rafmacalaba/datause-encoder-data huggingface dataset:
landings of _Tilapia niloticus_ fell to 16,251 tonnes in 2002, whilst landings of other tilapia became negligible<sup>44</sup> .

**Figure 22. Total landings of fish in Kenyan waters of Lake Victoria by species, 1996-2002:**

<!-- Start of picture text --> 250000<br>200000<br>150000<br>100000<br>50000<br>0<br>1996 1997 1998 1999 2000 2001 2002<br>Lates niloticus Other Tilapias Tilapia niloticus Rastrineobola<br>Tonnes<br><!-- End of picture text --> _Source: Fisheries Department_ Whilst the onset of restrictions on exports to the EU resulted in an immediate decline in the landed price of Nile perch, this was soon at least partially offset by a decline in fishing effort applied to Nile perch and demand from local markets (Abila and Jansen, 1997; Bokea and Ikaiara, 2000). Further, expanded markets soon became established in urban centres, particularly Nairobi, Mombasa and Nakuru. For example, Nile perch became regularly available at the City Market in Nairobi whereas previously it had been rarely offered for sale. Thus, the real price of Nile perch declined from KSh45.5 per kilogram in 1996 to KSh27.4 per kilogram in 1997, but recovered to KSh36.9 per Kilogram in 1998 (Figure 23). However, it was not until 2001 that the landed price fully recovered in real terms, despite a significant increase in the nominal price throughout the period 1997 to 2001. Subsequently, through 2002 the landed price of Nile perch increased significantly in both nominal and real terms, reflecting the decline in the volume of catch and competition between industrial fish processing operating well below capacity.

Whilst the landed price of Nile perch declined directly restrictions were imposed on exports to the EU, the price of other fish increased or remained constant (Figure 24). In particular, the landed price of _Tilapia niltoticus_ increased significantly through 1997 and 1998, and in both years actually exceeded the price of Nile perch. This is in stark contrast to the previous twenty years, during which time the price of tilapia was both lower than and declined relative to the price of Nile perch. Thus, one of the responses by fishers to the diminished market demand and price for Nile perch was to increase the effort devoted to other fish species and landed volumes increased significantly, although subsequently declined through 2002.

> 44 These variations in the level and composition of catch cannot be entirely attributed to the restrictions on exports to the EU.  Local environmental conditions (in particular the spread of water hyacinth) and the impact of the intensity of fishing on the fish population have also had a major effect.

49
#### _Source_ : Global Findex database.

_Note:_ Data for Northern Europe, the South Caucasus, and Western Europe are not available.

### b. By country

<!-- Start of picture text --> Albania<br>Armenia<br>Azerbaijan<br>Belarus<br>Bosnia and Herzegovina<br>Bulgaria<br>Croatia<br>Georgia<br>Kazakhstan<br>Kosovo<br>Kyrgyz Republic<br>North Macedonia<br>Moldova<br>Montenegro<br>Romania<br>Russian Federation<br>Serbia<br>Tajikistan<br>Turkey<br>Turkmenistan<br>Ukraine<br>Uzbekistan<br>0% 5% 10% 15% 20% 25% 30% 35%<br>Pay online Pay in cash when order is delivered<br><!-- End of picture text --> _Source_ : Global Findex database.

_Note_ : The length of the bars represents the percentage of adults aged 15 and above who report buying online.

# Informal Saving and Borrowing Is Prevalent in Developing Economies of ECA

## _Formal Saving_

People save for future expenses and to be prepared to respond to uncertainties. They may save for a large purchase, investment in education or a business, financial needs in old age, or emergency expenses. To meet immediate expenses, they may borrow. Global Findex data show how and why people save and borrow and shed light on their financial resilience to unexpected expenses.

Formal saving is more common in high‐income economies than in developing economies (figure 9). In 2017, 48 percent of the world’s adults reported having saved or set aside money in the previous 12 months. The figure was 72 percent in high‐income economies and 43 percent in developing economies. In high‐income economies, more than 75 percent of savers (55 percent of all adults) save formally at a financial institution. In developing economies, slightly less than half of savers (21 percent of all adults) save formally. By contrast, in the developing economies of ECA, less than 40 percent of adults report having saved the previous year. Only 38 percent of savers (14 percent of all adults) saved formally at a financial institution. The share of adults who saved formally was only 7 percent in 2011 and 11 percent 19
property. The second important feature of the ejidal sector in the context of this paper relates to governance. All ejidos have the same minimal administrative structure: a president, secretary, oversight committee, and treasurer are elected by vote for three year terms, and are not allowed to serve consecutive terms in the same position. Many villages also maintain committee structures beyond this basic arrangement. Communities hold regular assemblies to make decisions on a variety of issues, such as enrollment in government programs that affect the entire ejido, including the PSA. In all of the communities that we interviewed, decisions are made based on majority voting. Evidence of a vote to participate in the program is required along with the other application materials. In order that there be a formal vote for participation in a PSA (or other government) project, a clear sequence must occur. First, a general announcement of a meeting must be made. When the meeting is held, there must be 50% plus 1 in attendance. In the case that attendance is insufficient, the meeting ends and there is another meeting announcement. In the second meeting the assembly is held, with a formal vote, even if attendance does not reach 50%. The vote is validated by the National Agrarian Registry (RAN).

In the surveys used for this study, we asked a series of questions about these meetings. Participation in community meetings is mandatory, although not all communities sanction lack of attendance – 59% do have some kind of punishment for non-attendance, and within the past year, 70% have applied these sanctions. Attendance rates tend to be fairly high; we asked leaders the number of members attending the most recent assembly, and find the median attendance to be 62%<sup>6</sup> .

> 6For the random sample of households to whom we administered the household survey, we also asked how many assemblies a household member was sent to within the last year. Across our more than 8,000 households, less than 3% did not send a representative to a single assembly with in the past year. While households were asked about all assemblies, the leaders were responding about the most recent assembly, so these numbers are not necessarily inconsistent. Although this is quite a small number, we run a basic probit regression with non-attendance as the dependent variable and the available household characteristics as covariates (results shown in Table A5). Broadly speaking, non-attendance is greater for older and less educated households.

10
_Table 13: Mean frequency of plots with drought shocks within household, conditional on at least one drought shock being recorded_

|**Country**|**Survey**|**Mean drought shock**<br>**frequency of plots with**<br>**the same crop within**<br>**household**<br>_Restricted to plots with_<br>_maize_<br>(1)|**Mean drought shock**<br>**frequency of plots with**<br>**the same crop within**<br>**household**<br>_Restricted to plots with_<br>_maize_<br>(2)|
|---|---|---|---|
|Ethiopia|2011 – 2012|68.75 %|_n≤10_|
|Ethiopia|2013 – 2014|62.52 %|70.76 %|
|Ethiopia|2015 – 2016|82.33 %|91.07%|
|Ethiopia|2018 – 2019|59.68 %|72.81 %|
|Malawi|2010|88.08 %|93.52 %|
|Malawi|2013|65.77 %|100.00 %|
|Malawi|2016|71.77 %|88.89 %|
|Malawi|2019|38.89 %|40.00 %|
|Mali|2014|65.83 %|69.91 %|
|Mali|2017|62.97 %|62.72 %|
|Niger|2011|_n≤10_|90.29 %|
|Niger|2014|_n≤10_|69.01 %|
|Nigeria|2019|24.40 %|57.14 %|
|Tanzania|2008 – 2009|43.22 %|_n≤10_|
|Tanzania|2010 – 2011|52.65 %|67.39 %|
|Tanzania|2012 – 2013|56.57 %|_n≤10_|
|Tanzania|2014 – 2015|44.25 %|_n≤10_|
|Tanzania|2019 – 2020|51.19 %|_n≤10_|
|All countries||66.56 %|79.55 %|

_Note: These frequencies are conditional on at least one plot with the same crop (maize in col (1) or sorghum in col(2)) experiencing a drought shock, the household having more than one crop plot with the same specific crop (maize in col (1) or sorghum in col(2)), and a sample size of over 10 observations to calculate shares._ 34
occupations (Table 2), that are aligned with lower levels of education.

Table 4: The probability of being in a green job in 2019

|Model|Pr(strictly|greenjob)|Pr(broadly|greenjob)|
|---|---|---|---|---|
|Marginal effects|dy/dx|dy/dx|dy/dx|dy/dx|
|Female|-0.060***|-0.053***|-0.314***|-0.250***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|base=age 15-24|||||
|25-34|0.005***|0.005***|0.042***|0.033***|
||(0.001)|(0.001)|(0.002)|(0.002)|
|35-44|0.001|0.004***|0.037***|0.031***|
||(0.001)|(0.001)|(0.002)|(0.002)|
|45-54|-0.005***|-0.001|0.010***|0.013***|
||(0.001)|(0.001)|(0.003)|(0.002)|
|55-65|-0.003**|0.000|0.008***|0.013***|
||(0.002)|(0.002)|(0.003)|(0.003)|
|base=less than matric|||||
|Matric|0.007***|0.007***|-0.105***|-0.074***|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Other tertiary|0.036***|0.036***|-0.054***|-0.039***|
||(0.001)|(0.001)|(0.003)|(0.003)|
|Degree|0.034***|0.042***|-0.233***|-0.198***|
||(0.001)|(0.001)|(0.002)|(0.002)|
|base=black/African|||||
|Coloured|0.022***|0.018***|0.012***|-0.019***|
||(0.001)|(0.001)|(0.002)|(0.002)|
|Indian|0.014***|0.007***|-0.097***|-0.079***|
||(0.002)|(0.002)|(0.004)|(0.004)|
|White|0.033***|0.028***|-0.007***|-0.040***|
||(0.001)|(0.001)|(0.002)|(0.002)|
|Urban|0.011***|0.011***|-0.086***|-0.021***|
||(0.001)|(0.001)|(0.002)|(0.002)|
|Industrydummies|No|Yes|No|Yes|
|Observations|609,628|609,472|609,628|609,472|
|Standard errors inparenth|eses ***p<0.01,*|*p<0.05,*p<0.1|||

Logit estimate (equation 2), reporting marginal effects. The dependent variable takes a value of 1 if the observation reports a strictly (columns 1 and 2) or broadly (columns 3 and 4) green job (GTI-strict > 0). Survey weights applied in all regressions. Sample includes employed individuals aged 15-65. All columns include survey quarter dummies, province dummies. Source PALMS V3.3.

Black Africans are least likely to be in strictly green jobs, even when controlling for education level, but most likely to hold broadly green jobs. This may be expected due to the disproportionately high share of high-level jobs in strictly green occupations, where Black Africans are under-represented due to the persistent occupational segregation by race in the 20
some cost savings **.** On the positive side, the abolition of the agricultural tax has eliminated the need in local finance departments for the large corps of tax collectors who had been deployed to perform the mammoth task of going door-to-door to collect the tax.<sup>55</sup> Unfortunately, progress on downsizing appears elusive. Available data shows fiscal-supported population at the subnational levels continuing to grow, at least through 2005, with the share of county and township staff remaining stubbornly static (Table 23).

**Table 23.  Fiscal-supported staff at subnational governments by level.**

||Total<br>||%<br>||
|---|---|---|---|---|
||(million)|Province|Prefecture|County+ Township|
|2000|40|9.1|18.3|72.6|
|2001|41|10.5|18.0|71.5|
|2002|42|10.8|17.8|71.5|
|2003|43|9.8|17.7|72.5|
|2004|45|12.6|17.5|69.8|
|2005|46|13.9|17.5|68.6|

Source: Compendium of Fiscal Statistics for All Prefectures, Cities, and Counties.

# **5.4.  The Immediate Revenue Impact of the RTFR**

The revenue gap at the local level is substantial **.** As shown in Table 22 earlier, provincial officials in Henan had counted on the expanded agricultural tax to replace a significant part of the revenues lost from eliminating the fees and levies – roughly RMB 3 billion (compared with the RMB 2.9 billion in central subsidies in 2002). With the abolition of the agricultural taxes, however, these calculations became obsolete, and a larger gap emerged in rural finance. By 2005, agricultural tax revenues had fallen to RMB 360 million, and central subsidies were financing more than RMB 9 billion, or three-quarters of rural revenues in the province.

What is the size of the revenue gap nationwide? Many estimates have been made, and central government offices have been inundated by wildly inflated reports of “lost revenues” from local governments. Two sets of estimates are presented in Table 24. The first comes from a speech delivered by then-Vice Premier Wen Jiabao in April 2000 at a national conference on Anhui’s RFR experience, and the second from a group of experts at the State

> 55 In the counties visited by the mission, up to three-quarters of the staff in township-level finance departments were assigned to collect the agricultural tax.

51
3 FOREIGN DIRECT INVESTMENT IN KENYA FDI has grown rapidly, reaching US $23 million in 2008 from just US $1 million in 2003. Between 2009 and 2010, Chinese FDI increased 261 percent, the largest gain during the 2003-2012 period before returning to US $68 million in 2011. China’s FDI stock in Kenya has also grown 35.6 percent annually from US $26 million in 2003 to US $403 million in 2012. Figure 20 shows Kenya’s top FDI inflow sources in 2011 (UNCTAD 2015). China comes in second to the United Kingdom, and its share of Kenya’s FDI total inflows has also grown. Reaching a peak of US $101 million in 2010, or 57 percent of Kenya’s total FDI flows, it then fell to US $79 million in 2012, representing 31 percent of total flows (figure 18). China’s large FDI profile will lead to greater cooperation with Kenya, but Kenya must continue to lower the costs of business and investment and curb corruption to squeeze the most from foreign investment.

<!-- Start of picture text --> 100 400<br>75 300<br>50 200<br>25<br>100<br>0<br>2003 2006 2009 2012 2003 2006 2009 2012<br>Year Year<br>FDI Inflow (USD Millions) FDI Instock (USD Millions)<br><!-- End of picture text -->

- (a) Kenya’s FDI inflow from China (2003-2012) (b) Kenya’s FDI instock from China (2003-2012) Figure 17: Chinese FDI in Kenya is growing quickly since 2009 (2003-2012)

Source: UNCTAD FDI/TNC database 2015 <!-- Start of picture text --> 40<br>20<br>0<br>2003 2006 2009 2012<br>Year<br>China FDI (% of total FDI)<br><!-- End of picture text --> Figure 18: China’s FDI represents large share of total FDI (2003-2012) Source: Authors’ own calculation based on World Development Indictors World Bank 2015 21
**Table 2: Family background and Conditional Mean and Conditional Variance** **<u>( Full Sample Estimates)</u>**

||**CH**|**INA **|**IN **|**DIA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
||YrsSch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|Yrs. Sch.|**Res. Sq.**|
|Father's Edu|0.383***<br>(0.014)|**-0.483*****<br>**(0.036)**|0.621***<br>(0.005)|**-0.379*****<br>**(0.028)**|0.497***<br>(0.011)|**-0.514*****<br>**(0.052)**|
|Constant|5.943***|**18.823*****|4.244***|**20.134*****|6.437***|**16.784*****|
||(0.152)|**(0.424)**|(0.043)|**(0.144)**|(0.113)|**(0.429)**|
|Observations|94,159|**94,159**|86,748|86,748|18356|18356|
|R-squared|0.142|**0.009**|0.298|0.006|0.212|0.011|
|MeanofY|7.521|**16.83**|6.501|18.76|9.523|13.587|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia.

26
22 investing across borders: do fdi-specific regulations matter?

of contract enforcement rights, due process, and access to justice). It gives parties autonomy to create systems tailored to their disputes. According to World Bank Group (2010), countries that score well on these indicators have a strong arbitration legal framework, receive support from local courts for arbitration proceedings and efficient enforcement, adhere to international conventions, and provide autonomy to parties seeking to resolve their commercial disputes.

# 1.4.3 _Starting a Foreign Business_

Starting a Foreign Business indicators record the time, procedures, and regulations involved in establishing a local subsidiary of a foreign limited liability company. Here, I use the number of procedures required to establish a foreign business. This resembles a cost that affects the decision about whether and how much a firm invests in a foreign country. The steps include both pre- and post-incorporation procedures with which foreign businesses are officially required to comply. According to World Bank Group (2010), countries that score well on the Starting a Foreign Business indicators have simple and transparent establishment processes that abolish unnecessary steps (which create opportunities for rent-seeking). High scorers also treat foreign and domestic investors equally, and differences in treatment vary only by company size, legal form or commercial activity, not the nationality of shareholders. Tables 19 to 21 describe how indicators of FDI-specific regulations are constructed in the IAB data set. I separately construct composite country scores for the quality of selected FDI regulations in Table 22.<sup>22</sup>

> 22 The Investing Across Sectors score (1) averages the maximum percentage of equity permitted for foreign ownership in the 11 sectors mentioned in Table 19. The Ease of Process Index (2) and the Ease of Judicial Assistance Index (3) are obtained by normalizing the country score for those indices using the min-max rule: the score for a country is subtracted from the best-performing country, divided by the difference in scores between the best and worst-performing countries. The number is then multiplied by 100. Higher the score, better is the regime in place for resolving commercial disputes. The score for the Number of Procedures (4) is also obtained by normalizing the country score using the min-max rule. However, the final score is multiplied by, and subtracted from, 100. Higher the score, the fewer the number of pre- and post-incorporation procedural steps formally required to establish a wholly foreign-owned subsidiary.
Maloney, 2010). Hence, changes in the composition of the employed labor force over the business cycle might be more limited.

To assess the importance of compositional effects in estimates of wage cyclicality we exploit micro data from the Brazilian labor force survey, the Pesquisa Mensal de Emprego (PME), which enables us to derive a quarterly series of wages for the manufacturing sector over a fairly large period; in this case, 1984—2010.<sup>7</sup> We proceed in two steps. First, we exploit the individual worker data in the PME to purge the wage series from changes in observable worker characteristics over the business cycle. This is obtained by estimating a standard Mincer regression where wit denotes the hourly wage of worker i at time t, Xit is a matrix that includes quadratic terms on education and age, and τ t are quarterly dummies.<sup>8</sup> In a second step, we recover the coeffi cients of the quarterly dummies, and use their projection (ˆτ t) as our main wage variable in the vector autoregression analysis. This projection is naturally the average quarterly wage once changes in the education and experience of the employed population have been purged.

Figure 6 shows the evolution of the cyclicality of real wages in Brazil before and after controlling for compositional effects. The graphs in the left-hand panels reproduce our previous estimates, which do not take into account changes in the composition of the employed population over the cycle. The right-hand panels show the impact of controlling for observable worker characteristics on the cyclicality and volatility of all variables. We find that changes in the composition of the work force do not have a major impact on the comovement of wages and output. As before, we observe a decline in the elasticity throughout the sample period.

Our analysis so far has been confined to the manufacturing sector. We can also exploit the PME, which is representative of all sectors in urban areas in Brazil, to

> 7 See Appendix A for data details.

> 8 The regression is conducted after trimming the observations in the bottom 1 percent and top 99 percent of the distribution of hourly wages, and excludes public sector workers.

17
demographic, and political issues in Nepal. It is surprising how little is known about the economic effects of the country’s work-related migration and remittances, given the interest in the subject by the Nepali government and international development agencies involved in the country. We are unaware of any research that formally investigates the micro-level relationship between work-related migration and household well-being in Nepal. A few, mostly descriptive, studies by Nepali scholars establish no causal relationship between work-related migration, remittances, and poverty (for example, Acharya 2001 and Chhetry 1999, 2002; see also Kumar 2003). With this paper, the intention is to fill this gap by providing empirical evidence of the effect of migration and remittances on poverty in Nepal.

In this endeavor, we rely on two rounds of the nationally representative household surveys to measure the impact of increased work-related migration and remittance inflows on the economic well-being of Nepali households between 1995 and 2004. More specifically, we model the effect of remittances and work migration on household consumption and aggregate poverty and inequality rates. Using the cross-sectional sample of 2004, we estimate a model of household migration decisions jointly with the consumption equations by the method of full information maximum likelihood (FIML). The method takes into account unobserved household characteristics that could simultaneously affect household migration decisions and household income. We also simulate counterfactual expenditure distributions to determine the effect of work-related migration on poverty and inequality in Nepal.

The results of our estimations show that the increase in the number of working migrants during the past decade had a significant impact on poverty in Nepal. Almost 20 percent of the decline in poverty in Nepal between 1995 and 2004 can be attributed to increased work-related migration and remittance inflows. If the level of migration and the amount of remittances remained at the 1995 level, the poverty rate in Nepal would increase from the currently observed 30 percent to 32 percent; the mean per capita expenditure would decline from about 15,000 to 14,000 NPR. Almost two-thirds of this increase in poverty can be explained by the higher number of the would-be-poor among the households with international migrants. Work-related migration and remittances, however, have only marginal impact of the changes in income inequality in Nepal.

4