rafmacalaba/datause-encoder-data huggingface dataset:
11 Private sector workers have higher productivity than the average worker in the labor market. By contrast, public workers have below average productivity. The coefficient estimates of the selection term for individuals were statistically significant, being negative in the public sector and positive in the private sector. The positive coefficient for the private sector indicates that individuals who select jobs in the private sector have higher productivity than the average worker while the negative coefficient for public sector workers imply that they have lower productivity that the average worker.

**<u>Table 3 Djibouti: Log Wage Equations for the Public and Private Sector</u>**

|<br>|<br>**Selectiv**<br>|<br>**ity Corre**<br>|<br>**cted Regres**<br>|<br>**sion**<br>|<br>|<br>**O**<br>|<br>**LS Regr**|**ession**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
|**Variables**|**Publi**|**c**|**Priva**|**te**|**Pu**|**blic**||**P**|**riva**|**te**|
|age|0.007|(0.02)|0.108 ***|(0.03)|0.047|**|(0.02)|0.066|***|(0.02)|
|age squared/100|0.011|(0.03)|-0.123 ***|(0.04)|-0.037|*|(0.02)|-0.072|**|(0.03)|
|female dummy|0.176|(0.11)|-0.424 ***|(0.10)|-0.059||(0.06)|-0.314|***|(0.07)|
|married dummy|0.101|(0.06)|0.220 **|(0.09)|0.183|***|(0.06)|0.201|**|(0.09)|
|primary school|0.141 **|(0.06)|0.402 ***|(0.11)|0.233|***|(0.05)|0.280|***|(0.08)|
|middle school|0.375 ***|(0.10)|0.756 ***|(0.14)|0.551|***|(0.07)|0.613|***|(0.13)|
|high school|0.670 ***|(0.12)|0.954 ***|(0.19)|0.856|***|(0.07)|0.751|***|(0.10)|
|vocational training|0.659 ***|(0.13)|0.815 ***|(0.18)|0.870|***|(0.06)|0.667|***|(0.19)|
|university|0.942 ***|(0.12)|0.977 ***|(0.18)|1.137|***|(0.09)|0.822|***|(0.14)|
|constant|5.603 ***|(0.57)|2.747 ***|(0.93)|4.376|***|(0.28)|4.164|***|(0.42)|
|selection term|-0.274 **|(0.11)|0.318 *|(0.18)|-|-|-|-|-|-|
|Chi square|1167.|13|316.2|0|0|.42|||0.27||

_Note: Standard errors are in brackets. *** denotes statistical significance at 1% level, ** at 5%, and * at 10%. Source: Authors' estimates based on EDAM 1996._

# **3.3 Public-private wage differential decomposition**

Using a simple average of the wage difference between public and private (formal) workers to determine whether there is a wage premium is misleading, because doing so does not account for differences in human capital endowment and other personal or household characteristics that influences earnings. It is possible to breakdown various components that contribute to the difference between predicted public and private sector wages using an Oxaca-Blinder decomposition. There are four possible components, including the differential caused by selectivity bias,
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|**Table4.5**|**:Mover/Sta**|**yerMo**|**del:Wages**|**,Stayers**|**inNEa **|**ndMov**|**ersfromN **|**EtoSE**|
|---|---|---|---|---|---|---|---|---|
|Moverto|Southeast||||Stayers|inNort|heast||
|Numberof|obs=<br>511||||||Numberofo|bs=27642|
|F(14,496)|=17.84||||||F(14,27627|)<br>1413.36|
|Prob>F|=0.0000||||||Prob>F|=0.0000|
|R-squared|=0.3978||||||R-squared|<br>=0.4461|
|RootMSE|=.5985||||||Root MSE|=.72898|
||||**Wage**|**Regressio**|**ns**<br>||||
||**Mover**||||**stayer**||||
||Coef.|P>z|[95%Conf.|Interval]|Coef.|P>z|[95%Conf.|Interval]|
|Expir|0.0111|0.32|-0.0110|0.0333|0.0376|0.00|0.0350|0.0403|
|expir<sup>2</sup>|0.0000|0.89|-0.0004|0.0005|-0.0005|0.00|-0.0005|-0.0005|
|priml|-0.1135|0.14|-0.2624|0.0354|0.0959|0.00|0.0700|0.1219|
|prim2|0.0704|0.36|-0.0817|0.2225|0.0878|0.00|0.0592|0.1164|
|Secu|0.2523|0.01|0.0684|0.4361|0.6655|0.00|0.6360|0.6950|
|Uni|1.4701|0.00|1.1211|1.8191|1.7877|0.00|1.7399|1.8354|
|Female|-0.3927|0.00|-0.5129|-0.2725|-0.5613|0.00|-0.5812|-0.5413|
|Self|-0.1905|0.06|-0.3885|0.0075|-0.4623|<br>0.00|-0.4865|-0.4381|
|Informal|-0.3177|0.00|-0.4346|-0.2008|-0.5001|0.00|-0.5206|-0.4795|
|Ind|0.1848|0.16|-0.0759|0.4455|0.2374|0.00|0.2058|0.2690|
|Serv|0.1076|0.42|-0.1516|0.3669|0.2792|0.00|0.2484|0.3101|
|Public|0.5799|0.03|0.0589|1.1008|0.3837|0.00|0.3370|0.4305|
|Rural|-0.4018|0.00|-0.6572|-0.1463|-0.1198|0.00|-0.1447|-0.0949|
|Const.|5.6581|0.00|4.9256|6.3906|4.5566|0.00|4.4918|4.6215|
|_A:_|-0.0233|0.88|-0.3353|0.2886|0.2881|0.18|-0.1304|0.7067|

# **4.4. Returns to Migration**

As an estimate of the returns to migration, we use the coefficient estimates from the wage regression in Tables 4.4 and 4.5 to form linear predictions by region of the mean wages for actual movers and for movers had they stayed. The selectivity-corrected differences in mean wages for different levels of education are graphed in Figures 4.3 and 4.4. '<sup>0</sup> As a test of the robustness and stability of our findings over time, we repeat this exercise for information based on the PNAD 1995. This enables us to contrast the returns to migration for migrants from 1990 to 1995 (based on the PNAD 1995) with migrants from 1995 to 1999 (based on the PNAD 1999).

'° We also predicted mean wages from simple OLS regressions without correcting for self-selectivity. The findings did not differ from the selectivity-corrected estimates.

27
||4.3|The Com|position<br>and Level of Government<br>Revenue|23|
|---|---|---|---|---|
||4.4|Market|Regulation|24|
|||4.4.1|Labor Market Regulation|24|
|||4.4.2|Financial<br>Market Regulation|24|
|||4.4.3|Price Controls|25|
|5.|<br>Conc|lusion||25|
|Ref|erence|s||26|
Deaton, A. and A. Fantozzi (1999) _Prices and poverty in India._ Research Program in Development Studies Working Paper. Princeton University.

Deaton, A. and S. Zaidi (1999) _Guidelines for Constructing Consumption Aggregates for Welfare Analysis._ Research Program in Development Studies Working Paper No. 192. Princeton University. October.

Dercon, S. (2001) _Assessing Vulnerability._ Mimeo. Jesus College and CSAE, Department of Economics, Oxford University. August.

Frankenberg, E., K. Beegle, D. Thomas and W. Suriastani (1999) _Health, Education, and the Economic Crisis in Indonesia._ RAND draft. March.

Thomas, D., K. Beegle, E. Frankemberg, B. Sikoki, J. Strauss and G. Teruel (2001) _Education in a Crisis._ RAND mimeo. March.

Hentschel, J. and P. Lanjouw (1996) _Constructing an Indicator of Consumption for the Analysis of Poverty. Principles ad Illustrations with Reference to Ecuador._ LSMS Working Paper No. 124. World Bank. Washington, D.C.

Leamer, E. (1988) "Measures of Openness," in Baldwin, R. (ed.) _Trade Policy Issues and Empirical Analysis._ The Chicago University Press.

Moldova Economics Trend (2001) _Economic Trends Quarterly Issue._ July-September 2001.

Neri, M. and M. Thomas (2001) _Household Responses to Labor-Market Shocks in Brazil, 1982-99._ Mimeo.

Schady, N. (2002) _The (Positive) Effect of macroeocnomic Crises in the Schooling and Employment Decisions of Children in a Middle-Income Country._ World Bank Policy Research Working Paper No. 2762. January.

Signoret, J. and E. Murrugarra (2001) _Poverty Dynamics in Moldova._ Europe and Central Asia Unit - Human Development Department. World Bank draft.

Tibi, C., S. Berryman and M. Peleah (2002) _Moldova's Education Sector. A Financing Strategy to Leverage System- Wide Improvement._ Draft. February.

44
|**Barriers**|**Table A6:**<br>**Overall**|**Barriers of acce**<br>**Rural **|**ss (Kenya)**<br>**Urban **|**Female**|**Male**|
|---|---|---|---|---|---|
|Currently ineligible|7.4|6.1|10.0|7.4|7.3|
||(3.1 to 11.6)|(0.8 to 11.3)|(2.8 to 17.1)|(2.3 to 12.5)|(0.6 to 14.1)|
|Too far away|23.5|24.8|20.9|22.1|24.8|
||(17.7 to 29.2)|(17.1 to 32.5)|(13.1 to 28.7)|(14.8 to 29.5)|(16.0 to 33.6)|
|Don't know how to get|2.6|2.6|2.7|1.7|3.5|
||(0.1 to 5.1)|(-1.0 to 6.1)|(0.4 to 4.9)|(0.2 to 3.2)|(-1.1 to 8.1)|
|Too crowded at vax site|27.8|28.0|27.3|30.2|25.4|
||(21.6 to 33.9)|(19.5 to 36.5)|(20.2 to 34.4)|(21.7 to 38.7)|(16.6 to 34.2)|
|Facility not disability friendly|0.1|0.1|0.2|0.1|0.1|
||(0.0 to 0.2)|(-0.0 to 0.1)|(-0.0 to 0.3)|(-0.0 to 0.1)|(-0.0 to 0.3)|
|No transport|5.3|4.9|6.0|8.4|2.3|
||(2.3 to 8.3)|(1.4 to 8.5)|(0.4 to 11.5)|(2.7 to 14.1)|(0.6 to 3.9)|
|Not enough vaccines|25.8|26.6|24.4|25.1|26.5|
||(20.0 to 31.6)|(18.5 to 34.6)|(17.8 to 31.0)|(18.0 to 32.2)|(17.4 to 35.6)|
|Unable to register|0.8|0.9|0.5|1.1|0.5|
||(-0.0 to 1.6)|(-0.2 to 2.1)|(0.1 to 0.8)|(-0.5 to 2.7)|(0.2 to 0.8)|
|Too expensive to obtain|0.5|0.3|1.1|0.2|0.8|
||(-0.1 to 1.1)|(0.0 to 0.5)|(-0.7 to 2.9)|(-0.0 to 0.4)|(-0.4 to 2.0)|
|Discouraged in community|0.4|0.4|0.3|0.3|0.5|
||(0.1 to 0.7)|(-0.0 to 0.9)|(0.0 to 0.6)|(-0.1 to 0.7)|(-0.0 to 1.0)|
|No barriers|37.9|38.7|36.4|34.7|41.0|
||(31.4 to 44.5)|(29.9 to 47.6)|(27.8 to 45.0)|(26.7 to 42.7)|(30.9 to 51.2)|
|Other barrier|0.0|0.0|0.0|0.1|0.0|
||(-0.0 to 0.1)|(-0.0 to 0.1)|(-0.0 to 0.1)|(-0.0 to 0.1)|(-0.0 to 0.0)|
|_N_<br>Note: Main anticipated diffic<br>parentheses.|1,732<br>ulties to get vaccin|839<br>ated. All values i|893<br>n percent. 95% c|952<br>onfidence interva|777<br>ls in|

19
# **Table 2: Summary of Variables**

|||||**Expected**||
|---|---|---|---|---|---|
||**Logs?**|**Variable**|**Unit of analysis**|**Sign**|**Definition**|
||||||TFP calculated using Olley-Pakes|
||Yes|Productivity|Firm, Year|+|methodology|
|**Firm**|No|Age|Firm, Year|-|Age of the firm|
|**Characteristics**|Yes|Sales|Firm, Year|+|Sales (deflated)|
||||||Private domestic, private foreign,|
||No|Type|Firm||<br>public or mixed|
||Yes|Exporters|District, Year|+|Count of other exporters per capita|
|||Exporters by|||Count of other exporters within the|
||Yes|Industry|District, Industry, Year|+|same industry per capita<br>Agglomeration of firms with other|
||Yes|Localisation|District, Industry, Year|+|firms within the same industry<br>Agglomeration of firms with their|
|**Agglomeration**|||||suppliers (i.e. those who they buy|
||Yes|Input|District, Industry, Year|+|from)<br>Agglomeration of firms with their|
||Yes|Output<br>Industrial|District, Industry, Year|+|buyers (i.e. those that they sell to)<br>Industrial diversity within a district<br>(higher values mean lower industrial|
||Yes|diversity|District,Year|-|diversity)|
|||Market|||Accessibility indicator measuring|
|**First-Nature**|No|Access|District, Year|+|access to neighbouring regions|
|**Geography**|No|Port|District|-|Travel time (in minutes) to the<br>closestport|
||Yes|Roads|District, Year|+|Road Density per square km|
||||||Proportion of households with|
||Yes|Electricity|District, Year|+|access to electricity|
||||||Proportion of households with a|
||Yes|Telephone|District, Year|+|<br>telephone connection|
|**Infrastructure**|||||Proportion of population with a|
||Yes|Education|District, Year|+|high-school degree|
||Yes|Wages|District, Year|-/+|Non-agricultural hourly wages<br>Proportion of high-income|
||Yes|Wealth|District,Year|+|<br>households|
|||Labour|||Besley and Burgess classification|
|**Institutional**|No|regulations|State|-|(pro-employer=0, pro-labour=1)|
||Yes|Riots|District,Year|-|Incidents of social unrestper capita|

# **3.3 Sources of Data**

Firm-level data on export behavior (i.e. when a firm starts to export and following entry into export markets, the value of exports as a proportion of sales) and on output and inputs is drawn from the Prowess database. Prowess is a corporate database that contains normalized data built on a sound understanding of disclosures of over 20,000 companies in India. The database provides financial statements, ratio analysis, fund flows, product profiles, returns and risks on the stock market etc. The Centre for Monitoring the Indian Economy (CMIE), which collects data from 1989 onwards, assembles the Prowess database. The database contains information on 23,168 firms 12
# **Abstract**

In a recent paper, Fiess and Verner (2000) analyse sectoral growth in Ecuador and find significant long-run and short-run relationships between the agricultural, industrial and service sectors. They take this as evidence against the dual economy model which rules out a long-run relationship between agricultural and industrial output and show further that a more detailed picture of the growth process can be discovered, once the agricultural, industrial and service sectors are disaggregated further into intrasector components. This paper extends their initial results and provides insight from a multivariate cointegration analysis of intrasector components. We are able to identify three cointegrating relationships, each of which has its own meaningful economic interpretation: Two cointegration relationships capture the direct and indirect effects of the “petrolization” of the Ecuadorian economy. A third relationship clearly indicates a link between agriculture and industrial activity. Since this third cointegrating relationship seems to coincide in time with the trade liberalisation at the end of the 1980s, promoting agriculture appears to be an important way to promote sustainable economic growth in Ecuador.
percentage of adults with an account at a formal financial institution. At the same time, comprehensive and centrally collected Census data at the firm-level is available to researchers, thereby sidestepping many of the concerns arising from data comparability in cross-country studies. India is also an interesting case study because some recent studies (e.g. Hsieh and Klenow, 2012) suggest that India has a flat life-cycle where fo40-year-old plants are no larger than young plants. Our India sample comprises of repeated cross-section census data for the formal manufacturing sector at roughly five year intervals - 1983/84; 1989/90; 1994/95; 2000/01; and 2004/05<sup>5</sup> and census data on the informal sector for 1994/1995.

We have the following main findings: Despite considerable differences in financial depth across Indian states we find the role of financial development to be marginal in explaining lifecycles in the broad population of firms and in most of the sub-samples we analyze. These results are robust to a number of checks including looking at just the firms in the right tail of the size distributions, across states with flexible versus rigid labor market regulation, and using alternate indicators of financial development. We also find no differential impact of financial development on lifecycle when we look at firms born after India’s financial liberalization in 1991 or looking at periods after industry de-licensing. We also find only marginal differences in the proportion of manufacturing employment in old and new firms across different levels of financial development _._ Second, we find the extent of financial dependence of industries does not predict the life cycles of firms across states in India. We use the identification strategy introduced by Rajan and Zingales (1998) for classifying industries into financially dependent and financially independent depending on the extent to which firms can support their capital expenditures using cash flow

> 5 Repeated cross-sections helps us look at life-cycle patterns over a longer period of time in addition to providing us more observations at each point in the life-cycle. It also allows us to examine if our results are consistent over different periods of institutional change in India such as financial liberalization and industry de-licensing.

4
# **_Tables_**

|**T**|**able 1: Tre**<br>|**nds in basic**<br>|**assets and a**<br>|**menities**<br>|||
|---|---|---|---|---|---|---|
||**All hou**<br>|**seholds**<br>|**Bottom**<br>|**5 deciles**<br>|**Bottom**<br>|**3 deciles**<br>|
||2000|2005|2000|2005|2000|2005|
|Average real value of<br>livestock (tks)|4280|5281|3222|4653|2623|3919|
|Livestock ownership (%)|35.2|40.3|33.6|43.3|31.6|42.5|
|Wall of dwelling (% with<br>cement / CI sheet)|37.7|55.2|21.4|39.5|17.4|33.9|
|Roof of dwelling (% with<br>cement / CI sheet)|76.4|89.9|68.1|84.2|64.5|81.6|
|Safe latrine use (%)|52.0|69.3|35.2|55.6|29.4|50.0|
|Electricity connection (%)|31.2|44.2|14.6|25.4|10.0|20.2|
|TV ownership (%)|15.8|26.5|3.6|10.1|1.8|6.7|
|Phone ownership (%)|1.5|12.2|0.1|1.5|0.0|0.9|
|_Source_: HIES 2000, 2005|||||||

|**Tab**|**le 2: Demo**<br>|**graphic char**<br>|**acteristics of **|**household**|**s**||
|---|---|---|---|---|---|---|
||**All hou**|**seholds**|**Poor ho**|**useholds**|**Non-poor**|**households**|
|_Demographics_|2000|2005|2000|2005|2000|2005|
|Household Size|5.18|4.85|5.4|5.2|5.0|4.6|
|Dependency Ratio|0.77|0.69|0.99|0.91|0.60|0.57|
|Number of children|2.1|1.8|2.5|2.3|1.6|1.5|
|Number of Male Adults|1.6|1.5|1.4|1.4|1.7|1.6|
|Number of Female Adults|1.5|1.5|1.5|1.5|1.6|1.6|
|Head female|0.09|0.10|0.08|0.08|0.09|0.12|
|_Source:_HIES 2000,2005|||||||

24
## 3.3 Main independent variables: Macro-financial variables and other country

## characteristics

Table 2 provides definitions and sources of the main independent variables used in our empirical analysis. Table 3 and Table 4 provide summary statistics and correlation matrixes, respectively. In addition to the crypto-asset data from Chainalysis, we use countries’ financial and macroeconomic data from various sources. We focus on a parsimonious set of monthly indicators to ensure broad country coverage. Our monthly indicators are derived from three data sets: 1) IMF’s International Financial Statistics (e.g., country-month inflation and exchange rates); 2) IMF’s World Economic Outlook (e.g., country-year nominal GDP); and 3) US Federal Reserve economic data for global or U.S. asset prices, interest rates, risk appetite measures, and inflation expectations. The bitcoin and ether data are sourced from Coinbase, a large U.S.-based crypto-assets exchange.

Our set of broader annual country-level variable include GDP per capita, financial development indicators such as domestic bank credit to GDP, financial account ownership, remittances to GDP, and ICT development indicators (e.g., mobile phone subscriptions and fixed broadband subscriptions). We also include indicators on countries’ institutional frameworks proxied by economic freedom indicators. These indicators are taken from the World Bank World Development Indicators and the Heritage Foundation’s Economic Freedom Index, respectively.

# 4 Empirical approach

First, we start by running monthly panel regressions focusing on a set of country and global macroeconomic and financial factors with country-fixed effects. We do not include time-fixed effects since we are studying the global macro-financial factors. Second, we run basic cross-sectional regressions to explore the role of a broader set of annual variables. All estimations involve robust standard errors clustered at the country level. In light of the relatively small size and impact of crypto-assets activity on country and global macro-economic variables to date, we are not very concerned about endogeneity challenges.

We exploit within-country variation by deploying panel regressions. Our main specification is: 24
The results of these mean comparisons across key export promotion variables are provided in Table 6. Enterprise Ireland’s budget promotion is two orders of magnitude higher than the average for LAC EPAs. We know from Lederman et al. (2010) that there are diminishing returns to EPA expenditure. Ireland may lie on a portion of the curve so flat that no level of government support can increase the probability that a firm will export. However, diminishing returns do not explain why our results on the intensive margin of Latin American exporting firms were weaker than the results from Ireland. That is, conditional on being an exporter in 2006, Latin American firms that received export promotion services also appear to have increased their shares of exports over total sales, although this partial correlation was not statistically significant in all model specifications.

Enterprise Ireland also assigns top priority to increasing exports across all sectors and destinations, whereas Latin America’s EPAs on average assign higher priority to encouraging non-traditional exports, as can be seen from Figure 1. The focus on non-traditional exports can potentially be linked to the strong impact that export promotion activities have on entry in Latin American countries. Thus, differences in the institutional functioning of Enterprise Ireland and EPAs in Latin America could help explain the differences in results between the two studies.

# **5 Concluding Remarks**

There is a growing literature on the impact of export promotion services and subsidies. The latter have generally found that export promotion tends to increase the exports of existing products to existing markets, rather than the “extensive” margin of exports. However, this literature has tended to rely on data on exporting firms or product-level trade flow data. There are a couple of exemptions, such as Bernard and Jensen (2004) and Gorg, Henry and Strobl (2008), but these studies focused on developed countries (the U.S. and Ireland, respectively). Still, these studies found that export promotion is associated with the intensive margin, rather than with the extensive margin.

This result could be potentially worrying, as the economic justification often given for the existence of government intervention through export promotion programs often relies on information asymmetries and externalities that should affect the fixed cost of exporting and not the variable costs. Thus, if export promotion programs are correcting these market failures, one should observe an impact of these programs on firms' decision to start exporting or surviving in export markets, rather than on their export levels. In other words, export growth should come 13