rafmacalaba/datause-encoder-data huggingface dataset:
||**Table 1: B**|**ase regression r**|**esults (OLS)**|||
|---|---|---|---|---|---|
|Dependent variable: Female<br>workers|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Maternity leave (logs)|**2.845****|**2.068****|**2.129****|**2.193****|**2.076****|
||**(1.320)**|**(0.922)**|**(0.878)**|**(0.869)**|**(0.876)**|
|GDP per capita (logs)||4.606***|4.766***|4.555***|3.360**|
|||(0.555)|(0.551)|(0.547)|(1.373)|
|Growth rate of GDP per||0.513***|0.515***|0.519***|0.445***|
|capita (%, annual)||(0.181)|(0.175)|(0.174)|(0.164)|
|Females in adult||1.674***|1.722***|1.718***|1.567***|
|population (%)||(0.475)|(0.472)|(0.469)|(0.458)|
|Number of workers (logs)||-0.025|-0.385|-0.503*|-0.466*|
|||(0.299)|(0.282)|(0.290)|(0.279)|
|Female owner present Y:1||4.984***|4.949***|4.899***|4.481***|
|<br>N:0||(0.500)|(0.493)|(0.488)|(0.490)|
|Top manager is female||13.300***|13.172***|13.166***|13.046***|
|Y:1 N:0||(0.801)|(0.795)|(0.793)|(0.774)|
|Age of firm (logs)|||-0.237|-0.293|-0.380|
||||(0.382)|(0.391)|(0.387)|
|Labor productivity (logs)|||-0.220|-0.267|-0.236|
||||(0.194)|(0.200)|(0.187)|
|Exports (% of sales)|||0.029**|0.029**|0.023*|
||||(0.014)|(0.014)|(0.013)|
|Foreign ownership (%)|||0.023**|0.022**|0.023**|
||||(0.009)|(0.009)|(0.009)|
|Firm offers formal training|||2.282***|2.168***|1.938***|
|<br>to workers Y:1 N:0|||(0.572)|(0.567)|(0.560)|
|Inadequately educated|||-0.232|-0.179|-0.125|
|workers: Obstacle severity||||||
|<br>(0-4 scale)|||(0.189)|(0.203)|(0.206)|
|Firm registered when||||-0.179|-0.343|
|started operations Y:1 N:0||||(1.024)|(0.999)|
|Firm experienced losses||||0.672|0.701|
|due to crime Y:1 N:0||||(0.611)|(0.614)|
|Access to finance: Obstacle||||-0.589***|-0.505**|
|severity (0-4||||||
|<br>scale)||||(0.225)|(0.217)|
|Tax rates: Obstacle||||0.294|0.303|
|severity (0-4 scale)||||(0.232)|(0.223)|
|<br>Firm has own website Y:1||||0.586|0.469|
|N:0||||(0.550)|(0.538)|
|Fertility rate|||||-0.189|
||||||(0.966)|
|Gender parity in|||||0.064***|

50
35 We take as measures of financial deepening the ratios of various monetary and credit aggregates to income. Our main result -- which confirms the thinking of many development economists and which is compatible with the simple theory we outlined -- is that financial deepening is an important characteristics of the growth process. A one percent increase in real per capita income is typically associated with approximately a 1.5% increase in the various "financial deepening" measures. Further, this result is robust to a wide variety of perturbations. We also find a positive connection between private external borrowing at per capita GNP within the set of developing countries, as our theory predicts.

# _Va. Data_

Most of our data is taken from the IMF's International Financial Statistics (the data has been checked fc-~ errors and is available upon request). The data is annual, usually spanning 1950 through 1988. We usually focus on a set of 69 developing countries. These countries satisfy the criteria established by Gertler and Rogoff (1990); they are rnon-communist and have populations over a million.<sup>40</sup> For purposes of comparison, we have also collected data for 21 developed countries. A virtue of restricting attention mainly to LDCs is that the financial data in the IFS statistics summarizes virtually all of the formal credit flows in these countries. Missing are data on stock and bond markets, which are important conduits of credit in many industrialized countries.<sup>4 1</sup> Since we are not testing a specific structural model, but rather gathering stylized facts, we take a somewhat eclectic view about our measures

**4**<sup>**0**</sup> **The** countries are (listed in alphabetical order): Algeria; Argentina; Bangladesh; Benin; **Bolivia;** Botswana; Brazil; Burkina Faso; Burundi; Cameroon; Central African Republic; Chad; Colombia; Congo; Costa Rica; Cote d'lvoire; Dominican Republic; Ecuador; Egypt; EL Salvador; Ethiopia; Gabon; Ghana; Guatemala; Haiti; Honduras; India; Indonesia; Jamaica; Jordan; Kenya; Korea; Lesotho; Liberia; Madagascar; Malawi; **Hali;** Mauritania; Mexico; Morocco; Myarnar; Nepal; Niger; Nigeria; Pakistan; Panama; Papua; **New** Guinea; Piraguay; Peru; Philippines; Portugal; Rwanda; Senegal; Sierra Leone; Somalia; Sri Lanka; Sudan; S;yria; Tanzania; Thailand; Togo; Tunisia; Turkey; Uganda; Uruguay; Venezuela; Yemen Arab Republic: Zaire; and Zambia.

> 41Listed in alphabetical order, the developed countries are as follows: Australia; Austria; Belgium; Canada; Denmiark; Finland; France; Germany; Greece; Iceland; Ireland; Italy; Japan; Luxembourg; Netherlands; New Zealand; Norway; Spain; Sweden; Switzerland; **UK;** and USA.
# **III. Methodology**

One of the methodological challenges in correlating skills to labor force outcomes is that it is not possible to precisely measure a person’s ability. Standard tests are a proxy for actual abilities, but a range of factors, from environment where the test is being taken to intrinsic motivation will affect test scores leading to measurement error. Thus, the measured test scores are biased information and may result in an over‐ or under‐estimation of the importance of various skills in determining the outcomes of interest.

To overcome issues of measurement error in test scores, we can estimate an individual’s latent cognitive and non‐cognitive abilities. In other words, we can use information on the observed outcomes and on skills and other characteristics to “back‐out” a distribution of true skills, or ability, using a structural latent variable technique. A comparison of results from the OLS (using directly measured skills) and structural latent variable techniques (using MLE and a range of observed and unmeasured information) allows us to identify the direction of bias (if any) in OLS estimates resulting from measurement error.

In this paper we estimate the effect of skills on five labor market outcomes: log of hourly wages and the probability of being employed, being wage worker, being white‐collar worker and being formal worker. We first estimate OLS regressions of the form Where Y refers to one of the outcomes of interest listed above, �� are observable controls, � is either the individual’s years of schooling or a set of parental education dummies, � are observed measures of cognitive and non‐cognitive skills, and �<sup>�</sup> is the error term. We estimate (1) for a vector of cognitive skills (numerical skills, working memory, verbal ability, and verbal fluency) and a vector of non‐cognitive skills, consisting of the eight factors derived from the factor analysis. We also estimate (1) for Alpha, Beta, Grit and the cognitive aggregated factor, thus allowing comparability with the structural latent variable model. A shortcoming of the model is the correlation between T and �<sup>�</sup> , due to measurement error in skills. This leads to biased estimates of �<sup>�</sup> (Hansen, Heckman, and Mullen 2004). To address this issue, we turn to a methodology that allows us to indirectly proxy a truer measure of _T_ that can then be correlated with our outcome of interest.

Now, if we want to treat skills (T) as consequences of unobserved heterogeneity instead of true manifestations of the scores, we need to turn to the following structural specification:<sup>**28**</sup>

> 28 For a more detailed explanation of this type of models see Sarzosa and Urzúa (2013).

**11 |** P a g e
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_Table 7: Impact on Household’s Likelihood of Using a Savings Device & Level of Savings by Type of Device_

||HH has<br>|savings in: (yes<br>|=1)<br>|Bank/<br>|Am<br>MFI<br>|ount of saving<br>R<br>|s in:<br>SCC<br>|O<br>|ther<br>|
|---|---|---|---|---|---|---|---|---|---|
||Bank/MFI|RSCC|Other|Male|Female|Male|Female|Male|Female|
|round 3|0.122***|0.004|-0.022|52.903|127.520|77.706**|82.536***|1.474|17.116|
||(0.033)|(0.029)|(0.020)|(165.290)|(139.451)|(36.847)|(28.054)|(30.067)|(13.380)|
|FIF*round 2|-0.006|0.006|0.008|-162.859|-97.796|13.798|50.851**|-44.805*|-2.640|
||(0.022)|(0.026)|(0.016)|(166.269)|(179.314)|(18.751)|(19.040)|(24.879)|(7.551)|
|FIF*round 3|-0.008|0.044|-0.016|322.457|240.554|37.104|-19.603|-26.981|-19.741|
||(0.030)|(0.027)|(0.015)|(237.506)|(194.287)|(44.193)|(34.429)|(33.143)|(14.811)|
|WIF*round2|-0.003|-0.014|-0.015|27.427|-169.133|-21.249|33.532|-82.752***|-16.541|
||(0.043)|(0.045)|(0.041)|(336.408)|(258.751)|(33.626)|(35.954)|(24.727)|(11.880)|
|WIF*round3|-0.016|0.031|-0.005|1,185.622**|624.322|-68.833|-48.723|-75.689***|-15.779|
||(0.053)|(0.070)|(0.031)|(563.074)|(374.630)|(59.899)|(47.621)|(23.812)|(29.962)|
|WIF*FIF*round2|-0.003|0.024|0.073|-72.613|-289.268|70.102|-35.382|137.207*|70.630**|
||(0.056)|(0.060)|(0.072)|(441.727)|(319.206)|(64.171)|(43.411)|(69.858)|(35.083)|
|WIF*FIF*round3|0.114|-0.062|0.057|-778.447|-416.698|53.109|104.212|82.623|19.826|
||(0.091)|(0.086)|(0.041)|(911.166)|(655.143)|(76.494)|(70.175)|(53.957)|(39.593)|
|Observations|2,428|2,428|2,428|2,428|2,428|2,428|2,428|2,428|2,428|
|R-squared|0.091|0.074|0.011|0.133|0.054|0.042|0.044|0.016|0.008|
|Estimates: FIF2 + WIF2 + FIF_WIF2|-0.012|0.016|0.066|-208|-556.200|62.650|49|9.650|51.450|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.811|0.757|0.330|0.495|0.006***|0.266|0.195|0.897|0.187|
|Estimates: FIF3 + WIF3 + FIF_WIF3|0.089|0.012|0.035|729.600|448.200|21.380|35.890|-20.050|-15.690|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.142|0.791|0.376|0.238|0.370|0.804|0.673|0.691|0.564|
|Control Mean(BL)|0.123|0.161|0.048|373.500|163.600|58.840|40.030|16.980|12.510|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. MFI stands for Microfinance Institutions. RSCC stands for Rural Savings and Credit Cooperative. The amount of savings in Bank/MFI, RSCC, and other saving schemes are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

26
42 _economles do not show the full impact of a cooperative solution on the world economy as a whole, because there are indeed not just three loci, if you like, but rather, in this conception, there are as many as 25 or so economies which, either because of their_<sup>_size or because_</sup> _of the ratio of their trade shares to GDP, are crucial to the_<sup>_overall_</sup> _outcomes of global economic growth._ _So it is a much more diffuse process than the_<sup>_one of seek-_</sup> _Ing where the center of gravity of world economic growth is, because in fact there isn't one, but there are multiple_<sup>_centers._</sup> _MR. LINN: Maybe that distinguishes the post-war period from the other periods of growth, namely that if you look_<sup>_at the data_</sup> _on GDP growth rates, everybody grew very rapidly_<sup>_at the same time,_</sup> _the developing countries as well as the industri.l economics._<sup>_While_</sup> _in the earlier periods, the Americans were growing most_<sup>_rapidly in_</sup> _the late 19th century._ _So the question is: Was it coincidental that_<sup>_halppened?_</sup> _Is it something that won't come back? Will you have_<sup>_these smaller_</sup> _growth poles (such as East Asia), but they won't_<sup>_be able to pull the_</sup> _world along in the future? Or is there a way_<sup>_through coordinated_</sup> _policies to get everybody to come back up to a, say, 3.5_<sup>_percent [per_</sup> _annum] growth rate together? One group of 25 percent_<sup>_of world GDP,_</sup> _one relatively small group, growing at 4 or 5 percent_<sup>_[perannuml_</sup> _isn't going to do the trick._ _MR. BANETH: I would like to come back actually to something which Steve Marris said, about the movement_<sup>_in the weight_</sup> _of the developing countries in the world economy. If we focus our attention on the impact of developing countries on the industrial countries, then I believe_<sup>_the answer_</sup> _tends to be: relatively limited, because there are a_<sup>_lot of develop-_</sup> _ing countries that are so small that even together chey_<sup>_don't weigh_</sup> _much in the international economy._ _If we look at the Impact of the rest of the world_<sup>_on some_</sup> _of the developing countries, I think we get_<sup>_a somewhat more positive_</sup> _answer. While Mexico or Brazil or Korea, may_<sup>_not have much of an_</sup> _impact on the United States today, they may play a role._<sup>_Their_</sup> _weight relative co the weight of Africa is not very dissimilar_<sup>_to_</sup> _the weight Europe had relative to Africa 30 years ago._ _And in that sense, they may be increasingly replacing this particular growth impulse In terms of demand_<sup>_for primary commodities_</sup> _and the rest._ WP/BOLTHO/SF:19
However, it also turns out that the rate of return to education in informal sector employment that we read from the three tables is comparable to that of the formal sector. In other words, while the less educated have greater propensity for selfemployment, the more educated are more successful among the self-employed.

# **7 Conclusion**

Based on data from the Ethiopia Urban Household Socio Economic Survey over the period 1994 to 2004, this paper has assessed the extent to which the structure of the urban labor market has changed over the same period. Specifically, we have looked at what has happened to entry rates to the public and the formal sectors of the labor market and to sectoral earnings premiums over the survey period, as well as to the sensitivity of inter-sectoral mobility to earnings gaps. Our data show a large, persistent and unexplained public sector wage premium. However, the sensitivity of sector choice to earnings gaps has become more pronounced, not only in relation to the public vs. private sector divide, but also vis-à-vis the formal and informal sector dichotomy. In particular, the role of comparative earnings in selection into the informal sector has increased in recent years. The rate of workers’ mobility has also increased between the two pairs of sectors since the late 1990s as indicated by sample transitions rates. More importantly, state dependence in sector choice has decreased. In other words, the fact that we observe a randomly chosen individual in a given labor market state today is becoming a less and less powerful predictor of the probability that the individual will be found in the same state at a future date.

# **References**

Arulampalam,W., A. Booth, and M. Taylor. 2000. ‘Unemployment Persistence,’ Oxford Economic Papers, 52: 24-50.

Bigsten, A., P. Kimuyu, and K. Lundvall. 2004. ‘What to Do with the Informal Sector?,’ Development Policy Review, 22: 701-715.

Bulow, J. and L. Summers. 1986. A Theory of Dual Labor Markets with Application to Industrial Policy, Discrimination and Keynesian Unemployment,’ Journal of Labor Economics, 4:376-414.

Chamberlain, G. ‘Panel data,’ in Z. Griliches and M. Intrilligator (eds.) _Handbook of Econometrics_ . Amsterdam: North-Holland.

24
- Matteo Maggiori. Financial intermediation, international risk sharing, and reserve currencies. EFA 2008 Athens Meetings Paper. Available at SSRN: http://ssrn.com/abstract=1093137, 2008.

- Kiminori Matsuyama, Nobuhiro Kiyotaki, and Akihiko Matsui. Toward a theory of international currency. _Review of Economic Studies_ , 60(2):283– 307, 1993.

- Enrique G. Mendoza, Vincenzo Quadrini, and Jose-Victor Rios-Rull. On the welfare implications of financial globalization without financial development. _NBER International Seminar on Macroeconomics_ , 2007.

- Atif R. Mian and Amir Sufi. What explains high unemployment? the aggregate demand channel. SSRN working paper, 2012.

- Virgiliu Midrigan and Thomas Philippon. Household leverage and the recession. NYU Working Paper No. FIN-11-038, 2011.

- Pablo A. Neumeyer and Fabrizio Perri. Business cycles in emerging economies: the role of interest rates. _Journal of Monetary Economics_ , 52(2):345–380, 2005.

# **A Appendix**

## **A.1 Proof of Lemma 1**

Let _λt_ be the Lagrange multiplier on the aggregate budget constraint of time _t_ (equation 16). The FOC of the Lagrangian with respect to _kd,t_ +1 is: 40
There is a particularly rich literature on microeconomic transport-to-development linkages, particularly for rural roads and for Sub-Saharan Africa. This brings significant evidence that rural roads reduce poverty and contribute to household welfare by raising income and consumption, expanding (self-) employment opportunities, and improving food security. Rural localities benefiting from improved market access also see an increase in agricultural production, more widespread uptake of modern agricultural techniques, and reduced dispersion of prices. Reducing the isolation of small towns similarly leads to expanded output, increased specialization, and higher exports. Turning to national highway networks, the findings are that these contribute substantially to boost manufacturing output and associated exports, among other things by facilitating greater industrial specialization and enhancing competition. There is evidence that over time this may lead to the convergence of lagging regions, but at the same time tending to concentrate population and economic activity in regional primate cities.

In urban areas, public transportation systems have the dual effect of facilitating the decentralization of cities by improving the accessibility of lower cost outlying residential land, while at the same time facilitating the commuting of workers to access a larger range of employment opportunities in urban centers, thereby reducing informality.

Turning to other transport infrastructures, railways have been found to have a long-term impact on countries’ development patterns, with their tendency to lock-in spatial patterns of economic activity. The arrival of railroads in rural areas during the colonial period has been shown to have enhanced agricultural output and trade as well as improving food security. At the same time, inter-city railways have played a role in concentrating urban populations. Though not having comprehensive evidence in the dataset as in the other sub-sectors of transport infrastructure, the findings point out that port infrastructure also plays a vital role in international trade, significantly affecting a country’s international competitiveness, and – when operating efficiently – able to compensate substantially for the effect of geographical distance.

The exponential expansion of the academic literature on the development impacts of infrastructure has been fueled by the availability of much richer sources of microeconomic data on firms and households, often with geospatial characteristics, as well as the associated analytical techniques. Compared to the earlier literature that focused primarily on cross-country panel data econometrics based on crude high-level measures of infrastructure, the new body of work provides a much richer and more granular understanding of the specific channels through which different kinds of infrastructure data can contribute to development overall. While the results can sometimes be mixed, and methodological controversies remain, the overwhelming balance of evidence suggests that improvements in infrastructure play a vital role in supporting the development process.

44
In exports, the U.S. share has risen dramatically since 1980 to reach<sup>27 percent in</sup> 2000, far surpassing that of Japan and the EU (14 percent for both). This<sup>is a complete</sup> reversal of the share pattern in 1980 when exports to the U.S. made up the<sup>smallest share</sup> (5 percent) and the share for Japan was the largest (22 percent). This<sup>reflectsimproved</sup> relations between the U.S. and China over the last 20 years, as well as<sup>the fact that U.S.</sup> and EU TNCs are moving production to China and exporting from there.

# **Intrareglonal Trade Shares**

Figure 4.1 revealed that during the 1 990s the gap between the share of<sup>intra-</sup> ASEAN trade and the share of ASEAN's trade with its major non-Asian<sup>partners,the</sup> U.S. and the EU, had progressively widened. Does this mean that East<sup>Asia's trade is</sup> becoming more intraregional as opposed to global? Examining the intraregional<sup>trade of</sup> the entire region (not just ASEAN) in comparison with that of other regions<sup>over a longer</sup> period of time and in relation to (1) world trade as a whole and (2) East<sup>Asia's total trade</sup> (intraregional and extraregional) will help to answer that question.

The tables below present two measures based on trade shares for major<sup>Asian and</sup> non-Asian groups.

Table 4.2 <!-- Start of picture text --> Intraregional Trade<br>Merchandise Exports Within Regional Group (as share of world exports)<br>Absolute Measure<br>(% ofwad1d aests) 1970 1980 1990 1996 1998 1999 2000<br>APEC 20.9 19.1 27.1 33.2 32.2 33.5 35.6<br>ASEAN 0.5 0.7 0.9 1.6 1.3 1.4 1.6<br>ASEAN Plus Three n.a. 3.7 4.6 7.2 5.5 6.2 7.0<br>All EastAsia n.a. 4.6 7.8 12.7 10.5 11.2 12.7<br>European Union 27.3 1 24.4 29.5 24.1 22.7 24.8 22.3<br>NAFTA 7.91 5.5 6.8 8.3 9.71 10.3 10.7<br>NOTE: Table shows sum of exports by members of a group to other members of the group as a percent f woild eports.<br>Service spofts are excluded. AJthough data has been calculated back to 1970 or 1980 on the basis of current group membership, mcet of the groups<br>came Into existence In later years and their membership may have changed over lime. Intratrade In earlier years may not have been affected by the<br>same preferences (as set forth In preferential arrangements) as In rerent years.<br>I All East Asia includes ASEAN plus Japan, Korea, China. Hong Kong, and Taiwan. Tawan not Included In 1980 and 1990 data.<br>Source: For APEC, EU, and NAFTA: calculted using World Bank, World Deveiopment Indicators 2002; for all others: calculated using<br>IMF, Directin of Trade Statistics<br><!-- End of picture text -->
budget is set to equal 5% of this total value. We treat rural and urban areas separately and thus calculate this budget separately in rural and urban areas, respectively.

Gains from targeting also vary with the choice of poverty line. The higher the poverty line, the less need for targeting, as leakage to the non-poor diminishes to zero. In this study, we select as benchmark the official poverty line of 3098 DH in rural areas and 3421 DH in urban areas.. This line yields a rural poverty rate of 23% and urban poverty rate of 8% in 2004.

# **IV. Simulation Procedures and Results**

# _Simulating the Impact of Uniform Transfers_

Our policy simulation in the case of uniform transfers is calculated in a very straightforward manner. Budget _S_ is divided by total population _N_ . The resulting transfer _a_ is ( _r_ ) added to each predicted expenditure in our database, to yield _ych +a_ . For each replication _r_ we estimate post-transfer national poverty. The average across the _r_ replications of the estimated post-transfer poverty rates yields our expected poverty rate associated with the benchmark, untargeted lump-sum transfer scheme. This new estimated poverty rate can be compared to the original national-level poverty estimate from the poverty map to gauge the impact of the transfer.

11