rafmacalaba/datause-encoder-data huggingface dataset:
assessments of cost-of-living differences.

2.2 Experimental Design: Measuring the Responsiveness of Migration Intentions to Alternate Scenarios of Stabilization We attempt to measure the degree to which migration intentions respond to signals of political or economic stabilization through a randomized survey experiment. We introduce hypothetical scenarios of political and economic stabilization in the opening question described in the preceding sub-section to measure the willingness to migrate. The sample is randomly assigned to two treatment arms representing political and economic stabilization, respectively, or a control group that provides no signal of stabilization. Political stabilization is signaled by prefacing the introductory question with a brief description of positive political developments such that the question would read as, _“Imagine a situation:_ **_The political situation in Myanmar improves, violent conflict reduces substantially, and a political agreement is reached_** _. You are offered a job in a country of your choice which involves the same type of work that you currently do, and which pays you what your currently earn. Your employer at the new job will also help you with relocation, including any travel costs and visas. Will you take the job?”_ subsequent iterative questions follow and the three outcomes of interest are measured in the same way as described earlier.

Similarly, economic stabilization is signaled by prefacing the introductory question as, “ _Imagine a situation:_ **_The economic situation in Myanmar improves, growth picks up and prices stabilize._** _You are offered a job in a country of your choice which involves the same type of work that you currently do, and which pays you what your currently earn. Your employer at the new job will also help you with relocation, including any travel costs and visas. Will you take the job?”_ We then assess whether migration intentions vary with the prospect of political or economic stabilization by comparing migration outcome variables for these two scenarios with the control arm, i.e., the neutral framing of the question as described in Section 3. that does not make any mention of the economic or political situation and therefore implicitly elicits responses under the prevailing situation. Respondents are randomly assigned to the control group or to either of the two treatment arms (political and economic stabilization) with equal probability to be assigned to one of the three groups (control and two treatment arms). Random assignment ensures that observed differences between treatment arms and the control group can be interpreted as causal estimates of the effects of stabilization on migration intentions.

> they had to.

9
had on average two employees and monthly profit of US$214, while in the study in Sri Lanka (de Mel et al. (2013)), businesses had on average three employees and monthly profit of US$300.

Businesses selected for the study have very similar characteristics to the whole population of informal businesses surveyed, and the overall study shows good external validity.

Formal businesses had on average 3.2 employees and monthly profits of around CFAF 210,000 (US$960), while informal businesses had 1.1 employees and a monthly profit of CFAF 46,000 (US$210). Formal businesses also had higher access to the banking system: 80% of them owned a bank account, whereas only 20% of informal businesses did.

# **3.3. Evaluation Design**

The 3,600 informal businesses were randomly allocated into three treatment groups and one control group. The first group of informal businesses received package A of incentives, the second group packages A and B of incentives, and the third group packages A, B and C.

The randomization was done in the office using STATA. The following methodology was used for stratification:

- (1) 16 strata were created using the following variables: business owner gender, business operating in Dantokpa market, trader and business owner a bank account.

- (2) Inside each stratum a Z-score was created as the average of standardized profits, turnover and number of employees. Based on this Z-score, triplets of businesses were created and inside each triplet, businesses were randomly allocated to 3 groups: group 0, group "1 and 2", and group 3. Each group included 1,200 firms.

- (3) The 1,200 businesses in group "1 and 2" were randomly allocated into group 1 with 300 businesses and group 2 with 900 businesses.

As a result, 300 businesses were allocated to group 1, 900 to group 2, 1,200 to group 3 and 1,200 to the control group. Figure 2 describes the organizational chart of the interventions.

14
Institute for the Semi Arid Tropics, Patencheru (AP)), suggest that the resistance to synthetic pyrethroids was not a feature of the bollworms until late 1987 (McCaffery et.al, 1989). It thus seems clear that the pesticide resistant strains have spread from the coastal to the inland areas, creating another serious negative externality, on other crops, in areas quite distant from the "hot spot".

# Cotton as an Efficient ExRort Crop

The second important reason to focus on cotton is because, based on a comparison of domestic and international prices and subsidy estimates, long staple cotton lint appears to be an efficient export commodity from India. On average, for the eighties, annual cotton exports (valued at about $300m) comprise about 6% of total domestic production. (Imports form about 1% of domestic production on average). However recent studies (Gulati, 1991) and World Bank reports have emphasized that there is scope for increasing the production as well as exports of long staple cotton. This could provide a st.:ong growth impetus to agriculture as well as foreign exchange to the economy. This recommendation is based on a consideration of the direct (paid out) and indirect (imputed) costs of cultivation. However to get the full economic costs of cotton production we must look at the extended costs of cultivation, i.e. factor-in the potential externalities arising in cotton production. This will enable us to more fully address the issue of whether production and export of cotton is economicaly 8
U.S. held only 11% of their portfolios in foreign equity and bonds in 2003. A similar pattern is observed in Japan, where institutional investors held only 16% of their portfolios abroad in 2003.<sup>15</sup> Although domestic residents hold the majority of their assets in their own countries, a large number of foreign investors, if allowed, tend to hold these foreign assets as well. Survey data published by the U.S. Department of the Treasury shows that residents of Japan were the largest foreign portfolio investors in U.S. securities by a wide margin in 2005.<sup>16</sup> They held U.S. $1.1 trillion (or 16% of the total holdings of U.S. securities by foreign investors), whereas residents of the U.K., the second major investing country, had holdings of U.S. $0.56 trillion, only half the holdings of Japanese investors. Previous surveys show that this pattern is stable over time. For example, in 1994, when the first survey was conducted, Japan held 18% of the total foreign holdings of U.S. securities. At the same time, Japan has consistently been one of the main destinations of foreign purchases of securities by U.S. residents. U.S. investors held around 10% of total market capitalization of equity markets in Japan in 2005.<sup>17</sup> Moreover, in 1994 U.S. residents invested 15% of their foreign portfolio holdings in Japan – the country that attracted the largest share of U.S. portfolio investments abroad. A more recent survey shows that Japan is still a large destination for U.S. funds, attracting 12% of U.S. holdings of foreign securities in 2005. Although no data is available on the holdings of other foreign investors in Japan (or even other holdings of Japanese investors), the evidence presented here suggests that the U.S. has been a major participant in this market.

By focusing on the U.S. and Japan and their bilateral current account, given the survey evidence presented above, we are analyzing the two largest holders of U.S. securities: U.S. investors themselves and Japanese investors, the largest foreign holders. We also examine large

> 15 See IMF (2005) _._

> 16 See “Report on U.S. Portfolio Holdings of Foreign Securities,” U.S. Department of the Treasury.

> 17 See “Report on Foreign Portfolio Holdings of U.S. Securities,” U.S. Department of the Treasury.

20
The timing of phases, thresholds and other parameters of the contract were determined using the PNUTGRO crop model (Gadgil, Rao and Rao, 2002) and interactions with farmers. The upper threshold corresponds to the crop’s water requirement, while the second trigger is intended to equal the water requirement necessary to avoid complete harvest failure.

The policy premium was initially benchmarked on projected payouts using historical rainfall data (at least 25 years of data for each rain gauge was used). The premium was initially calculated as the sum of the expected payout, 25 percent of its standard deviation and 1 percent of the maximum sum insured in a year, plus a 25% administrative charge and 10.2% government service tax. In some cases, the premium dictated by this formula was then reduced, since it was believed to exceed farmers’ willingness to pay. The policy was targeted towards small and medium size farmers with 2-10 acres of land. However, sales were not limited to this group; any household in the targeted villages was eligible to purchase the insurance product.

# _3.2 Example_

Table 2 presents contract details and actual payouts for castor insurance policies sold in Mahaboobnagar in 2004. Mahaboobnagar includes three mandals (counties) with a reference weather station, Atmakur, Mahaboobnagar and Narayanpet, against which contracts are written. For example, in Narayanpet, the per-policy premium for a policy covering all three phases of the monsoon is Rs. 200. One policy is considered to be equivalent of one acre of coverage. In 2004, the start date for the monsoon is a fixed calendar date, June 10, and the first phase is 35 days in length. Narayanpet received 12mm of rain in the first phase, 84mm of rain in the second phase and 177mm of rain in the third phase. This resulted in a maximum lump sum payout of Rs. 1500 in the first phase, since accumulated rainfall fell below the lower trigger of 60 mm. Rainfall during the second phase was also deficient, but exceeded the lower trigger level, resulting in a payout at Rs. 240 per acre insured (240 = [100mm - 84mm] x 15). Rainfall exceeded the upper threshold value in the third phase. Thus, insured households in Narayanpet received total payouts of Rs. 1740 per policy.

7
changes in the relative prices faced by consumers. Lastly, step 5 accounts for economy-wide changes in per capita household consumption growth.<sup>6</sup>

# 3. Scenarios and assumptions

We first generate the long-term baseline, then run a number of counterfactual scenarios. By comparing the two, we can isolate the impacts of various policy changes.

## Baseline

The GTAP 9 database is benchmarked to 2011. We run the model to 2018, replicating the key macroeconomic aggregates from the World Bank’s _Global Economic Prospects_ report (World Bank 2016a).<sup>7</sup> Population growth is based on the medium fertility variant of the 2012 UN’s population projections. Labor force growth follows the growth of the working age population – defined here as the demographic cohort between 15 and 64 years of age. The evolution of supply of skilled and unskilled workers is consistent with the IIASA constant educational trends (CER) scenario, where growth rates of the supply of skilled workers exceed that of unskilled. Capital accumulation is equated to the previous period’s (depreciated) capital stock plus investment. Productivity growth in the baseline is “calibrated” to achieve the growth rates for the baseline scenario (as in the World Bank (2016a)) up to 2018, then we fix the productivity growth for 20182030 to be consistent with historical trends. These productivity growth rates remain fixed in the counterfactual scenarios.

The baseline scenario also incorporates tariff reductions in existing PTAs. These are based on the data set provided by International Trade Center, including all TPP members FTA commitments up to 2030 (ITC and MAcMap (2015)). In addition, the baseline also includes pre-existing trade agreements among member countries (e.g., NAFTA, AFTA, the ASEAN-Japan FTA, the ASEANAustralia-New Zealand FTA and the P4 Agreement among Brunei Darussalam, Chile, Singapore and New Zealand).

## RCEP, FTAAP and EU-IDN CEPA

RCEP and FTAAP scenarios include liberalization of tariffs and NTMs following Wignaraja et. al. (2015). Initial tariffs are based on the GTAP data whereas NTMs are provided by Petri and Plummer (2016). The (trade-weighted) ad valorem equivalent NTMs for the different scenarios are reported in Table 1. For the countries that are also CPTPP members we use the CPTPP tariff schedules from ITC and MAcMap (2015) and MAcMap (2016). In case of RCEP, the

> 6 For a detailed specification of the GIDD micro model see Osorio-Rodarte (2016).

> 7 For China, we replicate the growth projections of World Bank (2013).

7
characteristics of the neighborhood in which they locate.

# **1 Using Landscan Data and Defining Urbanized Areas**

## **1.1 Landscan data**

To analyze measures of economic density, we need fine spatial resolution data to calculate neighborhood effects and variation in clustering within a city. And we need the most accurate data available. For countries like the USA, both finely gridded population and employment data are available from censuses. However, in most developing countries that is not the case. Population data are only available at a coarse scale such as regional or local government political unit, and economic censuses in Sub-Saharan Africa are generally nonexistent. Even when they do exist (e.g. Uganda), they tend not to be publicly available.

Our primary data source is LandScan (2012) from Oak Ridge National Laboratory in the USA, which is now being used in some research (e.g. Desmet et al. (2018)). Oak Ridge takes population data from censuses and other sources worldwide on as fine a spatial scale for each country as they can obtain. They then create a measure of an ambient population for each 1km grid square on the planet, in a process we describe below. The ambient population is meant to represent where people are on average over the 24 hour day. To assess the ambient population, they appear to use nocturnal and diurnal population estimates for at least some areas of the globe, although these are not publicly available. Later in a ground-truthing exercise for Nairobi and Kampala, we will demonstrate our own interpretation of how nocturnal and diurnal populations might be estimated and combined.

For Landscan, as for WorldPop<sup>1</sup> , the Global Human Settlements Layer<sup>2</sup> , and similar data sets, a key element in this process involves taking population numbers at some upper level of spatial scale and allocating people to fine grid squares based on where they are likely to live and possibly work. The typical standard in such work has been to allocate people on the basis of the relative extent of ground cover in a grid square from Landsat satellite imagery, or its enhanced versions.

> 1http://www.worldpop.org.uk/data

> 2http://ghsl.jrc.ec.europa.eu/datasets.php

4
- 19 -

18. To establish the degree of reliability of the error cnrrection specification of the mcdel, the restrictions implied by the prior cointegrated parameter (Zt) can be relaxed and a free error correction equation estimated by including the lagged values of the commodity price indices and the interest rate. That is, the term Zt-1 is replaced by the lags t-l of the two co-integrated variables. The results of estimation of the unrestricted model are presented in Table 6. The findings from these equations are very close to those of Table 5. The diagnostic tests suggest no evidence of autocorrelation or non-normality. It is also interesting to note that the out-of-sample stability test results indicate considerable parameter stability for these equations. Moreover, the F-test fails to reject the hypothesis that the estimations of the restricted and unrestricted equations are significantly different.

19. Finally, we estimate the static long-run solutions _1/_ of the models estimated in Table 6. These results are presented in Table 7 and with one exception the coefficients on the real interest rate variables are statistically significant at 95Z confidence intervals and very close to the co-integrated parameters given in Table 3. The coefficient on this variable in the equation for Agricultural Foods is higher by 2.5. This has probably resulted because of the interaction of the interest rate variable with the price of oil in the unresitricted regression. These results are, of course, in line with the theory of co-integration which claims that biases in parameter 1/ The PC-GIVE software package used for these tests provides these cuiutions.
Table 4.1: Probability of being Employed in the Nonagricultural Sector, <u>Rural Dispersed Areas in Argentina, 2003</u>

||Nonagri<br>Emplo|cultural<br>yment|Low-pro<br>Nonagri<br>Employ|ductivity<br>cultural<br>ment~~1~~|High-pro<br>Nonagr<br>Emplo|ductivity<br>icultural<br>yment~~2~~|
|---|---|---|---|---|---|---|
|Worker Characteristics:|dF/dx|P>|z||dF/dx|P>|z||DF/dx|P>|z||
|_Education_|||||||
|Primary complete|0.069|0.000|-0.075|0.000|0.186|0.000|
|Secondary complete|0.189|0.000|-0.076|0.000|0.389|0.000|
|University complete|0.434|0.000|-0.041|0.000|0.524|0.000|
|_Skills_|||||||
|Age|0.010|0.000|-0.003|0.000|0.002|0.000|
|_Gender_|||||||
|Male|-0.202|0.000|-0.246|0.000|-0.017|0.000|
|_Land_|||||||
|Land per capita|-0.001|0.000|-0.001|0.000|0.000|0.660|
|_Family characteristics_|||||||
|Family size|-0.013|0.000|-0.013|0.000|-0.003|0.000|
|_Region_|||||||
|Mendoza|0.055|0.000|-0.033|0.000|0.102|0.000|
|Santiago del Estero|0.196|0.000|0.135|0.000|0.108|0.000|
|Chaco<br>|0.288|0.000|0.172|0.000|0.187|0.000|
|Pseudos R<br>2:|0.1|18|0.1|60|0.|074|

_Notes_ : Excluded categories: No education or primary incomplete and Santa Fe. (1) Low-productivity nonagricultural employment: average monthly nonagricultural labor income is below the poverty line (2) High-productivity nonagricultural employment: average monthly nonagricultural labor income is above the poverty line _Source_ : Own calculation based on RHS 2003.

Findings from above and recent research have shown that the nonfarm sector can often be seen as a source of both high-return employment as well as a “last resort” option (see Ferreira and Lanjouw 2001). Therefore, following Ferreira and Lanjouw, this paper presents estimations of two additional models with the same specification of regressor, but differentiating between high-return nonfarm activities as opposed to low-return nonfarm activities. The nonfarm subsectors are designated as either high return or low return depending on the average monthly earnings accruing to the individuals whose primary occupation is in that sector. If the average monthly labor income is below the poverty line, the sub-sector is designated as low return, or low productivity sector. Conversely, if the average monthly return from a sub-sector is above the poverty line, the sub-sector is designated as high return.

Poor households are often involved in low-return nonfarm occupations. This may be seen as the equivalent of subsistence farming as mentioned by Reardon, Berdegué and Escobar (2001); low productivity, low wage, rather unstable, and with low growth 14
Policy Research Working Paper 10188

# **Abstract**

The relationship between paid maternity leave and the share of female workers in registered private firms is analyzed using firm-level survey data for 111 developing and emerging countries. Theoretically, the relationship can be either positive or negative. Higher maternity leave raises the cost of female workers to the employer, discouraging female employment. However, higher maternity leave encourages more females to enter the labor market, implying greater female employment. The results show that the latter effect dominates. That is, a positive, large, and statistically significant relationship is found between maternity leave and female employment. A conservative baseline estimate is that the share of female workers in a firm increases by 2.08 percentage points for each log point increase in the number of days of paid maternity leave. Alternatively, an increase in the number of days of paid maternity leave from its smallest to highest value (0 to 410 days) increases the share of female workers by 12.5 percentage points. The positive relationship between female workers and maternity leave is much larger when maternity leave is fully funded by the government than when paid for by the employer, and in countries where there is a higher share of females in the childbearing age group of 20–29 years. These heterogeneities highlight channels that accentuate the relationship, thereby serving as checks against endogeneity concerns with the estimation. The distributional implications of paid maternity leave are also analyzed by estimating its impact on low-skilled versus high-skilled employment. Important policy implications of the findings are discussed.

This paper is a product of the Global Indicators Group, Development Economics and the Office of the Chief Economist, Middle East and North Africa Region. 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 mamin@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