rafmacalaba/data-use-mentions-extended huggingface dataset:
5 survey reports each year on a range of variables that form the basic data set. Questions are asked on subjects pertaining to the household and to individuals within the household. Information is recorded on the geographic location of the household; characteristics of the dwelling; household size; relationships between individuals in the household; activities of individuals; income from labor, transfers and other sources (such as land rents and capital); occupation and other labor characteristics; age; gender; education; ethnicity and literacy. The definition of income throughout the main analysis is gross monthly household income per capita and the population is all individuals in the population.<sup>7</sup> Monetary amounts are all measured in 2004 Brazilian Reais, with a dollar exchange rate of BR$/US$ = 2.89.<sup>8</sup> The Brazilian _INPC_ official consumer price index, which is listed in Appendix 1, is used to convert current incomes into real incomes. For a more detailed description of the data set and methodology see Litchfield (2001).

1 _n y_ Entropy (E) class of measures, _E_ (0) = ∑ log , also known as the mean log deviation or _n i_ = 1<sup>_y_</sup> _i_ Generalised Entropy class of measures is chosen because its members satisfy all of the desired axioms of inequality measures<sup>9</sup> . Whilst the Gini will only satisfy one of these principles under certain conditions, it is included in the analysis to allow some degree of comparability with other

> 7 In this paper, we do not deflate the raw PNAD incomes by a regional price index, nor do we impute rents for owner-occupied housing, since the assumptions required about the stability of certain estimated relationships over 23 years were deemed too strong. The consumption surveys which could be used to generate nationwide regional price indices, for instance, are so far apart (1975 and 1996), as to make sensible comparisons of regionally deflated
because of the prudent management of the fiscal, monetary and exchange policies, Costa Rica has been able to guarantee a neutral exchange rate (as measured by the real effective exchange rate index - REER) and relatively low inflation levels to its export sector (Figure 1.4).

<!-- Start of picture text --> Figure 1.4 Costa Rica: Evolution of Inflation and REER<br>1983-2003<br>115<br>105<br>95<br>85<br>75<br>65<br>55<br>45<br>35<br>25<br>15<br>5<br>1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>REER Inflation<br>Percentage FTZ Law Reform Intel’s Arrival<br>Export Promotion Import Liberalization<br>Capital Account Liberalization<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

Attempts have been made to minimize the significance of the response of Costa Rica’s business community to the liberalization of the economy, by pointing out that the increase in Costa Rica’s exports is mainly associated with the foreign sales of the multinational companies under the free trade zone regime. The truth is that, although multinational companies are currently an important source of wealth for the country—by generating new and better employment sources and technology transfer to domestic businesses, among other positive effects—the results shown in the previous figures underline the importance of the liberalization in achieving a better allocation of the resources of Costa Rica’s economy, along with the entry of multinational companies since the second half of the 1990s.<sup>4</sup> The sustained growth of FDI inflows into Costa Rica is another positive outcome of economic liberalization, as it is an evidence of the foreign investors’ confidence in the new development model (Figure 1.5
manufacturing and very late implementation. We have confirmed that that all results in the paper hold if excluding Arunachal Pradesh.

We conduct much of our empirical analysis at the state-industry level. Figure 3 repeats Figure 2 at this disaggregated level. The horizontal axis continues to be the timing of each state’s implementation, while the vertical axis documents pre-existing women’s entrepreneurship on a per-worker basis or as a share of activity. For the left two panels on women’s ownership shares, there is again little evidence of a relationship to the sequence of state implementations. The added data variation allows for more precise trend lines, but we continue to reject the hypothesis that the trend lines are different from zero. For the right two panels on new establishments, we do see more evidence that later-adopting states have lower entrepreneurship when each industry is treated separately. These trend lines are statistically significant. On the whole, however, they are very small in size relative to the magnitudes that we will estimate due to the reforms.

As a complement to these levels of activity and shares in 1994, we would ideally be able to examine changes in women’s entrepreneurship and ownership rates over the period immediately before 1994 to check whether there are pre-trends in the data that correlate with the relative timing of state implementations. We are unfortunately not able to conduct this analysis directly because 1994 is the first survey to collect gender-differentiated ownership information. We are, however, able to achieve the spirit of this check for pre-trends in two complementary ways.

First, the 1994 NSSO data include the ages of plants beyond the 0-3 years-old group that we use to define entrepreneurship.<sup>11</sup> One approach to testing for pre-trends is to look at the age structure of women-owned businesses in 1994 by the dates at which states will implement the elections. If there are substantial pre-trends for early adopters in the years immediately before 1994, one might anticipate seeing an abnormal profile for early adopters indicative of a pre-trend in entry levels.

Figure 4
often not available in labor force surveys, which usually exclude individuals outside the ages of 15–64. Second, using region- or country-specific estimates of completed family size can be complicated. For instance, observed average household size is affected by the distribution of household types and sizes. Single-person households would significantly reduce average household size even though this is unrelated to the typical household size of workers with children. 11 Consumer price index and PPP values are taken from WDI (World Development Indicators),World Bank, Washington, DC, https://datatopics.worldbank.org/world-development-indicators/.

> 12 HDI (Human Development Index) (dashboard), United Nations Development Programme, New York, https://hdr.undp.org/data-center/human-development-index#/indicies/HDI.

8
recent short-term period 2009-15 saw income redistribution accounting for more than half of total mobility.<sup>15</sup> This result is consistent with our earlier findings that both indexes M<sup>�</sup> and M<sup>�</sup> remained relatively stable over time, and that it was income growth that was the driving factor behind mobility for the country. It is also interesting to note that income growth is higher for longer periods: the average income growth for the four shorter periods is 34 percent, which increased respectively, by almost twice and three times to an average growth rate of 66 percent and 95 percent for the two medium-term periods and the long-term period.

Table 6 provides estimation results for Shorrocks’ mobility index M<sup>�</sup> , short-term inequality, and long-term inequality. We estimate M<sup>�</sup> in two different ways, using the Gini index and the variance of log income, for robustness checks. Estimation results for both methods are, however, qualitatively similar and suggest a couple of emerging patterns that are consistent with our earlier findings for the other mobility indexes.<sup>16</sup> First, both short-term and long-term inequality has been steadily decreasing over time for Russia, over the four shorter periods. For example, the Gini index over the short term decreased by 30 percent, from 0.44 in the 1994-98 period to 0.31 in the 200915 period. The corresponding decrease for the variance of log income over the same time span is even larger at more than 50 percent (i.e., from 0.76 in the 1994-98 period to 0.35 in the 2009-2015 period). There is a similar decrease in inequality for the two medium-term periods, but this is unsurprisingly smaller since inequality measures are averaged over a longer time span for the medium-term periods compared to the shorter periods. Second, long-term inequality is less than

> 15 Increased minimum wages may
# **Table 2: The effects of temperature on subnational inequality**

|Inequality:|Gi|ni|The|il|
|---|---|---|---|---|
||Panel FE –<br>all countries|Long<br>differences|Panel FE –<br>all countries|Long<br>differences|
||(1)|(2)|(3)|(4)|
|**_Panel A: Country-level_**|**_analysis_**||||
|Temperature|0.154***|0.171***|0.253***|0.275***|
||(0.029)|(0.040)|(0.049)|(0.063)|
|Precipitation|0.002|0.025**|-0.002|0.031|
||(0.007)|(0.012)|(0.012)|(0.021)|
|Country FE|Yes|No|Yes|No|
|Year FE|Yes|No|Yes|No|
|Mean dependent var.|34.406|34.406|23.417|23.417|
|Observations|423|90|423|90|
|Equality test (Panel vs.<br>long differences)|p = 0|.726|p = 0.|604|
|**_Panel B: Subnation-lev_**|**_el analysis_**||||
|Temperature|0.285***|0.349***|0.350***|0.592***|
||(0.086)|(0.049)|(0.032)|(0.082)|
!-- End of picture text --> _Note: These figures are based on the STATA command mmsel._ _The conditional gap is based on earnings equations including controls for years of education, potential work experience, search method (friends and relatives), unlimited contract, having had an apprenticeship contract, working in a small-sized firm (5 or less employees), union membership, living in a rural area. With a larger number of variables, the command does not work, due to the lack of sufficient information._ _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ 39
Why are small- and medium-size firms more likely to become monopsonist than large firms? To understand this result, recall the first-order-condition in equation (7): _wit_ = _γit_ ( _Rit_<sup>_L_).Itfollowsthatthedegreeofmonopsonypowerisgivenby</sup><sup>_γit_=</sup> 1+(( _ε_<sup>_L_</sup> _<u>wε</u>_<sup>_L_</sup> _w_<sup><u>)</u></sup><sup>_<u>it</u>_)</sup><sup>_it_where</sup> ( _ε_<sup>_L_</sup> _w_<sup>)</sup><sup>_it∈R_+is the wage elasticity of labor supply.Hence, if wages tend to be inelastic with</sup> respect to labor supply, then firms are likely to compress wages when faced with increased competition.

<!-- Start of picture text --> 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year Year<br>Small Medium Small Medium<br>Large Large<br>(a) (b)<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year Year<br>Small Medium Small Medium<br>(c) (d)<br>400<br>250<br>200 300<br>150<br>200<br>100<br>Employment level Real Wage Index<br>100<br>50<br>0 0<br>25 160<br>140<br>20<br>120<br>15<br>100<br>Employment level Real Wage Index<br>
Policy Research Working Paper 8741

# **Abstract**

Following a couple of decades of offshoring, the fear today is of reshoring. Using administrative data on Mexican exports by municipality, sector and destination from 2004 to 2014, this paper investigates how local labor markets in Mexico that are more exposed to automation in the U.S. through trade fared in exports and employment outcomes. The results show that an increase of one robot per thousand workers in the U.S. — about twice the increase observed between 2004–2014 — lowers growth in exports per worker from Mexico to the U.S. by 6.7 percent. Higher exposure to U.S. automation did not affect wage employment, nor manufacturing wage employment overall. Yet, the latter is the result of two counteracting forces. Exposure to U.S. automation reduced manufacturing wage employment in areas where occupations were initially more susceptible to being automated; but exposure increased manufacturing wage employment in other areas. Finally, the analysis also finds negative impacts of exposure to local automation on local labor market outcomes.

This paper is a product of the Social Protection and Jobs Global Practice and the Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ research. The authors may be contacted at hwinkler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/
food inflation is low. A 1% increase in the international price inflation leads to a 0.16% increase in the domestic food price inflation when the FAO food index is used as a measure of international food prices, and to a 0.13% when the FAO cereals sub-index is instead used. Also, the first-step equation shows that international food price inflation explains around one quarter of the changes in domestic food price inflation, meaning that changes of domestic food prices are attributed to factors other than the international food price inflation.

> 5 All selected coefficients are significant at least at 95% confidence level.

13
Global Recession. Washington, DC: World Bank.

Kose, M. A., F. Ohnsorge, and N. Sugawara. 2021. “A Mountain of Debt: Navigating the Legacy of the Pandemic.” Policy Research Working Paper 9800, World Bank, Washington, DC.

Kose, M. A., N. Sugawara, and M. E. Terrones. 2020. “Global Recessions.” Policy Research Working Paper 9172, World Bank, Washington, DC.

Kubota, M., and A. Zeufack. 2020. “Assessing the Returns on Investment in Data Openness and Transparency.” Policy Research Working Paper 9139, World Bank, Washington, DC.

Laeven, L., and F. Valencia. 2020. “Systemic Banking Crises Database II.” IMF Economic Review 68 (2): 307-361.

Lang, V., D. Mihalyi, and A. Presbitero. 2021. “Borrowing Costs after Sovereign Debt Relief.” CEPR Discussion Paper 15832, Centre for Economic Policy Research, London.

Lian, W., A. F. Presbitero, and U. Wiriadinata. 2020. “Public Debt and r - g at Risk.” IMF Working Paper 20/137, International Monetary Fund, Washington, DC.

Mbaye, S., M. M. Moreno-Badia, and K. Chae. 2018. “Bailing Out the People? When Private Debt Becomes Public.” IMF Working Paper 2018/141, International Monetary Fund, Washington, DC.

Melecky, M. 2021. Hidden Debt: Solutions to Avert the Next Financial Crisis in South Asia. Washington, DC: World Bank.

OECD (Organisation for Economic Co-operation and Development). 2021a. OECD Economic Outlook. May. Paris: Organisation for Economic Co-operation and Development.

OECD (Organisation for Economic Co-operation and Development). 2021b. Tax and Fiscal Policy in Response to the Coronavirus Crisis: Strengthening Confidence and Resilience. Paris: Organisation for Economic Co-operation and Development.

18
definitions stunting “reflects a process of failure to reach linear growth potential as a result of suboptimal health and/or nutritional conditions.” Stunting is an indicator of persistent, longer term chronic undernutrition and has long-term negative effects on the child’s development (Brown, 2017). We also calculate weight-for-height (wasting). A z-score of less than -2 for wasting suggests recent rapid and severe weight loss which is often associated with acute starvation and/or severe disease.

We use market prices for three crops – teff, wheat and maize, collected through the community questionnaire in the LSMS.<sup>10</sup> These are key crops in Ethiopia and account for the largest share of total cereal area cultivated (Taffesse, Dorosh, and Asrat, 2011). The data are collected at item level from two different vendors in each enumeration area across Ethiopia. An average price is computed from the two different vendors, which forms the price in that enumeration area. Table 2 presents summary statistics of the three crop prices in wave 1, wave 2 and wave 3.<sup>11</sup>

**Table 2: Summary statistics of market prices in ETB/kg**

||**Teff**<br>Observations|Mean|Std Dev|Min|Max|
|---|---|---|---|---|---|
|2011/12|258|9.64|2.3|3.5|15.67|
|2013/14|347|13.1|2.68|4|18|
|2015/16|365|16.42|3.52|4|22|
||**Wheat**|||||
||Observations|Mean|Std Dev|Min|Max|
|2011/12|248|8.2|2.58|4.46|15
expectancy, average education levels and per capita income. The gini coefficients are based on recent surveys of income distribution.

The quality of a government's economic management is assessed through performance indicators of fiscal policy (debt-GDP ratio), monetary policy (central bank independence) and trade policy (outward orientation). For monetary and trade policy, we were able to use indices which capture, to some degree, the institutional orientation of government. The central bank independence index is based on the legally stated independence of the central bank. The outward orientation index includes a component of investors' perceptions of the receptivity of a government to trade. Unfortunately, the fiscal policy indlex, debt-GDP ratio, is a particularly imperfect measure of institutional orientation. This limitation is offset to some degree by the historical perspective it provides since debt is a cumulative measure of a country's fiscal policies. The indices of fiscal and monetary policy may be subject to a bias against poor countries. It is possible, for example, that the debt-GDP ratio may be biased against countries that are growing rapidly, regardless of the quality of their economic management since public investment typically has very long term payoffs. Or, conceivably, a country with a poorly developed revenue collection system may find monetization of debt to be the most efficient form of financing. However, neither of these indices are strongly correlated with income, suggesting that bias, if it exists, is not strong.

A more general question regarding bias is whether aggregating these indices introduces a systematic bias towards or against any country or group of countries. For example, does the better availability of data from developed countries mean that these countries, as a group, are rated higher or lower than LDCs? Or, does the composition of these indices by western oriented academics, businessmen and economists lead to a bias against governments pursuing alternative goals? The answer to the first question is that it seems unlikely. The answer to the second question is, yes, although ideological differences are more likely to develop
5% 29% 50% line<br>-10% 20%<br>-6% 28%<br>-7% 27% -20% 0%<br>KHM BGD ALB ARM GEO<br>Baseline BRI Debt to GDP ratio including BRI Baseline BRI Debt to GDP ratio including BRI<br>In percent of GDP In percent of GDP<br>Debt to GDP at end-2030 Debt to GDP at end-2030<br><!-- End of picture text --> Sources: WEO, WIND Database, LIC DSF DSAs and de Sources: WEO, WIND Database, LIC DSF DSAs and de Soyres (2019). Soyres (2019).

In a second scenario, we have hardened the budget constraint of some countries by setting real interest rates to be the maximum between one percent and the value under the baseline (Figure 18). Under this scenario, four countries would accumulate a debt-to-GDP ratios above 50 percent by 2030 as a result of BRI. Also in this scenario all but three countries would face a negative interest rate growth differential, but this differential would reduce from a median of -7 percent in the base case to a median of -3 percent in this scenario.<sup>47</sup> This increase would be consistent with a number of plausible outcomes that could be realized in the medium and long-term: (i) normalization of monetary policy in advanced economies; (ii) 47 The authors also developed a stress test which would assume that the real interest rate would be not smaller than baseline growth, implying that all countries would face a positive interest-growth differential in a no-BRI scenario. Under this scenario a total of six countries would end up with a debt to GDP ratio that would increase above 50 percent by 2030. However, this scenario would be too extreme, as many countries would face an increase of real interest rates of more than 10 percentage points compared to the base case.

28
for retirement (e.g., Mitchell and Moore, 1998 for the U.S.). These findings prompted theoretical developments in behavioral economics aimed at capturing departures from rationality or time-consistency such as hyperbolic discounting (Laibson, 1997, Harris and Laibson, 2001). However, others have argued that properly modeling household consumption decisions (buffer stock behavior, end-of-life uncertainty, medical expenditures, asset-tested public transfers, family structure, etc.) can rationalize the saving behavior of American households (Scholz et al., 2006, Gourinchas and Parker, 2002).<sup>3</sup> Most of the literature describing how household net worth changes with age (e.g. Lydall (1953), Shorrocks (1975), Wolf (1981) or more recently Cagetti (2003)) uses data from rich countries and we are not aware of lowincome country descriptions of how the value and mix of assets owned by households evolve over the life cycle. An obvious reason is data scarcity. Such a description requires comprehensive data on household assets, but typical representative household surveys only ask about durable ownership and housing characteristics. Second, controlling for time and cohort effects requires repeated cross-sections spanning large periods of time, which are rarely available in low-income settings.

Here we are able to use detailed data from Pakistan to examine life cycle wealth accumulation, to understand trends in old-age savings and the changes in potential demand for long-term saving schemes.

> 2 Surveys of the literature on household saving decisions include Browning and Lusardi (1996) and Attanasio and Weber (2010).

> 3 Another related puzzle is that households do not dissave enough in old age, compared to what the LCH would predict and that consumption tends to drop after retirement (Banks et al. 1998). The literature has attempted to quantify the role of possible explanations including bequests, asset illiquidity and longevity and health expenditure risk (e.g De Nardi et al. 2010, Poterba et al. 2011, Nakajima and Telyukova, 2016