rafmacalaba/datause-encoder-data huggingface dataset:
## **Figure 20.  Unemployed by Gender and Job Search Methods, 1996-2002**

<!-- Start of picture text --> Unemployed males by main search method Unemployed females by main search method<br>100% 100%<br>90% 90%<br>80% 80%<br>active<br>70% 70%<br>60% friends 60%<br>50% read ads 50%<br>40% Private ES 40%<br>30% 30%<br>Public ES<br>20% 20%<br>10% 10%<br>0% 0%<br>1996 1997 1998 1999 2000 1996 1997 1998 1999 2000<br>job search methods: males job search methods: females<br>80.0 80.0<br>70.0 70.0<br>60.0 60.0<br>50.0 50.0<br>40.0 40.0<br>30.0 30.0<br>20.0 20.0<br>10.0 10.0<br>0.0 0.0<br>2001 2002 2001 2002<br>SES Private ES<br>SES Private ES read ads<br>read ads ask friends<br>ask friends contact employers place ads<br>contact employers place ads<br>try to start business<br>try to start business<br>percent percent<br><!-- End of picture text --> _Source:_ Calculation based on LFS.

# **E.  RELATIVE WAGES**

One of the most important features of a labor market is its wage structure. Are human capital related workers’ characteristics – education, general, and firm specific experience - rewarded in Latvia in similar ways as in established market economies? Empirical evidence from other transition countries suggests that returns to education have increased sharply, while returns to experience have decreased compared to the socialist wage setting system. However, the patterns of change and the end result differ across countries, reflecting differences in historical background, social norms, and the industrial structure of the economy.

In the following section, the major factors contributing to wage differences between individuals in Latvia are assessed. Particular attention is focused on differences in wages earned by individuals of differing education levels, gender and ethnic wage differentials, and regional differences in wages. Such an assessment is of great importance in designing policies which target individuals and regions which may have been disadvantaged in the move to a market-based economy.

29
|Dependent variable:|o|Bi|@)<br>Reform|a |<br> Mentions|oOo<br>- All Lan|©<br>guages|(7)|Sy)|
|---|---|---|---|---|---|---|---|---|
|Estimation:||OLS (Full|Sample)|||25|L5||
|GDP Growth|-0.015°*" <br>(0.005)|= -10.002<br>(0.002}|-0.011°<br>=<br>(0.006}|-0.016"*<br>(0.008)<br>||0.063<br>(0.042)|0.037<br>(0.025)|0.037<br>(0.027)|0.040<br>(0.029)|
|GDP Growth X Democracy||<br>-0.045***<br>(0.015)||||-0.116°**<br>(0.037)|||
|GDP Growth X Strong Executive Constraints|||-0).064°°*<br>(0.017)||||-0_124°**<br>(0.043)||
|GDP Growth X Strong Political Competition||||-0).046°"<br>(0.016)||||-0_123***<br>(0.040)|
|YearFE|rs|é|rs|rs|rs|v|v|vw|
|<br>ISOS|vo|re|wo|ra|ra|ra|re|ve|
|C-D F Statistic<br>‘Observations<br>NumberofClusters|5,603<br>182|4,822<br>160|4,694<br>160|4.694<br>160|19.056,<br>5,603<br>182|11.462<br>4 822<br>160|13.293<br>4,004<br>160|13.018<br>4.604<br>160|

Notes: This table presents the main results wing OLS and the instrumental variable strategy. The dependent vanable im all columns ts the economic reforms article count in all languages normalized by the total number of articles. GDP growth is instrumented using contemporaneous and lagged changes in the groee export commodity price index, as described in the text. Standard errors are clustered at the country level and reported in parentheses, stars indicate *** po 0.01, ** p< 0.08, * p< 01.
Table 3: Demand for Fertilizer / ha.

|_EXPLANATORY VARIABLES_|_SML_**_estimator_**|_TL_|_S_**_estimator_**|
|---|---|---|---|
||_ESTIMATES_<br>_STD. ERRORS_|_ESTIMA TES_|_STD. ERRORS_|
|Intercept|-3.01**<br>0.22|||
|Time Trend (T)|-0.41**<br>0.12|-0.59**|0.13|
|Log of HH adult population (POP)|1.01**<br>0.09|0.54-|0.18|
|Credit Received (1000 Ks)|0.48**<br>0.02|0.13*|0.07|
|Cattle Stock (1000 Ks)|0.05<br>0.07|0.11|0.10|
|Draught Animals and Implements (1000 Ks)|1.21**<br>0.25|0.46|0.77|
|DM01|-0.01**<br>0.00|-0.04|0.04|
|SexofHH's head (Women = 1)|-0.68**<br>0.13|||
|Distance to Markets|-0.22**<br>0.02|||
|Extension|-0.15<br>0.20|||
|Primary education|-0.09**<br>0.12|||
|Secondary Education|1.53<br>0.11|||
||3.34**<br>0.01|||
|Mean Log- Likelihood|-1.10792|||
|NumberofObservations|9706||9706|

- Indicates statistically different from zero at the 10% significance level.

Indicates statistcally different from zero at the 5% significance level.
13 (2004) (BFG) methods. The most striking result in these tables is the absence of a strong, positive relationship between education and household income (expenditure). In both tables the correlation between human capital variables and household income is only significant in the case of university education.

In general, the effect of selection bias on the coefficient estimates in Tables 4 and 5 does not seem to be severe. Table 4 shows that the selection control variable is insignificant for households receiving internal remittances (from Ghana) using both the Lee and the BFG methods. This suggests that selection is not a problem for households receiving internal remittances. However, for households receiving international remittances (from African or other countries) in Table 5 the selection control variable is significant at the 5% level using both methods, but its impact is ambiguous. The Lee method suggests that there is negative correlation between unobserved heterogeneity and household income, while the BFG method suggests just the opposite, namely, that there is positive correlation between unobserved heterogeneity and household income. Hausman tests performed for both the Lee and the BFG methods cannot reject the null hypothesis that the coefficients are similar. We also tested whether our instruments identify selection in the expenditure equations; this analysis shows that our instruments perform well.<sup>14</sup> To summarize, the correlation between unobservable household characteristics and the probability of receiving remittances is insignificant for internal, but significant for international remittances. We interpret these results as indicating that the receipt of internal remittances is correlated with the observable characteristics of households to the point that conditional on those characteristics, the unobservable component in income does not bias our estimations. On the other hand, given that observable characteristics seem to be less correlated with the receipt of
20 There are two tendencies at work which have already operated since the early history of financial institutions, tendencies which you may call the specialty shop and the department store principle. A specialty shop in finance holds, essentially, one type of financial instrument and raises most of its funds also by one, though a different instrument. At certain times and certain countries, the one or the other system has prevailed. England was, during most of its modern financial history, a financial specialty shop country, as was the United States. Germany, on the other hand, has always been more of a financial department store country, France being generally in the middle. In the last decade, however, there has been a definite tendency, in virtually all countries, for the department store model of finance to gain, particularly in the field of deposit banking . . . Finally, something happened during the fifties and the sixties. It probably became obvious to bankers that, if they continued to do business as before they would continue to lose in importance. This trend soon became worldwide, pa.tly because it is much easier to transfer financial technology from one country to another than to do so in the case of industrial technology . . . This movement towards financial department store operations is, I think, one of the most important trends to watch in developed countries in the next generation . . . It has always been present to some extent and is particularly pronounced in less developed countries. (pp. 82-84) For the rationale of such a strategy in oligopolistic markets, see Swati Bhatt, Non-Price Comnetition in Oligopolistic Industries. Phd. diss., Princeton University, 1986.

14. On technological changes in the electronics industry and their impact on structural changes in the financial institutions and markets, see C.A.E. Goodhart, Monetary Theory and Practice (London: MacMillan Press, 1984), chap. 5.

- 15.See the survey on "International Investment Banking" in The Economist, vol. 294, no. 7385, March 16, 1985, pp. 44-84.

- 16.Ibid.
Policy Research Working Paper 5989

# **Abstract**

This paper estimates how changes in China’s exchange rates would affect exports from competitor countries in third-country markets—in other words, the “spillover effect.” The authors use recent theory to develop an identification strategy, with a key role for the competition between China and its developing country competitors in specific products and export destinations. Using disaggregated trade data, they estimate the spillover effect by exploiting the variation across different exporters, importers, products, and time periods. They find a spillover effect that is statistically and quantitatively significant. Their estimates suggest that a 10-percent appreciation of China’s real exchange rate boosts a developing country’s exports of a typical four-digit Harmonized System product category to third markets by about 1.5 to 2 percent on average. The magnitude of the spillover effect varies systematically with the characteristics of products, such as the extent to which they are differentiated.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at amattoo@worldbank.org, pmishra@imf.org, and asubramanian@piie.com.

_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
O.A.M.D.G.

present in the original population, are then used to adjust the selected sample to the ‘true’ proportions. This procedure can be extended to a Tobit specification. Greene (1997) offers a procedure for sample selection correction if the principal specification is a Tobit model. However, given that often a Tobit can be approximated by OLS, see Greene (2001), in most cases it will be more practicable to use a linear specification and Heckman’s method.

Given the data we have available, we can attempt a correction for sample selection in some of our specifications. We have the original dataset from which the _Familias_ sample was drawn, the SISBEN register of households. This is a c20,000 household register of all beneficiaries of the national welfare system, which are in the lowest income stratum, ‘SISBEN 1’. The register identifies some basic characteristics, such as age, gender and education. However, information on these is missing for about half of the households. We can identify, which people stayed in and which left the original SISBEN population by comparing the households in the SISBEN register with those in the Familias dataset. Although Familias and SISBEN do not share the same household identifier, we can link the households up via the National ID number of the head. (A National ID number is unique and stays with the bearer for life, no matter where they move. That means, those SISBEN households which we no longer find in Familias did not just move from one place to another within the same sample. They are definitely not picked up anywhere in the Familias sample.) The SISBEN dataset can be merged at the municipality level with some municipal variables which are available for the entire country, such as violence data from before 2000 on armed group actions, as well as municipality level wealth indicators for various years. We include the sum of all bank assets per head in the municipality.
11

# <u>Figure 2: Structure of the 2018 HEFPI database</u>

<!-- Start of picture text --> Health Equity and<br>Financial Protection<br>Health equity Financial Protection<br>Service coverage Health outcomes Catastrophic expenditures Impoverishing<br>(CATA) expenditures (IMPOV)<br>Prevention Treatment MDG era SDG era CATA 10% IMPOV $1.90-a-day, and<br>other $ PLs<br>MDG era SDG era MDG era SDG era Infant mortality (IMR) Adult BMI CATA25% IMPOV 60% median per<br>capita consumption<br>Cervical cancer screening Skilled birth attendance Under-five mortality<br>Antenatal visits (4+) Inpatient admissions Adult overweight<br>(PAP) (SBA) (U5MR)<br>Treatment of child with<br>Child immunization Breast cancer screening acute respiratory infection Treatment for hypertension Stunting among under-5s Adult obesity<br>(ARI)<br>Sleeping under insecticide- Treatment of child with Treatment for Underweight among<br>treated bednet Hypertension testing diarrhea diabetes under-5s Adult height<br>Prevalence of raised blood<br>Contraception prevalence Cholesterol testing HIV prevalence<br>pressure<br>Raised blood glucose and<br>Family planning demands<br>satisfied Diabetes testing impaired fasting glycaemia<br>(IFG)<br>Condom use during risky<br>intercourse<br><!-- End of picture text --> Figure 3 shows the surveys used in the 2018 HEFPI database, where the size of each block is proportional to the fraction of total datapoints contributed by the survey in question. In contrast to the 2000 database, which was based entirely on DHS surveys, the 2018 database uses a variety of different surveys, albeit still, for the most part, highly standardized surveys from a few multicountry programs. For the MDG-era health service coverage and health outcome indicators, the DHS accounts for the majority of datapoints, but the MICS and the WHS are also important sources, contributing over 30% of the MDG-era service coverage datapoints. For the SDG-era health service coverage and health outcome indicators, the DHS is also an important source, but other sources are also important. These include the STEPS and the WHS, as well as the European Community Household Panel (ECHP), the International Social Survey Program (ISSP), the Eurobarometer, and
forms the bulk of the financial system in most developing countries. It must substitute for well-functioning18 equity and bond markets in achieving a sharing between the sectors (and between individual households and enterprises) of risk and of the timepazh of resource flows.

The banking system is limited in the degree to which it can perform this function. To take just one aspect, it provides to depositors as assets mainly or solely deposits which are denominated in money terms<sup>1</sup> 9. These do not, for example, protect against unexpected inflation. Furthermore, bank deposits do not enable saving households who do not own the borrowing enterprises to benefit from increases in the value of the assets of those enterprises. Many households would be willing to accept some increased risk in order to obtain such an opportunity. The limited menu necessarily offered by banks results in lower economic welfare than if a full range of well-functioning financial markets existed.

This is only one illustration of the unfulfilled potential of a bank dominated system. In order to provide a context for assessing the statistical findings of Section 5 below on the use of financial instruments in developing country flows, the following paragraphs Drovide an account of the particular characteristics of different inancial institutions and instruments for sharing resources.

18 The relative importance of the role of equities does not depend only on their relative share of total financing, but on the degree to which their use is widespread among enterprises, and also on the efficiency with which the equity markets function.

19 Except in countries where, because of chronic inflation, indexed deposits have become widespread.

22
# **TABLE 4. First stage.**

||I<br>|CT<br>|
|---|---|---|
||(1)|(2)|
|Information * Small|0.13***|0.12***|
||(0.029)|(0.029)|
|Information * Med‐size|0.11***|0.11***|
||(0.030)|(0.030)|
|Information * Large|0.097***|0.090**|
||(0.036)|(0.036)|
|Observations|3,434|3,419|
|F‐stat|13|12.2|
|Industry effects|Yes|Yes|
|Trends||Yes|

First stage regression of 2SLS. Dependent variable: dummy indicating whether firm invested in ICT during sample period. Instrument: dummy indicating whether firm was exposed to information on government program to finance ICT investment interacted with firm‐size indicators at the beginning of the sample period.

28
Stark, Helmenstein, and Prskawetz (1998) “Human Capital Depletion, Human Capital Formation, and Migration: a Blessing or a curse?” _Economics Letters_ 60 p363-367 Stark and Wang (2002)“Inducing human capital formation: migration as a substitute for subsidies.” _Journal of Public Economics_ 86 p29-46 Venables (1996) “Equilibrium Locations of Vertically Linked Industries” _International Economic Review_ 37(2) 341-359 Venables (2005) “Regional Disparities in Regional Blocs: Theory and Policy” Paper prepared for the IADB Vidal (1998) “The Effect of Emigration on Human Capital Formation” _Journal of Population Economics_ .

Wildasin (2000) “Labor-Market Integration, Investment in Risky Human Capital, and Fiscal Competition” _American Economic Review_ 90(2) p73-95

# **Online Resources**

UNDP online data: `http://esa.un.org/unpp/` UNESCO UIS online data: `http://www.uis.unesco.org/` World Bank Development indicators: `http: //publications.worldbank.org/WDI/` 27
4 where lower case for HK is logs and the A is the time rate of change<sup>5</sup> . In order to empirically imnplement this I have to choose a value for _r._ I assume it is 10 percent. This is based on a large number of empirical estimates from micro data. Since we use the same wage increment for every country, the growth rate of educational capital is very robust to variations in the value of _r._ I also assume r is constant across the years of schooling, an assumption which is more problematical, but not grossly inconsistent with the data6. While both of the sets of educational attainment data have been roundly criticized on a number of grounds (Behrman and Rosenzweig, 1993,1994) I will plow ahead for several reasons. First, using real. but flawed, estimates of the growth of educational capital stock for estimation of equations in which the growth of that stock is the conceptually appropriate variable must be better than the widely used alternative of using flow investment rates (enrollment rates) and simply pretending they are estimates of the growth of the stock. Second, the degree of pure measurement error is easy to overstate relative to the signal in the data and in any case pure measurement error can (and will) be handled econometrically. Third, even if years of schooling do not proxy well human capital accumulation, they do proxy the policy alternative often considered, which is not to improve human capital (since that is not under policy control), but to increase years of schooling.

That said, one should not be too facile about the association between the years of schooling (and derived educational capital) measures, which is all we really have to hand, and "human capital" which is a much broader concept. An individual's marketable human capital can be defined to be the annualized 5 Most previous studies use either the growth rate of the log of years of schooling or the growth rate of the level of years of schooling as proxies for growth of human capital. While the present specification is arguably ad hoc (for instance, this is not aggregated up from individua **l** evels), it is not clear what fundamental specification of the earnings function would generate either of the other two specifications.

6 This does not contradic **t** he widely asserted to be a fact that the rate of return to investment in primary education is higher than that of other levels of schooling to growth accounting. For growth accounting what is needed is the wage increment, which is the amount by which wages are higher ex-post, not the return anticipated ex-ante. This is important because much of the reason why calculations (such as those reported by Psacharopolous (1993)) show primary education having a higher return is not because the incremen **t** o wages from a year of primary school is higher, but because the opportunity cost of a year of primary schooling is much lower given the assumption that the foregone wage attributed to a primary aged unschooled child is very low (Bennell, 1994).
took control and is substantially higher among youth and women. Additionally, the higher rates of return for women may reflect the relatively lower initial levels of female education in Afghanistan, meaning that each additional year of schooling represents a more significant improvement in skills and productivity. Investing in female education can have far-reaching implications for the socio-economic development of Afghanistan, as educated women are more likely to invest in the health and education of their children, creating a virtuous cycle of human capital development.

Table 3: Monetary Return from an Additional Year of Schooling

|||OLS<br>|||2SLS<br>||
|---|---|---|---|---|---|---|
||Pooled|Female|Male|Pooled|Female|Male|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Education (years)|0.025***|0.066***|0.023***|0.038**|0.127***|0.029*|
||(0.004)|(0.01)|(0.003)|(0.016)|(0.034)|(0.015)|
|Experience (imputed)|0.023***<br>(0.004)|0.023***<br>(0.006)|0.022***<br>(0.004)|0.028***<br>(0.008)|0.038***<br>(0.008)|0.025***<br>(0.008)|
|Experience square|-0.000***|-0.000|-0.000***|-0.000***|-0.000|-0.000***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|6.843***<br>(0.067)|5.849***<br>(0.112)|6.942***<br>(0.073)||||
|R-squared|0.21|0.26|0.22||||
|N|13135|1288|11845|13135|1288|11845|
|ProvinceFE|X|X|X|X|X|X|
|Survey-Wave FE|X|X|X|X|X|X|
|First stage F-statistics|||||||
|Kleibergen-Paap||||87.42|26.51|89.69|
|Cragg-Donald||||1004.64|83.58|902.46|

_Note_ : Significance level: *0.1 ** 0.05, ***0.01. The dependent variable is log of annualized income in USD. The estimates can be interpreted as percent change in annual earnings.

We also report the annualized income breakdown by gender and education level in Figure 4. There are striking gender gaps in average income across all education levels; nevertheless, there is a greater premium at higher levels of education, especially for women. There are larger gains for women and men attaining higher education relative to those with high school or associate degrees.

11