rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Product varieties churning - Sectoral Differences <!-- Start of picture text --> Export Prod. Introduced Export Prod. Dropped<br>Food Bev TobacTextil Garments Leather Wood ProdPulp and PaperChemical ProdMineral BasedBasic MetalsMachin Equipment Others<br>.15<br>.1<br>.05<br>0<br>Ratio of No. of Products over Tot. Exported Products<br><!-- End of picture text --> While the international trade literature has examined the patterns of introduction and discontinuation of export products and contrasted changes at the “extensive margin” and “intensive margins” using national trade statistics (Hummels and Klenow 2005, Besedes and Prusa 2006, Besedes and Prusa 2007, Venables and Evenett 2002), export churning at the firm level has not been documented before. To the best of our knowledge, this is the first study to uncover a very high degree of export product churning within firms. Our finding is in line with Bernard, Redding, and Schott (2006b) who document a similar pattern for products produced and sold (in general) in the US.<sup>5</sup> Product churching within the firm has recently attracted a lot attention among theorists who suggested various explanations for this phenomenon. In particular, a theoretical contribution by Bernard, Redding, and Schott (2006a) puts forward the hypothesis that product churning is due to firm-level managerial capabilities interacting with productfirm level idiosyncratic expertise. Eckel and Neary (2006) build a model of multi-product firms characterized by core competencies and cannibalization effects among products. In both models, the range of products produced and exported responds to changes in trade

> 5They find product switching to be frequent, widespread and influential in determining both firm and aggregate outcomes.

12
retired. All adjustment on the extensive margin occurs via an increase in the fraction of people in a subregion who are out of the labor force. The impact is statistically significant for women, with no significant impact for men. The magnitude of the point estimate is consistent with the interpretation that the decrease in the net overall employment of women due to refugees, reported in Table 6, is entirely the result of women leaving the labor force. The impact on female wages, reported in Table 7, suggests that much of this decrease in labor force participation is due to falling potential earnings. However, there may also be non-labor market reasons why women leave the labor force. For example, in a 2014 survey 62 percent of Turkish agreed with the proposal that “Syrian refugees disturb the peace and cause depravity of public morals by being involved in crimes, such as violence, theft, smuggling and prostitution,” while only 23 percent disagree (Erdogan, 2014).

Another plausibly important adjustment margin is that Turkish respond to the Syrian refugees by relocating across NUTS 2 subregions. We test for population movements in response to the inflow of refugees in two distinct ways. First, by estimating the impact of refugees on the population growth (in percent) in each Turkish NUTS 2 subregion. Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey’s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year.

Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregionyear). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education.

For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low 25
12 In Table 1 the coefficients for both of the income variables – GDP and survey mean income-- are of the expected (negative) sign and statistically significant in all cases. However, the results for the model as a whole are better and more precise when estimated using survey mean income: the R<sup>2</sup> coefficients increase from the 0.4-0.5 range to 0.6-0.7. For this reason, we will focus on the results using survey mean income.

In Table 1 the poverty elasticities with respect to income inequality (Gini coefficient) are positive, as expected, and their magnitude is consistent with other recent analyses of poverty reduction.<sup>22</sup> The latter outcome suggests that countries with higher income inequality also have higher poverty.

When the dependent variable in Table 1 is poverty headcount or poverty gap, the results for the migration variable are negative and statistically significant. However, when the dependent variable is squared poverty gap, the share of migrants in the country’s population has no significant impact on poverty. For the poverty headcount measure, the estimates using survey mean income suggest that, on average, a 10 percent increase in the share of migrants in the country’s population will lead to a 1.9 percent decline in the share of people living on less than $1.00 per person per day. This means that for a “representative” country if exactly one-half of the population lives below the poverty line of $1.00/person/day, a 10 percent increase in migration will bring the proportion living in poverty down to about 0.49, holding the level and distribution of income constant. International migration has a small, but statistically significant impact on poverty reduction, independent of the level of income and its distribution.

Table 2 shows the results when equation (1) is estimated using remittances data. The remittances variable – remittances as share of country GDP – has a negative and
Firm-level surveys have been conducted since 2002 by the World Bank. The raw individual country datasets, aggregated datasets (across countries and years), panel datasets, and all relevant survey documentation are publicly available (see Appendix 2 for a description of the methodology). The Enterprise Surveys (ES) data used for this paper is for 138 countries which have a non-zero number for the measure of the tax corruption indicator. These surveys are conducted between the years 2002 and 2013.

In the questionnaire administered by the Enterprise Surveys, the following questions are asked about corruption in tax administration:

- “J3 question” from the survey: over the last 12 months, was this establishment visited or inspected by tax officials?

- “J5 question” from the survey: in any of these inspections or meetings was a gift or informal payment expected or requested?

Based on the response, the measure of percent of firms giving gifts to tax officials is computed. More specifically, for each country, the tax corruption indicator is defined as the ratio of the number of "yes" answers to “J5 question” to the total number of "yes" answers to “J3 question”. This is a direct measure of corruption in tax administrations.

It is worth noting that while calculating the tax corruption ratios, we do not use any aggregate data from the Enterprise Surveys Database. The tax corruption ratio is constructed by using firm-level data from the database; and then we calculate country averages by using this series based on firm-level data. The detailed information on firms from each country is presented in Table A1 in the Annex. It can be seen in the table that the number of firms interviewed is large and it includes firms with different characteristics. Thus it can be concluded that firms included in the Enterprise Surveys Database represent the average position of countries because the database covers a broad range of firms. The response rates on tax corruption are reasonably large in many countries. The size characteristics of firms are welldistributed. Almost 53 percent of firms are small firms, which are defined as having fewer than 9
|TableA4:Labor force participationbyrace,immigrationsta|tus, and time since arriva<br>|l inthe US.<br>|
|---|---|---|
||(1)<br>Males|(2)<br>Females|
|Dependent variable|Inthelabor force|Inthelabor force|
|White bornoutside the US, arrivedless than 10 years ago|-0.0944***|-0.1911***|
||[0.0029]|[0.0031]|
|White bornoutside the US, arrived between 10 and20 years ago|-0.0230***|-0.0829***|
||[0.0032]|[0.0040]|
|White bornoutside the US, arrivedmore than 20 years ago|0.0051***|-0.0195***|
||[0.0017]|[0.0023]|
|Blackborn inUS|-0.0808***|-0.0270***|
||[0.0009]|[0.0010]|
|Blackbornoutside the US, arrivedless than 10 years ago|-0.0755***|-0.0139**|
||[0.0052]|[0.0054]|
|Blackbornoutside the US, arrived between 10 and20 years ago|-0.0394***|0.0593***|
||[0.0045]|[0.0047]|
|Blackbornoutside the US, arrivedmore than 20 years ago|-0.0156***|0.0491***|
||[0.0044]|[0.0051]|
|Asianborn inUS|-0.0320***|0.0046|
||[0.0032]|[0.0038]|
|Asianbornoutside the US, arrivedless than 10 years ago|-0.1227***|-0.1914***|
||[0.0028]|[0.0028]|
|Asianbornoutside the US, arrived between 10 and20 years ago|-0.0652***|-0.0438***|
||[0.0026]|[0.0028]|
|Asianbornoutside the US, arrivedmore than 20 years ago|-0.0339***|-0.0198***|
||[0.0026]|[0.0029]|
|Hispanic born inUS|-0.0517***|-0.0343***|
||[0.0012]|[0.0014]|
|Hispanic bornoutside the US, arrivedless than 10 years ago|-0.0639***|-0.1512***|
||[0.0018]|[0.0023]|
|Hispanic bornoutside the US, arrived between 10 and20 years ago|-0.0807***|-0.0752***|
||[0.0017]|[0.0022]|
|Hispanic bornoutside the US, arrivedmore than 20 years ago|-0.0328***|-0.0275***|
||[0.0016]|[0.0021]|
|Observations|3472846|3649194|
|R-squared|0.15|0.10|

Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%.

Data from the 2000 Census IPUMS data (5% sample), population aged 25-65. The probit regressions (marginal effects shown) also include 5 year age group dummies, region dummies, education (HS drop out, HS graduate, some college, college and above), indicators for being married, disabled and in school, the number of children under age 5 in the household and the logarithm of total personal income earned by the other household members. The regressions use the sampling weights provided with the IPUMS data.

36
# **1.  Introduction**

The lower Mekong sub-region, consisting of Cambodia, Lao PDR and Vietnam, is one of the poorest areas in Asia. With per-capita GDP’s in the range $US 270-410, and poverty incidence rates of 35-40 percent, these three countries are experiencing serious natural resource degradation in rural areas and declining environmental quality in major cities. To improve its understanding of links between poverty and environmental degradation in the sub-region, the World Bank has undertaken a Poverty-Environment Nexus (PEN) study with a two-phase implementation.<sup>1</sup> The Phase I report, completed in October, 2002, provided numerous insights into the spatial relationships between environmental problem indicators and poverty in the three countries (Dasgupta, et al., 2003). A preliminary Phase II analysis has provided further understanding of spatial poverty-environment linkages, with a particular focus on the burden imposed on the poor by environmental degradation.

Concurrently with the World Bank PEN initiative, Cambodia, Lao PDR and Vietnam have begun to focus on poverty-environment linkages as they develop poverty reduction and growth strategies. In Lao PDR, the focal country for this paper, the PEN Phase I report has served as an input to the National Poverty Eradication Program (NPEP). While the NPEP addresses overall sectoral and macro policy issues, it also targets 46 priority districts for poverty eradication. The government has identified these districts using a comprehensive measure of poverty, as well as consideration for regional and provincial representation.<sup>2</sup> Yet to be determined, however, are the overall resource requirements for the priority districts, the allocation of resources across and within these districts, the magnitudes of central and local

> 1  See “The Poverty-Environment Nexus: A Strategic Approach for Cambodia, Lao PDR, and Vietnam,” (World Bank)

> 2  The poverty measure includes consumption and asset indicators, and access to basic services and infrastructure.

2
Only 45.7 percent of students from our entry cohort graduate—15.1 percent graduates on time (in five years) and 30.6 percent graduates late (in 6-8 years).<sup>6</sup> Dropout rates are far from uniform over time. As Figure 2 shows, over a quarter of college students (half of all dropouts) leave in the first year, and the first two years account for about 70 percent of all dropouts. Similar to enrollment rates, dropout rates vary widely across student types (Table 2). Conditional on income, higher ability students have lower dropout rates; on average, the dropout rate gap between the highest and lowest ability quintiles is equal to 25 pp. In contrast, dropout rates do not vary much by income—suggesting that free college might affect enrollment more than dropout decisions.

Final college outcomes vary substantially by ability. High-ability students are more likely to graduate, whether on time or late (Figure 3), whereas low-ability students are more likely to drop out, particularly in year 1. After year 1, dropout rates vary little across abilities.

**Figure 2: Dropout Timing.**

<!-- Start of picture text --> 60<br>% relative to entry cohort<br>% relative to all dropouts<br>50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5 6 7 8<br>College year<br>Percent<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES, for students from the 2006 entry cohort (first semester). _Notes_ : The blue line shows percent of students who drop out in a given college year. The red line shows, among all dropouts, percent of those who drop out in a given college year.

**Figure 3: College Outcomes by Ability.**

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Ability quintile<br>Dropout 1st year Dropout 2nd year<br>On−time graduate Late graduate<br>Dropout after 2nd year<br>Percent<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES. Students belong to the 2006 entry cohort (first semester). _Notes_ : For each quintile, the graph shows the percent of students who attained each outcome. Percents add to 100 by quintile.

# _Fact 3: Cumulative performance varies more within than across abilities._

Figure 4’s panel a shows average number of classes completed by ability quintile in year 1. The thick black line depicts average over all students, whereas the color lines depict averages among the students with the following final outcomes: on-time graduation, late graduation, dropout in year 1, and dropout later. On average, high-ability students complete more classes than lowability students in year 1. However, the figure also shows a pattern repeated every year: average classes completed varies little _across_ abilities—overall and conditional on outcomes—but varies greatly _within_ abilities. For a given ability, on-time graduates complete more classes than late graduates, who complete more than dropouts. In other words, number of classes completed—as

> 6For comparison, in the U.S. 59.2 percent of students from the 2006 cohort graduated within six years—39 percent on time (in four years), and 20.2 percent late  ̇Source: Digest of Education Statistics.

9
-20 -

of the real exchange rate Chart 3 plots three series. Like many Latin American countries that experienced heavy capital flows, it is clear that Turkey also experienced real exchange rate appreciation<sup>7</sup> . The abandonment of the real exchange rate rule in 1989 coupled with liberalization of the capital account indeed led to sharp real appreciation during 1989-90 which continued at a more moderate scale until 1994. With no fiscal adjustment and the persistence of high inflation, choosing the exchange rate (govemed with a managed float) as an implicit nominal anchor inevitably led to such real appreciation when combined with capital inflows. This policy process backfired when further deterioration of the fiscal stance combined with government's attempt to control interest rates on its domestic bonds-a fundamental policy error when capital account is open-led to a correction of the real exchange rate in the 1994 financial crisis, which is clearly visible in Chart 3.

**Chart 3**

<!-- Start of picture text --> Turkey: Real Exchange Rate<br>(1987=100)<br>140<br>130 . . . . \<br>120<br>110 -<br>90<br>80 l l<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997<br>- - RER: (1US$+1.5DM)ITL - RER: Trade Weighted (IMF) ..--- RER: US$/TL<br><!-- End of picture text --> The response to the crisis was a stabilization program announced on April 5, 1994, which was supported by a three year stand-by arrangement with the IMF. Demand for 7 Capital flows are believed to cause exchange rate appreciation insofar as the increased domestic absorption associated with the capital flow from abroad puts pressure on the non traded goods sector, and increase its relative price.
# **Assessing Effects of Large-Scale Land Transfers: Challenges and Opportunities in Malawi’s Estate Sector**

# **1. Background**

Since the 2007/08 commodity price boom, transfers of large tracts of land for agricultural production have been a key issue in policy debates on African agriculture (Collier and Dercon 2014; Cotula 2014; Deininger and Byerlee 2011). Yet, while there has been enormous interest in the size (Dell’Angelo _et al._ 2017; Holmen 2015), causes (Arezki _et al._ 2015), and the aggregate impact (Davis _et al._ 2014) of such transfers, actionable assessment of the extent to which transferred land is being used, the efficiency of such use, and potential impacts on neighboring smallholders has been limited. Evidence on these would be of importance for governments to manage public land transfers in ways that can reduce risks and maximize positive socioeconomic impacts. Latin America shows the advantages of combining administrative with remotely sensed data for real-time monitoring by the public (Assuncao _et al._ 2015) and private sectors (Gibbs _et al._ 2016), but use of such methods in Africa is still in its infancy (Lemoine and Rembold 2016).

In this paper, we show that land registries contain a wealth of information but that lack of maintenance and failure to use these data, partly because they were locked up in analog form, affected economic performance by reducing tenure security and the ability to harness land’s economic potential. We note that digitizing such data and combining them with survey information allow making inferences on the longer-term impact of large farm investment in Africa and drawing policy relevant conclusions for Malawi, a country where, as described in more detail in section 2.2 below, large areas had been transferred to estates in the 1980s and early 1990s (Mandondo and German 2015).

With some 1.35 million ha or about 25% of the country’s arable area, agricultural estates are an important part of Malawi’s rural economy. But their contribution to public revenue is negligible, as 70% of agricultural estate leases have expired and failure to index to inflation levels of the ‘ground rent’ that was supposed to be paid to the government for using the land has reduced revenue even for non-expired leases. Associated losses are large: charging half the market price for land rental would increase public revenue by US$35 million or 5% of total public spending a year. It also undermines incentives for record maintenance, possibly setting in motion a downward spiral whereby low rent collection makes record maintenance difficult while low quality of records implies that for any individual estate owner, spending resources on updating of records is not a desirable strategy. As a result, investment and productivity in the large farm sector remain low, smallholders fail to benefit from technology- or market-driven spillovers emanating from large farms, and an estate sector that could -with proper records to ensure management and integration into the broader rural economy- potentially have acted as a motor for rural change instead becomes an obstacle to progress.

2
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