rafmacalaba/datause-encoder-data huggingface dataset:
Costa, D. L. 2003 (editor). _Health and Labor Force Participation Over the Life Cycle: Evidence from the Past_ . Chicago and London: University of Chicago Press.

Docquier, F. and A. Marfouk. 2005. “International Migration by Educational Attainment (1990-2000).” Chapter 5 in this volume.

- Early version published as _Policy Research Working Paper_ 3381. World Bank.

_____, O. Lohest and A. Marfouk. 2005. “Brain drain in developing regions (19902000).” Mimeo. CADRE. University of Lille, and Research Program on International Migration and Development. International Trade Unit. DEC Research Group. World Bank.

Docquier, F. and H. Rapoport. 2004. “Skilled Migration: The Perspective of Developing Countries.” _Policy Research Working Paper_ No. 3382. World Bank (August).

_____________. 2004. “Skilled Migration and Human Capital Formation in Developing Countries – A Survey.” Mimeo. Bar-Ilan University (December, draft).

Dumont, J.-C. and G. Lemaitre. 2005. “Counting immigrants and expatriates in OECD countries: a new perspective. Directorate for Employment, Labour and Social Affairs. Paris: OECD.

Faini, R. 2005. “Does the brain drain boost growth?” Mimeo. University of Rome, and Research Program on International Migration and Development. International Trade Unit. DEC Research Group. World Bank.

Fan, C. S.. and O. Stark. 2005. “Addition through Depletion: The Brain Drain as Catalyst of Human Capital Formation and Economic Betterment.” Mimeo. University of Klagenfurt.

Haque, N. Ul. 2005. “‘Winner-Take-All’ Distribution of Talent: A New Approach to the Brain Drain.” Research Program on International Migration and Development. International Trade Unit. DEC Research Group. World Bank.

Jasso G. and Rosenzweig M.R. 1995. “Do immigrants screened for skills do better than family reunification immigrants?” _International Migration Review_ 29 (1): 85-111.

Javorcik, B. S., C. Ozden and M. Spatareanu. 2004 (Dec.) “Does South-North Brain Drain Contribute to North-South FDI?” Mimeo. Research Program on International Migration and Development. International Trade Unit. DEC Research Group. World Bank.

Kugler, M. and H. Rapoport. 2005. "Skilled Emigration, Business Networks and FDI Inflows." Paper presented at the Royal Economic Society (March). Nottingham.

Lopez, R. and M. Schiff. 1998. "Migration and the Skill Composition of the Labor: The Impact of Trade Liberalization in LDCs." _Canadian Journal of Economics_ 31 (2): 318-36.

Lucas, R. E., Jr. 1988. “On the Mechanics of Economic Development.” _Journal of Monetary Economics_ .

Lucas, R. E. B. 2004. _International Migration Regimes and Economic Development_ . Report prepared for the Swedish Ministry of Foreign Affairs (EGDI).

33
workers, e↵ectively making way for less organized forms of employment. Finally, we can detect similar e↵ects with respect to welfare and pension reform provisions that are included in these social pacts.

In our view, these results support the notion that governments deploy tailored policy reforms with the aim to minimize the downside e↵ects of CBI. However, even in light of these policy changes, it is unclear whether these changes leave a mark on economic outcomes. To verify that this is the case, we analyze central macroeconomic (and financial) responses to CBI.

First, we analyze the movement of central financial variables. Financial deregulation opens the doors for greater leverage in the financial system (Adrian and Shin, 2008). When financial players are able to leverage up their balance sheets, they e↵ectively create money. This implies that financial deregulation leads to a loosening of the connection between monetary policy and financial market outcomes (i.e., monetary decoupling). To capture this e↵ect, we take the annual M2 growth rate, for which we expect it to fall at higher levels of CBI. The data come from Eichengreen and Rose (2014). The reason is that an independent central banker who is concerned with maintaining a stable inflation rate will not bend to a government’s will to fuel economic activity through excess money creation. For example, one of the first steps – when announcing CBI in the United Kingdom in May 1997 – was to tighten monetary policy by increasing interest rates by 25 basis points (Corry, 2010). To o↵set this tightening e↵ect, policymakers can substitute central bank money creation through excessively loosening regulatory practices fueling credit growth and financial activity that translates into rising asset prices (Aklin and Kern, forthcoming). This asset price inflation, in turn, feeds the pockets of a society’s top-income segment (Piketty and Saez, 2014; Milanovi ́c, 2016). To capture credit growth dynamics, we calculate the annual credit growth rate from the World Bank (2019). Given the challenging nature of measuring asset prices, we use the price of the capital stock that is available from Feenstra, Inklaar, and Timmer (2015). A distinct advantage of using this comparably wide measure is that it “ _is computed by aggregating asset-specific investment prices using shares of each asset in the total (current cost) capital stock_ ” (Feenstra, Inklaar, and Timmer, 2015, 3172), instead of capturing only a country’s stock market performance in a given year.<sup>13</sup>

> 13This aspect is particularly important for the case of emerging market economies with a less developed financial infrastructure (e.g., bond or stock markets) where investors either directly channel funds into the real estate, the

19
(EBRD 2023). The survey covers the transition economies of Europe and Central Asia and several comparator countries in Western Europe, the Middle East, and North Africa.<sup>2</sup> The 2010 round includes 35 countries, the 2016 round 34 countries, and the 2023 round 39 countries. The survey is based on a nationally representative sample of around 1,000 households per country in the second and fourth rounds and around 1,500 households per country in the third round.

We capture support for tax-financed investments in education by examining responses to the question “ _would you be willing to give up part of your income or pay more taxes if you were sure that the extra money was spent on education?_ ” This dummy variable is the main dependent variable of our analysis.

Our measure of expectations of future mobility is based on the two social ladder questions: “ _Please imagine a 10-step ladder where on the bottom, the first step, stand the poorest 10 percent people in [COUNTRY], and on the highest step, the 10th, stand the richest 10 percent people in [COUNTRY]. On which step of the ladder is your household today?_ ” and “ _Where on the ladder do you believe your household will be four years from now?_ ” We take the difference between the expected future and current income ladder positions as our measure of expectations of future mobility, which ranges from –9 to +9. We also conduct a sensitivity analysis that relies only on the direction of the expectations by creating a variable with three categories (downwardly mobile, static, and upwardly mobile) based on the difference between the ladder position today and the ladder position four years from today.

The LiTS data also allow for assessment of the expectation of intergenerational mobility, which is captured by responses to the following statement: “ _Children who are born now will have a better life than my generation_ ,” with respondents answering on a five-step Likert scale ranging from strongly disagree to strongly agree. We construct a dummy variable that takes the value of 1 if the answer is either agree or strongly agree and zero otherwise.

> 2 We use the second, third, and fourth rounds of the LiTS, for which comparable information on parental education background is available. The classification of parental education in the first round of the LiTS is not comparable to that of the following rounds, which is why we excluded it from our study.

8
Second, it could be argued that selection into reform (and thus market structure) was all but random and that poorly performing countries were compelled to introduce reforms when performance deteriorated. A number of prima facie evidence elements however suggest that reform implementation has not been directly linked to market performance. Figures 1 and 2 plot yields and production against market structure. They suggest that (i) reforms took place in very different performance contexts (yields and production trending upwards in Benin for example while they were trending downwards in Zimbabwe) and (ii) countries with relatively similar performance have/have not adopted reforms (e.g. Burkina Faso and Mali in the early 2000s). It is to be expected that reforms have rather been influenced by the macroeconomic and political situation of countries and, most importantly, by the way in which international financial institutions (IFI) promoted structural adjustment plans. Additional evidence that reforms were driven by IFI specific determinants rather than country and cotton sectorspecific determinants, can be seen from the fact that reforms happened almost at the same time (1994 or 1995) in most countries of ESA. Conversely, in WCA, competition has been seldom introduced, partly because the French co-operation agency (the _Agence Française de Développement_ ) played an important role in the reform process – or rather, in the non-reform process – as it opposed the reform agenda pushed forward by the World Bank and promoted or supported regulatory systems instead (Bourdet, 2004).

The fact that reforms were more ideological than market-driven however suggests a potential endogeneity problem: what we capture as being the effect of cotton market reforms could reflect the impact of structural adjustment more generally. To deal with such potential endogeneity and address formally the reverse causality issue, one would ideally like to instrument the reforms. To our knowledge, there is, yet, no suitable instrument to do so. Instead, we try to include as an extra control in our baseline regressions a dummy variable that takes on the value one after a structural adjustment plan has been adopted. The variable is based alternatively on two different datasets displayed in Swinnen _et al._ (2010, Table A1) and starts either with the year the country received its first structural adjustment loan from the World Bank or with the year preceding continuous and uninterrupted openness of a country.<sup>19</sup> However, the fact of having adopted a structural adjustment plan is a neither meaningful nor significant determinant of production, whatever the definition of the variable used. With respect to yields, a small negative and significant impact is found. The inclusion of this variable therefore slightly reinforces the magnitude of the impact of our variables of interest contained in _I_ on

> 19 See Swinnen et al. (2010, Table A1) for data sources.

18
associated with the hiring of financial, legal, technical and economic advisors. These costs can be as high as US$5million for complex or large scale projects.

There is a correlation between the _time allowed to prepare compliant bids_ and _the quality of bids_ submitted, but, the lengthier becomes the bidding process, the higher are uncertainties faced by bidders. Tender rules and procedures should therefore reconcile the need to provide enough time for the preparation of compliant bids, and the need to complete the bidding process in a reasonable timeline. Tender documents should therefore provide accurate and consistent specifications about the service to be provided by the project developer. But, it is important to bear in mind that the procurement of PPI projects is a time consuming process. In Europe, the average timeline observed for light rail or road concession projects varies between 72 to 88 weeks<sup>4</sup> . In South Africa, PPI procurement guidelines recommend a timeline between 34 and 90 weeks. Clearly, the issue at stake is to implement a streamlined bidding process which is supported by clear and sound rules and procedures<sup>5</sup> . In the case of the desalination water Build Operate and Transfer (BOT) project in Israel (see Box 2), the government completed the process in 52 weeks, even though the two-stage bidding process included a third round during which preferred bidders had to submit their best available final offers.

For complex and very costly projects, it is often argued that governments should reimburse a portion or the totality of bids’ preparation costs. There is no conclusive recommendation on this sensitive issue although it is important to restate the rule of thumb that bids should be prepared at bidders’ costs and risks. However, in specific circumstances, the government can consider reimbursing a portion of the bids’ preparation costs. For example, some years ago the Greek government agreed to reimburse bidding preparation costs to unsuccessful bidders participating to the tender

> 4 A study published by the European Union finds that the average procurement time was:  (a) 22.3 months for Tram and light rail PPI projects (upper bound: 30 months, Lower bound: 13 months), (b) 18 months for roads PPI projects (upper bound: 20, lower bound: 15). Source: Official Journal of European Communities. 5 One anonymous reviewer suggests that the emphasis should be on the process of bid evaluation and contract award, because a protracted process of bid evaluation could undermine the transparency of the tender. We think that a streamlined bidding process underlined by sound rules and procedures will not only preserve bids’ sanctity but will also ensure bidding predictability, and certainty, and should allow enough time for the evaluation of competing bids.

7
- 45 -

# 2. Eamings

The earnings variable used in the study is the sum of monthly wage earnings and selfemployment income in the primary job in local currency with a few exceptions. In the 1990 El Salvador data, bonus earnings are included. Quarterly wage income is used for Mexico while income from the main job for the last week is used in the Peru 1985 data set.

# 3. Hours Worked

According to the over table information in the country data sets, there are three different time periods to which the hours worked variable refers to -- daily, weekly or yearly. In the 1984 Mexico data set, the hours worked information is not available.

|Daily<br>Daily hours worked in|Bolivia|1989|
|---|---|---|
|primary job|Jamaica|1989|
|Weekly|||
|Total hours worked per week|Argentina|1980, 1989|
||Panama|1979|
||Venezuela|1981|
|Weekly hours worked in|Bolivia|1986|
|primary job|Brazil|1979, 1989|
||Costa Rica|1981, 1989|
||Guatemala|1986, 1989|
||Honduras|1986|
||Panama|i989|
||Paraguay|1983, 1990|
||Uruguay|1981, 1989|
|Total hours worked last week|Dominican<br>Republic|1989|
||Ecuador|1987|
||El Salvado|r 1990|
||Peru|1990|
|Normal hours worked per week|Chile<br>Colombia|1989<br>1980, 1989|
||Honduras|1989|
||Venezuela|1989|
|Yearly|||
|Yearly hours worked|Peru|1985|
# **Connecting People to Projects: A New Approach to Measuring Women’s Employment in the Middle East and North Africa**

Ragui Assaad<sup>1</sup> and Caroline Krafft<sup>2</sup>

**Keywords:** Employment, gender, household enterprises, Egypt, Tunisia **JEL codes:** J21, J22, J43

# **Acknowledgments**

This work was supported by the MENA Chief Economist Office under the labor and gender research programs (TTLs: Nelly Elmallakh and Nazmul Chaudhury).. We appreciate the comments of participants in the MNACE authors’ workshop, particularly our discussant Layane Alhorr.

> 1 Corresponding author. Humphrey School of Public Affairs, University of Minnesota. Email: assaad@umn.edu

> 2 Department of Economics and Political Science, St. Catherine University. Email: cgkrafft@stkate.edu
Table 3 presents the results from Regression (6), assessing the impact of a change in market access on the change in cropland area, distinguishing between what is due to the change in roads and what is the due to the change in the population distribution. The impact is positive for both components, but most of the effect appears to be due to the change in population, not the change in roads.<sup>10</sup> This is qualitatively consistent with the findings of Blankespoor et al. (2016). We find here that an increase in market access due to changes in the road network has a much smaller effect on cropland expansion compared to an increase in market access due to changes in the population distribution. This is suggestive that, on average, the extensive margin in agricultural production could respond mostly to local population demand. This is consistent with possible barriers to trade beyond the local vicinity.

Although it was long taken for granted that, because of stagnating yields, production mostly increased at the extensive margin in Sub-Saharan Africa (Deininger et al., 2011), recent studies have found that increases have occurred through both the productive and intensive margins, starting in the 1990s (see Seck et al., 2013, and Goyal and Nash, _forthcoming_ ). To test whether this has an impact on the relation between market access and cropland we stratified the sample into two subperiods—1970-1990, during which yields were stagnating, and 1990-2005, during which some increases in yields have been observed—and re-estimated Regressions (5) and (6) for each subsample. Results (see Table A5 and A6 in the Appendix) show that the significant and positive relation remains. Interestingly, the coefficients on the change in total market access (Table A5) and on the roads and population components of the change in market access (Table A6) are greater for the most recent period, suggesting that the extensive margin response to market access has strengthened, if anything.

14
# **Appendix A: Comparison of data from disaster inventories and microdata**

Data from disaster inventories such as EM-DAT and from microdata such as the LSMS-ISA differ in a number of meaningful ways. This includes their coverage of different shock types and sizes, the affected population, and time frame, as well as their detail, accuracy, and comparability across countries. These dimensions determine the respective strengths and weaknesses of each data source and can give rise to differences in estimates of disaster incidence and severity.

Disaster inventories collate reports from a variety of data sources on a per-event basis and are subject to the coverage and detail of information provided in these underlying sources.<sup>37</sup> Typically, this data comes aggregated, most often at the country-level. On the other hand, microdata, in our case, is based on household surveys that interview a sample from the target population and population-level estimates are formed based on this sample.

## **Shock coverage**

Coverage of shocks in disaster inventories varies depending on the underlying source data for each event. While large, salient shocks are likely reported across one or more sources that disaster inventories rely on (such as news reports or data bases from international organizations) and are therefore likely covered in disaster inventories, this is somewhat less likely for small or idiosyncratic shocks.<sup>30,37</sup> Such small shocks may therefore go unrecorded. For similar reasons, some shock types are more likely to be covered in disaster inventories than others.<sup>37,42</sup> Conversely, microdata is based on reports directly elicited from those affected by the shocks in question. They are therefore more granular and able to cover small and localized shocks. At the same time, shock recording in microdata sources is subject to the design of the survey questionnaire such as the list of shocks covered, and the number of shocks recorded.

## **Population coverage**

Disaster inventories are not limited to a specific population of interest and can, in principle, cover any event affecting any population group so long as this is reported in a source the disaster inventory can draw on. The lack of an explicit focus and reliance on reports from underlying sources, in turn, means that coverage for poor and marginalized population groups is more likely to be incomplete.

Microdata from household surveys focuses on (stratified) samples from a well-defined target population. In the case of the LSMS-ISA surveys, the samples are typically drawn to be nationally representative of the general population at the national and sub-national levels, and of urban and rural areas. This means that even poor and marginalized populations can be explicitly covered. At the same time, coverage is limited to the sample and affected by gaps in coverage wherever the sample is not fully representative of the population affected by a shock.

## **Temporal coverage**

42
# **Appendix 3: Calibration**

|**Rel.**|**Symbol**|**Value**|**Description**|**Source(s)**|
|---|---|---|---|---|
|1|π|0.02|Effect of fertility on female labor supply|Ashraf et al. (2013)|
|2|θ_E|0.2|Effect of fertility on childhood education|Joshi & Schultz (2007); Rosenzweig & Wolpin (1980)|
|3|ψ|-0.15|Effect of women’s education on fertility|Osili & Long (2008)|
|4|θ_H|-0.00067|Effect of fertility on adult height|Giroux (2008); Joshi & Schultz (2013); Kravdal & Kodzi (2011); Stevens et<br>al. (2012); Victora et al. (2008)|
|5|α|0.33|Capital share of output in modern sector|Hall & Jones (1999)|
|6|β|0.167|Land share of output in traditional sector|Kawagoe et al. (1985); Williamson (1998, 2002)|
|7|γ|0.1|Economic returns to schooling|Banerjee & Duflo (2005); Oyelere (2010); Psacharopoulos (1994);<br>Psacharopoulos & Patrinos (2004)|
|8|λ|0.08|Effect of health on output|Schultz (2002, 2005)|
|9|δ|0.07|Depreciation rate of capital|Schmitt-Grohe & Uribe (2006)|
|10|φ_1|0.758|Effect of lagged savings on current savings|Bloom et al. (2007)|
|11|φ_2|0.133|Effect of wage rate on savings rate|Bloom et al. (2007)|
|12|φ_3|-0.006|Effect of squared wage rate on savings rate|Bloom et al. (2007)|
|13|φ_4|-0.209|Effect of ratio of old to working age<br>population on savings rate|Bloom et al. (2007)|
|14|υ|+-0.356|Elasticity of factor demand|Behar (2004)|
|15|τ|-0.326|GDPpc-to-poverty elasticity|Tuccio & Paci (2016)|

35
Table 1: Summary Statistics

|Year|Obs|Mean|StdDev|Min|Max|
|---|---|---|---|---|---|
|_A. The val_<br>|.<br>_ue of IC_<br>|.<br>_T as a Frac_<br>|. .<br>_tion of Total_<br>|.<br>_Capital_<br>|.<br>_Value (%)_<br>|
|1995|70|3.95|2.65|0.17|12.40|
|2011|70|3.88|1.56|1.60|8.92|
|_B. The val_|_ue of IC_|_T relative to_|_NICT (%)_|||
|1995|70|4.19|2.95|0.17|14.16|
|2011|70|4.07|1.73|1.63|9.80|
|_C. PPP Un_|_its of IC_|_T per Unit_|_of NICT (%,_|_only co_|_untries with ICP data in 2011)_|
|1995|68|0.89|0.70|0.10|3.13|
|2011|68|5.43|2.86|1.46|14.96|
|_D. Log Re_|_al GDP_|_Per Person_||||
|1995|70|-4.76|1.04|-8.41|-3.38|
|2011|70|-4.28|0.96|-6.42|-2.61|

_Notes:_ The table reports raw sample summary statistics for 70 countries (and 68 for the real ratios) in 1995 and 2011.

hensive account of ICT and NICT stocks at this point. That said, there are currently numerous WORLD KLEMS projects under construction to expand converge. The TED, on the other hand, has very comprehensive country coverage, yet it only contains measures of the growth in ICT capital services for the period 1990-2014 and does not specifically attempt to measure ICT capital stocks.

While our measurement efforts are clearly complementary to Jorgenson and Vu (2005), who also use WITSA and ITU data to estimate ICT, there are some differences. Specifically, they assume that ICT investment is proportional to ICT spending, while we try to construct an investment measure directly. Furthermore, most previous work does not count the category of “capital services” as an investment category, and rather focuses on projected values of hardware, software and telecommunications spending on hardware, software and telecommunications investment. Since the services category consists of some software investment (such as custom software or website design), the ratio of software spending and software investment in the US is above two (Vu, 2005). Our view is that the category “ICT services” represents pure investment spending and should be counted as such. Another important difference is that the data provided in his paper is data on ICT capital growth rather than on the stock of ICT.

8