rafmacalaba/datause-encoder-data huggingface dataset:
# 5.1. The Case of Homogeneous Capital and Labor Inputs

It is instructive to compare our results with an alternative calibration that treats labor and capital as homogenous inputs, rather than disaggregating labor into routine and non-routine, and capital into ICT and non-ICT. In particular, we use a CES production function with capital and labor inputs, as in Karabarbounis and Neiman (2014): We choose _σ_ to match an elasticity of substitution between capital and labor of 1.28—the point estimate in Karabarbounis and Neiman (2014). We calibrate _γ_ to best match the capital and labor income shares, given the levels of capital per worker in the US during our sample period, and calibrate _A_ to match the levels of output given the observed levels of the capital stock.

We construct a capital aggregate using 2005 prices. Formally, we define _kt_ = _pc,_ 2005 _kc,t_ + _pn,_ 2005 _kn,t_ . The resulting price index is _pt_ = _pn,t_ <u>1</u> _<u>pc,tkc,t</u>_ <u>+</u> _ktpn,tkn,t_ , using our finding that _pn,t_ is roughly equal to the GDP deflator. The aggregate price index declines relative to the GDP deflator by about 25%, consistent with magnitudes discussed in Karabarbounis and Neiman (2014). Note that Karabarbounis and Neiman (2014) study the steady state implications of the declining capital price, and focus on the world as a whole. Since we focus on the US and are interested in the transitional dynamics, our results are not directly comparable (in particular, our welfare analysis takes into account the transitional costs associated with accumulating capital to reach its higher steady state level). The purpose of this comparison is to illustrate the differences and similarities in the quantitative implications resulting from our disaggregation of capital and labor inputs.

Table 6 summarizes the results. Consistent with the findings of Karabarbounis and Neiman (2014), both simulations imply that the declining investment price results in a 2-3% decline in the labor income share, about half of the observed decline in the aggregate labor income share. However, the steady state implications and the welfare implications are somewhat more modest when considering the disaggregated specification. The increases in steady state output, consumption, and wages are over 1.5 times higher when assuming homogeneous inputs, and the corresponding welfare gain is nearly 40% higher.

32
Table 5 uses data from surveys of Tongan and Vanuatu RSE workers conducted by the authors at the end of the second season of the RSE to summarize the self-reported reasons for individuals not returning for a second season. Approximately two-thirds in each country say they would like to migrate again, but were not selected for a second year. Few migrants say they have reached a target level of savings, or that injury or sickness is preventing them from going. The launch of Australia’s seasonal work program had led some to consider working there instead, given the higher pay rates available in Australia. The ni-Vanuatu workers expressing dissatisfaction with earnings the first time attributed this in part to the deductions from their wages being higher than they had anticipated – both because of income tax (something they were not used to), as well as for airfare and accommodation. The Tongan workers expressing dissatisfaction with earnings mainly attributed this to there being fewer hours of work than they had anticipated.

Among those workers who did return for a second season, Tongans said they would like to work in New Zealand for a median of two more seasons, while ni-Vanuatu would like to work for a median of four more seasons.

**<mark>Table 5: Reasons for Not Returning for a Second RSE Season</mark>**

||Percent of<br>migrants wh<br>important reaso<br>for anot<br>|one season<br>o saythis is an<br>n for not returning<br>her season<br>|
|---|---|---|
||Tonga|Vanuatu|
|Dissatisifed with how much theyearned the first time|42.7|50.0|
|Missed their familytoo much while away|66.0|23.1|
|Would like togo again,but haven'tyet been selected|64.1|65.4|
|Earned enough the first time to reach their target|0.0|24.0|
|Personal sickness,injury,or familyemergency|4.9|8.0|
|Prefer to tryand work in Australia'sprogram|23.3|48.0|
|It was too cold in New Zealand|0.0|64.0|
|Other|4.9|7.7|
|Number of Respondents|103|26|
|Source: SurveyResponses of Single Season Migrants,fro<br>and McKenzie (forthcoming)|m surveys describe|d in Gibson|

15
bribes. After controlling for capacity and competition, we also find that bribes are lower in countries where the utility companies have been privatized. One potential explanation for this final result is that private owners might have a greater incentive than public managers to impose stiff penalties upon employees taking bribes, reducing bribe payments.

Characteristics of the enterprise offering the bribe also affect payments. For example, enterprises that are more profitable appear to pay higher bribes - a result that is consistent with both the queuing (Lui, 1985) and the endogenous harassment (Myrdal, 1968) theories of corruption. Also consistent with the endogenous harassment theory, firms with higher overdue payments to utilities appear to pay higher bribes, perhaps because they have a weaker bargaining position vis-a-vis the employees of the utility company. The duration of the relationship between the enterprises paying and receiving the bribe and ownership also appears to matter: _de novo_ private firms are found to pay higher bribes than established firms. Finally, we find strong support for the complementarity of the overall level of corruption in a country and bribes in the utility sector.

# **II.** CONCEPTUAL FRAMEWORK

Consider a company that provides utility services (i.e., power or telecommunications services) and many firms that demand the service. If there is excess demand for the service that the utility provides - for example if there are price ceiling or if limits on public investment have historically limited system expansion - there will be rents associated with access to utility services. 6 In this situation, if employees of the utility company have some discretion over which enterprises or individuals get connected to the service or have broken down connections repaired, utility employees will be able to demand additional payments from the firms and individuals

> _5_ See Bertrand and Mullainathan (2001) for a general discussion about problems related to subjective survey data.

> 6 In practice, utility services in many developing and transition economies have been severely rationed, with long waiting lists for connections. The average waiting list over the number of main lines (or the waiting list ratio) is 0.17 for the 17 countries in our sample that have non-missing values for waiting list ratio in 1998 (authors' calculation based on the ITU data). In addition, see footnote 9.

3
# 3.2 Explanatory variables

# <u>Weather variables</u>

The maximum and minimum temperature variables are constructed using the monthly average of the highest maximum and lowest minimum temperature of the highest and lowest six months’ average monthly temperature during the year.

The rainfall variable is defined using the twenty year monthly average rainfall in the six months of the wet season for each region. The wet season is defined as the six-month period of highest rainfall from January to December for each region. The reference category is zero months with 20 percent rainfall above the 20-year average. Excess rainfall for a particular month is defined as monthly rainfall that is 20 percent above that month’s 20-year average rainfall. The first rainfall dummy variable takes the value 1 if there is one month’s excess rainfall during the wet season. The second rainfall dummy variable takes the value 1 if there are two or more months of excess rainfall during the six months of the wet season. The use of two excess rainfall variables allows us to test for nonlinearity in the degree and duration of excess rainfall on the health factors in comparison with the reference category of no excess rainfall.

Long-term rainfall is captured by two-year average rainfall and five-year average rainfall.

# <u>Climate zone</u>

The global map of climate is constructed using the Köppen-Geiger climate classification system based on a large global data set of long-term monthly precipitation and temperature station time series data (Kottek et al., 2006, Peel et al, 2007). These climate variables were calculated from a 0.5<sup>0</sup> x 0.5<sup>0</sup> latitude and longitude grid of monthly climate data.

To match this climate classification with our regional health data, we overlay the Köppen-Geiger climate map for Sub-Saharan Africa with the administrative map to assign all 115 regions under study with an appropriate climate type classification.

The localities across countries in Africa in our sample fall into ten out of the 30 possible climate classifications, as detailed in Table 2. The majority of regions in our sample fall into the climate classification categories of equatorial savannah with dry winter (Aw) (51 percent), followed by warm temperate climate with dry winter and hot summer climate type (Cwa) (15 percent) and steppe climate and hot steppe or desert (Bsh) (12 percent).

# <u>Socioeconomic variables</u>

We include variables on the proportion of households with access to piped water, the proportion of households without access to a toilet, the proportion of the adult female population with no education and access to health care. The health access variable is an 8
Table E.10 Tariff Equivalent of Distance

|Elasticities|||||||||
|---|---|---|---|---|---|---|---|---|
|Tariff||-2.2|-3|<br>-0.5|-3|-0.5|-3|-0.5|
|Distance||-1.6|-1.5|<br>-1.5|-1|-1|-0.5|-0.5|
||1|0%|0%|<br>0%|0%|0%|0%|0%|
||1.5|34%|22%|<br>238%|14%|125%|7%|50%|
||2|66%|41%|<br>700%|26%|300%|12%|100%|
||2.5|95%|58%|<br>1463%|36%|525%|16%|150%|
||3|122%|73%|2600%|44%|800%|20%|200%|
|Distance|3.5|149%|87%|4188%|52%|1125%|23%|250%|
|Multiple|4|174%|100%|6300%|59%|1500%|26%|300%|
||4.5|199%|112%|9013%|65%|1925%|28%|350%|
||5|222%|124%|12400%|71%|2400%|31%|400%|
||5.5|245%|135%|16538%|77%|2925%|33%|450%|
||6|268%|145%|21500%|82%|3500%|35%|500%|
||6.5|290%|155%|27363%|87%|4125%|37%|550%|

Notes: The first column uses the elasticity estimates of Linneman et.al.

# Distance Matrix: Miles

||US|CA|MEX|CHINA|
|---|---|---|---|---|
|US||2238|<br>1843|<br>6616|
|CA|2238||1199|<br>8741|
|MEX|1843|<br>1199||7734|
|CHINA|6616|<br>8741|<br>7734||
|Distance|Ratios|CA|Supplier<br>MEX|CHINA|
|Market|US<br>|1<br>1.2|1<br>1.0|o<br>3.61|
||MEX<br>|I<br>1.0<br>|<br>||6.51|
103 In principle, the measurement of nonearned incom is to capture an exogenous difference across persons in their budget constraint that does not also induce a change in money or time prices of various types of consumption or behavior. In practice, nonearned income (rents, dividends, interest, and capital gains) could arise from inheritances that are similar to schooling, in that they are largely financed by parents and family and can be **viewed as** *xogenous at **the** start of adult life. But nonearned income also represents returns on a person's life cycle accumulation of savings, and hence captures in part the person's behavior. It then becomes, for some purposes, an endogenous choice variable. Hence, it is desirable for survey questionnaireni to pursue the source of current nonearaed incom, current ssets, and the date of receipt of bequests that led to these current assets, and whether they came from the husband's or wife's side of the family. The Rand Malaysian family life survey comes closest to asking these questions, but I know of no analysis of these data from the perspective outlined in this raper (Butz and DaVanzo, 1978).

L/JEidence compiled by Svedberg (1988) indicates that female nutritional status and survival prospects in subSaharan Africa are superior overall to male, possibly because women are economically more productive in converting calories into work than oen. As a consequence, perhaps, parents are paid brideprices for their daughters and have a stronger incentive to invest in their health. The one region of subSaharan Africa vhere Svedberg's anthropometric indicators of nutrition and mortality do not indicate as strong a bias in favor of females is in Nigeria and perhaps Senegal. Both of these countries contain a significant Islamic element and women's productive roles are more circumscribed in these segments of the population (Caldwell and
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was not. ToT estimates reported in columns (3)-(4) are in line with the previous findings and show that the effect of the docuseries on viewers precisely doubled.

Table 3: Impact on willingness to share the videos right after the intervention

|_Dep. Var. (Y):_|_Willing _|_to share v_|_ideos with F_|_acebook friends_|
|---|---|---|---|---|
||(1)<br>ITT|(2)<br>ITT|(3)<br>ToT|(4)<br>ToT|
|Drama|-0.023<br>(0.024)|-0.025<br>(0.024)|||
|Documentary|0.060<sup>_∗∗_</sup><br>(0.027)|0.050<sup>_∗_</sup><br>(0.027)|||
|Play Drama|||-0.035<br>(0.037)|-0.037<br>(0.036)|
|Play Documentary|||0.125<sup>_∗∗_</sup><br>(0.057)|0.105<sup>_∗_</sup><br>(0.057)|
|Controls||✓||✓|
|R-squared|0.003|0.033|−0.001|0.029|
|P-value equal coef.|0.002|0.005|0.002|0.005|
|Observations|2269|2269|2269|2269|
|Wald F-statistic|||444.5|432.9|
|Mean Y (Control)|0.529|0.529|0.529|0.529|

Notes: The sample considered here is made of all participants who self-reported to complete the intervention. Heteroscedasticityrobust standard errors in parentheses. Controls are described in the notes to Table 2. In columns (3)-(4), independent variables Play Drama and Play Documentary take value 1 if the respondent has played half or more of the assigned video clips, as objectively recorded by the bot. These variables are instrumented using the random assignment indicators to the treatment groups. The first-stage Wald F-statistic is reported. * p < 0.10, ** p < 0.05, *** p < 0.01

# **4.3.3 Information-seeking behaviors**

Panel A of Table 4 shows that both treatment arms were effective in promoting informationseeking behaviors, with effects generally being larger for treatment 2 (though impact differences between treatment arms are not statistically significant). Individuals in treatment 1 and treatment 2 were respectively 7.3 and 10 p.p. more likely to click on both gender-equalityrelated websites provided to them at the end of the survey, which represented increases of 85% and 116%, respectively.

The impact on time spent visiting these websites was almost twice as large for those treated with treatment 2 compared to treatment 1. While individuals in treatment 1 spent on average an additional 2 minutes on the websites (compared to the control’s average of half a minute), individuals in treatment 2 spent an additional 3.4 minutes, which is 5.6 times longer than the average visit duration in the control group. As expected, we observe no significant effects on the likelihood of clicking climate-change-related websites nor on the time study participants 19
well-being as measured by consumption, income, and employment. We further exploit the importance of mediating effects such as access to state and financial services within a developing country. A key contribution concerning the effects of regularization programs in the Global South is that forced migrants in countries with large informal sectors are already part of this sector even without a work permit. Consequently, these programs do not give forced migrants the right to work per se but instead provide them access to the formal employment sector. Regularization may not allow all forced migrants to join the formal sector, but it improves their material labor conditions, gives them access to social safety nets, and enhances their well-being. Moreover, our analysis leverages comparisons between migrants who were regularized and those who were not, a comparison group previously unavailable to researchers. In fact, prior work evaluated the impacts of amnesties by comparing regularized migrants and native workers or newly regularized workers with previously regularized ones, since no similar data was available for irregular ones. We are the first to survey irregular migrants as a comparison group for program beneficiaries. As such, this paper is extremely informative of the counterfactual for forced migrants if amnesties such as DACA in the United States had never been created.

This paper also relates to the large group of prominent studies examining migrant assimilation (Ferrie 1994, Ferrie 1997, Hainmueller et al. 2015, Abramitzky et al. 2014, Abramitzky et al. 2021, P ́erez 2021, Nakamura et al. 2021) and the economic and cultural performance of migrants across heterogeneous contexts (Borjas 1991; Antecol et al. 2006; Bauer et al. 2011, Kaushal et al. 2016, Abel 2019, Boberg-Fazlic and Sharp 2020).<sup>5</sup> Unlike most such literature, we focus on how economic rights and access to public social programs change the shortterm paths of forced migrants in hosting countries, not on the impacts of migration per se on migrants.

Finally, the work most closely related to ours evaluates PEP’s implications for Colombian hosting communities, including impacts on labor markets (Bahar et al. 2021), crime (Iba ̃nez

> 5A seminal group of papers also examines the impacts of forced migration on education outcomes for refugees (see Becker et al. 2020, Nakamura et al. 2021, and Toews and Vezina 2020).

8
Policy Research Working Paper 7301

# **Abstract**

Maternal and child undernutrition is a pervasive and detrimental condition in Papua New Guinea. Despite rapid economic growth during the past decade, the stunting rate for children under 5, one of the primary indicators for child undernutrition, was estimated at 46 percent in Papua New Guinea in 2010, stagnant from 44 percent in 2005. This paper analyzes the association between the demographic, socioeconomic, environmental, and health-related factors on nutritional status for children under age 5 years, using the 2009–10 Papua New Guinea Household Income and Expenditure Survey. Stunting and underweight rates sharply rise in the first 24 months. Even in the better-off quintiles, children suffer from suboptimal breastfeeding and complementary food in the first 24 months. In general, the regression results showed that household wealth and geographic location are crucial factors that contribute to children’s malnutrition. More importantly, food quality, measured by protein intake, has significant predicting power on child malnutrition. Broadly increasing socioeconomic status and improving the quantity and quality of caloric intake are general steps to improving health outcomes in Papua New Guinea. In addition, three key areas were identified as critical to alleviating the persistent and detrimental stunting rate in the country: (1) exclusive breastfeeding and complementary food; (2) interventions by health workers; and (3) nutrition education.

This paper is a product of the Health Nutrition and Population Global Practice Group. 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://econ.worldbank.org. The author may be contacted at xhou@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
Policy Research Working Paper 5054

# **Abstract**

The use of expert or qualitative surveys to rank countries’ business investment conditions is widespread. However, within the economic literature there are concerns about measurement error and endogeneity based on characteristics of the respondents, raising questions about how well the data reflect the underlying reality they are trying to measure. This paper examines these concerns using data from 79,000 firms in 105 countries. The findings show that first, qualitative rankings correlate well with quantitative measures of the business environment, using both quantitative measures from within the survey and from external sources. Second, there are systematic variations in perceptions based on firm characteristics— focusing in particular on size and growth performance. However, it is not that an optimistic view of the business environment is simply the expression of a firm’s own performance. Rather, firm size and performance affect the _relative_ importance of certain constraints, particularly in areas such as finance, time with officials/inspectors, corruption, and access to reliable electricity. The results also show that much of the variation in subjective responses by firm types is largely due to differences in the objective conditions across firm types. There is little evidence that size and performance have non-linear effects in how constraining a given objective condition is reported to be. Overall, concerns about endogeneity remain in using business environment indicators to explain firm performance, but this stems primarily from the fact that who you are and how well you are doing can affect the conditions you face rather than whether the indicator used is qualitative or quantitative.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to explore firm level data and the microeconomics of growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mhallward@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