rafmacalaba/datause-encoder-data huggingface dataset:
- 14 -

# _Table 6: Returns to Education (Mincenan Earning Function), 1993_

||_Basic_|_Dummry_|_Rate of_<br>_Return_<br>_(Percent)_|
|---|---|---|---|
|constant|10.12|12.03||
|Schooling(s)|12.20|||
|Schooling (S2)<br>squared|0.005|||
|Experience (E)|0.031|0.034||
|Experience squared (E<sup>2</sup>)|-0.0002|-0.0001||
|No education (Base)|0.000|||
|Primary||0.303|18.2|
|Secondary||0.517|13.6|
|University||1.488|18.9|
|R2|0.274|0.268||

Note: See Technical Annex A for detail.

_Source:_ Staff calculations from MOL survey 1993.

Finally, one important issue concerning the social rate of return is that it is based on monetary earnings (or productivity) and excludes any "externalities" associated with education. These are benefits of education that go beyond what the individual investor realizes in terms of increasing earnings, such as effects on others by having more literate people around, or better nourished and health children if a mother is educated. However, this is not really a critique of the rate of return analysis. It is a possible addition to education benefits that are unmeasured, as for any other investment in a country.<sup>6</sup> . Assessing education system's future output goals also need tracking labor market development that are not captured by the ROR analysis. Table 13 (Annex 1) presents some relevant market signals, such as job vacancy rates by educational qualification . From 1990 to 1995, while the demand for all types of labor has increased, but the growth in the demand for secondary and university graduates is particularly high, justifying expansion of these subsectors.

> 6For recent attempts to quantify education externalities, see P. Romer, "Increasing Returns and Long Run Growth" _Journal of Political Economy_ 94, 1986, and R. Lucas, "On the Mechanics of Economic Development" _Journal of Monetary Economics 21,_ 1988.
changed their access policy over time. For example, Tunisia initially did not share access to their 2015 survey, but later granted access as part of their engagement with the World Bank. Morocco did not share its microdata but decided in 2019 to publish online its most recent HBS. Importantly, there are instances where the World Bank provides substantial technical assistance to the National Statistics Office but where data are not shared, or where there are substantial delays before data are shared; for example, Morocco only granted access to its 2013 survey in 2018. Algeria granted access to the World Bank to group data only.

Table 3. Status of public and WB access to household budget surveys in MENA region as of August, 2019

|Public or licensed access/ partial or full|nopublic access|no WB engagement|
|---|---|---|
|Djibouti<br>Egypt, Arab Rep.<br>Iraq<br>Iran, Islamic Rep.<br>Yemen, Rep.<br>Tunisia<br>West Bank and Gaza<br>Morocco|Algeria<br>Jordan<br>Lebanon|Libya<br>Syrian Arab Republic|

Source: Authors’ compilation. Note: Licensed means the request to get the microdata should be cleared.

# <u>Comparability</u>

## _Regional picture_

In addition to needing recent HBSs to provide current welfare metrics, comparability of these surveys across time is important to estimate trends. Poverty estimates may not be comparable for different reasons. Non-comparability arises when surveys measure either the welfare aggregate (consumption or income) differently across different years following changes in survey design or instruments or because the statistical office changes the way it constructs welfare aggregates.<sup>10</sup> In the absence of at least two comparable data points, it is impossible to analyze poverty or shared prosperity across time.

Figure 7 shows the distribution of countries in each region that report shared prosperity in the most recent World Bank global shared prosperity database, 2012 to 2017. Excluded are those countries that did not have two recent and comparable welfare aggregates. We distinguish between countries reporting improved shared prosperity (positive consumption or income per capita growth of the bottom 40 percent of population) and countries reporting deteriorating shared prosperity. The regions with the highest share of countries not reporting shared prosperity are Sub-Saharan Africa and MENA. Only four of 12 developing MENA countries with at least one international poverty rate also report shared prosperity, less than onethird of all countries, and much lower than the 80 percent of ECA countries that report shared prosperity.

> 10 Surveys being incomparable could be caused by improvements in survey design or methodology.

10
Table 4: Controlling for additional factors

|_Dependent Variable:_||Migration|Flows||
|---|---|---|---|---|
||WGI CC <br>(1)|WGI GE<br>(2)|CPI<br>(3)|ICRG<br>(4)|
|WGI CC in _o_|0.09***<br>(0.02)||||
|WGI CC in _d_|–0.10***<br>(0.02)||||
|WGI GE in _o_||0.24***<br>(0.02)|||
|WGI GE in _d_||–0.08***<br>(0.02)|||
|CPI in _o_|||0.08***<br>(0.02)||
|CPI in _d_|||–0.09***<br>(0.02)||
|ICRG in _o_||||0.03*<br>(0.01)|
|ICRG in _d_||||–0.13***<br>(001)|
|GDP pc PPP (_log_) in _o_|–1.26***<br>(0.08)|–1.06*** <br>(0.08)|–1.32*** <br>(0.08)|.<br> –1.49***<br>(0.09)|
|Infation (_%_) in _o_|0.04***<br>|0.04***<br>|0.03***<br>|0.04***<br>|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Agriculture va in _o_|–0.30***<br>(0.02)|–0.32*** <br>(0.02)|–0.29*** <br>(0.02)|–0.27***<br>(0.02)|
|Services va in _o_|0.06***<br>(0.00)|0.06***<br>(0.00)|0.06***<br>(0.00)|0.06***<br>(0.00)|
|GDP pc PPP (_log_) in _d_|0.76***<br>(0.09)|0.96***<br>(0.09)|0.81***<br>(0.09)|0.54***<br>(0.10)|
|Infation (_%_) in _d_|–0.03***|–0.03***|–0.03***|–0.04***|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Agriculture va in _d_|–0.33***<br>(0.02)|–0.32*** <br>(0.02)|–0.32*** <br>(0.02)|–0.34***<br>(0.02)|
|Services va in _d_|0.09***<br>(0.00)|0.09***<br>(0.00)|0.09***<br>(0.00)|0.09***<br>(0.00)|
|Right to work|0.25***<br>(0.08)|0.28***<br>(0.08)|0.25***<br>(0.08)|0.34***<br>(0.09)|
|Constant|–771***|–1232***|–757***|–276*|
||. <br>(1.36)|. <br>(1.27)|.<br>(1.29)|.<br>(1.44)|
|Gravity controls|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Adj. R-Square|0.70|0.70|0.70|0.70|
|N|6053|6053|6053|4813|

The additional gravity controls are those included in Table 3. Robust standard errors clustered by county in parenthesis. ***, **, and * indicate statistical significance at the 1 _%_ , 5 _%_ , and 10 _%_ levels, respectively.

18
only. If _X_ can be partitioned into two not necessarily disjoint sets ( _Q,T)_ such that _Q_ affects the outcome equation while _T_ determines the selection part it is possible to estimate the following model: Outcome equation _: Y_ = _g_ ( _Q_ ) + δ _D_ + _u_ Selection equation: _D_ * = _h_ ( _T_ ) + _v_ where _D=1_ if _D*>0_ and _D=0_ otherwise. Selection bias is then treated as an omitted variable problem. As is common, linearity is assumed in the _g_ and _h_ functions. This set up imposes a much stricter parameterization on the outcome equation than matching does and than we would prefer since we think that treatment may be accompanied by changes in the production process. In our case no justifiable exclusion restrictions can be found to separate Q and T and therefore semiparametric estimators cannot identify g(.)<sup>8</sup> . Imposing assumptions about the joint distribution of _u_ and _v,_ i.e. that they follow a bivariate normal distribution offers another alternative although Heckman, Tobias and Vytlacil (2000) have shown that distributional misspecification in this setting can impart important bias.

# **III. Data**

We employ the National Survey of Microfirms (Encuesta Nacional de Micronegocios, ENAMIN) for the years 1992, 1994, 1996 and 1998. This survey covers a sample of individuals who declare that they are self-employed in a broader labor survey, the Encuesta Nacional de Empleo Urbano (ENEU). It is restricted to micro-firms with at most 6 workers (including owners) for all economic sectors except manufacturing where the range is from 1 to 16 workers. We restrict the sample to micro-firms with 1 to 6 workers to be consistent across industries. The ENAMIN allows a relatively precise construction of a wide variety of variables that represent basic firm and entrepreneur characteristics: profits, employment size, capital stocks, time in business, and engagement with a wide variety of societal institutions, including those for which treatment effects are estimated in this paper. In addition, we are able to construct, from the ENEU, several variables representing personal characteristics of the entrepreneur and

> 8 See Heckman and Navarro-Lozano (2004) for a review of the different assumptions needed for each estimation procedure and recent development in control function semiparametric approaches.

11
43. When interpreting these results, it is important to notice that, since one adult 2 remains unemployed, the household is still entitled to child allowances and the proposed 'replacement ratios' are really the lower bound of all possible values.

|**Benefit regime**<br>**Income**|**UI**<br>**First3 months**<br>**(RR50%)**|**UT**<br>**Afterfirst3 months**<br>**(RR45%)**|**SA/SS**<br>**(Objective)**|
|---|---|---|---|
|**Adultl'sincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|3,230<br>(0.29)|
|**HHincome(AW)**|9,670<br>(0.79)|9,670<br>(0.79)|9,670<br>(0.79)|
|**Adultl'sincome(MW)**|3,230<br>(0.90)|3,230<br>(0.90)|3,230<br>(0.90)|
|**RHincome(MW)**|9,670<br>(1.00)|9,670<br>(1.00)|9,670<br>(1.00)|

# **_Adult_** _2_ **_employed at average wage_**

44. When the adult 2 is paid the average wage, adult I can be thought of as the secondary earner in the household. He still receives the same amounts in the form of unemployment insurance, but he no longer qualifies for social assistance/support since household income is above the minimum subsistence level.

45. As long as one of the adults remains unemployed, the family qualifies for child support, so this payment is included in the calculation of household income during adult l's unemployment, but excluded after re-employment. Then, assuming he could obtain employment at the average wage level, the household replacement ratio is 78-76 percent with unemployment insurance, and 56 percent without it. These numbers increase up to 94 and 84 percent, respectively, when he expects to work at the minimum wage.

|**Benefit regime**<br>**Income**|**UI**<br>**First3 months**<br>**(RR 50%)**|**UI**<br>**Afterfirst3 months**<br>**(RR45%)**|**SA/SS**<br>**(Objective)**|
|---|---|---|---|
|**Adultl'sincome(AW)**|5,400|**(0.45)**||
|**HRincome(AW)**|17,190<br>(0.78)|16,717<br>(0.76)|12,210<br>(0.56)|
|**Adultl'sincome(MW)**<br>**Adult l's**<br>**income**~~|1,800<br>**_(0.50)_**|1,620<br>**(0.45)**|**0**|
|**HHincome(MW)**|13,590<br>(0.94)|13,410<br>(0.93)|12,210<br>(0.84)|

# **_Adult_** _2_ **_employed at minimum wage._**

46. The perverse effects associated with safety net payments are stronger when adult 2 is employed at the minimum wage, rather than the average wage, since the household qualifies for both social assistance/support and child allowances. At the individual level the situation is identical to the one where adult 2 is also unemployed. If adult I can earn the average 12
**Table 14: Organized Sector Log (Real Output): Lag Specification**

||(1)|(2)|(3)|
|---|---|---|---|
||Lead = 2|Lead = 4|Lead = 9|
||_Other-_|_sector Output Sp_|_illovers_|
|Horizontal Output Spillovers from||||
|Unorganized Sector|0.00668|-0.0109|-0.0250|
||(0.58)|(-0.70)|(-1.72)|
|Forward Output Spillovers from||||
|Unorganized Sector|-0.560|1.549*|1.278|
||(-0.83)|(2.07)|(1.56)|
|Backward Output Spillovers from||||
|Unorganized Sector|-0.321|0.343|-0.420|
||(-0.71)|(0.71)|(-0.84)|
||_Other-sec_|_tor Productivity_|_Spillovers_|
|Average TFP of Horizontal-linked||||
|Unorganized Plants|0.00543|0.0552|-0.0427|
||(0.08)|(0.53)|(-0.44)|
|Average TFP of Forward-linked||||
|Unorganized Plants|4.125*|-0.0310|-1.298|
||(2.36)|(-0.02)|(-1.15)|
|Average TFP of Backward-linked||||
|Unorganized Plants|-0.892*|0.798|0.182|
||(-2.29)|(1.18)|(0.24)|
|d ill f|_Own-s_|_ector Output Spi_|_llovers_|
|Forwar Output Spovers rom||||
|Organized Sector|-3.463**|-5.042*|-1.824|
||(-3.16)|(-2.02)|(-0.89)|
|Backward Output Spillovers from||||
|Organized Sector|2.129**|-2.473*|-3.539*|
||(2.74)|(-2.10)|(-2.54)|
|Constant|52.27**|148.0**|123.6***|
||(2.66)|(3.02)|(3.38)|
|Observations|5888|4416|4416|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. All specifications include state-year and industry-year fixed effects. Standard errors are clustered at the state-industry level. Significance at the 10% level is indicated by *, at the 5% level by ** and at the 1% level by ***._ 43
the government can tax at rate _μt_ . As in Eichenbaum et al. (2021), the tax rate _μt_ will be the instrument by which the policy maker implements a lockdown.<sup>6</sup> Tax revenues are then remitted to households in a lump-sum manner. The budget constraint of household _j_ is: where _cjt_ is total household consumption expenditure. Household income on the right-handside of (4) consists of after-tax labor income and the government transfer Γ _jt_ . If the working adult is infected ( **I** _jt_ (2) = 1), the effective labor supply falls by a fraction _φ ≤_ 1. After the death of the working adult ( **D** _jt_ (2) = 1), household _j_ lives off government transfers.

The amount Γ _jt_ transferred to households is determined by the government’s budget constraint, i.e.

where Γ<sup> ̄</sup> _t_ is some exogenous development assistance revenue.

**Firms** There is a unit measure of competitive firms that produce consumption goods _Ct_ using the aggregate labor input _Nt_ :

International Labour Office and United Nations Children’s Fund (2021, p. 74) reports that worldwide, 9.7% of children 5-11, and 9.3% of children 12-14 are engaged in child labor, often less than full-time. The highest percentages are in Africa (22.1% age 5-11; 23.5% age 12-14) and especially sub-Saharan Africa (24.7% age 5-11; 25.8% age 12-14). In all other world regions, these fractions are below 10%. While the percentages remain high in Africa, the study reports that 82.6% of children aged 5-11 and 74.5% of children 12-14 who engaged in child labor are “contributing family workers.” Similarly, 76.6% of children aged 5-11 and 75.8% of children 12-14 engaged in child labor are working in agriculture. Thus, the large majority of children engaged in child labor are either contributing family workers and/or working in agriculture. In both cases, the labor takes place within the household or in agricultural fields, and there would not be a disease transmission benefit from work-related social distancing.

> 6We interpret lockdown policies broadly to include multiple forms of containment, such as encouraging social distancing, restricting public events, school and border closures, and so on. As will become clear below, in the calibration the lockdown policy will also lower community transmission.

7
# **The Internet as a General-Purpose Technology:**

# **Firm-Level Evidence from around the World**

George R.G. Clarke Christine Zhenwei Qiang Lixin Colin Xu<sup>1</sup>

**_JEL codes:_** H4, L9, O1, O3.

**_Key words_** _:_ Telecommunications; internet; broadband; general-purpose technology.

1 <mark>George Clarke, Texas A&M International University,</mark> <u>george.clarke@tamiu.edu</u> <u><mark>; Christine Qiang</mark></u> <mark>and L. Colin Xu: World Bank,</mark> <u><mark>Cqiang@worldbank.org, lxu1@worldbank.org. We thank Sole Martinez Peria for</mark></u> <mark>comments.</mark>
share combined with 1% nationally), while the maximum winsorized level is 40.0 (e.g., a 40% local share combined with 1% nationally).

Is a value of 6.65 high or low? Table 1c compares the 2005 Indian values to what we observe in the United States. Looking at Panel A, the mean and median values for specialization increase as one includes more spatial areas with smaller sizes. This is not surprising given the properties of the indices that we highlighted in the previous section. Recalling that the typical Indian district is about the size of two U.S. counties, we believe that Indian data most closely compare to the last two columns, which include 193 and 746 U.S. cities, respectively. It is very interesting that the Indian level of specialization falls right around what one would expect based upon the U.S. levels, despite the substantial differences between the two countries.<sup>6</sup> Returning to Table 1a, this comparability of Indian specialization to U.S. values is not present at the start of our sample period. Even with the annual winsorization, the 1989 mean value is significantly higher than in 2005 and is declining consistently in the interim surveys. The same holds true for the other metrics regarding specialization like standard deviation and maximum values. Thus, around the time of liberalization, we estimate India’s manufacturing distribution to have been more specialized in most districts and to have displayed a much larger spatial variation than the U.S. data. India has converged towards the U.S. baseline in the two decades since then.

Panel B of Table 1a considers the relative diversity index. The average diversity in 2005 is 1.41, which again is in general agreement to the United States’ average level in 2011. The longitudinal pattern for the Indian diversity index is similar to those in Panel A—decreasing specialization corresponding to increasing diversity—but the patterns are more muted, likely in large part due to the diversity index’s metric design being more stable than the design of the specialization index. For both specialization and diversity, we see some re-widening of the distributions in 2010 (greater standard deviation). Table 1b shows a very similar set of patterns when considering only the urban areas of districts, with the additional observation that the specialization of urban areas tends to exceed that for the district as a whole.

> 6 We have not winsorized the indices for the United States since we want to report multiple levels of aggregation. Nonetheless, the same comparability is also evident for the median values that are unaffected by winsorization.

11
<!-- Start of picture text --> Agriculture Electronics<br>e 8<br>38 &<br>Ro 2<br>o<br>2 =<br>°o 2019 2021 °o 2019 2021<br>IT services Tourism<br>ey 3<br>8 8<br>z 8 Re<br>2&<br>oo<br>2019 2021 2019 2021<br><!-- End of picture text --> <!-- Start of picture text --> Electronics<br>High income Low income<br>Es<br>8<br>¢<br>o<br>N<br>o<br>2019 2021 2019 2021<br>DS<br>Lower middle income Upper middle income<br>8<br>8<br>N<br>oo [oo<br>2019 2021 2019 2021<br>ME oo MN <10% ME 10-25%<br>MO 25-50% MN 50-75%<br>Graphs by inc_group<br><!-- End of picture text -->
**Table 5: Summary of Recommendations**

||Key working objectives|Data|
|---|---|---|
|**Macroeconomic**<br>**aspects**|**Theory**:<br>-<br>Optimal infrastructure investment needs and<br>fiscal constraints.<br>-<br>Interplay between country and sector<br>governance and different aspects of<br>infrastructure development.<br>-<br>The link between financial constraints (of<br>government and operators) and involvement<br>of external financing agents and how the<br>multiprincipal nature of this situation affects<br>the level and performance of investments.<br>**Empirical**:<br>-<br>Step up current efforts to analyze the<br>empirical link between infrastructure sectors’<br>organization (degree of competition,<br>ownership, etc.), regulatory governance, the<br>sequencing of policy measures, the impact of<br>measures geared towards the development of<br>capacity to coordinate infrastructure<br>development, etc., and sectors’performance.|-<br>Strengthen the<br>systematic collection<br>of data on institutional,<br>regulatory and political<br>economy<br>characteristics of<br>sectors / regions /<br>countries.<br>-<br>For infrastructure<br>access / quality / cost<br>data, use output from<br>microeconomic data<br>gathering effort.|
|**Microeconomic**<br>**aspects**|**Empirical**:<br>-<br>Define gaps and adjustment to be made to<br>current household survey data collection and<br>strategy implementable at the WB level to<br>generate such data, taking into account<br>institutional / political constraints related to<br>the ownership of such surveys.<br>-<br>Retake and evaluate existing ICA work and<br>define gaps and adjustment to be made to<br>current data collection to make infrastructure<br>(one of) the prime objective of such surveys.<br>-<br>Address endogenous technological choice<br>issue in the context of firm-level data.|-<br>Generate data on<br>access, quality and<br>costs in the four main<br>sectors, to feed in both<br>macroeconomic and<br>economic geography<br>research.|
|Economic<br>Geography|**Theory**:<br>-<br>Develop new economic geography models<br>that include more explicitly the different<br>infrastructure sectors.<br>-<br>Address shortcomings to the current<br>modeling of transportation.<br>-<br>Include other aspects, such as<br>telecommunications, electricity and water, in<br>the theoretical framework.<br>**Empirical**:<br>-<br>Develop suitable econometric techniques and<br>paneldata to test thesemodels.|-<br>Use output from<br>microeconomic data<br>gathering effort, to<br>develop adequate GTC<br>measures for<br>developing countries,<br>as well as indicators<br>for other dimensions of<br>infrastructure.|

41
**Table 3: Characteristics of girls exposed and not exposed to conflict.**

||Characteristics<br>(mean)|Age|HH size|Years at<br>residence|Years of<br>education|Number<br>of IGAs|
|---|---|---|---|---|---|---|
||Not exposed|22.4|8.7|5.4|7.9|0.8|
|Internal|Conflict exposed|21.5|12.6|5.9|7.3|1.1|
||Not exposed|22.3|8.8|5.4|7.4|0.9|
|External|Conflict exposed|21.5|12.5|6.0|8.4|1.0|

While both indicators have some caveats, they are both complementary. According to both indicators, about 1 in 3 girls were exposed to the conflict. Rumbek had the highest percentage of conflict exposed clusters (92 and 78 percent respectively) and the highest percentage of non‐consent to conflict questions (Figure 11 and Figure 2). Therefore, households that were most affected may also have been unwilling to respond to conflict questions. The correlation between the internal and external indicator is significant and positive (P<0.1). The moderate correlation coefficients further warrant the claim that the self‐reported index measured the self‐perceived exposure to conflict while the external index provides a more objective but also less nuanced indication of conflict exposure.

**Figure 11: Percentage of clusters categorized as conflict‐affected.**

<!-- Start of picture text --> 100%<br>90%<br>92%<br>80%<br>70% 78%<br>60%<br>50% 56%<br>40%<br>30%<br>34% 33%<br>30%<br>20%<br>10%<br>4% 0% 13% 0%<br>0%<br>Juba Rumbek Torit Yei Total<br>Internal External<br><!-- End of picture text -->

# 3.3. Outcome Indicators

The dependent variables for the analysis are individual level outcome indicators. These variables cover a range of economic, social, and household condition indicators. A total of 27 outcome variables are 15