rafmacalaba/datause-encoder-data huggingface dataset:
input data file, (2) the number of subgroups, (3) the number of records, (4) the type of data configuration (codes 1 through 8), and (5) the DOS name for the output file. Once this input has been received for each of the two specifications of the Lorenz curve underlying the simulation tool, the program provides an estimate of the Lorenz curve, along with relevant statistical summary measures. It prompts the user to supply a poverty line and a different estimate of the mean of the distribution (if he/she does not want to use the estimate based on the data).

# **Box 1: Lorenz Curve Approach: POVCAL Assessing Poverty Dynamics in Madagascar**

Essama-Nssah (1997) uses POVCAL to analyze the dynamics of poverty in Madagascar between 1962 and 1980. Over this period, different governments showed various levels of concern over poverty issues. Some of the policy choices targeted either the rural or the urban sector. From the early 1960s to the mid-1970s, public policy favored the rural sector by lifting the poll and cattle tax applicable to the sector and by providing farmers free access to agricultural inputs. In 1977, an urban bias was introduced in public policy when the government increased the minimum wage and decided to subsidize basic items such as rice, edible oils, and condensed milk.

The study is based on two published aggregated data sets on income distribution in the rural and urban sectors. These data are in the form of class intervals with the associated frequency and mean incomes. The analysis through POVCAL revealed that, while poverty in Madagascar remained a predominantly rural phenomenon, poverty generally increased between 1962 and 1980 in both rural and urban areas. A decomposition of the poverty outcomes into growth and inequality components showed that increased income inequality in the rural sector was the major cause of the observed increase in rural poverty (see the table below). In the urban sector, however, increased poverty was most likely due to the lack of economic growth.

The study concluded that the urban bias introduced in government social policies in the mid-1970s was not justifiable strictly on grounds of poverty reduction. A simulation of what the level of poverty would have been had rural and urban mean incomes been set to the national average showed that a significant reduction in aggregate poverty could have been achieved had the government pursued effective policies to reduce the regional disparities.

**Poverty Measures and the Decomposition of Poverty Outcomes in Madagascar 1962–80**

|Measure|Value(1962)|Value(1980)|Change|Growth|Inequality|Residual|
|---|---|---|---|---|---|---|
|||Rural|||||
|Headcount|46.65|42.25|−4.40|−17.72|6.24|7.08|
|Poverty Gap|10.50|15.24|4.74|−5.67|10.29|0.12|
|Squared Poverty Gap|3.15|7.51<br>Urban|4.36|−2.06|7.68|−1.26|
|Headcount|13.35|18.47|5.12|9.74|−1.34|−3.28|
|Poverty Gap|2.72|6.73|4.01|3.88|1.20|1.07|
|Squared PovertyGap|0.73|3.31|2.58|1.73|1.00|−0.15|

_Source_ : Essama-Nssah 1997.

10
interest rates since the 1960s to the worsening of government fiscal positions in industrial countries.'<sup>3</sup> If the decline in saving rates in industrial countries continues, world longterm real interest rates may rise even higher than the 4 to 4.5 percent average range observed since the mid-1980s. Fortunately, however, there are reasons to think that the declining trend in industrial country saving rates could be reversed, at least for a time.

Private saving rates in industrial countries over the next 15 years or so are likely to be boosted by a rise in the share of population of higher-saving age groups, as the baby boom generation enters middle age. The proportion of industrial country population aged between 40-64 years-the prime earning and saving years-is expected to rise from 40 percent today to 45 percent by 2010, with the proportion of population aged between 20-39 years-typically a time of net borrowing-declining from 42 to 34 percent (Chart 11). This demographic shift is expected to more than offset the negative effect on saving of a continuing rise in the share in population of those over 65 years-who typically save little or dissave-from 18 to 21 percent. Also, the baby-boom generation is much richer and appears to be saving at higher rates than its previous cohorts. It is estimated that these factors could cause aggregate net household financial savings in industrial countries to rise in real terms over a ten-year period by about $5 trillion on top of a trend increase of about $7 trillion, yielding a total of $12 trillion (Chart 12).14 The favorable demographic shift is expected to be especially pronounced in the United States, potentially raising its net saving rate from 4.9 percent in 1992 to 6.2 percent in 2010.

> " IMMF(1995a).

14 McKinsey (1994). See also Borsch-Supan (1995).

18
Bangladesh. Differences between the crest level of embankment of each polder and the inundation depths projected for 2050 ‐ with and without climate change have been computed to identify polders likely to be overtopped by intensified storm surges in a changing climate and to quantify the potential extent of overtopping. The result indicates 26 interior polders and 33 sea facing polders in the coastal region will be overtopped in a changing climate by 2050.

# b) _<u>Foreshore Afforestation</u>_ <u>:</u>

In the past, foreshore afforestation scheme proved to be cost effective in dissipating wave energy, and in reducing hydraulic load on the embankments during storm surges<sup>47</sup> , but Bangladesh has inadequate foreshore forests. At present, the total length of embankment of 49 sea facing polders is 957 km, of which only 60 km has forest belts; and in many areas the existing forest belt is degraded. Officials of the Department of Forests and experts of the Institute of Water Modeling (IWM) have commented on the successes, failures and sustainability of past foreshore afforestation measures. According to their recommendation, a minimum of 500 meter width of mangrove forest is required for protection of sea facing polders. In this analysis, existing length of coastal afforestation, between the coastline and polders, has been estimated from Google Earth using GIS methodology; and the gap between recommended 500 m wide mangroves and area‐specific existing ones has been computed.

# c) _<u>Cyclone Shelters</u>_ <u>:</u>

At present, cyclone shelters in the coastal region of Bangladesh play a very important role in protecting human lives and livestock during cyclones<sup>48</sup> . Although the need for and use of 47 Benefit of foreshore afforestation was evident during the 1991 cyclone, Sidr (2007) and Aila (2009). It was reported that massive devastation to property and loss of lives during the 1991 cyclone, in Chokoria and areas around it, was primarily the result of virtual absence of mangrove forests along their coasts (BCAS). An in‐depth study of the damages and losses inflicted by the Sidr (2007)has noted that even scattered and unplanned forestation on the foreshore of the embankments has substantially helped in breaking the velocity of storm surges (GoB, 2008).

> 48 During the Sidr (2007), 15% of the affected population took refuge in cyclone shelters; and it has been estimated that cyclone shelters saved thousands of lives.
treatment effects. One of the concerns, which was also noted by Lu and Yu (2015), is that the timing of China’s WTO accession may have had unobserved anticipatory effects that also affected the outcome variables. In view of the extended negotiations that took 15 years to complete, in which each WTO member had to agree to China’s accession, we argue that it was difficult to predetermine that 2001 would have been the year of China’s WTO accession. However, following Lu and Yu (2015) strategy, we also include �������� � �������� and ���������� � �������� in our _DID_ specifications in order to address this concern. The inclusion of this extra control enables us to check whether the expectation effect on the coefficient of the treatment effect a year before WTO accession. �������� and ���������� refer to dummies for districts that have neither mineral deposits nor active mining, respectively.

China-Africa relations have further deepened since 2000 when the forum on China-Africa cooperation (FOCAC) was created. It is possible that the creation of this forum had unobserved biases on the pre-treatment trends of our outcome variables. FOCAC has convened every three years since 2000, which means that two meetings were convened during the 2002–2007 period: one in 2003 (in Ethiopia) and one in 2006 (in Beijing). It is not unreasonable to assume that resolutions arrived at in these forums might have had some direct and indirect effects on our outcome variables. To address such concerns, we argue that the country-year fixed effects suffice to account for any potential unobserved bias.

The last concern is the likelihood that the accession impacts within the treated group also affect the control group; for example, through intra-districts trade or cross-border interactions – the violation of the classical stable unit treatment value assumption (SUTVA).<sup>19</sup> Indeed, we expect the treated and control districts to have been trading even before China’s WTO accession. Nonetheless, we do not have district-level trade data with which to squarely address this concern. As a way to control for this violation, we rely on both district and country-year fixed effects controls. We argue that these fixed effects implicitly capture the unobserved intra-trade or crossborder interactions between the treated and control districts.

# 4. RESULTS

# _A. Descriptives_

Table 3a reports the descriptive statistics for annual lights growth, population density, spatial Gini, and the spatial Sen Index from 1997 to 2007. The table shows that, on average, annual light growth was around 0.03 percent. Population density averaged 288 people per square kilometer. Spatial Gini and Sen Index averaged 0.254 and 4.003, respectively. Except for spatial Gini, the rest of the outcome variables show considerable variations around their mean.

> 19 We rule out the second SUTVA assumption, variations in treatment, since the treatment does not vary across the treated group: all districts are subject to the same post-WTO accession dummy for the treatment period 2002–2007.

14
The team was able to track the results of the RCT without additional development based on Babajob’s existing data tracking methods. Results tracking was performed through May 24, 2017.<sup>11</sup> Babajob tracked every application made by a user, storing a record of the same, as well as anytime an application is “Unlocked” (the primary result we are tracking, which signifies an employer is placing value on the applicant). In addition to this, Babajob has tracked which job seekers have completed the personality test and their answers.

# <u>Descriptive Analysis</u>

In this sub-section we show descriptive results to characterize the experiment. Personality traits were distributed as follows (including both treatment and control).

There is potential bias in the distributions due to voluntary decisions made by seekers to take the test. Therefore, the distribution is not representative of personality traits in the general population. For the same reason, there can be differences between this result and the first pilot done by phone as the pilot was conducted under strict confidentiality. Except in extraversion, the traits are one-sided: cooperative in agreeableness, organized in conscientiousness, imaginative in openness, and calm in emotional stability. Those who decided to take the test might have an intention to make them look attractive to potential employers by choosing particular answers.

Next, the treatment and control groups are compared in personality test scores as well as traits. In our experiment, 51.5% of the sample is in treatment (Table 2). Since the treatment is a random sub-sample of test takers, it is designed that the treatment and control groups are probabilistically ex-ante identical.

Table 3 shows that personality traits (defined in this study) are balanced between the treatment and control groups. Two-sided t tests could not differentiate the two groups (the equality of means is not rejected at the conventional level).

Table 4 compares gender and education distributions by treatment status. Though we do not perform a test to compare the two groups, it is evident that the two distributions extraordinarily resemble. Similarly, age distributions are compared between the treatment and control groups (Figure 1), which confirms that the two groups are balanced in age. The above descriptive results appear to provide a good justification to identify the impact of non-cognitive skills (additional information on profiles) on job market outcomes.

> 11 There are variations in the tracking period. The last test taker was recorded on May 16.

11
**TABLE 13. Job turnover. Share of unskilled workers**

||<br>|FE<br>|FE‐<br>|2SLS<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|‐0.0011|‐0.0056***|‐0.0011|‐0.038**|
||(0.0017)|(0.0016)|(0.019)|(0.018)|
|ICT|0.00018|‐0.0046***|‐0.0052|‐0.035*|
||(0.0016)|(0.0016)|(0.021)|(0.018)|
|ICT * High Productivity|‐0.0022|‐0.0019|‐0.00035|0.000036|
||(0.0021)|(0.0020)|(0.0049)|(0.0043)|
|ICT|‐0.0013|‐0.0056***|‐0.0052|‐0.040**|
||(0.0018)|(0.0016)|(0.018)|(0.017)|
|ICT * Skills|0.00098|‐0.00014|‐0.0038|‐0.0061|
||(0.0028)|(0.0026)|(0.0054)|(0.0048)|
|ICT|0.010**|0.0079|0.029|0.0096|
||(0.0039)|(0.0050)|(0.018)|(0.016)|
|ICT * Internet|‐0.011***|‐0.014***|‐0.021**|‐0.042***|
||(0.0039)|(0.0050)|(0.0098)|(0.0084)|
|ICT|‐0.00085|‐0.0028**|‐0.011|‐0.049**|
||(0.0016)|(0.0014)|(0.020)|(0.020)|
|ICT * Computers|‐0.00045|‐0.0064**|0.0019|0.0024|
||(0.0024)|(0.0027)|(0.0060)|(0.0057)|
|Observations|3,566|3,564|3,566|3,564|

Dependent variable: change in the share of unskilled workers. Regressors: ICT investment dummy, ICT investment dummy interacted with firm‐level indicator variables defined as in Table 5. Instruments defined as in Table 3. Robust standard errors in parenthesis.

37
# AN EXACT **APPROACH** FOR EVALUATING **THE** BENEFITS FROM **TECHNOLOGICAL** CHANGE

Given the central role of technical change in agricultural developmen **t** , he high and rising pressure on agricultural research budgets, and the need to allocate scarce research resources efficienty, a framework for the analysis of benefits from research and technical change is required. The task is complicated by the wide variety of forms of technical change that affect agriculture. An extensive literature on the evaluation of benefits from research has now developed (e.g., see the survey by Norton and Davis, 1981). This literature has provided many insights into the effects of different types of technical advance on the welfare of particular groups, and in the presence of particular distortions (e.g., Edwards and Freebairn, 1981; Alston, Edwards and Freebairn, 1988). However, it has proved difficult to generalize these insights to situations involving more than one technical change or more than one distortion, especially in aes where more than one price is endogenou **s** o that there is more than one source of general equilibrium feedback (e.g., see Thurman, 1991b, and Alston, 1991, pp. 41-46). In most countries agriculture is characterized by differential distortions among closely-linked markets, so that a methodology that cannot deal with new technology in a distorted multi-market setting is of limited applicability.

In addition, there are some other problems with the state of the art of evaluating technological change. For the most part, the analysis has relied upon standard Marshallian consumer and producer surplus measures which can, at best, provide only approximations to the true welfare effects, are not amenable to generaliztion, and do not exploit the many advantages offered by a modem, dual formulation. With the simple _ad hoc_ supply functions used in most
**The Effects of Business Environments on Development: Surveying New Firm-Level Evidence**<sup>**1**</sup>

Lixin Colin Xu World Bank

> 1 I am grateful to Manny Jimenez, Mohammad Amin, Robert Cull, Ann Harrison, Guofang Huang, Justin Lin, Rita Ramalho, Yan Wang and especially three anonymous referees for helpful discussions, comments, and suggestions. The opinions expressed here are the author’s alone and do not implicate the World Bank, its executive directors, or the countries that it represents.
A great advantage of this survey is its broad coverage of the extent of innovation that the firms undertake. Previously, there has been very little consistent data across countries on the nature of innovative activities undertaken by firms. Moreover, the available data typically covers only the developed countries and focuses on patents and R&D expenditures. However, the issues are likely to be different for most developing countries where imitation and adaptation of already-created and tested innovations, rather than cutting-edge innovations, are likely to be more important. Thus, in studying innovation in developing countries, it is all the more important to define innovation broadly rather than just as new inventions.

The new investment climate surveys allow us to capture the rate of firm innovation in this broader sense. Specifically, the surveys ask firms if they have undertaken any of the following ten innovative activities in the last three years: _Developed a major new product line, Upgraded an existing product line, Introduced new technology that has substantially changed the way that the main product is produced, Discontinued at least one product (not production) line, Opened a new plant, Closed at least one existing plant or outlet, Agreed to a new joint venture with a foreign partner, Obtained a new licensing agreement, Outsourced a major production activity that was previously conducted in-house, and Brought in-house a major production activity that was previously outsourced._ The firm responses are coded as 0-1 (No-Yes) dummy variables for each of the 10 questions.

In addition to the ten individual indicators of firm innovation, we analyze three aggregate indicators for each firm as reported in panel B of Table 1. Our approach parallels La Porta et al’s (1998) use of indices that aggregate individual characteristics in the study of corporate governance. **_INDEX8_** is an aggregate index obtained by summing the number of activities in which the firm engages, excluding two of the activities, _Discontinued at least one product line_ and _Closed at least one existing plant or outlet._<sup>11</sup> These activities are left out of INDEX8 because they are less likely to require outside financing than activities such as the introduction of new technology or the opening of a

> implementation can be found at

> <u>http://www.enterprisesurveys.org/documents/Implementation_note_January_2007.pdf</u> 11 We also experimented with an aggregate indicator of the ten individual indicators of innovation as well as a principal component indicator of the ten individual indicators of innovation and found our results to be consistent with the aggregate index, INDEX8.

9
# TABLE 8: COMMONLY USED PESTICIDES AND THEIR TOXICITY

|PESTICIDE|ORAL<br>TOXICITY|DERMAL<br>TOXICITY|TOXICITY<br>TO BIRDS|HONEY<br>BEES|TOXICITY<br>TO FISH|
|---|---|---|---|---|---|
|PYREThROIDS||||||
|FENVALERATE|XX|X|-|XXX|XXX|
|CYPERIETHRIN|XX|X|0|XXX|XXX|
|DELTAIIETHRIN|XX|X|XX|XX|XXX|
|OTHERS||||||
|MONOCROTOPHOS|<br>XX|XX|XXX|XXX|XXX|
|ACEPHATE|X|X|_|_|X|
|ENDOSULPHAN|XX|XX||_ .|XXX|
|CHLORPYRIPHOS|XX|X||XXX|XXOC|
|DIFLUBENZURON|XXX|XX|XXX|XXX|x|
|PHOSPHANIDON|XXX|XX|XXX|XXX|X|
|DIMETHOATE|XX|XX|XXX|_|X|
|CARBARYL|X|X|X|XXX|X|

Source: A.J. Shaw, Cotton Pesticides Guide 1991-92 = No Information NSW Agriculture. XXX Highly toxic XX toxic X Low toxicity O Non toxic Note that oral and dermal toxicity refer to mammalian toxicity. Pesticide residues in fish could lead to toxicity in humans.

# Morbidity costs of Pesticides

From Table 8, it can be seen that the three most widely used pesticides have low dermal toxicity and somewhat higher oral toxicity. There is sufficient qualitative evidence to indicate that farmers do not use pesticides in accordance with the safety 76
# Figure 10.  Country correlations for commodity exporters and political risk

<!-- Start of picture text --> Low Political Risk High Political Risk<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Low Political Risk = 0.28***<br>-0.2<br>High Political Risk = 0.49***<br>-0.4<br>-0.6<br>-0.8<br>AzerbaijanYemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep.NicaraguaEcuador UruguayGuyanaLiberia Argentina<br>Brazil Namibia NigerBolivia KenyaZimbabwe AngolaQatar<br>Sudan SenegalMali Burkina Faso<br>Ghana Saudi Arabia MozambiqueThe Gambia Colombia Tanzania LibyaGabon Iceland EthiopiaMongolia<br>AlgeriaMyanmarSouth Africa Paraguay<br>Iran, Rep. of Madagascar<br>UgandaCameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>NigeriaPeru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>Australia Guatemala<br>Brunei Darussalam United Arab Emirates<br>government spending and real GDP 1984-2020<br>Correlation between cyclical components of real<br>Canada<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) political risk, defined as political risk above (below) the sample median. The difference between the two averages is significant at the ten percent level. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Country Risk Guide and WEO (IMF) data.
An interesting feature of the European market is the growing strength of mutual funds in several countries with unfunded social security systems (Austria, Belgium, France, Greece, Italy, Portugal and Spain). Some of these countries have also experienced strong growth of their life insurance industries. For instance, French life insurance assets exceeded 55 percent of GDP in 1997, a level that is much higher than in Canada, Germany or the United States and close to the levels prevailing in the Netherlands, Switzerland and the United Kingdom. This provides indirect evidence that the saving public is responding to various tax incentives to accumulate long-term savings as a defense against the likely future inability of their' national social security systems to honor promised benefits in full.

Among Anglo-American countries, which generally have well-developed securities markets and common law traditions, Australia, New Zealand and South Africa are notable for their relatively underdeveloped mutual fund industries with total assets around 10 percent of GDP. However, in all three countries mutual funds experienced considerable growth during the 1990s. The presence of a well-developed contractual savings industry in South Africa and the continuing credibility of tax-financed universal pensions in Australia and New Zealand are clearly relevant factors. As already noted, inclusion of the compulsory pension funds, which are mostly based on DC plans, causes a very large increase in the reported Australian statistics.

In East Asia, the experience of Japan is worth noting. Since the collapse of the Tokyo stock market in 1990, the mutual fund industry has stagnated. Total assets under management declined in relation to GDP from 9 to 8 percent. Mutual funds were better developed in Korea at the beginning of the decade with assets corresponding to 28 percent of GDP. They subsequently rose further to 32 percent of GDP by 1994 but suffered a major setback in the aftermath of the East Asian financial crisis of 1997 when they fell to less than 20 percent of GDP. Mutual funds in Malaysia grew aggressively in the first half of the 1990s, rising from 10 to 24 percent of GDP but plummeted to 9 percent of GDP in 1997.

Among other middle-income countries Brazil has the most developed mutual fund sector with assets corresponding to over 13 percent of GDP. In most other developing 17