rafmacalaba/datause-encoder-data huggingface dataset:
whole samples. A positive score indicates that a caregiver ranked the child higher on the perceived intelligence scale than the child ranked on the ASQ-I. Given that small differences between the ranks may not be meaningful, we categorized these scores into under-estimation (difference less than -2), matched (difference between -1 and 1), and over-estimation (difference greater than 2).

# 2.3. Covariates

## <u>Child Characteristics</u>

We examined mean-centered child age (in months), gender, and birth order with caregiver under- and over-estimation. We also included height-for-age z-score (HAZ) and weight-for-age z- score (WAZ), measured according to the World Health Organization (WHO) Child Growth reference standards (WHO Multicentre Growth Reference Study Group, 2006). The WHO defines stunting and underweight as having a HAZ and WAZ, respectively, two standard deviations below the median of same-age and sex of a global reference population.

## <u>Caregiver Characteristics</u>

All caregiver variables are for the primary caregiver of the child. In the majority of cases, the primary caregiver was the mother, but in some, an older relative was the primary caregiver. We included mean-centered caregiver age (in years), education, depression, and belief of own influence on child intelligence in our analyses. We classified caregiver education into (1) Did not attend school; (2) Primary or less; (3) Secondary or Higher. Caregiver depression was measured using an adapted version of the Center for Epidemiologic Studies Depression Scale (CESD), a screening tool used to assess depressive symptoms (Radloff, 1977). The CESD was adapted, translated, and tested before use. The ten-item scale examines depressed affect, somatic symptoms, and positive affect. Items were summed and standardized to create a depression score. A higher 8
**Figure 11 Fiscal risks from early termination of public-private partnership portfolios of South Asian countries as a percentage of total government revenues**

<!-- Start of picture text --> (a) Low (b) Medium (c) High<br><!-- End of picture text --> <!-- Start of picture text --> 0.30 0.30 0.30<br>0.25 0.25 0.25<br>0.20 0.20 0.20<br>0.15 0.15 0.15<br>0.10 0.10 0.10<br>0.05 0.05 0.05<br>0.00 0.00 0.00<br>2020 2021 2022 2023 2024 2020 2021 2022 2023 2024 2020 2021 2022 2023 2024<br>Afghanistan Bangladesh Bhutan India Nepal Pakistan Sri Lanka<br>Percent<br><!-- End of picture text --> _Note:_ Each data point represents the maximum expected loss at the 99 percent confidence from early termination of the public-private partnerships in the portfolio over the period starting at the end of 2019 and ending at the end of the corresponding year as a percentage of the total government revenue over the same period. The analysis assumes that annual government revenues as percent of GDP during the period 2020– 24 remains the same as in 2019.

_Source:_ Authors’ calculations.

# **_4.4.2. Adverse macro-financial shock_**

Multiple types of macroeconomic shocks have significant effects on the probability of early termination (table 3). The fiscal risks presented so far assume there will be no macro-financial shock such as depreciation of local currency or a stress in the banking sector.

This section simulates the results of a macro-financial shock. The Systematic Banking Crises data set of Laeven and Valencia (2018) identifies 104 banking crisis episodes among the countries included in the PPI data set, out of which 13 also involved both sovereign debt and currency crises. In the episodes of banking, debt and currency crises, the maximum yearly deviation in depreciation rate from its long-run average ranged between 15.1 and 116 percentage points with an average of 48.3 percentage points.<sup>21</sup> The simulation assumes a 48.3 percentage-point depreciation shock,

> 21 The statistics exclude two episodes in Ecuador because the adjusted currency conversion factor remained stable, even though the official rate of depreciation was 56.8 and 77.2 percent in the banking crises of early 1980s and the late 1990s, respectively.

25
# **_Country‐specific factors affect the type and rate of technology transfer_** _._ A host of country‐

specific elements affect the rate of such transfers and adoption. These are trade policies, the strength of intellectual property protection in host countries, transport costs, the business climate (including finance), finding complementary labor skills at the right price and anything else that affects firm profitability. Policies influencing immigration and capital flows also affect the rate of technology transfer. The final impact on the _host_ country depends on the form in which technology or innovation crosses borders (for example, licensing agreements) as well as the conditions inherent in the country (e.g., on their economic structure, the business climate, how well markets work, human capital and other endowments, or the macroeconomic framework).<sup>29</sup> The paucity of R&D related innovation joined with a high prevalence of institutional barriers preventing “compensation” mechanisms from working effectively in developing and emerging economies (DMEs) means that the net effect of innovation tends to be labor‐saving to a larger degree than it is in more developed countries. Box 1 presents a possible scenario for a garment exporting developing country.

# **_So...new technology adoption by MICs today has similar effects on their economies as new_**

**_technology adoption historically has had on today’s advanced economies; lower income countries respond differently._** A number of studies examine the impact of innovation (generally in the form of technological adaptation, often through trade) on labor markets in developing countries. These tend to focus on the skill‐bias of technological adoption, rather than on overall employment. For example, Almeida (2009) using a firm level data set spanning 8 countries (in the early 2000s) finds that greater openness and technology adoption have increased the demand for skills in MICs. However, for manufacturing firms in China and low‐income countries there had been greater specialization in low‐skill intensive goods (such as garments in Bangladesh). This skill bias in MICs is consistent with their having

> 29 Note that there is a two‐fold effect. What technology is transferred depends on host country conditions, but the impact of technology is also determined by a variety of host‐country conditions.

22
<!-- Start of picture text --> Wage Inequality Decomposition Contribution to Changes in Wage Inequality<br>0.45 3.00% 3.00%<br>0.4<br>035 1.00% 1.00%<br>0.3<br>0.25 -1.00% -1.00%<br>o2 -3.00% -3.00%<br>0.15<br>0.1 -5.00% -5.00%<br>0.05<br>oO -7.00% -7.00%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>TMBetween-firm & Within-firm mem Between-firm me Within-firm ——Aggregate Wage Inequality<br><!-- End of picture text --> <!-- Start of picture text --> Decomposition of Between-firm Wage Inequality Contribution to Changes in Between-firm Wage Inequality<br>100% 6.0% 6.0%<br>90% 4.0% 4.0%<br>80% 2.0% 2.0%<br>70% 0.0% 0.0%<br>60%50% -2.0% -2.0%<br>40h -4.0% -4.0%<br>30% -6.0% 6.0%<br>20% -8.0% -B.0%<br>10% -10.0% -10.0%<br>0% -12.0% “12.0%<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2013’ Dots! 201s: 2ox6) 2017: zoRw 52019 i2oe0 {202%<br>=FirmPremium & Sorting mun Firm Premium smmSorting ——Between-firm Wage Inequality<br><!-- End of picture text -->
Table 2 _MEAN_ & STANDARD DEVIATIONS OF VARIABLES, 1965 AND 1987

|Variable|19|65|19|87|
|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|
|_RPS Variables:_|||||
|Per Capita GDP (Current Prices)|547.15|760.22|4291.63|5860.05|
|Per Capita GDP (Constant Prices)|2526.00|4968.00|3428.00|3780.00|
|Share of Industry in GDP|27.63|<br>13.18|29.73|<br>10.79|
|Share of Services in GDP|44.22|<br>10.46|49.49|<br>10.66|
|Share of Agriculture in GDP|28.18|<br>18.48|20.81|<br>16.94|
|Share of Industry in Employment|18.00|<br>13.80|14.30|<br>8.20|
|Share of Services in Employment|26.00|<br>16.00|43.20|<br>23.60|
|Share of Agriculture in Employment|<br>55.70|<br>29.30|42.40|<br>29.30|
|Degree of Urbanization|36.28|<br>24.59|48.99|<br>25.60|
|_LNSVariables:_|||||
|Female Primary School Enrollment|68.36|<br>35.24|86.00|<br>30.66|
|Male Primary School Enrollment|84.87|<br>29.80|95.68|<br>24.43|
|Female/Hale Primary School Ratio|0.76|<br>0.26|0.88|<br>0.18|
|Female Secondary School Enrollment|<br>19.76|<br>21.71|46.68|<br>34.02|
|Male Secondary School Enrollment|26.10|<br>22.19|50.47|<br>29.67|
|Female/Male Secondary School Ratio|0.60|<br>0.32|0.83|<br>0.33|
|Female Life Expectancy at Birth|55.16|<br>11.97|64.94|<br>11.35|
|Male Life Expectancy at Birth|51.82|<br>10.81|60.66|<br>9.99|
|Female/Male Life Expectancy Ratio|1.06|<br>0.03|1.07|<br>0.03|

_Notes: Enrollment percentages are ratlos of enrollment In school to the population Ln the relevant age group._ 33
# **5.1 A Measure of Aggregate Productivity Loss**

In this section we will use the results from the micro level regressions to construct a measure of aggregate productivity loss. The micro findings have the advantage that identification is stronger than in cross-country studies. Their disadvantage is that outside validity is not ensured. We therefore first take the micro findings at face value and analyze the implications for the macro economy in the long run. We then show that the cross-country data is consistent with a productivity loss of a size implied by the micro studies.

Akresh et al. (2012) provide estimates of the height loss in cm due to exposure to the Nigerian civil war for different age groups between 0 and 16. Females exposed to the conflict between age 0 and 3 lost on average of 0.75 cm in height as compared to non exposed females. This loss increased to 1.09 cm for the age group 4-6, 2.16 cm for the age group 7-12 and finally 4.53 cm for the age group 13-16. Identification relies on a difference-in-differences model in which authors interact an indicator for war-exposed ethnicity (the Igbos) with a continuous cohort-based measure of months of exposure to the war. Based on these relatively well identified micro level estimates, we propose an estimate of a long run macro-level productivity loss due to conflict.

Suppose workers enter the labor force at age 17 with differences in productivity levels due to previous exposure to conflict. Once in the labor market, productivity dynamics are the same for all cohorts, and workers exit the labor force at age 60. We assume that if there is a conflict at time _t_ in country _i_ , all children of age _j_ suffer a loss that depends on the intensity of the conflict.

We follow three alternative approaches to measure conflict intensity and map it into a persistent productivity loss. First, we proxy conflict intensity by the per capita number of battle related deaths. To mitigate the effect of very intense conflicts on our loss measure, we prefer, however, to categorize conflicts in two groups: intense versus non intense conflicts. We use a dummy that takes a value of 1 if the number of per capita battle related deaths exceeded the level suffered in Nigeria during the Biafra civil war.<sup>31</sup> In addition, we use data on the stock of refugees provided by the UNHCR. We believe that flows of refugees are a good proxy for civilian suffering during conflict. We consider therefore a dummy that takes a value of 1 if there is a net outflow of refugees in a given year. However, we are aware that a big challenge of this proxy is that some conflicts by nature cause more internal movements or no movements at all because people are just not able to flee the affected areas.

The loss for a worker of age _h_ at year _t_ in country _i_ is calculated as: and the total loss in country i at year t is given by where _Shti_ is the share of workers of age _h_ in total labor force, _It−h_ + _j,i_ is a measure of

> 31We discuss the effect of a change in the threshold below.

34
42 corresponding marginal cost statistics. These differentials are indicative of the imprecision of our estimated cost functions. Indeed, altering the specification of the primary restricted cost functions modestly (dropping this or that insignificant variable, for example) moves the estimated shadow prices about substantially. However, that the shadow prices of bought electricity exceeds the marginal tariff price (marginal cost) of bought electricity is a robust finding across specifications.

Also robust across alternative specifications is the finding that the shadow price of bought electricity falls with the level of bought electricity. Larger firms value bought electricity less at the margin than do smaller firms. Among firms with positive shadow prices for bought electricity, the elasticity of shadow price with respect to bought electricity is -.65 in Nigeria and -.44 in Indonesia, numbers that did not vary widely across alternative specifications. These elasticities are not far from the elasticities of marginal cost of own electricity with respect to own electricity: -.44 in Nigeria and -.60 in Indonesia. In broad brush, what these numbers suggest is that firms that firms' marginal willingness to pay for publicly provided electricity varies directly with the firms' marginal cost of providing their own electricity. The shadow prices of bought electricity shifts much like the marginal costs of own electricity does. Thus, the shadow price evidence reinforces the argument for a policy shift from decreasing to increasing block price tariffs for purchased electricity. Firms that purchase smaller quantities of electricity value electricity more highly at the margin. Redistributing the limited supply of publicly provided electricity from larger purchasers to smaller would increase economic efficiency by moving the electricity from lower valued uses to higher valued uses. (viii) Estimation of the Unrestricted Primary Cost Function for Thailand Only 6% of Thai manufacturing firms had their own electricity generators. Clearly, Thailand's electricity utility fills the needs of industry far better than do its Nigerian and Indonesian counterparts. Thai firms experience relatively few power outages and voltage fluctuations, sparing them the need for their own.
|**Table 5: FirmCrea**|**tion, North and Sout**|**h**<br>_New_|,<br>_s_<br>_d t_<br>_Firm_|||
|---|---|---|---|---|---|
||Light<br>manufacturing<br>(1)|Heavy<br>manufacturing<br>(2)|Other<br>industry<br>(3)|WRT<br>services<br>(4)|Other<br>services<br>(5)|
|**a. North**||||||
|−<br>,<br>1<br>d t<br>NewMFirm|0.111|0.059|0.143|0.242|0.633***|
||(0.206)|(0.131)|(0.148)|(0.221)|(0.100)|
|,<br>d t<br>NewMFirm|0.679*|0.046|0.130|–0.001|0.034|
||(0.359)|(0.095)|(0.104)|(0.112)|(0.267)|
|+<br>,<br>1<br>d t<br>NewMFirm|0.419*|0.406|0.032|0.058|–0.263|
||(0.226)|(0.293)|(0.046)|(0.249)|(0.161)|
|+<br>,<br>2<br>d t<br>NewMFirm|–0.429***|–0.128|–0.050|0.322|0.007|
||(0.106)|(0.085)|(0.049)|(0.213)|(0.232)|
|Constant|0.366***|0.153***|0.110***|0.425***|0.538***|
||(0.028)|(0.022)|(0.016)|(0.026)|(0.029)|
|_N_|<br>1,407|<br>1,407|<br>1,407|<br>1,407|<br>1,407|
|_R_<sup>_2_</sup>-statistic|<br>0.036|<br>0.018|<br>0.007|<br>0.019|<br>0.027|
|**b. South**||||||
|−<br>,<br>1<br>d t<br>NewMFirm|0.012|0.123***|0.239***|0.117*|0.058|
||(0.053)|(0.044)|(0.067)|(0.064)|(0.071)|
|,<br>d t<br>NewMFirm|-0.042|0.163***|-0.003|0.123*|0.049|
||(0.047)|(0.047)|(0.045)|(0.073)|(0.056)|
||(0.030)|(0.025)|(0.014)|(0.032)|(0.025)|
|+<br>,<br>1<br>d t<br>NewMFirm|0.167***|0.067|0.105***|0.213***|0.109***|
||(0.051)|(0.044)|(0.032)|(0.045)|(0.036)|
|+<br>,<br>2<br>d t<br>NewMFirm|0.060|0.080|-0.030|0.189***|0.046|
||(0.041)|(0.051)|(0.038)|(0.056)|(0.053)|
|Constant|<br>0.409***|<br>0.211***|<br>0.116***|<br>0.384***|<br>0.583***|
||(0.030)|(0.028)|(0.023)|(0.029)|(0.032)|
|_N_|<br>1,449|<br>1,449|<br>1,449|<br>1,449|<br>1,449|
|_R_<sup>_2_</sup>-statistic<br>_Source:_Authors’ estimat|<br>0.027<br>es based on data from the|<br>0.019<br>2014 Integrated Bus|<br>0.027<br>iness Establish|<br>0.050<br>ment Survey.|<br>0.028|

_Note:_ This table presents estimates for equation 2. The dependent variable is an indicator variable indicating entry of at least one new firm in year _t_ in district _d_ in the given sector. The database used consists of a district-year panel comprising 119 districts and 24 years (1990–2013) based on the location and year of entry of the firm. Other industry = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. Other services = services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

26
**_Figure 3:  Share of Services Sub-Sectors in Employment_**

<!-- Start of picture text --> Electricity, Gas & Water Wholesale, Retail Trade & Repair Motor Veh. Hotels & Restaurants<br>Macedonia Ukraine Croatia<br>Lithuania Latvia Hungary<br>Slovak Rep. Croatia Czech Rep.<br>Estonia Poland Slovak Rep.<br>Lithuania<br>Slovenia<br>Croatia EU-15<br>Estonia<br>Bulgaria Hungary<br>Bulgaria<br>Latvia Estonia<br>EU-15<br>Hungary Czech Rep.<br>Romania Serbia & Mont. Latvia<br>Bulgaria Lithuania<br>Czech Rep. Slovak Rep. Serbia & Mont.<br>Poland<br>Slovenia Macedonia<br>Slovenia Macedonia Poland<br>EU-15 Romania Romania<br>0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 0% 5% 10% 15% 20% 0% 1% 2% 3% 4% 5% 6%<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br>Transport, Storage & Communications Financial Intermediation Real Estate, Renting & Business Activities<br>Latvia EU-15 Poland<br>Estonia Slovenia Estonia<br>Hungary Croatia EU-15<br>Czech Rep. Poland Hungary<br>Serbia & Mont. Serbia & Mont. Latvia<br>Lithuania Czech Rep. Czech Rep.<br>Bulgaria Slovak Rep. Croatia<br>Croatia Hungary<br>Slovak Rep.<br>Slovak Rep. Latvia<br>Bulgaria<br>EU-15 Macedonia<br>Lithuania<br>Ukraine Estonia<br>Ukraine<br>Slovenia Lithuania<br>Romania<br>Macedonia Bulgaria<br>Poland Ukraine Serbia & Mont.<br>Romania Romania Macedonia<br>0% 2% 4% 6% 8% 10% 12% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 0% 2% 4% 6% 8% 10%<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br><!-- End of picture text --> Source: WIIW database 2005 and KLEMS database 2007.

Complementary evidence on the importance of specific services sub-sectors across ECA countries is provided in Appendix Figures A.1 and A.2, which show the average share of each sub-sector in the WIIW database in services value added and services employment in 1997-2000 and in 2001-2004. Appendix Figures A.1 and A.2 along with Figures 2 and 3 suggest a relative specialization of particular ECA countries in particular sub-sectors. In hotels and restaurants, Croatia exhibits a substantially higher share of services value added and employment than the average for the ECA region, especially since 2001. Russia and Poland exhibit a particularly high share of services value added in wholesale, retail, and repair of motor vehicles during the entire period. In financial intermediation, Serbia and Montenegro displays a relatively high share of 8
parameter values are selected as follows for Latin American countries: discount rate � = 0:988, sticky wage elasticity "w = 10:50 (matching 10% unemployment rate), steady-state government purchases Gss = 0:19 (from World Development Indicators), and capital share � = 0:35 (in order to match K/Y from KLEMS). Since macroeconomic and in‡ation volatility is larger in emerging markets, the price-setting behavior in those countries is less rigid (see Barros et al., 2009, for Brazil). For this reason, we calibrate a higher frequency of price adjustments in developing countries (1.5 times a year relative to once a year in the developed world). This implies Calvo probabilities �p = �w = 0:625. Based on this alternative calibration exercise, the quantitative STM delivered by the model on impact is 0.84 in the Latin American sample, only 0.08 smaller than the benchmark calibration. Moreover, the prevalence of HtM individuals, �, and the ...scal targeting e¤ort aimed to reach HtM individuals beyond their population representation, STT , help explains about 80 and 8 percent, respectively, of the overall di¤erence in the e¤ect of social transfers on output depicted by the model (i.e., 0.71). This implies that about 12 percent of the overall di¤erence in the e¤ect of social transfers on output depicted by the model (i.e., 0.71) is explained by other parameter di¤erences between developed and Latin American countries.<sup>37</sup> To sum up, a standard calibrated TANK model is able to match key STM empirical regularities shown in Section 2 in terms of the crucial role of the higher share of ...nancially constrained individuals who live HtM in the Latin American sample relative to the developed countries has in explaining the large di¤erence in the size of STMs previously estimated as well as regarding the main macroeconomic variables involved, and temporal pro...le for both the developed and Latin American samples.

# 6 Final thoughts

Based on novel empirical evidence and quantitative results, we have shown the critical implications of having large shares of ...nancially constrained individuals on the size of the STM. A larger share of individuals living hand-to-mouth causes social transfer shocks to easily reach individuals with a high marginal propensity to consume which, in turn, increases aggregate consumption and output. For this reason, the e¤ect on output is mainly driven by consumption while investment remains mostly unchanged. These ...ndings coupled with the evidence supporting a larger share of HtM agents in emerging markets (beyond that of our six Latin American countries) suggest that a larger STM may be expected for emerging market economies in general.

Two further re‡ections emerge from our analysis, especially when translating our ...ndings into ...scal policy action. First, given the large size that the STM can achieve especially in emerging markets, social transfers emerge as a natural ...scal policy tool to help vulnerable families who are

> 37 In particular, 6 percent is due to di¤erences in monetary policy, 4 percent given by heterogeneity in nominal rigidities and 2 percent of the di¤erence by other factors.

22