rafmacalaba/datause-encoder-data huggingface dataset:
In table 8, poverty and exposure are broken down by region and income category. A large share of the population is exposed and poor in SAR and SSA by both poverty lines. The shocks important to poor households at the $6.85 poverty line are split relatively evenly across type of shock for EAP and LAC and split relatively evenly across flood, drought and heatwave for MENA and LAC. In SAR, heatwaves still dominate as the most important event type for poor people also, and in SSA exposure to drought and heatwaves is high among poor people.

**Table 8: Number of people exposed and poor, by region and income group (millions)**

||Flood|Drought|Heat|Cyclone|Any shock|Any shock<br>(% ofpopulation)|
|---|---|---|---|---|---|---|
|**$2.15 poverty line**|||||||
|_Region_|||||||
|East Asia & Pacific|3|3|2|2|8|0|
|Europe & Central Asia|1|2|3|0|5|1|
|Latin America & Caribbean|3|6|1|3|11|2|
|Middle East & North Africa|2|4|11|0|15|4|
|Other High Income|1|1|1|1|3|0|
|South Asia|25|36|155|14|167|9|
|Sub-Saharan Africa|22|127|69|3|181|16|
|_Income group_|||||||
|High|1|1|1|1|3|0|
|Upper-middle|5|9|2|1|15|1|
|Lower-middle|36|94|199|16|260|9|
|Low|16|74|40|6|110|18|
|_FCV_|16|69|75|3|125|17|
|**$6.85 poverty line**|||||||
|_Region_|||||||
|East Asia & Pacific|116|95|199|88|372|18|
|Europe & Central Asia|5|16|11|0|28|6|
|Latin America & Caribbean|17|32|12|15|66|11|
|Middle East & North Africa|27|18|46|0|78|19|
|Other High Income|1|2|2|3|6|1|
|South Asia|201|271|1,215|109|1,302|71|
|Sub-Saharan Africa|58|287|170|4|424|38|
|_Income group_|||||||
|High|2|4|2|3|9|1|
|Upper-middle|114|118|196|45|365|13|
|Lower-middle|273|438|1,355|158|1,652|56|
|Low|35|163|100|13|251|41|
|_FCV_|48|153|199|10|312|41|

Notes: Regions use the Poverty and Inequality regional classification, which differs from the regional classification used by the World Bank. Some economies, mostly high-income economies, are excluded from the geographical regions and are included as a separate group referred to as “other high income” (or “industrialized economies” or “rest of the world” in earlier publications). <u>https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html#regionsandcountries. Income groups are for FY21</u> (data for calendar year 2019).

# 5.2 AT RISK TO EXTREME WEATHER EVENTS

For these same events and risk thresholds, we consider the share of households that are exposed and highly vulnerable. Being highly vulnerable is defined as failing to reach the threshold in or lacking access to one or more dimensions (e.g., lacking access to electricity or coverage by social protection or having an insufficient income). The share of the global population for which we have data varies across the dimensions so in addition to presenting the number of people exposed and highly vulnerable we present the share of the global population that is exposed to a shock and highly vulnerable by each dimension 27
Some authors argue, in spite of the lack of more comprehensive evidence, that goods trade is the main channel of technology transfer for most countries. See e.g. Gomulka (1990), p. 161 and Kaplinsky (1990), p. 21.

2One of the few comprehensive quantitative assessments of the importance of imitation and reverse engineering is de Melto et al. (1980). They report that half of a sample of 280 significant innovations commercialized in Canada between 1960 and 1979 could be characterized as "imitations", and that more than half of these resulted from reverse engineering. Mansfield, Schwartz, and Wagner (1981) find that 60 per cent of the patented innovations in their sample were imitated within 4 years. Kim and Kim (1985) also present evidence of imitation and informal technology transfers in 42 South Korean firms. We will not examine these studies in detail because they do not focus on foreign investment, but it should be remembered that MNC presence in the host country probably facilitates imitation of MNC technology.

3 The determinants of the MNCs' choice between exports, licensing, and foreign direct investment are not discussed in this study, but treated in detail by e.g. Blomstr6m and Zejan (1991), Contractor (1984), Davidson (1980, 1983), Davidson and McFetridge (1985), Stobaugh (1988), Telesio (1979), Vernon and Davidson (1979), and Zander (1991).

4Mansfield and Romeo (1980) present indirect evidence also for this argument. They claim that the export of technologies from parents to U.S. affiliates abroad speeded up the emergence of competing products or processes in the host countries by an average of 2.5 years in about a third of their cases. In addition, they report that more than half of the managers of a sample of British firms believed that they had introduced some products and processes earlier as a consequence of transfers of technology to U.S. affiliates operating in the United Kingdom. More evidence will be presented later, in the survey of empirical studies of spillovers.

_5_ For theoretical studies, see e.g. Findlay, (1978), Koizumi and Kopecky (1977), Das (1987), and Wang and Blomstrom (1992). Some other early empirical studies are Balasubramanyam (1973), Brash (1966), Deane (1970), Dunning (1958), Forsyth (1972), Gabriel (1967), Rosenbluth (1970), and Safarian (1966).

34
# **Who are the Microenterprise Owners?: Evidence from Sri Lanka on Tokman v. de Soto**

Suresh de Mel, David McKenzie, and Christopher Woodruff<sup>*</sup>

> * University of Peradeniya, World Bank, and University of California, San Diego, respectively. The authors thank Josh Lerner, Antoinette Schoar, Tavneet Suri and participants of the NBER conference on International Differences in Entrepreneurship for valuable comments and Susantha Kumara and Jayantha Wickramasiri for outstanding research assistance. AC Nielsen Lanka administered many of the surveys on which the data are based. Financial support from NSF grant numbers SES-0523167 and SES-0617424 is gratefully acknowledged. This paper was drafted in part while Woodruff was visiting LSE, whose support is also greatly appreciated.
# **Table 2: Sex-Differential Fertility Stopping Behavior and Effect of the Islamic Inheritance Rule on the Actual and Ideal Proportion of Sons**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||||Dependen<br>|t Variable:<br>||
||Actua|l Proportion|of Sons|Ideal Proport|ion of Sons|
|||Ever Born||Women’s Ideal|Men’s Ideal|
|2 Children|-0.13<br>(0.077)||-0.15*<br>(0.072)|0.0021<br>(0.18)|-0.22<br>(0.16)|
|3 Children|-0.12||-0.16*|-0.089|-0.26|
||(0.074)||(0.072)|(0.18)|(0.16)|
|4 Children|-0.14||-0.18*|0.0047|-0.26|
||(0.075)||(0.071)|(0.18)|(0.16)|
|5 Children|-0.13||-0.18*|-0.067|-0.32|
||(0.074)||(0.072)|(0.18)|(0.16)|
|6 Children|-0.14||-0.20**|-0.072|-0.33*|
||(0.078)||(0.076)|(0.18)|(0.16)|
|7 Children|-0.15*||-0.21**|-0.16|-0.28|
||(0.074)||(0.076)|(0.19)|(0.18)|
|8 Children|-0.15||-0.22**|0.051|-0.35*|
||(0.077)||(0.077)|(0.18)|(0.17)|
|Muslim* At least one brother at||0.054|0.058|0.17**|0.13*|
|childbearing||(0.068)|(0.066)|(0.061)|(0.054)|
|Muslim * At least one brother at||-0.47|-0.60*|-0.13|-0.93***|
|childbearing * Childbearing after 1994||(0.26)|(0.29)|(0.21)|(0.22)|
|Mean, dependent variable|0.52|0.52|0.52|0.40|0.36|
|Mean, dependent variable if 1 child|0.65|0.65|0.65|0.44|0.44|
|R-squared|0.01|0.08|0.10|0.13|0.16|
|Observations|1931|1931|1931|1614|1671|
|Sample|Wife age|Wife age|Wife age|Wife age|Wife age|
||40 to 49|40 to 49|40 to 49|40 to 49|40 to 49|

* p<0.05,  ** p<0.01,  *** p<0.001.

Note: OLS estimates of interactions between indicators of Muslim religion, having at least one surviving brother at the time of first child birth, and starting childbearing in the post-compilation period. Parentheses contain standard errors clustered at the PSU-level. Regressions use survey weights. Baseline sample excludes mixed-religion and polygamous couples. Columns (1) to (5) report estimated coefficients for a subsample of couples in which women are 40 to 49 years old. All regressions include fixed effects for the men’s total number of siblings, women’s age, the year of first child birth, and area and region of residence. Other controls included are men and women’s educational attainment and indicator of household wealth. Data summary is provided in Appendix 1.

33
Policy Research Working Paper 9769

# **Abstract**

Do larger interventions improve longer run outcomes more cost effectively? And should poverty traps motivate increasing intervention size? This paper considers two approaches to increasing intervention size in the context of temporary unconditional cash transfers—larger transfers ( _intensity_ ), and adding complementary graduation program interventions ( _scope_ ). It does so leveraging 38 experimental estimates of dynamic consumption impacts from 14 developing countries. First, increasing intensity decreases cost effectiveness and does not affect persistence of impacts.

This result can be explained by poverty traps or decreasing marginal return on investment in a standard buffer stock model. Second, increasing scope increases impacts and persistence, but reduces cost effectiveness at commonly evaluated time horizons and increases heterogeneity. In summary, larger interventions need not have more persistent impacts, and when they do, this may come at the expense of cost effectiveness, and poverty traps are neither necessary nor sufficient for these results.

This paper is a product of the Development Impact Evaluation 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 fkondylis@worldbank.org and jloeser@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
Columns 3 and 4 of Table 6 show a more sizeable impact of migration on birthweights after instrumentation. Being in a household with at least one migrant is estimated to raise birthweight by 364 grams, or 0.64 of a standard deviation, and 335 grams once state level controls are included. Birthweight is likely to be better measured for children delivered by doctors, and so in Column 5 we show the results just for this group of children, obtaining a very similar estimate of the increase in birthweight from migration as for the full sample. Column 6 shows a one standard deviation increase in household migration prevalence raises birthweight by 140 grams, or 0.25 standard deviations. This effect is five times as large as that associated with a one standard deviation increase in mother’s schooling. Columns 6 and 7 provide similar results in terms of the impact of migration on the probability of being born underweight. Migration lowers this probability, with a stronger effect found after instrumentation.

Both the infant mortality and birthweight results show stronger improvements in child health from migration after instrumentation. Failure to consider the selectivity of migration therefore understates the impact of migration. This suggests that, in the absence of migration, children in what are currently migrant households would have poorer health status than children in observationally similar non-migrant households. From this we infer that on net, Mexican migrants to the U.S. are negatively selected in terms of the health status of their children.

# **5.3. Impact on Health Inputs and Behaviors**

The ENADID also provides results on several health inputs, both during the time of birth and during infancy. Table 7 examines the impact of migration on whether a child was delivered by a doctor, whether they were breastfed at all, whether they visited a doctor at least once in the first year of life, and whether they received vaccinations for tuberculosis, DPT, polio and measles. After instrumenting, we find children in migrant households to be significantly more likely to be delivered by a doctor, but less likely to be breastfed, vaccinated, or visit a doctor in the first year of their life. Although migration appears to result in lower infant mortality and higher birthweights and better care at the time of delivery, it therefore appears that children of migrants receive less preventative health care in their infancy. One possible reason for this may be a higher opportunity cost of time for migrant parents, and periods where one or both parents are absent from the children, making it more difficult to breastfeed and take the child to health

- 13 -
specialization since it enables small, innovative firms to access a larger pool of potential buyers and complementary services that cannot be provided in-house. Larger cities also provide a larger home market for end products, make it easier to attract skilled employees who are attracted by urban amenities not available in smaller towns, and support a large number of complementary service providers such as financial and legal advisers, advertising and real estate services.

The well-known Herfindahl measure is used to examine the degree of economic diversity in each district. The Herfindahl index of a district _j_ ( _Hj_ ) is the sum of squares of employment shares of all industries in district _j_ : Unlike measures of specialization, which focus on one industry, the diversity index considers the industry mix of the entire regional economy. The largest value for _Hj_ is 1 when the entire regional economy is dominated by a single industry. Thus a higher value signifies lower level of economic diversity. Therefore, for more intuitive interpretation of the measure, for the diversity index in our model, _Hj_ is subtracted from unity. Therefore, _DVj_ =1- _Hj_ . A higher value of _DVj_ signifies that the regional economy is relatively more diversified.

Variables on district specific characteristics were computed from various sources. The human capital variable reflects the share of each district’s working age population with primary school or higher education. This comes from the 2002 Uganda Census. Other variables such as terrain roughness were derived from USGS/NASA SRTM data. In order to analyze roughness per district, these data were aggregated into 100 by 100 groups of cells (approximately 10 km by 10 km). For each aggregation, a mean was computed in order to determine overall elevation variability within a district (Thomas, 2007). In addition, we were also able to disaggregate major crop production by district using a Spatial Allocation Model (SPAM) developed by IFPRI (see You et al., 2007 and You, Wood and Wood-Sichra, 2007). The complete list of data sources are provided in the Appendix.

14 -
As is now standard in the literature, we include domestic trade flows (Yotov 2021) as well as zero-trade flows, and estimate the model multiplicatively with the Poisson Pseudo Maximum Likelihood (PPML) estimator (Santos Silva and Tenreyro 2006) using a panel of consecutive years (Egger, Larch, and Yotov 2022); standard errors are clustered three-way (importer, exporter, and year) (Larch et al. 2019).<sup>2</sup> We regress exports _Xijst_ of sector _s_ from source country _i_ to destination _j_ on dummy variables that capture the different types of trade agreements, i.e., _EnClijt_ , _shallowijt_ , and _deepijt_ , while controlling for the effectively applied tariffs _ln_ (1 + _τijst_ ). Including tariffs allows us to disentangle between the effects of tariff cuts and changes in non-tariff trade costs due to RTAs. Additionally to the measures for policy changes, we include exporter-sector-time and importer-sector-time fixed effects _νist_ and _νjst_ , which control for multilateral resistances Ω _ist_ and Ω _jst_ (Anderson and Van Wincoop 2003) as well as for any other country-sector specific trends such exchange rate fluctuations or sector-specific demand or technology shocks. These terms also capture all multilateral policy changes (both of tariffs and of NTBs). Lastly, the country pair-sector fixed effects, _μijs_ , absorb all possible time-invariant bilateral trade costs consequently controlling for omitted variables such as bilateral distance or common language.

# **2.2 Accounting for Omitted Variables**

Next, we describe how our empirical approach departs from the traditional identification strategy to account for confounding factors, that are especially caused by measurement error in the standard sources for tariff data as well as general trends in trade policy. Both omitted variables substantially bias the NTB effect of deep trade agreements upwards.

The advantage of the indirect approach is that it is less demanding in terms of necessary data: instead of gathering information on changes in NTBs that are hard to quantify and to compare across different types of barriers, we only need information on tariffs and the presence of trade agreements. However, the indirect approach hinges on the correct measurement of tariffs: If tariffs are mismeasured the agreement dummies pick up not only changes in NTBs but also in tariffs happening simultaneously with the implementation of the trade agreement. Deep trade agreements typically reduce tariffs much more, while shallow ones often exclude entire sectors such as agriculture. Hence, the finding of the literature that only deep trade agreements reduce NTBs could be the result of a classical omitted variable problem whenever the empirical approach does not adequately control for tariffs.

> 2 Three-way clustering by importer, exporter, and year is the most conservative approach and yields the largest standard errors. For robustness, we also use alternative clusters that have been used in the existing gravity literature i.e., the pair level.

7
innovation, and of those, 4,222 invested exclusively in external sources of innovation. A total of 5,225, firms invested in internal innovation, but only 534 invested in only internal sources of innovation.

_Table 3. Determinants of Innovation Decision, Panel Firm Fixed Effects_

|Dep. Var.<br>{Z’_ijt_}|(1)<br>Internal<br>Only|(2)<br>Internal All|(3)<br>External<br>Only|(4)<br>External<br>All|(5)<br>Both|(6)<br>All|
|---|---|---|---|---|---|---|
|Firm size|-0.007<br>(0.269)|0.015<br>(0.328)|0.021<br>(0.146)|0.043<sup>**</sup><br>(0.005)|0.022<br>(0.138)|0.036<sup>*</sup><br>(0.015)|
|Tech staff ratio|0.003<br>(0.741)|-0.003<br>(0.914)|0.049<sup>*</sup><br>(0.023)|0.043<br>(0.076)|-0.006<br>(0.796)|0.046<br>(0.058)|
|Skill|-0.009<br>(0.179)|0.006<br>(0.689)|0.026<br>(0.091)|0.041<sup>**</sup><br>(0.009)|0.015<br>(0.289)|0.032<sup>*</sup><br>(0.037)|
|Export status|0.004<br>(0.605)|0.021<br>(0.189)|0.037<sup>*</sup><br>(0.012)|0.055<sup>**</sup><br>(0.001)|0.017<br>(0.281)|0.058<sup>***</sup><br>(0.000)|
|FDI status|0.001<br>(0.898)|0.005<br>(0.781)|-0.026<br>(0.167)|-0.022<br>(0.284)|0.004<br>(0.818)|-0.021<br>(0.299)|
|Firm age|-0.000<br>(0.665)|0.001<br>(0.275)|-0.001<br>(0.290)|0.000<br>(0.874)|0.001<br>(0.233)|0.000<br>(0.963)|
|Dominance|-0.002<br>(0.943)|0.289<br>(0.408)|-0.258<br>(0.385)|0.033<br>(0.915)|0.291<br>(0.401)|0.031<br>(0.920)|
|# of obs.|13,257|13,257|13,257|13,257|13,257|13,257|
|# of dep.=1|534|5,225|4,222|8,913|4,691|9,447|
|<br># of group|3,332|3,332|3,332|3,332|3,332|3,332|
|R<sup>2</sup>, within|0.002|0.005|0.006|0.010|0.006|0.011|
|Rho|0.458|0.647|0.610|0.601|0.659|0.598|

_Note:_ Firm fixed-effects (within) regression; _t_ statistics in parentheses, standard error are clustered and robust; year and industry dummies are included but not reported. Significance level: * = 5 percent, ** = 1 percent, *** = 0.1 percent Columns (2) and (4) show the factors that influence the probability of firms engaging in internal and external sources of innovation respectively. Column (6) shows probability of investment in any source of innovation (all). I added columns (1), (3), and (5) to see whether 32
38

**Table 8. Marginal Effects of Tobit Regression on Determinants of Rural Income in Egypt**

|||Marginal Effects F|rom Income Equ|ation Based On:||
|---|---|---|---|---|---|
|Variable|Non-Farm|Agnicultural|Transfer|Livestock|Rental|
|Land<br>Amount of land owned<br>by household|-26.186**|68.388**|1.358|7.913**|20.773**|
|Amount of land rented<br>in by household|-84.577**|-146.313**|-23.624|29.023**|15.421**|
|Labor||||||
|Household size|-18.352**|9.702|0.265|1.204|4.191|
|Mean age of all household<br>members|24.180**|-7.345|8.573**|0.253|0.649|
|Mean age of all members<br>squared|-0.393**|0.179*|-0.031|-0.002|0.036|
|Number of household males<br>over 15 years|37.100*|36.468|-26.311**|3.912|3.449|
|Capital<br>Mean education of household<br>males over 15 years|11.939|4.468|-10.887*|-2.403|-3.814|
|Mean education of household<br>males squared|1.205|-1.134|0.692*|0.179|0.293|
|Value of farm equipment|-0.004|-0.001|-0.001|0.003**|0.001|
|Value of enterprises owned|0.011**|-0.002|-0.002|0.001|0.001|

Notes: Marginal effects calculated from tobit results in Table 7; effects of governorate-level dummy variables are not reported.

> * Coefficient in income equation significant at .05 level.

> ** Coefficient in income equation significant at .01 level.