rafmacalaba/data-use-mentions-extended huggingface dataset:
report that they are not employed, the annual census enumerates the reasons., placed into several categories. These categories do not permit a clear distinction between people who are unemployed and looking for work and those who are not in the labor force. For this reason, the able-bodied people of working age who report that they are not employed will be referred to, awkwardly, as the "not-employed." 17. It is generally accepted in most transition countries that official unemployment registrations are not very good indicators of the actual level of unemployment,3 and Mongolia is no different. Although someone who has recently lost a formal sector job would have incentive to register at the ERO, the incentive to remain registered declines rapidly as benefits expire<sup>4</sup> Furthermore, those who haven't paid social insurance contributions for the requisite two years have even less incentive to register since they are not eligible for benefits.

18. It is important to understand the processes that generated the statistics on the following pages. Each _duureg/soum_ questions each household about the characteristics of household members, including whether or not they have a job. If they are able-bodied, of working age, and report that they are not employed, the _duureg/soum_ then asks the reason, providing several categories of responses. The final category is "other" which ultimately gets reflected in the Annual Labor Force Balance as "without any reason." Some of the other categories, such as "watching a child," can be inferred to indicate people who are not in the labor force, and in Figure 1 below, these categories have been grouped together as "clearly not in the labor force."

# **Trends in Employment in Ulaanbaatar's Informal Sector**

19. The number of the "not-employed," as well as the "reasons for being not-employed" are instructive in the trends that they display. Recognizing that informnals often do not consider themselves "employed," an expansion of the informal
though recent efforts have begun to gather data that could support the development of such functions (Phi _et al._ 2012). Given the lack of good data on vulnerability, we have developed candidate depth-damage curves, but we treat key parameters of this relationship as uncertain and include them in the exogenous factors varied in the analysis. Importantly, as our analysis in Chapter 5 reveals, although vulnerability is deeply uncertain, the comparative performance of the strategies we consider is not strongly affected by this uncertainty. Other factors play a much more important role. Thus, uncertainty regarding vulnerability turns out not to be decision-relevant in this study.

## **Population Vulnerability**

In the absence of other information, we have chosen a sigmoid function to describe the population depth-damage relationship shown in Figure A.5. Sigmoid functions have _________

> 27 Tax revenue data are from the Ho Chi Minh City Statistics Book, Table 3.11.
The first two rows in table 5 report the effects of the distortions on the level of informality and the relative price. Informal employment rises significantly, from an 8% share in the U.S. to 18% under entry barriers, 35% under misallocation, and 58% for the combination of distortions. Relative to the informal employment share of 70% in our Ghanaian data, the model with both distortions can account for about three-quarters of the observed value. The table also shows the change in the relative price of formal to informal goods as the general equilibrium force driving the reallocation of expenditure, firm entry, and employment into informal production. The relative price increases more than threefold relative to its value in the undistorted economy.

In terms of the productivity losses, table 5 shows that these reach up to 12% in the case of entry barriers and idiosyncratic distortions. The last row of the table allows for a decomposition of the aggregate losses. Formal output plummets by up to 60% due to the multiple effects created by the distortions, namely, a misallocation of resources among surviving incumbents, a reduction in formal sector entry, and subdued innovation expenses. However, the informal sector, in so far as it is captured in official statistics, partially compensates for the losses in the formal sector, by increasing the number of varieties and thereby informal aggregate output. The aggregate productivity still falls due to the lower aggregate productivity in the formal sector and due to the reallocation of resources towards the informal sector, which is less productive than the formal one.

# **7 Conclusion**

This paper investigates the size distribution of manufacturing plants in Sub-Saharan Africa, where the distortions are all the more ubiquitous. It uses firsthand establishment censuses from Burkina Faso, Cameroon, Ghana, and Rwanda to identify four stylized facts: (i) there exists a drop in the employment share accounted for by the middle range of the size distribution, providing some support to the missing middle hypothesis; (ii) informal firms are
feed-in tariffs have been employed in developing countries, they often have to be met by government budgetary allocations to cover the differential between the guaranteed price that the utility pays to the renewable energy suppliers, and the average rate that it is allowed to charge consumers for each kilowatt hour of electricity. This dependence on national budgets to cover the difference places a cap on the total expansion of renewable energy that can take place in a developing country, and thus creates a disincentive for expanded renewable energy investment. Within this context, alternative support schemes, including through a “global feed-in tariff”, a payment-on-delivery mechanism, whereby funds flow only when electricity comes on line; that is, when real and tangible benefits are being provided. Whereas they have been cases where costs has been borne by the taxpayers, in most cases will be passed through consumers, raising formidable affordability issues. There are ways to protect poor consumers, but at the same time allowing for some partial pass through of feed-in tariffs. The incremental cost associated with renewable based generation can be, for instance, recovered for instance only through large customers.

# **References**

Burer, Mary Jean; Wustenhagen, Rolf; 2009. Which Renewable Energy Policy is a Venture Capitalist‟s Best Friend? Empirical Evidence from a Survey of International Clean Tech Investors, Energy Policy, Vol. 37, 49975006.

Cory, Karlynn; Couture, Toby; Kreycik, C.; 2009. Feed-in Tariff Policy: Design, Implementation and RPS Policy Interactions, Technical Report NREL/TP-6A2-45549, http://www.nrel.gov/docs/fy09osti/45549.pdf 23
above the 1991 level in four of the seven countries and progress was also visible in the remaining three countries.

Relatively to other CIS countries, the CIS-7 suffered from a much worse recession in industry while they show a better performance in agriculture (table 2.1). It is also evident that the industrial recession has not been clearly reversed during the recent growth period which is an opposite trend to what it can be observed in other CIS countries during the same period.

**Figure 2.1 GDP growth (1991=100)**

<!-- Start of picture text --> Industry Agriculture<br>160 140<br>140 120<br>120 AzerbaijanArmenia 100 AzerbaijanArmenia<br>100 Georgia 80 Georgia<br>80 Kyrgyzstan Kyrgyzstan<br>Moldova 60 Moldova<br>60 Tajikistan Tajikistan<br>40 UzbekistanCIS 40 UzbekistanCIS<br>20 20<br>1991 1992 1995 1996 1997 1998 1999 2000 2001 2002 1991 1992 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: http://www.cisstat.com

**Table 2.1 Volume Indices of Agricultural and Industrial Output (1991=100)**

||1991|1992|1995|2000|2001|2002|2003|
|---|---|---|---|---|---|---|---|
|Industry CIS-7|100|70.2|43.1|47.7|51.1|54.0|47.8|
|Industry other CIS|100|87.6|56.0|70.8|77.3|82.0|89.3|
|Agriculture CIS-7|100|88.6|78.1|82.8|92.4|97.8|98.0|
|Agriculture other CIS
**Table 1: The size of the Shadow economy in the New EU Member countries and Croatia (Shadow economy as a percentage of official GDP)**

|Countries|1999/00|2006/07|
|---|---|---|
|1. Slovakia|18.9|17.4|
|2. Czech Republic|19.1|18.2|
|3.Hungary|25.1|24.4|
|4. Slovenia|27.1|26.4|
|5.Poland|27.6|26.5|
|6. Lithuania|30.3|28.2|
|7.Estonia|38.4|36.0|
|8. Croatia|33.4|36.5|
|9. Latvia|39.9|37.1|
|10.Romania|34.4|37.4|
|11. Bulgaria|36.9|39.4|
|Unweighted average|30.1|29.8|
|Unweighted average<br>of 21 OECD<br>countries|16.8|14.8<br>(2004/2005)|

_Source:_ Schneider 2009.

10. In an international business survey for the IMD World Competitiveness Yearbook 2009, people were asked about the importance of tax evasion for hampering business activity. Among NM-EU countries, the level of tax evasion was seen as least problematic in the Czech Republic (ranked 11<sup>th</sup> among 57 countries), followed by the Slovak Republic (ranked 17<sup>th</sup> ), while in Hungary (ranked 53<sup>rd</sup> ), Romania (ranked 55<sup>th</sup> ) and Croatia (ranked 56<sup>th</sup> ) tax evasion was seen as most problematic (Table 2). It
Policy Research Working Paper 9327

# **Abstract**

The Syrian crisis has led to rapid and large-scale population displacement. This paper has two main aims. (i) It documents the size and timing of the Syrian refugee influx into Jordan, Lebanon, and Kurdistan, characterizing the forced nature of displacement and exploring factors that influenced the decision to flee and subsequently move within the host country. (ii) The paper describes the daily living conditions of refugees after displacement, documenting vulnerability along several dimensions, such as housing access and quality, labor market attachment, and financial security. The data sources include the United Nations High Commissioner for Refugees’ registration database and multi-country, multi-topic surveys conducted in 2015‒16.

This paper is a product of the Poverty and Equity Global Practice. 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/prwp. The authors may be contacted at nkrishnan@worldbank.org, flaviorussoriva@gmail.com, dsharma5@worldbank.org, and tvishwanath@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/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **5. Empirical Evidence**

In the following subsection we analyze whether a macro wage curve exists in Zimbabwe using aggregate data from 1964 to 1994. The statistical model in use is an autoregressive distributed lag model for wages consisting of: (1) pressure of demand that we proxy by the unemployment rate/informal sector and GDP; (2) expectational influences that we proxy by prices; (3) productivity, captured by a deterministic, linear trend; and, (4) important policy changes captured by including dummy variables.

The following three subsections present findings. Subsection 5.1 gives statistical information and subsections 5.2 and 5.3 analyze wage determination in the total economy and the manufacturing sector.

# **5.1 Statistical information**

Visual inspections indicate that wages and unemployment have strong positive trends of a similar magnitude, so that they may be modeled as stationary deviations from a linear trend or alternatively as variables with stochastic trends within a co-integrated system (see details in Banerjee, _et al._ (1993)). The unemployment or informal sector series also exhibits deviations from the trend in form of cyclical movements that will be important to capture in the modeling of wages. These properties, possibly resulting from changes in economic policies and "autonomous" events such as droughts and changes in commodity prices, e.g., crude oil, may be important to take into account when modeling wages.

The inspection of correlograms for the variables w, p, w-p, and u reveals high first order serial correlation coefficients (around 0.9) with the higher order coefficients declining extremely slowly, and correspondingly the spectra have peaks at the zero frequency. This information is consistent with each of the series having a stochastic trend and, therefore, being non-stationary and presumably integrated of order one, I(1). The result of the Augmented Dickey-Fuller test supports these hypotheses (see Appendix A). Hence, in modeling wages it will be important to choose models that
MOB methodology, further partitioning of the global sample into geographic regions is not necessary. When estimating the poverty rate for India, in addition to the poverty derived from the 0.67 pass-through rate, we also present a range of poverty estimates derived using the 95 percent confidence band for the 0.67 pass-through rate.

Our preferred pass-through rate of 0.67 is in line with the broader literature on India. Sen (2000) finds that the ratio of survey mean to consumption in national accounts to be between 0.6 and 0.7 for the period 1972 to 1997. For the same period, Datt and Ravallion (2002) find that the ratio between the survey mean and consumption in national accounts to be between 0.575 and 0.645 – depending upon, as discussed above, which national accounts series (new or old) one uses. Ravallion (2003) estimates this ratio for 1997 in India to be 0.55. The author also finds that, using 22 survey spells of South Asian countries in the 1980s and 1990s, β=0.525.<sup>26</sup> Deaton and Kozel (2005) estimate the ratio between the two sources to be “currently around” 0.667. The implied pass-through estimate for fiscal year 2014/15 using the poverty rates from the survey-to-survey estimation by Newhouse and Vyas (2018) is around 0.65.<sup>27</sup> The ratio of survey mean to per capita HFCE using all available CES surveys for India is 0.751, and this ratio using comparable spells is 0.765.<sup>28</sup>

# **4.3. Concerns over National Accounts growth rates**

There have been recent debates around the soundness of the official GDP growth rates. A. Subramanian (2019) argues that the official GDP growth for the 2011-2016 period might have been overestimated by as much as 2.5 percentage points due to methodological changes that the
used in Lakner et al. (forthcoming).<sup>21</sup> Here, we use per capita HFCE growth instead of per capita GDP growth as the main independent variable. The partitioning algorithm, referred to as model-based recursive partitioning (MOB), takes equation 1 as the starting point and subsequently adds various input variables interacted with the growth in per capita HFCE, gNAS. Each interaction is added one at a time. For each interaction a Wald test is conducted to determine whether the coefficients on these interactions are statistically significant (at the 5 percent significance level). The input variables are geographical region, a dummy for whether consumption or income is used in the survey, mean consumption, median consumption, the Gini index, population, per capita GDP, and the year of the survey.<sup>22</sup> When a significant interaction is found, the sample is partitioned using that input variable as a splitting variable and the algorithm is applied on each of the sub-samples separately. Splits are only made if at least 10 observations will be in each subsample. For non-

> 19 The precise assessment of comparability is country-dependent, compiled from the World Bank’s economists who are in close dialogue with national data producers and have intimate knowledge of the survey design and methodology. More details on the comparability metadata can be found in Atamanov et al. (2019). The comparability data set can be accessed here: https://datacatalog.worldbank.org/node/506801.

> 20 The exception is Sub-Saharan Africa, where GDP per capita is preferred.

> 21 The algorithm is a variant of Classification and Regression Tree (CART), pioneered by Breiman, et al. (1984).

> 22 We consider two regional definitions. First, the standard World Bank regions, in which all countries are classified according to geography. Second, the regions used by PovcalNet, where most high-income countries form a separate region.

13
to technology adoption by female workers (Devadas and Kim 2020), the effects of greater female entrepreneurship via TFP (Cuberes and Teignier 2016), higher hours per female worker (McKinsey Global Institute 2015; henceforth MGI), or greater allocational efficiency of female labor across sectors or firms (MGI 2015; Bertay et al 2020) — see Cuberes and Teignier (2014) for a literature review. In addition, the GEGIs only capture gender gaps in employment—as their name suggests—rather than broader measures of gender gaps in terms of pay (Woden and De La Briere 2018) or education (Dollar and Gatti 1999, Devadas and Kim 2020).

There are relatively few other papers that try to estimate the effects of closing gender employment gaps on GDP for a large number of developing countries. The closest studies are Cuberes and Teignier (2016), MGI (2015) and Devadas and Kim (2020), which are discussed in detail in Section 6.<sup>3</sup> As mentioned above, the GEGIs are highly correlated with these other studies (>95%). Relative to the effect on GDP in those studies, the GEGIs are similar on average, or a little smaller because the other studies include other mechanisms beyond those considered in the GEGI. However, when focusing on comparable mechanisms, the GEGIs are similar or slightly larger.

The contribution of the GEGI to this literature is to provide a simple and transparent measure of the growth impacts of gender gaps that is available for almost every country. Simplicity and transparency are needed because alternatives do not have closed-form solutions for the size of the effect on GDP, and are not easy to calculate. In contrast, the GEGIs can be calculated easily in a spreadsheet from employment rates using closed-form expressions. Data availability is important because the above measures exclude many developing countries. For example, the sample size of the basic GEGI is double that of MGI (2015) and Devadas and Kim (2020), and 40% larger
Policy Research Working Paper 10170

# **Abstract**

Most welfare measures, including food expenditure and diet quality, are based on household aggregates and assume an equal or equitable distribution of resources among members within the household. But it is unlikely that resources are distributed equally or equitably within most households. As such, individual food expenditure and diet quality measures, rather than household aggregations, may paint a more accurate picture of intra-household welfare. This paper assesses the disparity between household and individual measurement of food expenditure and diet quality in the Philippines using data from 2013. It finds evidence of intra-household inequality for food expenditure and for diet quality. In particular, for the consumption of starchy staples, meat, fish, and legumes, women and children do not meet the recommended consumption, even within households that, in aggregate, are able to meet the recommended consumption. However, intra-household inequality is not observed under circumstances in which no one in the household meets recommended consumption, as is the case for many food categories in our analysis.

This paper is a product of the Agriculture and Food Global Practice. 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/prwp. The authors may be contacted at fdizon@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/World Bank and its affiliated organizations, or those of the Executive Directors of the World
_Figure 26: Real change in services consumption during crisis (2014‐2016) for major socio‐economic groups depending on initial level of food consumption, %_ <!-- Start of picture text --> 2 expenditure on services per capita in 2014, in BYN in prices of 2014<br>0 hh_single_parent<br>15 20 25 30 35 40 45 50 55<br>-2 retiree<br>hh_3more_kids nonemployed<br>-4 hh_2kids child<br>rural large_cities<br>other_edu<br>-6<br>average<br>small_cities<br>-8 hh_no_kidsworking_age hh_1child<br>employed<br>-10 sec_specialized_edu<br>minsk<br>high_edu<br>-12<br>partly_employed<br>-14<br><!-- End of picture text --> Note. Services exclude utilities, education and healthcare. Source: Authors’ estimates based on HSS data.

# 5.3. Savings

Precautionary savings could be an important consumption smoothing mechanism for households, and as such, the evolution of saving (and dissaving) during the crisis is of interest to this inquiry. Unfortunately, the HBS data do not have data on the savings stock or loans taken, but do have data on saving, dissaving and loans paid. In 2014, 42 percent of the population reported positive net savings, while 19 percent of the population dissaved (negative net savings). In 2016, at the height of the crisis, only 35 percent of the population saved a portion of their incomes, while the number of those who dissaved increased to 24 percent. Those with relatively higher incomes also had the highest dissaving rates in 2016 (32 and 38 percent for 8‐9<sup>th</sup> and 10<sup>th</sup> decile correspondingly).

In other words, while savings are an important insurance mechanism, they are available mostly for the well‐ off households that