rafmacalaba/datause-encoder-data huggingface dataset:
_Gender Equality and Economic Development: The Role for ICTs_ 14 developed countries, participate in the labor market in a non-secondary breadwinner capacity. In such countries, high rates of unemployment should not have effects on gender equality in employment.

# **6. Data and Empirical Methodology**

This section focuses on the econometric analyses of effects of ICT on gender inequality in education as well as gender inequality in employment. The sources and definitions of the variables used will be first presented and a description of the estimation methodology will then follow.

The data set underlying the gender inequality regressions attempts to include all of the 209 countries in the World Bank World Development Indicators (WDI) database for the years 1960 through 2002. However, due to missing observations, the number of countries and years included in the actual regression samples are significantly less.<sup>8</sup>

## **6.1 Definitions of Variables and Sources of Data**

### **Dependent Variables**

For the purpose of econometric estimation, this paper focuses a measure of gender inequality in education and a measure of gender inequality in employment. The ratio of girls to boys in primary and secondary education is used as a measure of the degree of gender inequality in education, while the ratio of the female to male labor force activity rates acts as the measure of gender inequality in employment.

1. _Ratio of girls to boys in primary and secondary education_ : the percentage of girls to boys enrolled at primary and secondary levels in public and private schools. (Source: WDI)

2. _Ratio of the female to male labor force activity rates_ : this ratio is constructed as the ratio of the female to male labor force crude activity rates. The female (male) labor force crude activity rate is expressed as a percentage of the total female (male) population of all age groups. Activity rate measures the labor force as a percentage of the working-age population. The labor force comprises people who meet the ILO definition of the economically active population: all people who

> 8 Please refer to the tables of regression results for the number of countries and years included in each regression.
amounted to reallocation of labor from established and larger businesses to micro startups as an increasing number of people who could not get similar opportunities in established businesses in the other sectors took up to self‐employment in activities where start‐up costs or entry costs seemed to be far lower.

# **_2.4. More on reallocation within and across sectors_**

# 2.4.1. Across enterprise size groups: Concentration vs. informalization

More than 93 percent of business establishments on the 2010 register were microenterprises engaging at most 4 persons each. This was also more or less the proportion of microenterprise in the UBR 2001 even though there were a little over 275,000 more microenterprises on UBR 2010 than on UBR 2001.

The 428,000 micro businesses on UBR 2010 had a combined workforce of 642,000, which was 60 percent of Ugandans working outside of the subsistence sector, but this comes to 1.5 persons per enterprise, reflecting the fact that the typical microenterprise in 2010 engaged one or two persons as it did in 2001.<sup>14</sup> Based on UBR‐2010, the rest of Uganda’s non‐subsistence economy employed a workforce of some 431,000 in 2010 spread across some 30,000 business establishments and public agencies on the register. Just a little shy of 27,000 of these were small businesses, which are defined as establishments employing anywhere between 5 and 20 workers (inclusive) and had a combined workforce of about 219,000. The balance of the workforce of the “formal economy” split almost evenly between those employed by about 3, 000 medium sized enterprises, each of which had more than 20 but no more than a 100 workers, and those working for some 340 larger establishments.

Because the structure of product markets markedly differed between tradables and non‐tradables, the seven‐percentage point shift in employment (shares) from the traded sector to the non‐traded sector that we saw between UBR 2001 and UBR 2010 also had implications to the distribution of employment across the size distribution of firms. Specifically, it led to a 12 percentage shift in employment share from the “formal economy” to microenterprises. This second shift raised the employment share of microenterprises from about 48% in 2001 to 60% in 2010 while reducing the share of large establishments by about the same percentage points thereby leaving share of SMEs more or less unchanged at about 30%, which in turn split 2:1 between small enterprises and medium enterprises respectively.

This was not surprising as employment was far more concentrated in microenterprises in the non‐traded sector than in the traded sector. Thus in UBR 2010, more than 65 percent of employment in the non‐ traded sector was in microenterprises as compared to only about 34 percent of in manufacturing and

> 14 The share of microenterprise in the workforce of the non‐subsistence economy would be 48 percent based on the 2001 UBR, which is far below that inferred from the 2010 UBR, but the gap that this bears in relation to the proportion in the 2010 is probably mainly  explained as bias stemming from the omission of farm related microenterprises by the 2001 register.

18
# **APPENDIX C: TRANSLATION OF LETTER SENT FROM THE INDONESIAN NATIONAL MINISTRY OF EDUCATION TO THE DISTRICT EDUCATION OFFICES (COPIED TO HEAD TEACHERS OF TREATMENT SCHOOLS WITH A LIST OF ELIGIBLE TEACHERS)**

To the head of the district education office: Following up on our letter dated May 18, 2009 regarding the Evaluation Study of the Teacher Certification Implementation, we would like to inform you that we have already received the schools and teachers data from the schools that have been assigned as the sample for the evaluation study of teacher certification. Based on these data, we have conducted data analysis and grouped the teachers into three groups as follows:

1. Certification participants under the additional quota from year 2009 These are teachers that already fulfilled the requirements for certification in 2009, and have been included in the additional quota from year 2009.

2. Candidates of certification participants under the quota from years 2010-2014 These are teachers that have not yet fulfilled the requirements as certification participants from year 2009, and who will be included as certification participants as soon they fulfill the requirements (eligible starting in year 2010 and ending in year 2014).

3. Teachers who were certified in years 2007-2009 and participants from year 2009 quota This group consists of teachers who already passed the certification in years 2007-2008 and teachers that were already included as certified teachers in the 2009 quota.

The list of names of the teachers in each of the above groups is provided in Appendix I. In accordance with this grouping, we would respectfully request that you pay attention to and conduct several things as follows:

1. Teachers who are included in Group 1 will be assigned as the participants of the additional teacher certification quota through the Head of the District Education Decree, and will be asked to arrange their portfolio based on Book 3 of the Portfolio Development Guideline. Assignment of teacher certification participants’ numbers will be in accordance with the regulation regarding the provision of participation numbers and sequence numbers (digits 11-14), which is the subsequent numbering of the participants from quota from year 2009. The portfolio of these teachers should be sent to the university in accordance with their respective area, by July 15, 2009 at the latest. The process of registering the participants is the same as with the quota from year 2009, and teachers are obligated to fill in format A1 and send it to the nearest Education Quality Assurance Agencies by July 10, 2009 at the latest.

2. Teachers who are in Group 2 will receive priority access and can be immediately appointed to join the certification process of the current year when the candidates have fulfilled the requirements. This regulation is only applicable for years 2010-2014.
Cambodia, are excluded.<sup>9</sup> In Latin America, food insecurity is surprisingly high (34 percent).

This may relate to the greater prevalence of urban poverty and of relatively poor net food consumers, although this is only a speculation.

# **Table 1. Regional Unweighted Means for the Two GWP Measures, Circa 2005, for Developing Countries Only (Percent)**

||**Food i**<br>|**nsecurity**<br>|
|---|---|---|
||**Mean**|**No. of obs.**|
|sub-Saharan Africa|58.3|27|
|South Asia*|31.2|5|
|East Asia*|24.0|6|
|Middle East & North Africa|26.5|2|
|Central America & Caribbean|34.7|9|
|South America|36.0|10|
|Transition<sup>a</sup>countries|29.1|23|
|OECD<sup>b</sup>|8.3|22|
|Low income<sup>c</sup>|48.6|49|
|Middle income<sup>c</sup>|29.6|28|
|Upper income<sup>c</sup>|11.0|34|
|Mean, total sample|31.7|433|

_Source_ : Data are from the GWP (Gallup 2011).

_Note_ : *Note that two outliers are excluded. Nepal is excluded from the South Asia results, and Cambodia is excluded from the East Asia results. In the case of Nepal, its food insecurity score is much lower than that of the other South Asian countries, whereas Cambodia’s is much higher. With the inclusion of these two outliers, the food insecurity scores for South Asia and East Asia are roughly equal at 31 percent.<sup>a</sup> _Transition_ refers to former Communist countries.<sup>b</sup> Members of Organization for Economic Co-operation and Development.<sup>c</sup> . _Low income_ is defined as a 2005 GDP per capita of less than USD 5,000 purchasing power parity; _middle income_ , as USD 5,000–13,000; and _upper income_ , as greater than USD 13,000.

The data also suggest a strong income gradient for food insecurity. Low-income countries have food insecurity rates that are 17 percentage points higher than middle-income countries, and the same difference is observed between middle- and upper-income countries. In terms of correlations with other welfare indicators (table 2), there is some support that cross-country patterns impart meaningful information. Of course, extremely high correlations are not necessarily expected given the well-known problems associated with measuring hunger and

> 9 Self-assessed food insecurity in Cambodia is unusually high (67 percent), but in Nepal, it is extremely low (9 percent).

> Including these two countries leaves the South and East Asian means roughly equal, at 31 percent.

10
where the term _xh nh_ is the per capita expenditure of household _h_ . The higher the inequality aversion parameter ε , the higher is the weight given the lower per capita expenditure households in the computation of<sup>λ</sup> _i_<sup>. In the case that εis zero, all</sup> households have the same weights; therefore the effect of tax reform will be calculated on the average household.

To see how price elasticities enter the calculation of changes in social welfare, the denominator of equation 18 can be written as: where θ _ii_ is the own price and θ _ij_ the cross price elasticity and<sup>_w_%</sup> _i_<sup>is the budget share for</sup> the average households.

The usual practice, which is followed in this paper, is to analyze the results of the various cost-benefit ratios<sup>λ</sup> _i_<sup>calculated for a range of values ofε .25 Results are given in table 5.</sup> The first column gives the OECD estimates of the Mexican tax structure.<sup>26</sup> It represents the tax (or subsidy) in percentage terms that each of the goods is subject to. These are calculated taking into account tariffs, taxes, subsidies and other transfers, all of which contribute in setting the domestic price of the product. OECD estimates suggest that government intervention causes higher prices for all goods, with the exception of

> 25 For the exact formula used in the calculation see the appendix.

> 26 Producer and Consumer Support Estimates, OECD Database 1986-2002

17
<!-- Start of picture text --> —m—Price(Tk/Wp) —#—Subsidy (Tk/Wp)<br>_ 450<br>a<br>= 400<br>> 390<br>~<br>> 300<br>oS<br>250<br>& 200<br>Ta<br>< 150<br>8 100<br>wa 50 ee<br>x 0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012<br>SHS adoptionyear<br><!-- End of picture text -->
# **Trade and Growth in Ecuador** - A **Partial Equilibrium View'**

# Jesko Hentschel

# 1. **Introduction**

When the outbreak of the debt crisis halted private international capital flows to _most_ developing countries in 1982, Ecuador was not in an easy position to cope with the changed international circumstances. Foreign savings had been relied on heavily to finance capital formation during the late 1970s. Investments consisted largely of impored machinery as domestic capital goods production was in its infancy. Further, Ecuador's exports were concentrated in petroleum and several agricultural products and exports could not be relied on to increase foreign exchange availability. Instead, trade balance improvements in the first half of the l980s were achieved by a reduction in imports.

This paper examines the behavior of the Ecuadoran economy in a period of scarce foreign exchange. Parameters of significant importance in this context are the trade elasticities which determine to what extent relative price changes can be relied on to achieve trade balance improvements.

1 This paper is partly based on my PhD dissertation at dte University of Konstanz (Henrschel 1992).
# **_Fiscal Policy_**

Despite the economic and political turbulence experienced between 2009 and 2012, fiscal policies remained consistent with macroeconomic stability. The government’s fiscal policy was broadly consistent with macro-economic programs agreed with the IMF. The fiscal deficit (including grants) increased only slightly from 2.4 percent of GDP in 2010 to 3.0 percent of GDP in 2011. For 2012, the deficit is projected to increase to 3.5 percent of GDP.

**Table 5:  Central Government Operations (% of GDP), 2009-2015**

||**2009**|**2010**|**2011pr **|**2012p**|
|---|---|---|---|---|
|Domestic Revenue|14.7|13.6|14.3|16.0|
|Current Expenditure|12.1|13.0|12.7|12.4|
|Capital Expenditure)|12.5|7.7|8.4|13.3|
|Net lending|0.0|0.0|1.5|0.0|
|Fiscal deficit (commitment basis, excl. grants)|-9.9|-7.0|-6.8|-9.7|
|Grants|4.5|4.6|3.8|6.2|
|Net. Loans|1.4|0.9|2.8|4.5|
|Domestic financing|4.5|1.9|0.3|-0.7|

_Source:_ IMF Niger data base. _Note:_ pr – preliminary; p – projected.

# **_Monetary and Exchange Rate Policy_**

Niger’s monetary and exchange rate policy is determined within the West African Monetary and Economic Union (WAEMU) mechanisms. In particular, Niger’s exchange rate – the CFA Franc – is tied to the Euro. Niger does not have an independent monetary policy. In the analysis, exchange rate appreciation is mainly about the relative price of tradable and nontradable goods.

**Table 6: Money and credit, 2007-2012, CFAF billion**

||**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**Average annual**<br>**change 2007-2012**|
|---|---|---|---|---|---|---|---|
|**Net foreign assets**|**231.8**|**285.0**|**193.8**|**292.9**|**263.5**|**388.7**|11%|
|Central Bank of West African States|240.7|297.3|224.2|292.6|265.2|363.8|9%|
|Commercial banks|-9.0|-12.3|-30.4|0.3|-1.7|24.9|0%|
|**Net domestic assets**|**123.9**|**114.2**|**278.5**|**283.1**|**348.1**|**394.8**|26%|
|Domestic credit|141.4|147.8|311.4|351.2|418.8|472.5|27%|
|Net bank claims on government|-50.5|-114.8|0.5|4.1|16.2|-16.2|-20%|
|Credit to the economy|191.9|262.6|310.9|347.2|402.6|488.7|21%|
|**Money and quasi money**|**355.7**|**399.2**|**472.3**|**576.0**|**611.6**|**783.5**|17%|
|Currency outside banks|132.8|147.7|187.0|234.9|269.9|344.6|21%|
|Deposits with banks|221.5|250.0|285.3|341.2|341.6|438.9|14.7%|
|Private sector|208.4|230.7|263.5|312.0|323.0|416.6|14.9%|
|Public institutions|13.2|19.3|20.0|27.8|18.7|18.7|7.2%|
|Velocity of circulation of money||||||||
|(GDP/broad money)|5.7|6.0|5.3|4.9|4.9|4.3||

_Source_ : IMF Niger data base.

9
the effect of misallocation in input markets on access to external finance. We begin by defining the following outcome variables at a district-industry-year level:

- (i) Total loan amount is defined as the sum of all plant-level outstanding long-term or short-term loans in a given district-industry-year cell.

- (ii) Loan intensity is defined as the average total loans over total output in a given district-industry-year cell.

- (iii) Misallocation in loans: Building upon Duranton et al. (2015), we obtain three distinct metrics for misallocation in loans that parallel our earlier work with other inputs. The simplest misallocation index of a factor is computed as the difference between the plant-level TFP and the average district TFP, weighted by the share of the plant in the given factor vis-à-vis the average establishment share in the district. Duranton et al. (2015) compare this factor misallocation index, labelled as the “without the industry aggregation step,” with two other factor misallocation indices. Their preferred estimates, and our baseline approach in this paper, compute the factor misallocation index at the district-industry level and then aggregate to the district level, weighting the district-industry misallocation measure by the share of the given factor in a given district-industry cell. This measures the extent of misallocation within industries in a given district. Additionally, the misallocation of the district should also account for the between-industry misallocation. A formal framework and methodology for computation of misallocation is offered in Duranton et al. (2015), and Appendix Box 1 summarizes this methodology. In both the indices mentioned above, the LPSivadasan style TFP estimation is used. In a third version, the misallocation index is computed without the industry aggregation step and using OLS-based TFP rather than LP-Sivadasan style TFP.

- (iv) Sum of absolute deviations in actual loans from expected loans: This and the next metric provide alternative approaches to OP metrics. To obtain this, we calculate predicted loan values from a regression of actual loan values as the dependent variable (in levels, retaining zeros) on total output, total employment, young plant

27
17

# How to Make Decentralization Work

To the extent local people decide to carry out some activity through an organiizational structure that they form or that is under their control, _annd_ they bear the fiull costs of their decision, they should be free, even encouraged, to do so. Of course, there may remain many potential problems even with such small groups e.g. enforcing accountability, preventing shirkcing, and so on (Box 8). But on the whole it is probably not misleading to say that the more decentralized the decision structure in such instances, the better the **-** infrastructure should be from the only point of view that matters namely, that of the local beneficiaries (and payors).

> W_~~~~ . .. ... " - ........-....... ,~~~~~~~~~~~~~~~. c. EX.t E.. E 4.44 **_XOm3g_** te aomah Vnfe~snae 3J >E '! J** 5* __ aD *.r F. 3: .. . .. . 32 =3ea **_MDiNg_** oi aboe (Bo 5) ut lso th proidm. E3 adqzt inomtint local.anut 33X3 .- yCi ;2X W>3 3_wa N?;;'** lg ee c~~~~~~~~~lotb deiDitio mus =e stitrdIl.iirhe an toInrouo. -h Eoid~ i_ o ~4~o ot 4 Sl~~~~~trpnvmit foma R,oven ta ogn.' ..... ::..4..::..:U **_73#,]WL3170_ M~01~Q** pi7d.UDs ,i incasls and sgs infor;a4io Is d avilbe § ofte ilag nudnSlU what may e miuin e lainy a:tousI n.. the:: B:invilg g.a s|ded iui; toinlil did= Teugi hneb~m hr a **_owf_** R R.t .. ~ ~ :::.:j :.o,::~ ' :,:::: jo **:** , ::i ~ : :::::S: **:** .::::.;,.~ **::** ':e:: : :: :.:a g e wometimav tuhe doer taiation hft~vlagescho **_bhee_** dcnsidrdan impossi ublc ha depamnd waoor

countries owing to the lack of experience at local levels and the difficulty of figuring out who the likely beneficiaries of projects are going to be, let alone making them pay for what they get. In fact, however, in many countries it appears that even the poorest people opeating informally, within the severely limited conditions open to them, can sometimes manage to provide the local public services that they want and are willing to pay for but that they cannot obtain from the established (and usually much too centralized) provision system (Box
differences between the two groups as discussed above. However, as we show in the next section, neither the urban-rural shift nor a range of other potential confounders can fully account for the observed drop in subjective well-being following the devaluation. Detailed descriptions of all variables used in the econometric analyses along with summary statistics are available in Appendix S4.

# **IV. Empirical Results**

To estimate the effect of the currency devaluation on subjective well-being, our econometric analyses use models for continuous and categorical data. First, we treat the dependent variable as continuous by converting the categorical responses into a variable that assigns a number to each response. Following this strategy, we construct a variable, which holds the values 0, 0.25, 0.5, 0.75, and 1 corresponding to the five response categories and use this as our dependent variable in a series of linear regressions.<sup>9</sup> As an alternative, we maintain the categorical nature of the data and estimate an ordered logit model, using the appropriate link function. In what follows, we report the coefficients of interest using both estimators to show that the results are qualitatively identical. Our starting point is the following linear regression model.

where the dependent variable, _yi_ , is respondent _i_ ’s evaluation of her present living conditions; _Ti_ is the devaluation treatment indicator; and _Xi_ is a vector of controls. The identifying assumption in (1) is that that _T_ and _e_ are orthogonal, Cov( _T_ , _e_ )=0, conditional on _X_ , where the most import element in

> 9 This effectively amounts to a rescaling of the numerical values assigned to each response in the _Afrobarometer_ survey such that our variable runs in the interval from 0 to 1.

17