rafmacalaba/datause-encoder-data huggingface dataset:
data, implying that the labor market looks more dynamic than it really is. Even a simple classification of workers by industry suffers from non‐random measurement error (Bassi et al., 2008). Retrospective data suffer, on the other hand, from recall error as respondents may not accurately remember the details of previous employment spells. For this reason, scholars have argued that retrospective questions can be used to gather qualitative data on labor market outcomes in previous periods, such as the occupation and the sector, but not for quantitative data such as earnings or hours worked (Grosh & Glewwe, 2000, chapter 9).

Although the LSMS reference work retrospective questions, they seem to have fallen out of favor. DHS, LSMS‐ISA surveys and most LSMS surveys do not include retrospective questions, while LFS cover them. For instance, the 2014 Guatemala LFS includes the question _‘What was your last employment?’._ The 2012 Indonesia LFS, instead, includes several retrospective questions, such as _‘Did (name) ever have a job/business before?’, ‘Did (name) quit or moved out into another job during the last one year?’, ‘What was (name’s) previous job main industry before quit or moved into the last job?’, ‘What is employment status of the previous job of (name) before quit or moved into the last job?’. T_ he 2007 Nepal and 2013 Malawi LFS include the same questions for the previous job as for the current job. Finally, the 2014 Tanzania LFS includes the following questions: _‘In which year did you start working in economic activities for wage job or self‐employment to support your life for the first time?’, ‘What type of work/activity were you doing at your first employment?’, ‘Have you ever worked or having any economic activity?’, ‘What type of work did you do in your last job?’._ 5. How and what will we measure in a changing labor market? Recommendations and concluding remarks Demand for employment data as well as the type of data required to monitor and explain labor markets is changing. One reason is the SDGs, which put more emphasis on labor markets than its predecessor, the Millennium Development Goals (MDGs), and the new ILO‐resolutions concerning (unpaid) work and employment.

In this final section, we attempt to set an agenda to improve the collection of individual‐level employment data in LSMS‐ISA surveys in the short and medium term. The first part deals with short‐term objectives, i.e. actions that can be implemented in the near future and do not require (much) additional research. The second part, medium‐term objectives, discusses how to measure unpaid domestic and care work, and informality. These issues are part of the SDGs and methodological work is currently ongoing to develop (standardized) tools to capture those concepts.

We refrain from discussing long‐term objectives, such as the skills agenda, the issue of capturing (international) migration and its implications for local labor markets, the use of technology to collect individual‐level employment data or the integration of survey data with administrative data sets. These discussions are of foremost importance but go beyond the scope of this paper.

24
The rise in inequality was far greater for China than India. The Gini index in India rose from about 0.31 around 1990 to 0.33 in 2005, as compared to a rise from 0.29 to 0.42 in China’s reform period (Table 1). However, there are reasons for caution in this comparison. First, there are data concerns. India’s inequality measure is based on consumption rather than incomes. Consumption inequality tends to be lower.<sup>12</sup> Income measures (from a different survey) suggest that inequality in India may well be far higher (Appendix). The other side of the coin to the rising gap between aggregate consumption from the sample surveys and that from the national accounts may well be that the rise in inequality has been underestimated. India may not be a low inequality country after all.

A second reason for caution is that there are important dimensions of inequality in India that are not evident in a conventional inequality index based on consumption or income. (This is true of China and Brazil as well, but India is where the concern is clearly greatest.) I refer to inequalities associated with identity, such as gender or caste, and inequalities in access to key social services, particularly health care and schooling.

The rest of this article discusses the differing performances against poverty of these three countries, and what factors came into play, including initial conditions, changes in income distribution associated with the pattern of growth, and policies, including direct interventions aiming to reduce inequality. The discussion begins with China.

# **China: Substantial but uneven progress against poverty**

While certainly impressive in the aggregate, China’s progress against poverty has been uneven over time and space. As can be seen from Figure 1, Progress was far greater in some periods (the early 1980s and mid-1990s) than others (the late 1980s). And far more progress was made in coastal than inland areas (Ravallion and Chen, 2007). This variance contains some lessons for China and other countries hoping to emulate China’s success against poverty.<sup>13</sup> An important role was played by the geographic and sectoral pattern of growth. Like most developing countries, living standards tend to be lower in rural areas of China, but China’s disparities between rural and urban areas are particularly large. Around 1980, the chance of being poor was about 10 times higher in rural areas than urban areas. Thus it was very important

> 12 Consumption smoothing by households is the likely reason; low incomes in a given year are supplemented from savings or borrowings, and unusually high incomes are used to supplement wealth or pay off debts.

> 13 For an attempt to draw lessons for Africa from China’s success see Ravallion (2009a).

7
family indices for the set of states that did not follow guidelines regarding the population eligibility threshold. If villages above the PMGSY thresholds are changing in ways other than through eligibility for roads, we would expect to find similar reduced form effects in these placebo villages as well. Specifically, we include villages close to the two population thresholds in states that built many roads but did not follow the rules at all (Andhra Pradesh, Assam, Bihar, Jharkhand, Karnataka, Uttar Pradesh and Uttarakhand), and villages close to the 1,000 threshold in states that used only the 500-person threshold (Gujarat, Maharashtra, Orissa and Rajasthan). Table A7 presents the estimates. There is no evidence of either a first stage or reduced form effect on any outcomes in the placebo sample, suggesting that our primary estimates can indeed be interpreted as resulting from new roads.

In Table A8, we present the five family index results for bandwidths from 60 to 100, for both triangular and rectangular kernels. The results are consistent with the those in our main specification (Table 3).

If rural roads are causing selective migration, as some studies on transport costs have found (Bryan et al., 2014; Morten and Oliveira, 2017), compositional changes in village population could bias treatment estimates. In Table A9, we examine three proxies for permanent migration.<sup>21</sup> First we test for impacts on village population in 2011 (Panel A). We find no evidence for significant impacts on total population, either in logs or levels. The limitation of population growth as an outcome is that any impacts on net migration could be offset by changes to fertility and mortality. But such offsetting effects would cause changes in village demographics, which we can estimate in the comprehensive census data. In Panels B and C, we show that roads cause no changes to the age distribution or gender ratios in any age cohort. Taken together, these three pieces of evidence suggest that new roads do not lead to major changes in out-migration.<sup>22</sup> The absence of an impact on migration also allows us to 21Short-term migrants and commuters are considered resident in the village, and thus covered in both the Population Censuses and the SECC. 22This difference with Morten and Oliveira (2017) may be due to the differences between rural feeder roads 22
firm characteristics can interact with a range of investment climate variables in determining who exits.

When taking the firm perspective, models of exit generally emphasize the importance of productivity and productivity shocks in predicting exit. Jovanovic’s (1982) model is based on firms that learn about their productivity as they operate. Size and age are linked to exit rates as entering firms are generally small. Based on their realized productivity, firms will either grow and continue in operations or decline and eventually exit. In Hopenhayn (1992)’s model, firms have different productivity levels that evolve according to a Markov process. If firms experience a series of negative productivity shocks, they exit. Melitz (2002) and Ericson and Pakes (1995) extend the framework to include monopolistic and oligopolistic competition. Again, younger firms tend to be smaller and to exit more frequently.

Taking these models to the data, a number of papers look to explain such patterns, relying on firm characteristics such as age, size and sector. Using census data from the United Kingdom, Disney, Haskel and Heden (2003) show the importance of these firm characteristics in predicting the exit of a firm. Controlling for firm exit, they then show that greater competition can raise the productivity of continuing firms. However, they do not link competition to firm exit itself.

Bernard and Jensen (2002) look at the closing of manufacturing plants in the United States. In addition to finding that size and age are negatively correlated with exit, they find that firms that are part of a US multinational face greater rates of exit once other firm characteristics are controlled for, as do plants that are part of multi-plant firms. They also find that increased competition, in the form of imports from low wage countries, significantly increases the probability of exit, particularly for low-wage and labor intensive plants.

6
Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey” highlighting increasing regional divergences (Berkoz, 2001; Erkut and Baypinar, 2003; Berkoz and Turk, 2009).

Lenger and Taymaz (2007) study innovation and technology transfer activities in Turkey and observe that foreign owned firms are on average more innovative that domestic ones. They also find that the main channel of spillovers is workers’ acquired knowledge. There does not seem to be much evidence of significant horizontal or vertical spillovers at the industry level. Therefore, not all of the alleged benefits of FDI may be easily and unconditionally achieved in reality.

Whether these studies usually contain useful guidance from a managerial perspective (joint venture choice, location...), they do not always provide hints for the policy maker. As it is acknowledged by some of the authors, this approach needs to be complemented with a macroeconomic perspective to fully understand the determinants of FDI in Turkey (Tatoglu et al, 2003).

A second strand of literature has a wider focus which normally considers macroeconomics and policy determinants of FDI. Their aim is varied, but they have in common the use of a quantitative analytical approach, often in the form of cross country regression.

Erdilek (2003) analyzes the causes for the low levels of both inward and outward FDI in Turkey. He blames internal factors such as _<<chronic high inflation, increasing economic instability, inward orientation until 1980, lack of protection of intellectual property rights, lack of inflation accounting and internationally acceptable accounting standards, failure of privatization, insufficient legal structure and inadequate infrastructure>>._

# _Erdilek (2003)_

Similarly, Basar and Tosunoglu (2006) conclude that Turkey seems to be attracting less FDI than expected (given the size of its economy), and this is probably a result of high inflation rates and macroeconomic instability.

During the 1980s and 1990s it was believed that capital account liberalization would result in increased foreign investment attraction (see, for instance, Desai et al., 2006). However, this resulted to be a necessary but not sufficient condition to enhance FDI inflows. Apart from counting with reliable macroeconomic management, a stable investment framework and efficient institutions seem to be necessary to appeal the foreign investor. Dumludag et al (2007) perform cross-country regression to find that lack of corruption, transparency, rule of law or security of property rights contribute positively to FDI attraction. In the second part of the paper the authors present the results of a survey which seem to 7
Compared to simple means, application of the OLS allows adjusting for the difference in human capital characteristics between workers in public and private sectors. Next, we employ a sector selection bias correction ( **SBC** ) method which models wage setting mechanisms in each sector as a function of both observable and unobservable characteristics of workers and employers. Finally, we use a non-parametric technique called Propensity Score Matching ( **PSM** )<sup>7</sup> .

Our empirical analysis is motivated by a model of an individual’s choice of sector of employment that is commonly presented in terms of utility maximization and human capital. Suppose the marginal productivity of a worker is determined by her/his human capital and personal characteristics. To choose between the sectors, an individual compares expected net benefits in each sector and selects the job that best rewards his individual set of characteristics. Once an individual decides on the sector in which to seek employment, she/he enters the pool of applicants from which employers select. The probability of being selected in a particular sector depends on the individual’s characteristics (observed and unobserved) as well as on characteristics of the employer. The observed individual outcome is a combination of preferences and rationing. First, an individual seeks to join, for example, the public sector if the expected benefits in that sector exceed her/his expected benefits in the private sector and in the informal sector. Second, he is selected by the employer and actually hired into the sector.

To formalize this approach, let _Vis_ be the indirect utility function of individual _i_ employed in sector _s,_ we assume that _Vis_ can be linearly characterized as: where **Z** i is a vector of characteristics of the respondent _i_ , γ is a vector of parameters, _s_ is a categorical variable describing sector choice, and η _i_ • _’_ s are IID error terms. The individual is employed in sector _s_ if: The wage in a particular sector is observed only if the sector is chosen:

> 7 We know of only one attempt to use PSM for measuring inter-sectoral wage differential, see Bales and Rama (2002). While in Bales and Rama the matching is based on the probability to be employed in the public sector, this study matches workers based on their predicted probabilities to work in each of three sectors.

14
**Table 2. Brazil: Composition of expenditure by** **<u>population quintile (in %)</u>**

|**Quintile**|**Apparel**|**Food &**<br>**Beverages**|**Transport &**<br>**Communications**|**Housing**|**Health**|**Other**|**Total**<br>**Expenditure**|
|---|---|---|---|---|---|---|---|
||||**1988**|||||
|**1**|10.17|38.31|8.94|21.95|6.91|13.72|5.76|
|**2**|11.67|31.36|9.94|23.60|6.94|16.49|8.96|
|**3**|11.88|26.17|13.88|22.57|7.15|18.35|13.45|
|**4**|10.13|19.81|15.09|26.45|7.33|21.19|21.43|
|**5**|8.22|11.79|20.02|26.96|6.30|26.72|50.40|
|**All**|9.54|18.72|16.60|25.67|6.73|22.75|100.00|
||||**1996**|||||
|**1**|6.42|37.21|11.79|27.98|7.63|8.98|5.89|
|**2**|6.67|30.68|14.99|29.75|7.92|10.00|8.73|
|**3**|6.30|25.40|16.69|31.16|8.33|12.14|13.41|
|**4**|5.42|21.16|19.91|30.97|8.81|13.73|21.32|
|**5**|4.16|13.20|25.25|35.51|7.61|14.27|50.65|
|**All**|5.07|19.47|21.27|33.01|7.99|13.19|100.00|

_Note:_ The table reports the composition of expenditure by population quintile of the expenditure distribution corresponding to six expenditure categories in each of the surveys analyzed.

18
<!-- Start of picture text --> Inventory of Public Lands - Anmedabad 2013<br>Classification by Marketable and Non-Marketable Lands<br>Total Non-Marketable 57.16%<br>Indian Space Reasearch OAPs<br>Cantonment - Operational 3.31%<br>Airport - Operational 79S<br>20% of Textile Mills" land 0.60%<br>Small non-vacant plots <1 ha Bal<br>Utilities and Industrail Estates 16.87%<br>Slums 411%<br>Heritage Building | 0.12%<br>Garden/ Open Spaces TAGS<br>Cemeteries | 0.25%<br>Water Bodies 10.77%<br>Total Marketable 42.82%<br>Consumed FSI >85% 2.36%<br>45%<Consumed FSI <B5% 5.5895<br>Consumed FSI <45%5 11.93%<br>‘Obsolete use | 0.20%<br>Cantonment- Marketable 2.99%<br>Airport - Marketable O.45%<br>20% of Green Belt lands 0.81%<br>Public land incl. TRS 18.50%<br>0% 10% 20% 30% 40% so% 60%<br><!-- End of picture text -->
Promoting Democracy in Fragile States Table 4: Effects of programs on diversity of vote choice (

||Index<sup>_‡_</sup>|Herf. Pres.|Herf. Sen.|Herf. Hou.|
|---|---|---|---|---|
|(Control mean)|0_._16|0_._53|0_._37|0_._41|
||(0_._08)|(0_._01)|(0_._01)|(0_._01)|
|Civ. Ed. ATE|0_._26<sup>_†_</sup>|0_._01|0_._05<sup>_⇤_</sup>|0_._02|
||(0_._15)|(0_._02)|(0_._02)|(0_._02)|
|Sec. Com. ATE|_−_0_._20|_−_0_._04<sup>_†_</sup>|_−_0_._02|_−_0_._03|
||(0_._17)|(0_._02)|(0_._03)|(0_._02)|
|Combined ATE|_−_0_._11|_−_0_._02|_−_0_._04<sup>_⇤_</sup>|0_._02|
||(0_._10)|(0_._02)|(0_._02)|(0_._03)|
|_N_|142|142|142|142|
|_R_<sup>2</sup>|0_._71|0_._86|0_._65|0_._80|

WLS estimates accounting for blocking using centered block interactions (coefficients omitted). _‡_ First column outcome is a standardized, inverse-covariance-weighted average of the other columns outcomes. “Herf.” stands for Herfindahl index. “Pres.”, “Sen.”, and “Hou.” stand for president, senate, and house votes. Standard errors account for clan-level clustering. _†_ significant at _p < ._ 10; _⇤p < ._ 05; _⇤⇤p < ._ 01; _⇤⇤⇤p < ._ 001 the Herfindahl index measures), with votes from parochial candidates being reduced again, but then being more evenly spread across the national party candidates. The increased appeal of national-level candidates above parochial candidates (and in particular, regional and ethnic candidates) is also apparent in the results for Table 6. Thus, while the effects on vote diversity are mixed and quite noisy, we see rather compelling evidence that all types of programs induced a shift toward national level candidates away from parochial candidates.

Tables 7 and 8 display results related to manipulation and surrendering of rights, which we expect to be lower to the extent that voters are better able to coordinate and are shielded from local factions. Table 7 includes nine indicators based on the survey data, capturing various forms of manipulation, while Table 8 shows effects on invalid vote counts based on the polling place data. At first glance, the effects for the civic education program appear puzzling: based on the index measure, we find a 0.11 standard deviation _increase_ in the index of manipulation and surrendering of rights. No such aggregate effects are apparent for the other two programs. The effect for the civic education program is driven primarily 28
# Box 1. On the Openness of the Bulgarian Economy

The long term macro-scal model presented here considers Bulgaria a small and open economy, where capital is the mobile factor. This means that the agents in Bulgarian economy take global prices as given, and whenever there are dierences between the domestic and foreign returns to investment, capital will ow out or ow in to equalize the risk adjusted expected returns. For many developing countries this assumptions would not hold. However, Bulgaria demonstrates characteristics of an open economy to a large extent. In addition to the EU membership status, historically large foreign capital ows across the border imply that this assumption is not unrealistic. The following gure shows that capital ows has been more responsive to changes in the economic environment in Bulgaria than the averages of EU-10 and EU-15 countries within the last decade.

Box-Figure 1. Capital Flows as a Share of GDP <!-- Start of picture text --> 30% 30% 30%<br>2001-04 2005-08 2009-11<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>-10% -10% -10%<br>-20% -20% -20%<br>Bulgaria EU10 EU15 Bulgaria EU10 EU15 Bulgaria EU10 EU15<br>FDI Portfolio Other CA Balance Total Capital Flows<br><!-- End of picture text --> However, the open economy environment, along with no eort to solve the households' maximization problem, prevents us from tracing the source of investment in the domestic economy. Investments only respond to risk adjusted interest rate dierentials between the domestic and global economies, and these investments are not necessarily nanced by the Bulgarian household. In comparison, in a closed economy, all investment would need to be nanced by domestic savings. Therefore, a closed economy would face an additional channel through which the demographic dynamics aect the macro-scal outcome in the economy. More broadly, the impact of aging on domestic savings and, hence, on investment and capital accumulation, is not uniform across countries. The magnitude of the impact, and even the direction of it, depends on the determinants of aging and the prevailing social security system. As shown by Dedry, Onder, and Pestieau (2014), a decline in fertility unambiguously fosters savings, whereas an increase in longevity in a society with exible retirement age and pay-as-you go social security has an ambiguous eect on saving. Even though an increase in savings would be expected under reasonable assumptions, if the pensions system is rather generous, then a decrease in savings is also possible.

9