rafmacalaba/datause-encoder-data huggingface dataset:
derivatives which further spurred the real estate boom.<sup>4</sup> Rising wages and the real estate boom initially helped increase fiscal revenue and allowed deficits to remain in line with the Maastricht criteria (except for Greece), in spite of government expenditure that rapidly rose due to increasing government wage bills and social transfers. Yet, with growth increasingly driven by unsustainably high domestic consumption, periphery countries lost export competitiveness and the manufacturing sector declined. At the same time, core countries‘ competitiveness and their external surpluses improved, as a result of wage restraint and the relative undervaluation of the euro compared to the earlier national currencies.

4. The global financial crisis since September 2008 led to a recession in Europe and triggered the burst of the real estate bubble; both resulted in a ballooning of fiscal deficits and a massive deterioration of debt indicators that set the stage for the sovereign debt crisis in the Euro Zone that began with the Greek crisis in early-2010.

<!-- Start of picture text --> Percent change, Figure 1: Euro Zone growth: Projections and reality<br>year over year<br>4 Actual<br>Projected<br>3<br>2<br>1<br>0<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br>-1<br>-2<br>-3<br>-4<br>-5<br>Note: Projection data from previous year of actual GDP growth.<br>Source: World Economic Outlook, various years.<br><!-- End of picture text --> 5. The dynamics between core countries, especially Germany, and non-core countries in the Euro Zone appear analogous to those between East Asian surplus economies and the United States in the run-up to the global financial crisis.<sup>5</sup> Excess consumption in the United States brought about by a real estate boom and low interest rates allowed East Asian countries to expand their exports, until the burst of the real estate bubble and the ensuing crisis of the financial sector triggered a global recession. The consumption boom in the United States went

> 4 Lin and Treichel (2012).

> 5 _Ibid_ .

3
**Table A.1a – Business outcomes at baseline (mean values, in TZS)**

||Control|Basic training|Enhanced<br>training|All|
|---|---|---|---|---|
|Typical monthly revenues|667,575|652,700|661,407|660,669|
|Revenues in previous month|690,983|952,442|737,512|793,977|
|Profit in in previous month|259,150|371,994|253,658|295,573|
|Profit last year|2,074,347|8,529,796|4,849,750|509,665|
|Monthly earnings|467,688|916,654|509,534|633,197|

Note: sample includes only entrepreneurs who could be interviewed at baseline and endline

**Table A.1b – Business outcomes at baseline (mean values, in US$)**

||Control|Basic training|Enhanced<br>training|All|
|---|---|---|---|---|
|Typical monthly revenues|445|435|441|440|
|Revenues in previous month|461|635|492|529|
|Profit in in previous month|173|248|169|197|
|Profit last year|1,383|5,687|3,233|340|
|Monthly earnings|312|611|340|422|

Note: sample includes only entrepreneurs who could be interviewed at baseline and endline. The exchange rate was USD 1 = TZS 1500 26
use of electronic information management and built-in incentives for self-compliance. Trade in these countries takes place in large-scale lots and duty rates are generally low. In this context, departure from routine business practice is costly, e.g., retrieving additional information in response to a valuation inquiry. Importers normally conduct the valuation process themselves, including application of the add-ins and take-outs needed to comply with the rules. In Norway, a paperless customs declaration system operates around-the-clock, average clearance takes 15 minutes. About 85 percent of declarations pass through the system without being stopped for further investigation.<sup>12</sup> Enforcement is risk based and involves random selection of entries that will be checked. That selection is grounded in the customs office's record of compliance by the traders. For example, in Mexico, 90 percent of shipments receive the "green light" overall, but historic information affects the probability of the light flashing red for any shipment.

Questioning and verification of the importer-submitted customs value does not normally cause physical delay of the shipment. Instead, the importer posts a customs bond sufficient to cover the amount in question. Financial institutions in many least developed countries do not offer such bonds.

# **SUPPORTING REFORM IN DEVELOPING COUNTRIES**

Colleagues at the Bank who proudly point out that "their" countries have eliminated all quantitative restrictions and have tariff rates at intermediate levels often admit that customs administration is a mess. Reform is needed in many least developed countries. The Uruguay Round Agreement does not prescribe changes particularly suited to the problems and to the administrative environments that exist in these countries; however, it does allow leeway for development and adoption of such reforms. We review in this subsection the relevant provisions of the agreement, and we suggest guidelines for reforms.

# **Customs valuation agreement provisions aimed particularly at developing countries' concerns**

The agreement and its annexes. The customs valuation agreement provides for delayed application by developing countries, those who were not signatories to the Tokyo Round customs valuation code have until January 1, 2000.13 As noted above, such countries can take an additional three years before they implement the constructed value method of valuation. Annex III to the agreement acknowledges that the five-year delay in implementation may not be sufficient for certain developing countries, and provides for a developing country to apply for an extension of the deadline (no length is specified) "it being understood that Members will give sympathetic consideration to such a request in cases where the developing country Member in question can show good cause." 12 WCO 1999.

> 13 The Tokyo Round customs valuation code went into effect in 1990, hence at the time the Uruguay Round agreement went into effect the industrial countries had 15 years' experience with implementing basically the same requirements.

11
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<!-- Start of picture text --> 5%<br>4.5%<br>4% 4<br>3<br>> 3.5% +<br>= 42.94%<br>gs 3%<br>©<br>Qz 2.5%9<br>£<br>© 2%<br>D<br>c<br>£ 15%<br>oO<br>1% +0.84%<br>0%<br>below median above median<br><!-- End of picture text -->
|||**Appe**|**ndix Table A4: A**|**nalysis of Profits**|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Mean|SD|Bartlett Test<br>p-value|<br>Levene Test<br>p-value|<br>Bartlett On<br>p-v|e-Sided Test<br>alue|
||||SD(C)= SD(T)|SD(C)= SD(T)|SD(C)> SD(T)|SD(C)< SD(T)|
|**Baseline:**|||||||
|Control Group|889.58|1167.42|--|--|--|--|
|Book Only|961.10|1207.89|0.59|0.45|0.71|0.29|
|Book & Movie|926.78|1192.31|0.74|0.45|0.63|0.37|
|Book & Assistance|825.25|1107.45|0.40|0.45|0.20|0.80|
|All Three|934.66|1249.39|0.28|0.45|0.86|0.14|
|**Midline:**|||||||
|Control Group|896.85|1106.40|--|--|--|--|
|Book Only|805.06|1006.01|0.14|0.23|0.07|0.93|
|Book & Movie|971.75|1185.84|0.29|0.18|0.86|0.14|
|Book & Assistance|1148.67|1268.23|0.04|0.00|0.98|0.02|
|All Three|1033.00|1260.63|0.05|0.03|0.98|0.02|
|**Endline:**|||||||
|Control Group|891.81|1155.26|--|--|--|--|
|Book Only|837.76|1088.17|0.40|0.45|0.20|0.80|
|Book & Movie|1040.76|1368.84|0.02|0.02|0.99|0.01|
|Book & Assistance|1223.05|1488.37|0.00|0.00|1.00|0.00|
|All Three|1138.58|1359.07|0.02|0.04|0.99|0.01|

**Notes:** This table provides a variance comparison for profits between treatment arms and control across three sruvey rounds (baseline, midline, and endline). Columns 1 and 2 report mean and standard deviations. Bartlett and Levene tests for the equality of standard deviations of the control and treatment groups are reported in columns 3 and 4. Columns 5 and 6 report one-sided Bartlett tests for differences in standard deviations between treatment and control groups.
Table A5: Access to basic amenities and location

|||Water|||Sanitation|||Electricity||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**Panel A: DOU**||||||||||
|Urban center<br>Urban cluster|0.269***<br>(0.004)<br>0.131***<br>(0.006)|0.248***<br>(0.005)<br>0.127***<br>(0.006)|0.190***<br>(0.006)<br>0.104***<br>(0.006)|0.227***<br>(0.006)<br>0.094***<br>(0.005)|0.176***<br>(0.006)<br>0.084***<br>(0.005)|0.136***<br>(0.007)<br>0.068***<br>(0.005)|0.479***<br>(0.005)<br>0.167***<br>(0.006)|0.379***<br>(0.005)<br>0.146***<br>(0.006)|0.258***<br>(0.006)<br>0.098***<br>(0.006)|
|Log of real exp.||0.036***<br>(0.003)|0.019***<br>(0.004)||0.089***<br>(0.003)|0.088***<br>(0.003)||0.177***<br>(0.003)|0.151***<br>(0.003)|
|**Panel B: DB**||||||||||
|Core|0.202***|0.179***|0.129***|0.181***|0.139***|0.104***|0.331***|0.242***|0.147***|
||(0.006)|(0.006)|(0.007)|(0.005)|(0.005)|(0.006)|(0.006)|(0.006)|(0.006)|
|Suburb|0.149***|0.130***|0.094***|0.170***|0.137***|0.113***|0.234***|0.164***|0.094***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.009)|(0.009)|(0.008)|
|Town|0.073***|0.065***|0.056***|0.064***|0.050***|0.042***|0.087***|0.058***|0.039***|
||(0.006)|(0.006)|(0.006)|(0.005)|(0.005)|(0.005)|(0.006)|(0.006)|(0.006)|
|Log of real exp.||0.056***|0.023***||0.099***|0.088***||0.212***|0.160***|
|||(0.003)|(0.004)||(0.003)|(0.003)||(0.003)|(0.003)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|No|Yes|No|No|Yes|No|No|Yes|
|Education|No|No|Yes|No|No|Yes|No|No|Yes|
|Employment|No|No|Yes|No|No|Yes|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.090|0.092|0.103|0.173|0.192|0.225|0.255|0.309|0.354|
|Panel B Adj.R<sup>2</sup>|0.066|0.073|0.093|0.160|0.185|0.222|0.186|0.269|0.339|
|Nr. of countries|16|16|16|16|16|16|16|16|16|
|Nr. of hh|134624|134624|134624|134624|134624|134624|131294|131294|131294|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a dummy variable taking the value 1 if a household has access to a respective amenity. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

45
# 1. Introduction

Since its inception, observers have speculated about the potential of microfinance to lift individuals and households out of poverty (Morduch, 1999). The “joint liability” contracts pioneered by the Grameen Bank were quickly replicated in many developing countries and, by 2006, microfinance loans had been received by over 100 million households in developing countries (Daley-Harris, 2007). Under these joint liability contracts, often referred to as group lending, members of the group use borrowed funds for their own purposes but all members are severally liable in the event of a default. Group members therefore have incentives to communicate with and monitor one another, and most group lending methods involve regular meetings. While these methods have achieved very high repayment rates on loans, borrowers may find them time consuming and not always well suited to their credit needs. We refer to these methods as ‘group liability loans’ in the rest of the paper.

Grameen founder Muhammad Yunus’s original vision was that group liability loans would provide credit to support micro-entrepreneurs that would lift households out of poverty. While early research findings based on non-experimental methods indicated that use of microcredit brought about reductions in poverty (Pitt and Khandker, 1998), subsequent research based on field experiments suggests a more nuanced view. For example, based on analysis of six recent microcredit experiments designed to measure the same outcomes (but in different country contexts), Banerjee et al. (2015) find no evidence of transformative effects in terms of lifting households out of poverty.<sup>1</sup> However, the studies did show modest gains in the profitability and investment of microenterprises that were offered credit, and increased flexibility of choice in terms of occupation (wage labor or business owner), business scale, consumption (with a shift toward durables), female decision power, and improved risk management. These findings and conclusions echo those from a previous survey that covered a broader set of experimental papers (Banerjee, 2013). Moreover, research based on financial

> 1 Those authors acknowledge that failure to find significant effects could stem from the design of the experiments themselves. Take-up of microcredit products was low and unpredictable (in a statistical sense). The resulting lack of statistical power to detect potentially significant effects remains an important challenge in field experiments designed to identify microcredit impacts on poor households (Banerjee, et al., 2015).

2
is comparable to the population fall in the wake of the Black Death in Europe from 1347 to 1351, i.e., 30 percent to 60 percent of Europe’s total population died.<sup>39</sup> The effects of Taiping occupation, in contrast, are statistically insignificant. Since Taiping-occupied areas behave similarly to the control group, from now on we will absorb these areas into the control group and focus on the Taiping areas.<sup>40</sup> The long-term impact on population by the Taiping Rebellion is also illustrated clearly in Figure 2, which depicts the evolution over time of the total population and the share of the Taiping areas in the total population of the sample prefectures. The Taiping prefectures’ population share has dropped from 34.2 percent in 1820 to 22.7 percent in 2000.

We conduct several robustness checks. First, to ensure that our results are not driven by the prefectures that fought the heaviest rebellion battles, we exclude six such prefectures (i.e., Wuchang, Songjiang, Anqing, Jiangning, Suzhou, and Hangzhou) (Platt 2013) from the sample. The results (column (3)) are similar. Second, the literature suggests that the Taiping Rebellion affects the subsequent imperial exam quotas (Li 2014). To ensure that our results are not driven by its omission, we include in column (4) the number of newly selected Palace Scholars (“ _jinshi_ ”) since the end of the last period.<sup>41</sup> Its coefficient is not significant; and the Taiping coefficients remain similar. Third, in columns (5) and (6), we replace the Taiping dummy with the Taiping interaction with the log of the number of battles during the rebellion, and with the log of duration of the Taiping occupation (in months). Here, we allow the effects to differ by Taiping “dose.” The qualitative results remain similar: the Taiping effects remain stable, and no recovery in population had taken place relative to the comparison group.

_Instrument Variable Estimates_ . The estimate of the rebellion’s effect would suffer from the omitted variable bias if the Taiping areas differ systematically from the other areas conditional on our controls. To tackle this, we pursue an IV approach: historical documents show that the rebellion’s top leaders pursued a military strategy that resulted in a spatial pattern of Taiping Army occupation that is related to the longitude, and we thus use the longitude of the prefectural seat as the IV for the Taiping analysis, holding key geographical features constant.

Firstly, the longitude satisfies the relevance condition as the instrument of the Taiping Rebellion. The rebellion officially started in the remote Guangxi Province in early 1851. A key

> 39 See <u>https://en.wikipedia.org/wiki/Black_Death.</u>

> 40 Indeed, when we repeat the analysis in column (1) but include the TP-Occupied prefectures in the control group in column (2), the results are almost identical.

> 41 The data set of Palace Scholar covers the period 1793-1911. For the base year 1820, Jinshi measures the newly selected Palace Scholar in the period between 1793 and 1820. This variable is automatically zero after year 1911 (since this exam was no longer held after the fall of Qing).

27
Eastern Europe and the CIS suggest that new firms appear to be as productive as or more productive than firms privatized to domestic owners and that foreign start-ups appear to be more efficient than domestic start-ups. Studies of employment find that privatizations in Central and Eastern Europe, the CIS, and China are not associated with reductions in employment.

Finally, it is important to emphasize that the empirical evidence accumulated regarding firm performance is mainly based on data from member countries of the Organisation for Economic Co-operation and Development (OECD), Latin American countries, and economies in transition. The empirical evidence on the effects of privatization in Africa is not as deep and robust as the evidence discussed above. Thus, caution is necessary in generalizing the results presented above to low-income countries or small states.

# **Distributional Impact**

In contrast to the huge number of studies on the impact of privatization on firm performance, relatively few studies analyze the impact on social welfare and its distribution. Moreover, these studies mostly cover OECD and Latin American countries; they focus mainly on the impact of privatization on consumer welfare in the utility sector; and they are less successful in controlling for potential biases. Birdsall and Nellis (2003) review some of this literature. As it is outlined here, our knowledge falls short of what is desirable for adequately assessing the impact of privatization on social welfare.

Galal et al (1994) is the most persuasive of the studies. Its main strength is the construction of a clear counterfactual that allows for the measurement of the financial as well as the welfare impacts of privatization. The authors analyze 12 privatized companies (mostly airlines and regulated utilities) in Chile, Malaysia, Mexico, and the United Kingdom to determine whether the transfer to private ownership increased efficiency and, if so, how the costs and benefits of the adjustment were allocated to government, foreign and domestic buyers, competitors, consumers, and employees. Galal et al. document welfare gains in 11 of the 12 cases analyzed. In no case were workers worse off after privatization, and in three instances they were better off. Although this study is rigorous, it examines only a small number of firms in four countries and does not cover the wider range of aspects described earlier in this paper.

14
|**Table 8: Attitudes and Associations**|Own Labor E<br>Hours|ntrepreneurial<br>Self‐Efficacy|GET Ahead<br>Attitudes|Women's<br>Association|# Discuss<br> Business|Works together<br> with other women|
|---|---|---|---|---|---|---|
|**Panel A: Impact of Assignment to Trainin**|**g, Allowing fo**|**r Spillovers**|||||
|Assigned to Training*1 Year|0.059|‐0.037|‐0.018|0.003|0.181|0.035|
||(1.106)|(0.181)|(0.021)|(0.017)|(0.210)|(0.024)|
|Assigned to Training* 3 Years|4.596***|||0.057***|0.584**|0.033|
||(1.274)|||(0.018)|(0.277)|(0.026)|
|Spillover Group * 1 Year|‐2.060*|‐0.180|‐0.047**|0.026|0.032|‐0.000|
||(1.138)|(0.171)|(0.022)|(0.019)|(0.224)|(0.025)|
|Spillover Group * 3 Years|‐0.192|||‐0.013|0.066|0.010|
||(1.356)|||(0.017)|(0.308)|(0.024)|
|Mean of Pure Control Group|42.6|4.457|3.802|0.115|4.672|0.490|
|Sample Size|6215|3059|3059|5400|5398|5403|
|P‐value: Training 1 year=3 years|0.001|||0.042|0.259|0.959|
|P‐value: Spillover 1 year=3 years|0.189|||0.155|0.934|0.731|
|P‐value: Training effect zero both years|0.001|||0.005|0.072|0.242|
|<br>**Panel B: Impact of Mentoring Compared**|**to Training Al**|**one, Allowing f**|**or Spillovers**||||
|Assigned to Mentoring|4.777***|||0.055**|0.920**|0.027|
||(1.642)|||(0.023)|(0.362)|(0.039)|
|Assigned to Training Alone|4.300**|||0.062***|0.303|0.030|
||(1.677)|||(0.023)|(0.329)|(0.032)|
|Spillover Group to Mentoring|‐0.429|||‐0.029|0.040|‐0.016|
||(1.758)|||(0.020)|(0.417)|(0.032)|
|Spillover Group to Training Alone|0.101|||0.002|‐0.003|0.041|
||(1.659)|||(0.025)|(0.390)|(0.031)|
|Sample Size|2635|||2234|2235|2236|
|P‐value: Mentoring= TrainingAlone<br>Notes: Robust standard errors in parent<br>10, 5, and 1 percent levels respectively.<br>All regressions control for randomization<br>Self‐efficacy and Get Ahead attitudes onl|0.814<br>heses, clustere<br>strata fixed ef<br>y asked in rou|d at the market<br>fects. Mentorin<br>nd 2.|level. *, **, a<br>g regression|0.795<br>nd *** den<br>s only for thr|0.135<br>ote significa<br>ee year fol|0.940<br>nce at the<br>low‐up.|

35
**Table 7: Vegetation indices and soybean crop rotation in Ukraine, 2016-18 period**

||**E**|**VI**|**L**|**AI**|**FAP**|**AR**|**LS**|**WI**|
|---|---|---|---|---|---|---|---|---|
||**2017**|**2018**|**2017**|**2018**|**2017**|**2018**|**2017**|**2018**|
|After sunflower|-1.082***|-1.415***|-28.75***|-24.20***|-4.933***|-5.189***|-0.911***|-0.927***|
||(0.0592)|(0.0600)|(1.421)|(1.500)|(0.276)|(0.271)|(0.0835)|(0.0743)|
|After cereals|0.0479|-0.568***|-6.243***|-15.06***|-0.0661|-1.981***|0.301***|-0.220***|
||(0.0545)|(0.0546)|(1.312)|(1.366)|(0.254)|(0.247)|(0.0771)|(0.0676)|
|After other|1.224***|0.944***|5.303***|7.335***|4.769***|5.111***|2.107***|1.826***|
||(0.0683)|(0.0560)|(1.640)|(1.397)|(0.318)|(0.252)|(0.0964)|(0.0692)|
|Self twice|0.212***||3.665**||1.262***||0.122||
||(0.0627)||(1.505)||(0.292)||(0.0884)||
|Self twice only||-0.160**||-1.451||-0.387||-0.264***|
|||(0.0770)||(1.926)||(0.348)||(0.0954)|
|Self thrice||0.169||11.94***||0.873||-0.0813|
|||(0.136)||(3.388)||(0.612)||(0.168)|
|No. of VCs|3146|3394|3129|3380|3129|3380|3129|3381|
|R<sup>2</sup>|0.223|0.322|0.148|0.152|0.196|0.309|0.201|0.314|
|Mean dep.|4.467|4.895|70.63|78.41|23.79|25.08|-0.0188|0.745|
|SD dep..|0.646|0.644|14.77|14.36|2.949|2.873|0.897|0.791|
|Elast. twice|0.00838||0.00917||0.00937||1.143||
|Elast. twice||-0.00368||-0.00207||-0.00173||-0.0397|
|Elast. thrice||0.00124||0.00547||0.00125||-0.00393|
|El a sunflower|-0.0362|-0.0430|-0.0608|-0.0460|-0.0310|-0.0308|-7.227|-0.185|
|F-test|||||||||
|Self 2x=self 3x||3.987**||10.56***||2.869*||0.802|

_Note:_ Results are from regressions at village council level and the maize-soybean rotation is the omitted category. A constant is included throughout but omitted. Standard errors in parentheses: * p<0.10, ** p<0.05, *** p<0.010.

21