accept and retain formal jobs despite lower starting salaries.

We conduct an experiment in partnership with the Mexican government to test the effectiveness of this formal employment incentive in a sample of almost 2,000 graduating students across 13 upper secondary schools in the San Luis Potosi metropolitan region. This area has a large industrial base (including automotive production), and firms report struggling to fill vacancies, frequently ensuing from high worker turnover. About 80% of students in our sample attend schools with a vocational focus while the remaining 20% are enrolled in general schools that traditionally prepare them for tertiary education. We randomly assign half of the graduating students in each school to be eligible for the wage incentive and test its effect on employment outcomes over two years using survey and high-frequency social security data.

We offer four main results. First, the employment incentive does not lead to (short-run) changes in employment for graduates of general schools, nor for students who plan to continue their education at baseline. This is an important finding for policymakers who are concerned that programs offering (short-term) benefits to encourage formal work may have hospitality and 41% in warehousing (Moon et al., 2021). High turnover can adversely affect firm performance. Moon et al. (2022) find that an additional percentage point in turnover among Chinese consumer mobile devices workers increases quality failure rates by 0.74%-0.79%.

2