rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A28: Second stage: Effects of refugee presence on household welfare disaggregated by location, with analysis at cluster level and the distance weighted refugee -** **<u>index at 100km from the clusters (Panel 2009 2012)</u>**

|||||L|og of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|||||||Panel A|(Rural)||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.10990|0.10939|0.13063*|0.11843*|||||||||
||(0.06781)|(0.06764)|(0.07237)|(0.06446)|||||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.11814*<br>(0.06773)|0.11812*<br>(0.06767)|0.14870*<br>(0.07655)|0.13834**<br>(0.06846)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.11739<br>(0.07433)|0.11660<br>(0.07408)|0.12254*<br>(0.07335)|0.11292*<br>(0.06564)|
|Observations|4,541|4,538|3,648|3,640|4,541|4,538|3,648|3,640|4,541|4,538|3,648|3,640|
|Kleibergen-Paap rk Wald F|15.20|15.20|13.19|12.45|18.02|18.03|15.24|14.30|12.58|12.59|11.36|10.80|
|Root MSE|0.582|0.581|0.532|0.364|0.582|0.582|0.533|0.365|0.582|0.582|0.532|0.364|
|||||||Panel B|(Urban)||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|-0.34333<br>(0.42013)|-0.35743<br>(0.42162)|-0.16438<br>(0.64743)|-0.10562<br>(0.80678)|||||||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||-0.31907|-0.33449|0.26373|0.48580|||||
||||||(0.42075)|(0.42024)|(1.21304)|(1.75363)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||-0.31919<br>(0.37713)|-0.33145<br>(0.37849)|-0.33726<br>(0.48258)|-0.31661<br>(0.62433)|
|Observations|902|902|450|450|902|902|450|450|902|902|450|450|
|Kleibergen-Paap rk Wald F|2.202|2.203|3.141|2.485|2.253|2.254|1.365|0.821|2.082|2.083|3.667|3.165|
|Root MSE|0.681|0.678|0.498|0.321|0.679|0.677|0.502|0.337|0.679|0.676|0.499|0.321|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|

47
|**Exhibit 3c:**<br>|**Evide**<br>|**nce from cross-region**|**al studies**<br>**Implications for**|**(III/V)**<br>|
|---|---|---|---|---|
|**Brief**<br>**description**<br>**Study**|**Time**<br>**period**|**Key finding**|<br>**market vs.**<br>**intervention question**|**Implications for centre**<br>**vs. region question**|
|• Study of 97<br>regions<br>(excluding<br>Austria,<br>Finland,<br>Sweden)<br>Cuadrado-<br>Roura et. al.<br>(2000)|1980-1993|• Absoluteβ-convergence of 2.8-3.5%, 1981-90<br>• Three regional groups for conditionalβconvergence<br>- +ve, significant fixed effects growing faster<br>-<br>-ve, significant fixed effects growing slower<br>- Zero fixed effects<br>• Five situations from disaggregated analysis of gross<br>value added, productivity & employment<br>- Advanced regions, e.g., Hesse<br>- Dynamic intermediate regions, e.g., Lombardy<br>- Intermediate regions with less dynamism, e.g.,<br>Basque country<br>- Regions in decline, e.g., old UK industrial<br>regions<br>- Miscellaneous heterogeneous cluster|• Regions better able<br>to adapt to changing<br>global technological<br>and market<br>environments do<br>better<br>• However, unclear if<br>better performance<br>due to market<br>friendly or<br>interventionist<br>policies|• Potentially decentralized<br>regions may have more<br>autonomy and flexibility<br>to adjust faster to<br>changing global<br>environments|
|Cuadrado-<br>Roura (2001)|1960-mid<br>1970s|• σconvergence in income|• Shows that<br>government policies<br>|• Shows institutional<br>environment is important<br>|
||Mid 1970s-<br>1988|• No convergence in income|are important<br>• However, does not<br>show which policies|• However, does not show<br>which environment is<br>better for growth|
||1983-1996|• Again no significant convergence|||
|**Evidence for marketEvi**|1977-1994<br>**dence for inte**|• Strong national influence on the performance of<br>regions, regions tend to cluster together both in<br>terms of initial development level and per capita<br>GDP growth rates<br>**rventionist policyEvidence for decentralized app**|**roach**<br>**Evidence for**|**centralized approach**|

27
# **2. Country context – macroeconomic indicators and recent poverty surveys in Tanzania**

Tanzania has experienced substantial improvements in macroeconomic indicators in recent years. At the national threshold, poverty headcount has decreased from 34.4% in 2007, to about 26.4% in 2018, and it stood at 44.9% in 2018 using the 2017 international poverty line of US$2.15. Poverty in Tanzania, like other countries in the region, is predominantly rural. Similar to other SSA countries, food constituted about 59.9% of the consumption basket in 2017 in Tanzania, about 4 percentage points increase from 2011, with rural and urban shares being <mark>63.2% and 52.6%, respectively</mark> (Ministry of Finance, NBS, and World Bank 2020). Food security is a major developmental challenge in Tanzania. The FAO (2021) estimates that about 34.4 million Tanzanians were moderately or severely food insecure in 2021, an increase from 25.1 million in 2014. As an agrarian economy, the agriculture sector continues to be critical to Tanzania, especially the rural poor. The sector contributed about 30% to GDP in 2021 (NBS 2022a), employed nearly 62% of the population in 2021 (NBS 2022b), and generated about 30% of foreign exchange earnings (Government of Tanzania 2017).

The apex government agencies, NBS in Mainland and OCGS in Zanzibar, are mandated with the production of official statistics, including poverty and inequality in the country. The NBS and OCGS have, in the past, independently published official poverty statistics for their jurisdictions using data from the HBS where food consumption is captured using diaries. Besides the HBS, the NBS and OCGS also implement the NPS as part of the Living Standards Measurement Study Integrated Survey on Agriculture (LSMS-ISA) project, which uses 7-day recall for food consumption data collection.

Both the NPS and HBS are multi-topic household surveys. Though the HBS and NPS are implemented by the NBS and OCGS, the focus of each survey is different – HBS provides data 5
|||λ (ratio of private and|Excess<br>returns|
|---|---|---|---|
|Country|code|public investment)|(%)|
|Armenia|ARM|5.39|505.51|
|Burundi|BDI|4.09|253.29|
|Benin|BEN|1.78|22.32|
|Burkina Faso|BFA|1.77|21.83|
|Bangladesh|BGD|2.33|54.61|
|Bolivia|BOL|1.73|19.79|
|Central African Republic|CAF|0.99|-4.64|
|Côte d'Ivoire|CIV|1.38|5.83|
|Cameroon|CMR|7.31|1074.88|
|Congo, Rep.|COG|3.32|147.06|
|Comoros|COM|0.88|-6.59|
|Djibouti|DJI|1.39|6.38|
|Ethiopia|ETH|0.92|-5.93|
|Ghana|GHA|2.70|83.42|
|Guinea|GIN|3.00|112.08|
|Guyana|GUY|0.78|-8.28|
|Honduras|HND|3.43|160.52|
|Kenya|KEN|3.85|216.11|
|Cambodia|KHM|1.96|31.51|
|Sri Lanka|LKA|5.39|506.36|
|Moldova|MDA|4.91|401.68|
|Madagascar|MDG|1.19|0.26|
|Mali|MLI|2.22|47.16|
|Mozambique|MOZ|1.06|-3.11|
|Mauritania|MRT|3.32|146.60|
|Malawi|MWI|1.17|-0.36|
|Niger|NER|1.68|17.61|
|Nicaragua|NIC|4.24|277.37|
|Nepal|NPL|2.90|102.38|
|Pakistan|PAK|2.85|97.66|
|Rwanda|RWA|1.35|5.00|
|Sudan|SDN|5.13|448.69|
|Senegal|SEN|1.87|26.78|
|Sierra Leone|SLE|1.43|7.71|
|Togo|TGO|1.75|20.96|
|Tanzania|TZA|3.70|194.92|
|Uganda|UGA|2.95|107.07|
|Uzbekistan|UZB|3.75|202.69|
|Yemen,Rep.|YEM|2.61|76.37|

Table 4: The estimated excess returns by country, for the point estimates _β_ = 2 and _βy_ = 1 _._ 5.

26
under control. The main pillar of the Convertibility Plan was the April 1991 Convertibility Law, which pegged the new Argentine peso to the U.S. dollar and forced the Central Bank to restrict the monetary base to the dollar value of international reserves.<sup>6</sup> The Convertibility Law, and the new 1992 Charter of the Central Bank that supported it, had a profound effect on the provincial governments: "First, the charter dictated that the central bank could not take any new domestic assets. This meant that the provinces could no longer count on the central bank to rediscount loans by provincial banks to provincial governments, ending their access to seigniorage and the inflation tax. Second, the charter prohibited the central bank from guaranteeing bank deposits. Then provincial banks had to rely on depositor confidence to maintain liquidity. Both measures reduced the central bank's role as a lender of last resort and hardened the budget constraint on provinces, limiting their ability to borrow (indirectly) from the central bank or from depositors." (Dillinger and Webb, 1999, p.16) Because they could no longer rely upon the central bank, these changes meant that the provinces would have to bail out depositors from their own resources in the case of bank failure. In this way, by decreasing the benefits associated with owning a bank and increasing the risk, the Convertibility Plan made owning a poorly performing bank significantly less attractive than it had been before.

These changes had an immediate impact on several provinces.<sup>7</sup> In November 1991, the provincial government of Corrientes passed a law authorizing the privatization of _Banco de Corrientes,_ and by December 1994, when the "Tequila Crisis" hit Argentina, six provinces had authorized the privatization of their provincial banks. The Tequila Crisis had a large impact on the performance of the provincial banks, imposing substantial fiscal costs upon the provinces (Clarke and Cull, 1999). Nervous depositors withdrew deposits from weak banks in both the public and private sectors, hitting the weak public provincial banks hard. This forced the provinces to consider ways to re-capitalize their insolvent banks and to reassess the costs and benefits of privatization.

> **6** See World Bank (1998) for a discussion of the Convertibility Plan and its impact on the financial sector. Clarke and Cull (2000a) discusses how different incentives in different provinces affected the timing of privatization in the separate provinces.

7
**Figure 11: Openness to trade**

<!-- Start of picture text --> 77.8<br>62.7<br>58.7<br>47.3 46.0<br>42.5<br>38.1<br>28.0 29.1 30.8<br>21.7 24.1 25.6 23.6<br>14.9<br>NON-OECD NON-EAP OECD EAP NON-OECD<br>1961-70 1971-80 1981-90 1991-00 2001-05<br>Sources: author’s own calculations using WDI data. Trade<br>openness is the group’s average (unweighted) of the ratio of total<br>trade to GDP for the year 2005.<br>Trade Openness<br><!-- End of picture text -->

# **Improved Macroeconomic Policies**

One of the most relevant sources of domestic shocks is poor management of fiscal and monetary policies. Erratic macroeconomic policies, leading to volatile public consumption growth and inflation volatility, have been repeatedly proven to be a major source of output volatility.

Several econometric studies find that a more stable fiscal policy lowers output fluctuations. Among others, Fatás and Mihov (2006) and Loayza _et al_ . (2006) find a positive causal link between fiscal volatility and output growth volatility, and argue that a more stable fiscal policy can help diminish, or at least not amplify, output fluctuations. Accordingly, East Asia displays a markedly high correlation between the volatility of fiscal policy and output growth volatility (0.89; see Figure 13), and econometric evidence shows that significant part of that correlation stems from the causal link from volatile fiscal policies to output growth volatility (see next section). It is worth observing that what causes increased output growth volatility is likely to be the _pro-cyclicality_ of fiscal policies. Instead of being a-cyclical or counter-cyclical (assisting people by means of higher spending in times of crises), Gavin and Perotti (1997) and others have shown that in most developing countries fiscal policy is generally pro-cyclical (that is, expanding in booms and contracting in recessions), aggravating both output and consumption volatility, and Talvi and Végh (2005) find that the correlation between the cyclical component of government consumption and GDP is positive for every single of 36 developing countries in their sample (with an average of 0.53), among which are Indonesia, Malaysia, Singapore and Thailand (see also Ilzetzki and Vegh, 2009).

The volatility of fiscal policy in non-OECD East Asian countries has decreased by more than half between the 1970s and 2000s (from a quite high 6.89 percent in the 1970s, to 2.91 percent in the 2000s). It is now close to the non-OECD world average (2.88 percent), but still remains far above the OECD average (1 percent). Improvements in fiscal policies have been observed in all groups of countries (i.e. high, middle, and low-income countries).

15
|[rble5**-**A||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|TheWorld at Work||||||||||||
|_Raw data_**_(part_**_1):_||||||||||||
||Working age|Participation|Labor force|Unernpl-|Work Force|Share in|Share in|Share in|Labor in|Labor in|Labor in|
||population|rate||oyment rate||agriculture|industry|services|Agriculture|Industry|Services|
|._|__.____<br>(thousands)|(% O)|(thousands)|(%)|(thousands)|(%)|(%)|(%)|(thousands)|(thousands)|(thousands)|
|World|3540558|69.89|2474324|4.85|2354254|45.65|20.38|33.97|1074625|479843|799787|
|**lliph**inconme**econondes**|547300|69.58|380811|8.30|349204|4.84|29.91|65.25|16901|104447|227856|
|Mliddle**income econondes**|1030643|63.83|657859|6.90|612467|30.75|26.80|42.45|188334|164141|259992|
|**Low**incoie econondes|1962615|73.15|1435653|3.00|1392583|62.43|15.17|22.40|869390|211255|311939|
|**East Asia and Pacific**|1176166|81.91|963398|2.80|936422|57.93|17.97|24.10|542470|168275|225678|
|**Europe and Central**Asia|323679|73.94|239328|7.90|220421|24.02|35.50|40.48|52945|78250|89227|
|**Latin America and Caribbean**|294319|56.47|166202|5.40|<br>157227|22.87|25.38|51.75|<br>35958|39904|81365|
|**Middle East and North Africa**|154506|51.66|79818|13.70|<br>68883|33.27|23.35|43.38|<br>22917|16084|29881|
|O.E.C.D.|534231|69.64|372038|8.30|<br>341159|4.89|29.88|65.23|<br>16683|101938|222538|
|Smalih Asla|743762|59.11|439638|2.90|<br>426888|62.31|14.68|23.07|<br>265994|62667|98483|
|**SuliI-Ssmiarson**<br>**Af.ica**|313895|68.181|<br>214014|6.00|201173|64.57|9.21|26.22|<br>129897|18528|52748|

36
Booth, Alison L. and Hiau Joo Kee ( 2009). ―Birth order matters: the effect of family size on educational attainment‖. _Journal of Population Economics_ 22, 367-397.

Conley, Dalton and Rebecca Glauber (2008). ―All In the Family? Family Composition, Resources, and Similarity in Socioeconomic Status‖. _Research in Social Stratification and Mobility_ , 26, 297–306.

Dahan, M and A. Gaviria (2001), ―Sibling Correlations and Intergenerational Mobility in Latin America‖, _Economic Development and Cultural Change_ ‖, Vol 49, no.3.

Das, Jishnu and Zajonc, Tristan, 2010. " India Shining and Bharat Drowning: Comparing Two Indian States to the Worldwide Distribution in Mathematics Achievement,‖ _Journal of Development Economics_ , Elsevier, vol. 92(2), pages 175-187, July.

- Datt, G and M. Ravallion (2010), ―Shining for the Poor, Too?‖ _Economic and Political Weekly_ , February 13 2010.

Dearden, L and S. Machin, and H. Reed (1997): Intergenerational Mobility in Britain, Economic Journal, January, pp.47-66.

Deaton, A, and J. Dreze (2002). ―Poverty and Inequality in India: A Re-Examination‖, _Economic and Political Weekly_ , Vol. 37, No. 36 (Sep. 7-13, 2002), pp. 3729-3748.

Dreze, J and A. Sen (2011), ― Putting Growth In Its Place: It has to be but a means to development, not an end in itself‖, Otlook India.com, Nov 14, 2011.

Duraisamy, P. (2002). ―Changes in the returns to education in India, 1983–94: by gender, agecohort and location‖, _Economics of Education Review,_ 21(6), 609–622.

Emran, M. Shahe and F. Shilpi. 2011. ―Intergenerational Occupational Mobility in Rural Economy: Evidence from Nepal and Vietnam‖, _Journal of Human Resources_ , 46(2), p.427-458.

Emran. M. Shahe and Yan Sun (2011). ―Magical Transition? Intergenerational Educational and Occupational Mobility in Rural China: 1988-2002‖, Paper presented at American Economic Association Annual Conference, Denver, 2011.

Fields, G, D. Grusky, and S. Morgan (Eds) (2005): Mobility and Inequality: Frontiers of Research from Sociology and Economics, Stanford University Press.

Goldberger, A. (1989), "Economic and Mechanical Models of Intergenerational Transmission," _American Economic Review_ , vol. 79, no.3, 504-513.

Green, F, S. Machin, R. Murphy, and Y Zhu (2010), The Changing Economic Advantage From Private Schools, Working Paper.

Hnatkovska, V., A. Lahiri and S. Paul. 2011. ―Breaking the Caste Barrier: Intergenerational Mobility in India,‖ Mimeo.

33
productivity by 5.5 percent. Arnold et al. (2011) find that a standard deviation increase in foreign presence in the services sectors in the Czech republic is associated with a 3.8 percent increase in the productivity of manufacturing firms, which is in line with our findings.

## **Endogeneity issues**

Industries may lobby the government to liberalize services. More productive firms that are larger and better politically connected have a stronger inluence on the government’s decision which services sub-sectors to liberalize. Hence, the positive link between services liberalization and productivity may be due to reverse causality. To adress the concern, we instrument _indexjt_ and _FDIsharejt_ by the log of the sub-sector specific outward services FDI of the EU to the rest of the world, ln( _FDIjt_ )<sup>7</sup> . The argument goes as follows. The EU has been a major bilateral negotiator over the WTO accession of Ukraine. We expect that the EU put more pressure on liberalization of those services sub-sectors in which there are large FDI outflows from the EU. Results of the first stage IV regression, presented in the appendix, indicate the EU FDI outflows are good predictors for both the EBRD index of liberalization and for the FDI share of employment.

In the second stage we replace our indices of services liberalization with the indices of services liberalization instrumented with outward FDI in services sub-sectors and report the results in panel B of Table 4.

# **4.4 Sub-sample results**

We further test the robustness of our results by looking at sub-samples of data along the time, ownership and size dimensions. The results are presented in Table 5.

> 7Data is available from Eurostat

23
poverty was 1.36 times the aggregate measure of absolute poverty. This upward revision in the extent of poverty reflects the aforementioned fact that our weakly relative measures imply that the economic gradient in poverty lines emerges at a lower level than was found using the AB poverty lines calibrated on the Ravallion et al. (1991) data set.

# **7. Conclusions**

While we can accept that people care about their relative position in society (at least above some level of living) it is very hard to accept that they only care about relative income. And while one can agree that certain goods have a social role it is hard to accept that the expenditure required to attain those goods is negligibly small for very poor people; recalling Adam Smith’s example of the role of a linen shirt in eighteenth century Europe, a sociallyadequate shirt would not presumably have cost any less to the poorest person than the richest. While poor people may be highly constrained in their spending on things that facilitate their inclusion, and so be more socially excluded, that does not mean that their social inclusion needs are negligible. Thus, the prevailing justifications for strongly relative poverty measures are hard to accept on theoretical grounds.

Our weakly relative poverty lines relax these assumptions. From a welfarist perspective, our measures place a natural upper bound on the weight attached to relative deprivation, namely that it cannot matter so much that measured poverty does not fall when all incomes increase by the same proportion. From a non-welfarist perspective, we impose a positive lower bound on the cost of social inclusion. Our poverty lines are calibrated to a new compilation of national poverty lines, drawing on a vast amount of new poverty studies done since the 1980s. A simple, dataconsistent, schedule of relative poverty lines is shown to provide an excellent fit to these data on national lines, but with an elasticity that rises from zero to unity, but never reaches unity.

On implementing our weakly relative poverty lines using almost 700 surveys for 115 countries we find that there is more relative poverty in the developing world than has been thought and that the pace of progress against relative poverty over 1981-2005 is less encouraging than that against absolute poverty. We find that 47% of the population of the developing world lived in relative poverty in 2005, down from 53% in 1990 and 63% in 1981. This was not a sufficient rate of decline in the incidence of poverty to prevent a rise in the number of poor, in contrast to our absolute poverty measures that show falling poverty counts in the aggregate. With 18
# **_2. “Change the system”_**

Before economic reform China had a planned system based on collectivized agriculture and state ownership of the means of production. In 1978 3% of retail goods were traded at market prices, and 6% of farm commodities. State-owned enterprises accounted for 77% of industrial production, and the rest came from collectives that were basically local state enterprises (Table 1). China has gradually reformed ownership over the past 25 years, starting with agricultural reforms detailed in a later section.<sup>2</sup> In the first decade of reform it opened up some locations to private foreign investment, allowed more scope to township and village enterprises to operate on a market basis, and introduced various reforms in state firms. These reforms accelerated after a famous trip by Deng Xiaoping to the dynamic southern coastal areas in 1992. By 1995 China’s industrial output was divided roughly evenly among state enterprises, collectives and private firms.

Since 1995 the private sector has expanded very rapidly and by 2003 accounted for 72% of industrial output. In this period many collectives and state firms were privatized, and entry of new private firms was encouraged. In 2005 the statistical bureau and the World Bank carried out a large survey of 12,400 manufacturing firms in 120 cities (World Bank 2006). The firms in the stratified random sample had in the aggregate more than 10 million employees, so that this is roughly a 10% sample of manufacturing. In this sample only 8% of firms are majority state-owned; 27% are foreign-invested; the large majority of firms are domestically owned private firms (Table 2). The Chinese economy is now largely based on the Chinese private sector. The sample also found that

> 2 Early stages of China’s reform are described in Lin (1988) and Lin (1992).  See Rawski (1994) on the industrial reforms in the 1990s.

5