rafmacalaba/datause-encoder-data huggingface dataset:
Consider the CDF in Figure 1, giving the proportion with income below each point on the horizontal axis. The numerator is _k_ times the aggregate poverty gap, which is the area under the CDF from zero up to<sup>_z_</sup> _p_<sup>, marked “A” in the unbroken CDF in Figure 1. The denominator is the</sup> tax base (the total income in excess of<sup>_z_</sup> _r_<sup>), which is the area marked “B,” above</sup> the CDF and above<sup>_z_</sup> _r_<sup>, but below the line for “1”. The MTR required to cover the poverty gap (</sup><sup>_k_=1) is the</sup> ratio of area A to B. Equation (1) can also be written in the following form, which is convenient for calculating  _k_ from existing data sets such as _<u>PovcalNet</u>_ <u>:</u> where _PG_ ( _z_ ) is the poverty gap index: The following remarks can be made about the properties of  _k_ ( _z p_ , _zr_ ) : (i) It is strictly positive (since the mean income of those living above<sup>_z_</sup> _r_<sup>must exceed</sup><sup>_z_</sup> _r_<sup>)</sup> but it can exceed unity (since the poverty gap below _z p_ can exceed the total income above<sup>_z_</sup> _r_<sup>).</sup> (ii) It is strictly increasing in both arguments. This is obvious with respect to _z p_ . Note that  [(1  _PG_ ( _zr_ )) _zr_ /]  _zr_  1  _F_ ( _zr_ )  0 implying that  _k_ ( _z p_ , _zr_ ) is strictly increasing in<sup>_z_</sup> _r_<sup>.</sup> (iii) In the special case  1( _z p_ ,<sup>_z_</sup> _p_ ) , the measure collapses to that found in Anand (1977), Kakwani (1977) and subsequent literature in which the poverty gap is normalized by income of the non-poor net of the poverty line.

(iv) It can also be readily shown that 1  1( _z p_ , _z p_ /)  1( _z p_ , _zr_ ) is the share of the total tax burden that is borne by the middle class ( _z p_  _y_  _zr_ ).

* * (v) If there exists a solution for<sup>_z_</sup> _r_<sup>defined implicitly by</sup>  1( _z p_ ,<sup>_z_</sup> _r_ )  1 then it is the “affluence line” proposed by Medeiros (2006) for Brazil. Note, however, that this line does not exist if  1( _z p_ ,<sup>_z_</sup> _r_ )  1for all _zr_  _z p_ .

9
The estimates in Table 5 show that in locations where governance improved between 2002 and 2005 (indicated by an increase in the value of the index), the likelihood of web-use by firms increases while that of ISO certification decreases.

Finally, the estimated effects of access to international knowledge on technology adoption are strong in the panel regressions and qualitatively similar to those in the cross-sectional regressions. Majority as well as minority foreign ownership and the participation in export markets increase significantly the likelihood of firms in the ECA region being ISO-certified and using the web.

# **V.  Conclusions**

This paper examines the relationship between the investment climate and technology adoption for firms in Eastern European and Central Asian countries using the 2002 and 2005 rounds of the World Bank Business Environment and Enterprise Performance Surveys as well as a panel based on those two survey rounds. We divide investment climate factors into three groups - complementary inputs, market incentives, and access to international knowledge - and estimate several strong correlations of those factors with the adoption of advanced technology as proxied by ISO-certification and web-use by firms.

Our findings have implications for policy reforms aimed at enabling faster and greater adoption of advanced technologies by the private sector in Eastern Europe and Central Asia. Specifically, we find that firms with access to the appropriate complementary inputs, namely skilled labor, managerial capacity, R&D, finance (and to a lesser extent good infrastructure) and to international knowledge, either through FDI or exporting activities, are more likely to be ISO-certified or use the web **.** Our results suggest that the relationship between market incentives and ISO certification or web-use is more nuanced. While pressures from consumers generate demand for firms in Eastern Europe and Central Asia to adopt advanced technology, pressures from competitors do not, which may seem counter-intuitive in a developed economy setting but is consistent with previous literature that argues that most firms in Eastern Europe and Central Asia face very substantial resource constraints (particularly financial resources) and thus only those with rents are able to finance technology adoption. Accordingly, we find that larger firms are significantly more likely to adopt advanced technology, particularly through web-use. Our findings also suggest that privatized firms exhibit better technology adoption outcomes only when there is a clear private owner with a profit incentive and empowered to change. As with competition, the negative association that we find between governance and technology adoption on average in the ECA region is counter-intuitive but the effects are very

- 17 -
dummy variable captures whether the interview consisted of one visit but with multiple respondents . The omitted category is when the interview was undertaken over “Several visits.” Location fixed effects (IILLRRLLEEL RRh) and sector fixed effects (E ) are included in the estimation. To account for interviewer or supervisor bias, interviewer fixed effects (LLEEIIR SSEELL) IIRRE ) and supervisor fixed effect ) are also included in the specification. (IILLRRE EESSEEIIE (EEMMRRE EESSRRE

# **5. Results**

_Mean Differences in Business Environment Evaluation_ Table 1 presents the mean differences for all the 15 elements of the business environment. Only 4 elements show a statistically significant difference in means due to changes in the question order of the business environment perception variables, indicating that perceptions for these elements may not be stable. These are corruption, political instability, electricity, and business licensing and permits. For corruption, a standard WBES questionnaire order format results in an 9-percentage point higher perception that corruption is a major or severe obstacle than the alternative format with business environment perception questions at the beginning of the survey. The difference is statistically significant at the 5 percent level. Similarly, for political stability, the standard WBES question order format results in an 8.7 percentage point higher perception that political instability is a major or severe constraint than the alternative format. This difference is statistically significant at the 5 percent level. Political instability is the one category among the four where it does not have a full section in the questionnaire dedicated to it. Electricity has the highest difference across the two questionnaire formats, 10.8 percentage points higher for the standard WBES question order format than the alternative format. The difference is statistically significant at the 1 percent level. Finally, Business, Licensing and Permits is the only business environment element where a higher percentage of firms declared it a major or severe constraint in the alternative format than then standard 13
Table 2.1: Population Size and Growth and Household Size in Urban and Rural Haiti, 1982-2003

|||Urban<br>|||Rural<br>|||Tot|al<br>Annual<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|||v|ae o||v|ae o|||ouaon|o|
|Region|Population|g.<br>House-<br>hold<br>Size|<br>Female<br>Ratio<br>(%)|Population|g.<br>House<br>-hold<br>Size|<br>Female<br>Ratio<br>(%)|Population|Avg.<br>House-<br>hold Size|p<br>Growth<br>1982-2003<br>(%)|p.<br>Density<br>2003<br>(pop/km<sup>2</sup>)|
|Artibonite|278,290|4.58|85.6|792,107|4.21|96.0|1,070,397|4.30|1.9|215|
|Center|90,843|4.65|90.7|474,200|4.51|100.8|565,043|4.53|2.2|154|
|Grand-Anse|90,095|4.47|93.2|513,799|4.45|105.7|603,894|4.46|1.0|182|
|North|295,624|5.20|85.1|477,922|5.07|96.9|773,546|5.12|1.6|367|
|Northeast|112,782|4.76|88.5|187,711|5.13|99.6|300,493|4.99|2.3|166|
|Northwest|102,338|5.30|84.9|342,742|5.00|96.0|445,080|5.07|2.1|205|
|West|2,070,799|4.72|86.5|1,022,900|4.39|95.9|3,093,699|4.61|3.4|641|
|South|98,506|4.96|89.3|528,805|4.80|105.3|627,311|4.82|1.1|225|
|Southeast|65,688|4.51|88.3|383,897|4.40|93.9|449,585|4.42|1.0|222|
|Haiti|3,204,965|4.76|86.7|4,724,083|4.55|98.5|7,929,048|4.63|2.2|286|

_Source:_ IHSI 2003.

The West and Artibonite regions have the largest population shares of Haiti’s 9 regions: 39.0 and 13.5 percent, respectively. In the West region, the people mainly reside in Port-au-Prince. The other regions have each between 3.8 (Northeast) and 9.8 (North) percent of the total population. The West also has the largest share of total urban and total rural populations, 65 and 22 percent respectively (Figure 2.1).

<!-- Start of picture text --> Figure 2.1: Rural and Urban Population and Total Population Growth Rates<br>Haiti and its Regions, 2001<br>2,500,000 4.0<br>Urban population 3.5<br>2,000,000 Rural population<br>3.0<br>Yearly pop. growth 1982-2003<br>2.5<br>1,500,000<br>2.0<br>1,000,000 1.5<br>1.0<br>500,000<br>0.5<br>0 0.0<br>Source: IHSI 2003<br>SoutheastGrand-Anse South NorthArtiboniteNorthwest CenterNortheast West<br>Population<br><!-- End of picture text --> 5
in the bottom percentiles in almost all countries, due to the association between fertility and education, income, and life expectancy.

**Figure 5. Top 60 percent households tend to have lower child dependency ratios than bottom 40 percent households in countries in all income categories**

Child dependency ratios in LIC countries Child dependency ratios in LMC countries <!-- Start of picture text -->
1.6 1.6<br>B40 T60 B40 T60<br>1.4 1.4<br>1.2 1.2<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>Child dependency ratios in UMC countries  Child dependency ratios in HIC countries<br>1.6 1.6<br>B40 T60 B40 T60<br>1.4 1.4<br>1.2 1.2<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>NER BEN BFA MOZ BDI MWI ETH UGA MDG GIN ZAR TZA AFG GNB MLI TCD TGO RWA COM NPL LBR BGD HTI KHM SLE CMR SEN YEM ZMB TMP SWZ CIV PAK VUT GHA WBG GTM STP GUY SYR NGA LAO BOL FSM PHL HND SLV MAR DJI SLB PRY CPV NIC EGY LSO IND IDN BTN MNG LKA VNM UKR<br>AGO BLZ BWA IRQ GAB MHL JOR MYS PAN DOM ZAF MEX VEN PER COL ECU JAM TUR LBN PLW BRA ARG CRI ALB IRN SYC THA FJI MUS MNE ROM HUN BGR BIH CHN BHS URY ISL SWE NOR USA CHL GBR IRL NLD FRA FIN DNK BEL CHE LUX AUT MLT CZE ITA SVN CYP HRV DEU ESP POL PRT LTU EST LVA GRC SVK RUS<br><!-- End of picture text -->

_Source:_ World Bank staff based on data from household surveys, circa 2010 but spanning 2001–10. _Note_ : The sample covers 33 high-income (HIC), 35 upper-middle-income (UMC), 37 lower-middle-income (LMC), and 25 low-income countries (LIC). Classificationof households into the top 60 and bottom 40 percent are based on the income distribution.

These findings suggest that there might be important distributional effects associated with demographic transition. The labor-market implications of rising levels of education, particularly for women, influence fertility. While higher educational attainment (especially of females) and higher household income are both associated with declines in fertility, the importance of education 20
The composition of services exports in the developing East Asian economies varies by country and over time (Table 2). For most developing East Asian countries (Indonesia, Cambodia, Lao PDR, Myanmar, Malaysia, Philippines and Thailand), travel services exports are at around 50 percent of total services exports or more. This is not the case in China and the Philippines, where “other business services” exports are currently relatively larger than other services.

Table 2. Importance of services in trade and in GDP, and the structure of services exports

|ISO3||Services ex<br>goods and<br>expo|ports (% of<br>services<br>rts)|Transport se<br>services|rvices (% of<br>exports)|Travel serv<br>services|ices (% of<br>exports)|Other ser<br>services|vices (% of<br>exports)|Servic|es values a|dded (% o|f GDP)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|code|Country|2005|2015|2005|2015|2005|2015|2005|2015|1985|1995|2005|2015|
|CHN|China|9.3|8.7|19.8|17.8|37.6|20.8|25.6|50.3|29.4|33.7|41.3|50.2|
|IDN|Indonesia|12.7|12.6|22.5|16.0|35.8|49.8|41.2|31.2|n.a.|41.1|40.3|46.5|
|KHM|Cambodia|25.6|30.6|12.8|11.8|78.9|82.9|8.3|5.3|n.a.|35.6|41.2|42.3|
|LAO|Lao PDR|25.0|22.4|19.8|8.5|75.4|85.1|4.8|6.4|n.a.|25.1|39.2|41.7|
|MMR|Myanmar|6.4|25.0|45.7|11.6|25.8|55.3|19.2|25.4|n.a.|n.a|35.8|38.7|
|MNG|Mongolia|27.8|12.8|48.7|34.7|43.2|35.7|8.1|28.4|56.4|31.0|41.7|51.6|
|MYS|Malaysia|12.2|14.8|20.7|11.6|45.0|50.9|33.4|30.2|40.5|45.6|45.8|55.1|
|PHL|Philippines|17.3|33.1|10.9|6.7|26.6|18.2|62.1|74.9|40.4|46.3|53.5|59.0|
|THA|Thailand|15.1|22.3|23.4|9.4|48.4|73.2|28.2|17.5|52.3|53.4|52.2|55.1|
|VNM|Vietnam|11.5|6.4|27.6|21.9|54.3|66.2|18.1|12.0|n.a.|n.a|n.a|44.2|
|dev. E|ast Asia|16.3|18.9|25.2|15.0|47.1|53.8|24.9|28.1|43.8|39.0|43.5|48.4|

Sources: WTO Notes: Nearest later or previous years used when data not available for 2005 and 2015. The aggregate for developing East Asia is a simple average.

# **iii. Integration in global value chains**

Developing East Asian countries are among the most integrated into global value chains (GVCs). Their integration is mostly occurring via backward participation in GVCs – that is, via using imported intermediate inputs in their exports (Figure 5a). Trade in value added (TiVA) data indicate that the share of foreign value added in gross exports in China and the other developing East Asian economies was higher than the aggregate for other regions during 1995‐2011.<sup>4</sup> The levels of forward participation (intermediate exports used in other countries’ exports) are lower than backward participation and also lower than forward participation of other regions, except for the countries that have commodities as significant items in their export bundles, such as Indonesia (Figure 5b).

> 4 Based on EORA data, developing East Asia is one of the three groups of countries with the highest levels of backward participation, the other two being Western Europe and the industrialized East Asia. China’s share is lower because the EORA input output tables do not account for processing trade.

11
# **<mark>Table 3: Estimated Class Membership Probabilities</mark>**

||π��|s.e.|
|---|---|---|
|Relations with Suppliers|||
|Pure bilateralism|0.657|0.024|
|Bilateralism with private support|0.166|0.022|
|Bilateralism with legal support|0.160|0.017|
|Strong comprehensive governance|0.017|0.004|
|Relations with Customers|||
|Pure bilateralism|0.565|0.032|
|Bilateralism with private support|0.242|0.000|
|Bilateralism with weak support|0.145|0.025|
|Weak comprehensive governance|0.050|0.008|

32
# **1 Introduction**

In recent years, the effect of ICT adoption over firm-level productivity has been widely studied and there appears to be consensus about the importance of ICT for firms’ performance (Syverson, 2011). However, in the context of models of Skill-Biased Technical Change (SBTC), ICT also affects firms’ organization, skill composition, demand for labor and therefore, wage inequality.

Since governments increasingly invest in programs aimed at promoting ICT adoption as a mechanism for boosting firm-level productivity, it is important to understand not only the effects of these policies over firm-level performance, but also how they could affect labor demand and wage inequality.

Most of the studies analyzing labor market outcomes, either in the framework of the SBTC theories or in the more recent skills and tasks (Autor et al., 2003; Acemoglu and Autor, 2011) and polarization (Michaels et al., 2014) strands of the literature, have focused on these outcomes for the case of developed countries, which tend to have better data availability for these kinds of analyses. Therefore, the evidence on ICT use and labor for developing countries is still scant.

Moreover, the small set of papers that have studied the impact of exogenous shocks on wages and labor demand in the case of Mexico have focused on the effects of its trade opening process, which according to the predictions of theoretical trade models should have decreased the wage gap between unskilled workers and skilled workers considering that Mexico attracted production processes that are intensive in low-skilled labor. However, these effects were not observed during the first decade after the North American Free Trade Agreement’s (NAFTA) entry into force, which has been the most important milestone of this process. This fact led to search for another explanation of the wage dynamics that were observed during this period such as changes in the supply of skilled-labor and the adoption of technologies.

This paper analyzes the effects of ICT adoption over labor demand of skilled and unskilled workers for the case of Mexico between 2008 and 2013 using data from Mexico’s Economic Censuses. Furthermore, we study whether a more intensive use of ICT is associated to a higher wage gap between these two groups. We also take advantage of a very detailed survey regarding ICT use in Mexico (ENTIC 2009 and 2013) in order to better understand some of the mechanisms that can explain what firms do when adopting ICT. Our main contribution is the analysis of wage-inequality and demand for skilled labor from a firm-level perspective for the case of Mexico, by using novel firm-level microdata (Census and ICT survey data) that to the best of our knowledge has not been used for this purpose before. A second contribution is the study of this relation for three different sectors (manufacturing, services and commerce), given the fact that the literature has focused on manufacturing due to its importance for productivity growth.

2
Policy Research Working Paper 11014

# **Abstract**

This paper examines the impact of livelihood diversification on food insecurity amid the COVID-19 pandemic. The analysis uses household panel data from Ethiopia, Malawi, and Nigeria in which the first round was collected immediately prior to the pandemic and extends through multiple rounds of monthly data collection during the pandemic. Using this pre- and post-outbreak data, and guided by a pre-analysis plan, the paper estimates the causal effect of livelihood diversification on food insecurity. The results do not support the hypothesis that livelihood diversification boosts household resilience. Although income diversification may serve as an effective coping mechanism for small-scale shocks, the findings show that for a disaster on the scale of the pandemic, this strategy is not effective. Policy makers looking to prepare for the increased occurrence of large-scale disasters will need to grapple with the fact that coping strategies that gave people hope in the past may fail them as they try to cope with the future.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at jdmichler@email.arizona.edu or tkilic@worldbank.org.

_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
Table B1 Estimates of Labor and Capital Adjustment Costs Leontief and Cobb-Douglas Production Functions

|Estimates|Baseline|Cobb-Douglas||
|---|---|---|---|
|Fixed Cost _γ_1|0.0379|0.026||
||(0.008)|(0.005)||
|Quadratic Cost _γ_2|0.178|0.163||
||(0.016)|(0.067)||
|Irreversibility Cost _γ_3|0.84|0.854||
||(0.057)|(0.096)||
|Mobility cost across employment sectors_Ce_|3.72<br>(0.48)|2.51<br>(0.22)||
|Mobility cost in or out of non-employment _Cu_|4.22<br>(0.26)|4.24<br>(0.20)||
|Moments|Baseline|Cobb-Douglas|Data|
|_Corr_(_I, I−_1)|0.163|0.173|0.145|
|_Corr_(_I, A_)|0.048||0.046|
|_Corr_(_I, A_)||0.044|0.044|
|_Negative spike_|0.004|0.002|0.014|
|_Corr_(_w −_<br>_w, N −_<br>_N_)|0.301|0.173|0.313|
|_Corr_(_N_0_, N_0_,−_1)|0.828|0.738|0.887|
|_Corr_(_N_0 _−N_0_,−_1_,_<br>_w_ _−_<br>_w−_1)|0.101|0.299|0.101|

Notes: The baseline column reproduces results from Table 2. The Cobb-Douglas column shows results under the Cobb-Douglas technology assumption in equation (23).

values of _b_<sup>0</sup> _jt_<sup>and</sup><sup>_pjt_,which we need to input separately into the prot function:log</sup><sup>_b_0</sup> _jt_<sup>= 0</sup><sup>_._5 log</sup><sup>_bjt_;</sup> log _b_<sup>0</sup> _jt_<sup>= 0</sup><sup>_._5 log</sup><sup>_bjt_.</sup> We calibrate the output elasticities _αL_ , _αK_ and _αM_ from the 1997 input-output table for Argentina. From the rm data we estimate the stochastic process for _bjt_ and _Aijt_ given by equations (18) and (19). We estimate the adjustment cost parameters Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3 _, Ce, Cu_ ) following the SMM objective function (20). We rst recompute the second empirical moment, which is the correlation between investment and idiosyncratic shocks _Corr_ ( _Iijt, Aijt_ ) , as the shocks _Aijt_ need to be recomputed under the new technology specication.

Table B1 reports the results, both for our baseline Leontief case and the alternative CobbDouglas specication. The estimates of capital and labor adjustment costs are comparable in order of magnitude under the two technology specications. The moments are also well-matched in the Cobb-Douglas case, although moments related to labor allocations are a better matched in the baseline Leontief case.

55