rafmacalaba/datause-encoder-data huggingface dataset:
8 alleged that foreign banking affiliates facilitate the export of capital in circumstances in which foreign returns exceed domestic returns or there are major uncertainties facing domestic savers.<sup>7</sup>

# C. Banking Systems in Develolng Countries

The above discussiot, takes fo- granted that the main function of the financial services industry is to provide financial services to clients efficiently and on a more or less competitive basis. But this is not true in all countries. There are importait differen.- .etween the industrial and developing countries and very large differences among developing countries that bear on the question of liberalizing entry.

# Role

Despite some trend towards reform, in a large number of developing countries, financial intermediaties are still expected to distribute subsidies by means of below-market credit, to cross-subsidize across clients, and to finance the government through forced investments and high reserve requirements. This role may be part of broad development policy, or it may be imposed to satisfy special interests. To give some idea of the size of such impositions, subsidies to government and others through financial systems frequently amount to 3-4 percent of GNP and may reach 7-8 percent of GNP--this for a sector in which normal value added amounts to only 5-6 percent of GNP. For a set of ten developing countries the inflation tax averaged 2.6 percent of GNP in 1987 (World 7 According to IMF data, over 1981-87 gross flows of foreign investment to developing countries were $118 billion, but net flows, taking into account dividend remittances, were negative $10 billion. For non-oil developing countries, gross and net flows were $85 billion and $21 billion, respectively.
# INCOME GROWTH

With the IFLS panel it is possible to calculate directly the income growth for individuals who were working in particular sectors or regions. Table 4 shows the median income growth for individuals according to whether they lived in rural or urban areas and whether they worked in agriculture or outside of agriculture for 1993 and 2000. Table 4 shows an intriguing story of how income and income growth are associated with sectoral transition and rural-urban migration. First, the dramatic impact of both _urban location_ and _work outside of agriculture_ is evident: agricultural workers earn about half the income of those working outside agriculture in both rural and urban areas; similarly rural workers (whether farm or non-farm) earn little more than half the income of workers in the same sector in urban areas.

Next, consider how the typical worker in each group fared. The vast majority of rural farm workers in 1993 continued to be rural farm workers in 2000 and saw their real incomes decline by 11%. Almost all of those who moved out of farming in rural areas did so by moving to non-farm activities in rural areas; these individuals saw their real incomes rise by almost a third. Very few rural agricultural workers moved to urban areas, so we do not place any confidence on the income growth figures for these transitions. However, we note that real income fell by 7% between 1993 and 2000 for this poorest category of worker, with only those that shifted out of agriculture managing to increase their incomes significantly.

19
the crisis (which are smaller than the premia presented in panel A because the analysis is confined to firms that are more similar to each other). During the post-Suharto era connected firms remain on average slightly larger than non-connected firms, but, importantly, the size and market share premia on being connected to Suharto seem to have substantially eroded because of Suharto’s ousting.

# **4.2 Impact of Political Turnover on Firms**

The key challenge is to disentangle the impact of the fall of Suharto from the potential confounding effects of the events that happened in Indonesia at the same time. The Asian financial crisis led to drastic currency devaluation, the collapse of the banking system, and numerous defaults. Firms (and industries) that were more import oriented, less export oriented, and more reliant on external finance before 1997 were hit hardest by the crisis and had different recovery trajectories, as credit conditions were durably altered and because the rupiah did not recover. This exchange rate adjustment enhanced the competitiveness of (net) exporters but hurt (net) importers. Suharto’s fall also precipitated regulatory reforms and decentralization.

To isolate the impact of (disruption of) political connections from these other developments, we use two main strategies. To start with, we use difference-in-difference and event study strategies to assess performance differences between connected and nonconnected firms before and after the fall of Suharto. Anticipation effects are likely limited (Fisman, 2001), given that his fall was largely unexpected, which aids identification. Propensity score methods are used to limit selection bias. Second, we exploit the differential timing of the removal of Suharto appointed mayors, who were allowed to finish their terms, across districts to break the simultaneity between the financial crisis and regime change. In both cases we follow Blalock et al. (2008) and exclude the crisis period and its immediate aftermath (1997– 99), because it was characterized by turmoil and adjustment.
where ‘rest’ is the _weighted_ restrictiveness index faced by manufacturing sector ‘s’ at time t, ‘w’ is the share of the input bill of manufacturing sector ‘s’ accounted for the service sector ‘j’ at time ‘t’, and ‘OECDr’ is the OECD restrictiveness index of the service sector j at time t. The weights obtained from Indonesian input-output tables are available for 1995, 2000, and 2005, disaggregated at the 2 digit level of the 3<sup>rd</sup> revision of the International Standard Industrial Classification (a total of 18 sectors), while the service sector OECD restrictiveness indices used are available for 1997, 2003, 2006, and 2010, and for Electricity, Construction, Distribution, Transport, and Communications.<sup>11</sup> To establish whether there exists a causal relationship between manufacturing firm performance in Indonesia, and how restrictive policies are toward FDI in the services sectors, in the second stage, we regress the productivity of manufacturing firms on the restrictiveness measure ‘rest’ as calculated above.<sup>12</sup> Formally, we estimate equation (2):

> where ln _TFP_ is the average log TFP of firm ‘i’ over the years t+1, t+2 and t+3, α _i_ are firm fixed effects, that capture unobserved heterogeneity, such as firm location, size, specificities related to products produced by the firm, and other time-invariant factors that may affect firms’

> performance, α _t_ are year fixed effects, that capture economy-wide shocks such as technological advances, across-the-board business climate changes and other macro shocks that may affect firms’ performance.<sup>13</sup>

> 11 Data availability constrains us to assume that the input cost shares of manufacturing sectors remain relatively stable around each data point we have available. Then, we match these shares in 1995 with the restrictiveness indicator in 1997, the shares in 2000 with the restrictiveness indicators in 2003, and the shares in 2005 with the restrictiveness indicators for 2006 and 2010. Given that we do not have data on manufacturing TFP beyond 2010, we do not use the available OECD indicators for 2011 and 2012. In any case, there are no changes apparent in those two periods with respect to the previous scores. 12 Our approach follows that of Arnold, Javorcik and Mattoo (2011).

> 13 TFP is measured using a multilateral index as suggested by Aw, Chen and Roberts (2001).

11
58 _yuan_ would tend to redistribute income to the disadvantage of agriculture (because China is increasingly a price taker on markets for agricultural products).

# **III:2 Income Transfers and Social Insurance**

In urban areas, China has recently started to build up mandatory systems of income insurance akin to social arrangements in developed countries in the west (“social insurance”), combined with elements from to the so-called “provident funds” in Singapore and Malaysia. The main programs comprise unemployment benefits, social assistance, pensions and health-care insurance (the latter will be discussed in section III:3).<sup>115</sup> The fairly high generosity of these programs, although confined to a small part of the population, is reflected in the large social security contributions required to finance them: payroll taxes amounting to about 40 percent of the wage bill for those involved (30 percentage points being paid by the employer).<sup>116</sup> By comparison, in today’s developed countries figures around 30 percent are more common. In a short and medium-term perspective the high payroll taxes may also have retarded the expansion of employment in manufacture, since it often takes time before such taxes are fully shifted onto correspondingly lower pre-tax wages.

We would expect that social insurance in China, as in other countries, will be designed not only to provide income smoothing and income insurance, but also to assist individuals with low life-time income, hence mitigating long-term poverty. In today’s developed countries, this is usually brought about by benefit floors in various social insurance systems, such as basic (or guaranteed) unemployment benefits, sick-pay benefits or pensions. In developed countries, social insurance often also contributes to an _overall_ redistribution of lifetime income (across the entire distribution of income). In addition to the benefit floors, this is brought about by also having benefit ceilings, without corresponding ceilings for the contributions. Of course, since such non-actuarial arrangements weaken the link between contributions and benefits, they are bound to create implicit tax wedges, which tend to distort labor supply and investment in human capital.

> 115 See, for instance, China’s Social Security White Paper (2006) and World Bank (2003a, Section 5). 116 China’s Social Security White Paper, 2006.
_-4-_

# THE STOCK hARKET

The first Kuwaiti Shareholding Company (KSC) was formed in 1952, and at the time of the moratorium there were 40 publicly owned companies listed on the Kuwait stock exchange. There were also 46 unlisted Kuwaiti shareholding companies' known as KSCCs, and 38 companies established in Gulf countries that traded on the informal exchange called the Suq Al Manakh, after the name of the building in which most of the brokers had their offices. The companies listed on the official stock exchange were established under Kuwaiti commercial law ./ ; the rest were traded on the unofficial exchange since they did not meet the exchange's legal requirements.

Kuwait's securities industry was regulated by the Committee on Securities appointed by the Minister of Commerce. Although the committee had wide powers for regulating the stock market, and its executive board met twice a month to follow market developments, there were no established procedures po supervise the system. Moreover, the committee was limited in its supervision to only those shares registered with it, which effectively excluded trades in the parallel market that accounted for more than 80 percent of trading volume. The Securities and Investment Department of the ministry was responsible for registering trades, supervising brokers, and generally executing the committee's decisions.

_Market Expansion._ During the early seventies, surging demand for equities forced prices higher, prompting buyers to pay a premium for newly issued shares, even while companies were being established. In fact, the subscription rights of Kuwaiti citizens could be bought through brokers, and almost all the companies founded were oversubscribed. Merchants responded to shareholder enthusiasm by forming new companies at a rapid pace, assuring themselves of a certain number of shares at a nominal price. New issues on the stock exchange rose from KD 33 (Kuwaiti dinars) million a year in 1974 to an annual KD 165 million between 1982-84 2/ .

This growth was accompanied by a rapid rise in new KSCCs and Gulf companies when the government suspended the formation of KSCs for almost two years, from mid-1977 to mid-1979. Between 1976 and 1982, 38 Gulf-incorporated companies were established with subscribed capital of $3.5 billion 3/ . These were essentially Kuwaiti-owned but incorporated in the Gulf Emirates. Regulations permitted Kuwaiti investors to incorporate a company in any Gulf state. The firm would have a long list of Gulf founders (although most were Kuwaitis) and each share was denominated at 1/30 the Kuwaiti price ($1 as compared to $30). Within a relatively short time the share would be actively trading in the parallel 1/ See Statistical Appendix 8 and 9.

_2/_ See Statistical Appendix 8.

- 2/ Ibid.
**Table 8:** CC mean yields and differences with ML and SR yields by crops, in the overall sample and by region for 2017.

||Millet|Sorghum|Rice|Maize|Cowpea|Groundnut|
|---|---|---|---|---|---|---|
|||||National|||
|CC<br>ML - CC|934.3<br>2.4|1045.5<br>-6.4|2542.8<br>95.5|1684.7<br>-28.8|320.5<br>-6.8|1866.3<br>108.3|
|SR - CC|-104.0<sup>_∗_</sup>|-326.2<sup>_∗∗∗_</sup>|-1108.7<sup>_∗∗∗_</sup>|-470.0<sup>_∗∗∗_</sup>|152.2|-890.5<sup>_∗∗∗_</sup>|
|N|1607|1155|699|1162|342|1300|
|||||Kayes|||
|CC|548.0|754.7|4114.4|774.9|216.3|2090.8|
|ML - CC|198.8<sup>_∗∗∗_</sup>|155.2<sup>_∗∗∗_</sup>|22.4|336.5<sup>_∗∗∗_</sup>|62.7<sup>_∗_</sup>|47.1|
|SR - CC|619.1|-196.2<sup>_∗∗_</sup>|-3098.6<sup>_∗_</sup>|128.1|113.8|-753.3<sup>_∗∗∗_</sup>|
|N|64|327|26|284|47|594|
|||||Koulikoro|||
|CC|1024.3|1057.1|1523.0|1650.5|288.6|1398.4|
|ML - CC|-44.6<sup>_∗_</sup>|-4.9|267.4<sup>_∗_</sup>|24.2|-5.8|510.0<sup>_∗∗_</sup>|
|SR - CC|-400.2<sup>_∗∗∗_</sup>|-483.5<sup>_∗∗∗_</sup>|-330.5|-712.5<sup>_∗∗∗_</sup>|342.3<sup>_∗_</sup>|-696.8<sup>_∗∗_</sup>|
|N|276|305|110|247|58|235|
|||||Sikasso|||
|CC|832.8|1079.5|2299.3|2141.8|330.6|1518.6|
|ML - CC|-97.8<sup>_∗∗∗_</sup>|33.4|98.8|-222.3<sup>_∗∗∗_</sup>|-7.6|255.4<sup>_∗∗∗_</sup>|
|SR - CC|-121.2|-184.1|-1473.5<sup>_∗∗∗_</sup>|-548.2<sup>_∗∗∗_</sup>|278.3<sup>_∗∗∗_</sup>|-640.7<sup>_∗∗∗_</sup>|
|N|166|222|156|534|79|239|
|||||Segou|||
|CC|1132.3|1438.9|5786.8|2196.4|433.6|2566.8|
|ML - CC|-103.3<sup>_∗∗∗_</sup>|-212.5<sup>_∗∗∗_</sup>|-526.6|-256.6<sup>_∗∗_</sup>|-67.8<sup>_∗∗∗_</sup>|-559.9<sup>_∗∗∗_</sup>|
|SR - CC|-142.5|-527.4<sup>_∗∗∗_</sup>|-4268.2<sup>_∗∗_</sup>|-1144.7<sup>_∗∗∗_</sup>|14.0|-1837.8<sup>_∗∗∗_</sup>|
|N|485|243|40|93|68|168|
|||||Mopti|||
|CC|862.0|839.3|1760.5|768.3|315.9|1711.1|
|ML - CC|71.2<sup>_∗∗∗_</sup>|-25.5|585.1<sup>_∗_</sup>|-0.0|2.9|108.7|
|SR - CC|-54.2|-37.0|-376.4|-108.7|-11.4|-1142.2<sup>_∗∗∗_</sup>|
|N|594|54|154|4|88|63|

_Notes:_ The training set represent 1/3 of the total sample size. The mean CC yield and differences with ML and SR yields are computed using survey design weights. Statistical significance are assessed using standard errors clustered at the enumeration area level. In the case of the ML, the standard errors used for statistical significance tests account for the between imputations variance computed with 10 simulations. Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 24
status, while Figures A.3 and A.4 in the Annex present the population shares for the eligible populations.

_Table 13: Intrahousehold inequality in the financial security dimension_

|Country|Populati<br>Populati|on Share<br>on (_n_)|of Wo|rking-Age|Working<br>MPI po<br>employe<br>working<br>looking|-age perso<br>or househ<br>d, and a<br>-age person<br>for work (%|ns who re<br>old where<br>t least<br>is unemp<br>)|side in an<br>they are<br>one other<br>loyed and|Chi-<br>squared<br>test<br>for<br>gender|Chi-<br>squared<br>test for IDP<br>status|
|---|---|---|---|---|---|---|---|---|---|---|
||_IDP_||_Non-ID_|_P_|_IDP_||_Non-IDP_||||
||_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|||
|ETH|4,751|5,687|808|934|46.45|51.52|||*|N/A|
|NGA|1,068|1,095|2,905|3,166|[16.02]|[12.95]|[13.95]|57.08|***||
|SOM|2,371|2,432|4,268|4,535|19.95|17.15|28.86|34.05|||
|SSD|173|209|1,226|1,475|||45.63|42.56||N/A|
|SUD|1,598|1,578|3,416|3,537|26.34|24.45|25.44|23.77||**|
|Country|Populati<br>|on Share of|Individu<br>|als (_n_)<br>|Individu<br>househo<br>identific<br>|als who re<br>ld and ha<br>ation, and a|side in an<br>ve a form<br>nother do<br>|MPI poor<br>of legal<br>es not (%)<br>|||
||_IDP_<br>||_Non-ID_<br>|_P_<br>|_IDP_<br>||_Non-ID_<br>|<br>|||
||_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|||
|ETH|11,435|11,897|1,538|1,648|48.14|42.33|[4.87]|[4.66]|||
|NGA|2,673|2,330|5,910|6,630|[9.68]|[7.37]|48.97|33.97|*|**|
|SOM|4,850|4,880|8,888|8,670|45.66|35.27|10.24|[8.84]|***|***|
|SSD|286|318|1,840|2,107|[23.52]||[42.50]|[31.72]|[***]|[]|
|SUD|2,911|2,832|5,916|5,986|32.75|33.52|17.02|16.71|||

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

According to Table 13, intrahousehold inequality in unemployment is relatively infrequent among the multidimensionally poor populations, and thus displacement status does not significantly correlate with intrahousehold inequality for unemployment. The exception is Sudan, where displaced persons are significantly more likely to live in MPI poor households where at least one working-age member is employed, and another is unemployed and looking for work, although there are no significant differences by 25
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108
**<u>Table 9: Change in Exports from India to Bangladesh</u>**

||**Share in Base**<br>**Year Export**<br>**to Bangladesh**|**India–Bangladesh**<br>**Full FTA (%**<br>**Change)**|**Bangladesh Gets DF**<br>**Market Access in**<br>**India (% Change)**|**India–Bangladesh Full**<br>**FTA + Improved**<br>**Connectivity (%**|
|---|---|---|---|---|
||**(percent)**|||**Change)**|
|Textiles|14.24|259.25|0.15|353.88|
|Processed rice|12.47|60.25|0.84|84.94|
|Chemical, rubber, and plastic<br>products|11.64|106.99|0.66|141.23|
|Machineryand equipment nec|8.34|98.73|0.19|145.46|
|Vegetables,fruits,and nuts|6.77|48.38|0.85|59.01|
|Wheat|4.59|44.82|0.66|64.91|
|Vegetable oils and fats|4.58|41.17|0.34|64.26|
|Plant-based fibres|4.30|17.96|0.25|23.69|
|Ferrous metals|4.30|82.97|0.25|120.80|
|Metalproducts|3.60|202.44|0.55|285.40|
|Petroleum and coalproducts|3.36|163.84|0.16|216.27|
|Motor vehicles andparts|2.25|147.34|0.12|195.95|
|Cerealgrains nec|2.24|0.57|0.18|0.88|
|Paperproducts andpublishing|1.98|177.10|0.31|213.37|
|Transport equipment nec|1.89|388.39|0.43|518.27|
|Metals nec|1.83|89.93|0.81|111.32|
|Coal|1.54|0.13|0.13|0.20|
|Crops nec|1.49|149.30|2.08|195.45|
|Electronic equipment|1.42|223.79|0.19|344.64|
|Mineralproducts nec|1.38|191.66|0.49|230.91|
|Dairy products|1.29|316.61|0.53|422.49|
|Minerals nec|1.05|19.28|0.13|24.73|
|Apparels|1.02|352.72|0.53|555.95|
|Foodproducts nec|0.94|85.75|0.36|135.97|
|Manufactures nec|0.64|413.95|0.64|550.97|
|Sugar|0.13|278.35|0.52|370.18|
|Forestry|0.11|83.54|0.63|128.66|
|Fishing|0.09|54.62|0.77|65.80|
|Leatherproducts|0.09|169.47|0.00|226.14|
|Woodproducts|0.08|299.14|0.86|383.68|
|Business services nec|0.08|1.71|0.00|2.70|
|Beverages and tobacco<br>products|0.05|81.58|0.00|116.66|
|Cattle,sheep, goats,and horses|0.04|1.85|0.00|2.60|
|Transport nec|0.04|1.89|0.00|2.18|
|Insurance|0.04|1.61|0.00|2.37|
|Public|||||
|admin/defence/health/education|0.04|3.13|0.00|4.21|
|Paddyrice|0.02|392.00|4.00|569.44|
|Communication|0.02|3.57|0.00|6.08|
|Trade|0.01|0.0|0.00|0.00|
|Sea transport|0.01|0.0|0.00|0.00|
|Air transport|0.01|0.0|0.00|0.00|
|Financial services nec|0.01|0.0|0.00|0.00|
|Recreation and other services|0.01|0.0|0.00|0.00|
|**Total **|**100.00**|**125.64 **|**0.47**|**172.20**|

_Source_ : GTAP simulation results.

17