rafmacalaba/datause-encoder-data huggingface dataset:
19 Table 1: Area of GM cotton and other GM crops, by country, 2002 to 2005 (million hectares)

# (a) Total area

||**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|
|United States|4.2|3.9|4.2|5.6|
|China|2.1|2.8|3.7|3.3|
|Australia|0.2|0.1|0.2|0.3|
|South Africa|0.0|0.0|0.02|0.03|
|India|0.0|0.1|0.5|1.3|
|Mexico|0.0|0.0|0.07|0.12|
|**Total, cotton**|**6.8**|**7.2**|**9.0**|**9.8**|
|**TOTAL of all GM crops**|**58.7**|**67.7**|**81.0**|**90.0**|

# (b) Area by product and variety, 2005

|||Global GM<br>area (m. ha)|Crop’s share<br>of global<br>GM area (%)|Area under<br>GM varieties<br>as a % of<br>crop’s global|
|---|---|---|---|---|
|||||area|
|Cotton:|Bt (insect resistant)|4.9|6||
||herbicide tolerant|1.3|2||
||Bt/herbicide tolerant|3.6|4||
||ALL COTTON|**9.8**|**11**|**28**|
|Soybean||54.4|60|60|
|Maize||21.2|24|14|
|Canola||4.6|5|18|
|**TOTAL**|**of four crops**|**90.0**|**100**|**30**|
|**TOTAL**|**of all crops**|||**5**|

Source: James (2005) and earlier issues.
||**Table 4.**<br>|**Impact on e**<br>% change<br>|**conomic and f**<br>% change<br>|**inancial variables**<br>||Current|
|---|---|---|---|---|---|---|
|Dependent|Exchange Rate|in|equity prices|GDP Growth|Investment|account|
|Variables|Depreciation|Reserves|(real)|(yoy)|Growth(yoy)|balance/GDP|
|Quarter 1|10.414***|-15.331***|-16.479***|-2.437***|-6.379**|-0.859|
||[4.25]|[4.85]|[5.35]|[3.01]|[2.68]|[1.37]|
|Quarter 2|13.568***|-7.060***|-10.997***|-5.721***|-9.255**|0.966|
||[3.42]|[3.00]|[3.22]|[4.88]|[2.14]|[1.07]|
|Quarter 3|3.427**|-8.104|3.097|-6.089***|-17.550***|2.804**|
||[2.23]|[1.50]|[0.82]|[4.45]|[3.88]|[2.55]|
|Quarter 4|5.894|-5.113|-0.210|-5.477***|-15.336**|3.304***|
||[1.66]|[0.65]|[0.05]|[2.82]|[2.43]|[2.80]|
|Constant|1.735***|2.628***|2.625***|4.279***|8.110***|-1.648***|
||[16.21]|[19.01]|[22.41]|[66.39]|[39.97]|[39.01]|
|Observations|2,569|2,573|2,284|2,160|1,959|2,000|
|R-squared|0.030|0.026|0.034|0.082|0.036|0.012|
|Number of countries|34|34|31|32|29|30|
|Adj. R-squared|0.0289|0.0243|0.0324|0.0805|0.0343|0.00985|

Data are quarterly over the period 1991-2014. Dependent variables are as indicated in the first row. All variables are in percentage. GDP growth and investment growth are year-over-year. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, or *** indicate the coefficients are significant at 10, 5 or 1 percent level of significance. Regressions with year fixed effects instead of a different intercept for post 2003 period yield similar coefficients.

21
identical across all jobseekers assigned to the treatment group, and the categorization of jobseekers into specific and general matching was one result of this flexibility.

Beneficiaries in the control group received job counseling services but did not have access to job matching. These services are similar in content to those provided by the public employment services. The sample for the analysis included a total of 1,541 jobseekers including 765 jobseekers in the treatment group receiving job matching services and 776 in the control group receiving only basic counseling services. A downside to this design, it is important to note, is that this study does not have a “pure” control group – i.e., a group receiving services only from the PES. As a result of this, the paper cannot compare the efficacy of a private employment service provider relative to the PES.

# **3. Data Sources**

The analysis made use of three sources of data which are described below.

## **3.1 Administrative Data from Federal Employment Institute (FEI)**

Data from FEI provided two strands of information. The first was information on the individual characteristics of program participants including gender, age, education level and length of unemployment. An individual is classified as unemployed if they are registered as such with the FEI, an approach followed by Balavac-Orlic and Posadas (2023). In some sense, this is a less strict definition relative to ILOs criteria, which additionally requires active job-search efforts and readiness to start a job to be classified as unemployed. As benefits for the unemployed are only available upon registration with the FEI, we view it as unlikely that large shares of those out of work are not registering. The FEI also provided information on the formal employment status of jobseekers after their engagement with the private provider. To do this, the FEI drew on a database maintained by the Tax Administration. As described above, payment to the private employment service provider was based on independent verification of a jobseekers’ formal employment status. Whenever the private employment service submitted a payment request for a successful placement, FEI would independently verify the employment status of the jobseeker. This typically happened 3-6 months after the jobseeker started receiving program services and is used in this analysis as an indicator of employment in the short-term. In March 2023 (2125 months after program services were first delivered), another round of employment status verification was conducted which is used as a measure of medium-term employment outcomes. These data are available for the full sample of beneficiaries and includes 1,541 observations.

9
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# Household Welfare and Natural Resource Management around National Parks in Zambia

Sushenjit Bandyopadhyay<sup>a</sup> and Gelson Tembo<sup>b</sup>

## Acknowledgements

We are sincerely thankful to our donors, the Government of Sweden, for funding the survey and the analysis. We are grateful to Jean-Michel Pavy, Phyllis Simasiku, David Mulolani, and Goodson Sinyenga for comments and advice. We would like to thank Kirk Hamilton, team leader, Policy and Economics Team, World Bank, for valuable guidance and support. We are also particularly thankful to the Central Statistical Office, Government of the Republic of Zambia for implementing the survey. The authors are responsible for all errors and omissions.

> a Environment Department, World Bank. b University of Zambia
**1**

# 1. General Considerations

Financial reforms -- and doubts about them -- are documented at least as far back as the Scottish free banking era of the 18th - 19th century, and it is likely that, in the wake of the banking crisis of 33 A.D., Romans debated putting a hitherto liberal banking system under government co..:rol.I Financial reform in the modem era is unusual at least in its frequency: most industrialized countries have embarked on measures to liberalize finance in some manner in the last two decades, and a growing number of developing countries are moving along that course more recently. Even if university courses in developnunt traditionally have attributed little role to finance in the development process, practitioners in the field as well as increasing numbers of academics seem to be convinced both that finance matters and that "market-oriented" financial systems can exert a positive influence on the economy, albeit with significant differences as to the proper or optimal role for maricet forces.

This paper presents lessons on financial reform, which are derived from two World Bank research projects on both the real and financial sector effects of financial reforms in selected countries (Caprio, Atiyas, and Hanson, 1992]. Table 1 presents a thumbnail description of the reforms in the cases studied -- Chile, Indonesia, Korea, Malaysia, New Zealand, and Turkey. These countries differ markedly in their economic, political, and institutional development, which, along with their meager number, raises problems in drawing lessons.<sup>2</sup> Moreover, it is difficult to describe any of these efforts as complete. Reforming the financial system should be thought of as a process, not an event, both because most governments enact reforms in stages and since institutions take substantial time to adjust. Even in the case of New Zealand, where many liberalizing changes were introduced abruptly ard virtually simultaneously in late 1984, it would be premature to consider reform as complete.

Conclusions about financial reforms also should be viewed with caution because these experiments -- even in Malaysia and Chile, where reforms began in the early 1970s -- are
# **ACKNOWLEDGMENTS**

The authors wish to acknowledge the support of the World Bank’s Financial Sector Board and Africa Regional Programs in carrying out this study and the country background studies over nearly two years and the guidance received from Gerard Byam and Marilou Uy. The authors are grateful to Carlos Cuevas, Richard Rosenberg, and Andre Ryba for comments on a previous draft, as well as to workshop participants and those who contributed to or commented on the country papers and issues paper.

iv
Figure 3.1 presents a picture of poverty in Indonesia in 1993 based on data that we describe below in the empirical section, using the normalized _TIP_ curve. This curve shows a graphical summary of incidence, intensity and inequality dimensions of aggregate poverty based on the distribution of normalized poverty gaps. Indeed, the length of the non-horizontal segment of the curve reveals incidence. The height of the curve represents intensity, while the degree of concavity translates inequality among the poor.

The following theorem identifies the form of the measure of _pro-poorness at percentile p_ , when this is expressed as the effect of a growth pattern _q(x)_ on _JP(p)_ over and above the effect that would obtain under distribution neutrality.<sup>14</sup> This measure also decomposes across income components: <!-- Start of picture text --> Figure 3.1. A Normalized TIP Curve for Indonesia in 1993<br>24<br>20<br>16<br>12<br>8<br>4<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Cumulative Percentage of the Population<br><!-- End of picture text --> 14 If _ζP(q│p)_ is the growth elasticity of _JP(p)_ for the growth pattern _q(x)_ , then _πJ(q│p)_ is defined by the equation _πJ(q│p) = JP(p)_ .[ _ζP(qo│p) - ζP(q│p)_ ], in the same way that (3.3) defines _πP(q)_ .

14
find that a single savings goal is more likely to be attained, whereas setting multiple goals may dilute the importance of each goal. Ülkümen and Cheema (2011) observe that for more ambitious savings targets, having concrete goals as opposed to general goals can increase the perceived importance of and commitment to the target.

In our study, the goal setting treatment was administered to respondents who received the financial education treatment. Among this pool, half the sample was randomly selected to receive a goal setting treatment, while the remaining half formed the control group. The goal setting assignment was orthogonal to the financial counseling assignment. Treated individuals received a household visit and were first asked about whether they currently used, or planned to use, financial services and financial planning tools. Next, they were asked to set a target date for several concrete financial planning goals (i.e. opening a savings account, increasing savings, reducing expenditure, purchasing insurance). These target dates were then marked on a calendar provided to respondents at no cost.

Further, to isolate the effect of goal setting versus the effect of the household visit, the goal setting control group was visited by surveyors as well and was asked about their use of financial services. However, they were not asked to set a target date for financial goals or provided with free calendars. Both goal setting treatment and control household visits happened within four weeks of the follow up survey on financial knowledge.

# **3. Empirical Methodology and Summary Statistics**

# _3.1. Empirical Methodology_

The main analysis of this paper estimates causal impacts on financial knowledge and behavior. First, we analyze impacts on three distinct components of financial knowledge, namely financial numeracy, awareness, and attitudes. We study pay for performance impacts using both data from the short-term survey and long-term persistence using endline data.

Since financial education and pay for performance were orthogonal treatments both randomized at the individual level, we estimate causal effects on financial knowledge with the following OLS model: 13
**20**

activities.<sup>17</sup> This implicitly assumes that it is difficult to transfer income generated in one sector/location (e.g. people employed in industry or services in urban areas) to another sector/location (e.g. people employed in agriculture in rural areas). This may be because of market segmentations or a political economic constellation unfavorable to redistribution. Second, given that the major asset of poor people is usually their labor, differences in labor intensity across sectors might generate sectoral differences in poverty reduction from growth as emphasized by Loayza and Raddatz (2005). Third, the distribution of other (complementary) assets (e.g. land in agriculture, capital in industry) may further affect the poverty reducing effect of growth in different sectors.

Few studies have explicitly compared the GDP elasticities of poverty across the sectors. And some studies even hint that the GDP elasticities of non-agricultural sectors might be greater, contrary to what is often implicitly assumed. Ravallion and Datt (1996) for example, find that the elasticity of rural headcount poverty with respect to agricultural growth in India is -0.9, compared with -2.4 for tertiary sector growth. The latter they conjecture being attributable to growth in the informal sector. For China however—and consistent with expectations—Ravallion and Chen (2004) estimated that agricultural growth has the greatest impact on poverty reduction (by a factor of 4 compared with the secondary and tertiary sectors) underscoring the existence of potentially important differences across countries in the participation effect depending on the structure and institutional organization of the economy.<sup>18</sup> Inspired by this work and in the absence of good country time-series data, other authors have most recently compared GDP elasticities across sectors using cross country data. Using fiveyear panel data for the period 1960-2000, Bravo-Ortega and Lederman (2005) find that agricultural output per worker is not as effective as non-agricultural output in raising the incomes of the poorest quintile. Quintiles 2 and 3 appear to gain most from increases in agricultural output. Loayza and Raddatz (2005) argue that the labor intensity of the production process is a critical factor in determining the poverty reducing effect of growth. Linking sectoral growth rates in different

> 17 For a more extensive review of this literature we refer to Timmer (2005) and Byerlee, Diao and Jackson (2005).

> 18  Similar findings are reported for China by Fan _et al_ (2005).
diverge from the U.S.-based estimates by the Bureau of Economic Analysis around this same time period. We can trace this surge in the macro data directly to a change in reporting regulations in Mexico. De Luna Martínez (2005) notes that before 2002 only commercial banks reported their remittance transactions to Mexico’s central bank, but then in 2002 a new regulation required all money transfer companies to register at the central bank and report their remittance transactions on a monthly basis.

Figure 5 A to D plot the data for Peru, Honduras, Pakistan and El Salvador. Peru follows a similar pattern to Mexico, with micro and macro remittances having similar trends in the 1990s, and then the macro growth surging in the 2000s. Macro remittance growth was 154 percent between 1999 and 2010, compared to 75 percent in the micro data. Honduras is even starker, with the macro data showing growth of 679 percent over 1997 to 2010, compared to –3 percent growth in the micro data. Pakistan has growth of 294 percent in the macro data between 1998-99 and 2007–08, compared to 24 percent in the micro data. The difference is less in El Salvador, but the 109 percent macro remittance growth over 1999 to 2008 still exceeds the 79 percent growth in the micro data.

All four countries in Figure 5 appear to have been affected to various extents by the AML regulations coming into place after September 11, 2001. Endo et al. (2010) report that Honduras enacted an AML in 2002 which regulated money transfer companies and imposed know-yourcustomer laws. CEMLA (2009) reports that El Salvador changed its reporting of remittances with the 2001 monetary integration law and 2003 AML, and CEMLA (2010) reports on a number of new regulations on remittances in Peru in the early 2000s. Kock and Sun (2011) report that in the wake of the 2001 terror attacks, there was increased scrutiny of Pakistani nationals’ bank accounts, and a crack-down on informal hawala systems of sending money, which are likely to have increased formal remittances.

However, there appear to have been other notable changes in measurement apart from those arising from the FATF recommendations for AMLs. Figure 6A shows the rarer case of Turkey, where the macro data show a 50 percent _decrease_ in remittances between 2002 and 2006, despite 18
attainment of men and the increase in male incomes. Changes in the structure of employment also appear to have contributed to lower participation rates. The employment shares of agriculture, manufacturing, and white-collar services declined, while these are the sectors positively associated with FLFP. The decomposition analysis in Section IV quantifies the contributions of changes in the different covariates included in the model. Before turning to those results, we first discuss the potential influence of migration on our estimation results.

# **_The Potential Role of Rural-Urban Migration_**

Rural to urban migration is an important part of India’s development process. The population numbers in appendix table A1.1 show that the total female population grew by 50 percent in the period 1987-2011, comprising a 91 percent growth of females in urban India and 38 percent growth in rural India. Rural-urban migrants are typically not a random selection of the rural population, so one might worry that the stagnation of urban FLFP and the changes in labor force participation determinants are driven by the inflow of migrants.

The National Sample Survey Organisation (NSSO) integrated the collection of migration data with the employment surveys in 1987 and 1999 but not with those of 2004 and after. In 1987 and 1999, almost 70 percent of women in our sample have a migration background, of which almost 60 percent (i.e., around 40% of the total female urban sample) migrated from a rural area. The most important reason for migration is marriage (about 65 percent of all female migrants). On average, rural-urban migrants are more likely to be at either end of the education distribution (illiterate or with at least secondary education) than nonmigrants, and less likely to have low or intermediate educational attainment (see Fig. A1.3 in the appendix). Given the U-shaped relationship between education and labor force participation, the compositional impact of migration should have been to increase urban FLFP, if anything. But despite the large share of migrants and differences in 25
purchases of foreign exchange, with occasional sales in response to short-lived depreciation pressures. The amount of net FX purchased reached the equivalent of 0.7 percent of GDP in 2007.

<!-- Start of picture text --> Figure 3.4: India -- FX Interventions and nominal effective<br>0.8% exchange rate (2000-12) 110<br>ρ= 0.45<br>105<br>0.6%<br>100<br>0.4% 95<br>90<br>0.2%<br>85<br>0.0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 80<br>-0.2% 75<br>70<br>-0.4%<br>65<br>-0.6% 60<br>6-month moving average of monthly net FX interventions in % of GDP NEER of Indian Rupee (right axis)<br>Sources: IMF World Economic Outlook, Reserve Bank of India, and WB Global Economic Monitor.<br><!-- End of picture text --> More recently, since August 2011, the RBI has intervened in FX markets in response to depreciation pressures, but has not attempted to change the direction of exchange rate movements. During the recent turmoil, from August 2011 to August 2012, the RBI conducted a combination of spot and forward FX market intervention. Direct sales in the spot market amounted to over US$ 20 billion from September 2011 to March 2012—reaching about 0.2 percent of GDP (Figure 3.4). From April to August 2012, the RBI intervened mainly in the forward FX market, with outstanding net FX sales approaching US$ 15 billion in June and July 2012. During this period, when intervention in the forward market was prevalent, FX sales in the spot market were kept to a minimum.

# **3.5.   Advanced Economies**

Japan and Switzerland illustrate cases of advanced economy central banks that have practiced various degrees of FX intervention. **_Japan_** operates a floating exchange rate but the authorities have frequently intervened to counter disorderly conditions in the market, and often with the aim 15
eligible individuals in village _v_ . The targeting differential can range from Tv = −1, where the program in village _v_ fully covers all non-eligible individuals with no benefits going to eligible individuals, to Tv = 1, where only eligible individuals are beneficiaries and they are all covered by the program. We find that the targeting differential has improved in the 2010/11 season compared to the start of the program, and that the targeting in study villages in Arusha is worse compared to Morogoro but much better than in Kilimanjaro. In fact, targeting in Kilimanjaro by 2010/11 was only 6 percent (0 percent would be consistent with lack of targeting, that is, when eligibility status plays no role in who becomes a beneficiary). In contrast, the targeting in Arusha by 2010/11 is 30 percent.

Table OA5 in the Online Appendix uses data collected at baseline to compare baseline characteristics of eligible households in the three regions of Arusha, Kilimanjaro and Morogoro. Households in Arusha appear to be richer and more educated than those in either of the other two regions. This perhaps explains the overall higher use of improved seeds and chemical fertilizer prior to 2008/09 in our study villages compared to villages in Kilimanjaro or Morogoro. Taking together the results of Table 2 and Online Appendix Table OA5, households and villages may not be readily comparable across regions but the trends in targeting over time are roughly similar.

Table 3 uses data from the eligible survey to compare the characteristics of eligible households that became beneficiaries to those that did not in lottery villages (columns 3-4) and in non-lottery villages (columns 7-8). Column 1 of Table 2 reports the data sources, column 2 the mean of the characteristics of all eligible farmers interviewed in lottery villages, and column 6 reports the analogous mean for eligible farmers interviewed in villages without the lottery. P-values associated with a t-test that the characteristics of beneficiaries and non-beneficiaries are different from zero in lottery and no lottery villages are reported in columns 5 and 9, respectively, and calculated using the t-asymptotic wild cluster bootstrap procedure (Cameron et al., 2008).

According to column 5, we find no statistically significant differences in the characteristics of beneficiaries and non-beneficiaries in lottery villages, except for past maize yields. While beneficiaries appear to have higher yields during the year prior to the intervention, non-beneficiaries seem to have used fertilizer more often compared to beneficiaries, although the difference in fertilizer use is not statistically significant. In contrast, in non-lottery villages beneficiaries appear to be younger, more able 14