rafmacalaba/datause-encoder-data huggingface dataset:
but are still in need of addressing. Understanding how the crisis might relate to these issues, and what policy opportunities might present themselves in the times ahead is an ongoing challenge.

The focus of this study is on the issue of global financial secrecy and its institutional implications for emerging economies. Many policy discussions on making the global financial system ‘work for all’ focus on financial secrecy: its beneficiaries and losers. Over the last two decades and especially actively since the 2008 financial crisis, policy makers introduced new rules and regulations to counter the negative spillovers from secrecy jurisdictions around the world. However, as with many past crises, the 2020 pandemic is likely to intensify the adverse effects of these jurisdictions for developing countries as capital would try to flee the most vulnerable economies. Developed economies also need more caution and oversight as emergency fiscal spending related to Covid-19 creates a moral hazard and an increased potential for fraudulent activities. Global Financial Integrity (GFI), a DC-based think tank, has already issued warnings about a potential uptick in illicit financial flows even as legitimate economic activity slows down.<sup>5</sup> Scholarly attention to these issues has been growing along with the enhanced policy attention. Besides intensifying public concerns, the infamous data leaks connected to the Panama and Paradise Papers also turned into a productive source of ‘big data’ for examining related issues. This paper reviews the key findings in the literature that explores the drivers and effects of financial secrecy on the emerging economies. We show that research on the drivers of financial secrecy has been focused on issues of tax avoidance, neglecting institutional arbitrage problems that go beyond taxation issues. Additionally, while the literature on economic effects of financial secrecy has advanced from country-level findings to firm-level observations, further research is needed to understand the political and institutional effects of global financial secrecy at the domestic level (in home countries). In the concluding section of this paper, we suggest some promising avenues for future research as well as new items to be included on the policy-making agenda in relation to financial secrecy. Specifically, we note the limits of the institutionalist paradigm that treats businesses solely as rule-takers and call for more attention to business agency and responsibility. We suggest that discussions about corporate social responsibility in emerging economies should incorporate thinking about the potential role businesses and especially big businesses could play in promoting more effective institutions at home.

> 5 https://gfintegrity.org/covid-19-and-illicit-financial-flows-whats-to-come/

3
||Log [Self-re<br>(1)<br>|ported Produc<br>(2)<br>|tivity (XOF/ha)]<br>(3)<br>|<br>(4)<br>|
|---|---|---|---|---|
||All crops|All crops|Export crops|Food crops|
|Household head has Primary education|<br>-0.002|<br>-0.003|<br>-0.004|<br>-0.006|
||(0.004)|(0.004)|(0.005)|(0.005)|
|Household head has Secondary education|-0.012|-0.012|-0.011|-0.017*|
||(0.009)|(0.009)|(0.008)|(0.010)|
|Household head has Tertiary education|-0.006**|-0.006*|-0.005**|-0.003|
||(0.003)|(0.003)|(0.002)|(0.003)|
|Household head disability|-0.005|-0.004|-0.002|-0.006|
||(0.006)|(0.006)|(0.009)|(0.006)|
|Dependency ratio|-0.003|-0.003|0.001|-0.014|
||(0.009)|(0.009)|(0.004)|(0.012)|
|Household Male Labor Use (Number)|0.062|0.058|0.034|0.110***|
||(0.038)|(0.037)|(0.031)|(0.042)|
|Household Female Labor Use (Number)|0.016|0.005|-0.002|0.015|
||(0.018)|(0.018)|(0.011)|(0.023)|
|Household Child Labor Use (Number)|0.003|0.003|0.001|0.004|
||(0.005)|(0.005)|(0.002)|(0.007)|
|Hired Labor Use (Number)|0.004|0.004|0.006|0.005|
||(0.006)|(0.006)|(0.005)|(0.007)|
|Number of Fields Managed by the household|0.052***|0.048***|0.009|0.175***|
||(0.013)|(0.013)|(0.008)|(0.036)|
|Total number of crops produced|0.002|0.003|0.009|-0.050*|
||(0.007)|(0.007)|(0.010)|(0.029)|
|Household used any purchased seed|0.002|0.001|0.001|0.000|
||(0.003)|(0.002)|(0.002)|(0.000)|
|Plots that Use Chemical Fertilizer (% of total)|0.025***|0.026***|0.031***|0.022*|
||(0.009)|(0.009)|(0.011)|(0.011)|
|Plots that Use Pesticide (% of total)|0.065***|0.067***|0.053***|0.030*|
||(0016)|(0016)|(0018)|(0016)|
|Export crops dummy (1 if Household produced any|.|.<br>0.179***|.|.|
|<br>export crop)||(0.035)|||
|<br>**unexplained**|||||
|<br>Log [Total land area cultivated in ha]|0.185***|0.201***|0.064|0.163***|
||(0.047)|(0.048)|(0.104)|(0.049)|
|Household head age (years)|-0.051|0.021|-0.037|-0.120|
||(0.263)|(0.252)|(0.271)|(0.302)|
|Married|<br>0.347***|<br>0.348***|<br>0.216***|<br>0.193|
||(0.120)|(0.117)|(0.077)|(0.170)|
|Divorced/Separated|<br>0.056**|<br>0.056**|<br>0.016|<br>0.046|
||(0.025)|(0.025)|(0.020)|(0.034)|
|Widowed|<br>0.322***|<br>0.329***|<br>0.168|<br>0.246**|
||(0.092)|(0.092)|(0.148)|(0.113)|
|Household head has Primary education|0.131***|0.138***|0.093**|0.103***|
||(0.037)|(0.036)|(0.037)|(0.040)|

21
Table 1: First stage

||(1)|(2)|(3)|(4)<br>(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|
|||Quota quan.||Quota income|Quota|quan.|Quot|a income|
|Clim. index|142.936<br>(45.623)<sup>_∗∗∗_</sup>||||||||
|FAO index||70.833<br>(25.530)<sup>_∗∗∗_</sup>|||||||
|Rice area|||91.357<br>(17.026)<sup>_∗∗∗_</sup>||||||
|Quota price|||||-6.376<br>(4.774)|17.917<br>(7.791)<sup>_∗∗_</sup>|||
|Clim. index x price||||.314<br>(.071)<sup>_∗∗∗_</sup>|||.190<br>(.049)<sup>_∗∗_</sup>|<sup>_∗_</sup>|
|FAO index x price||||.321<br>(.092)<sup>_∗∗∗_</sup>||||.213<br>(.085)<sup>_∗∗_</sup>|
|Mean dep. var.|195.22|195.22|195.22|2.065<br>2.065|204.813|224.527|2.225|2.225|
|Obs.|23558|23558|23511|27779<br>29009|16174|11539|16203|16203|
|Fixed efects||Prov.-year||Village + prov.-year|Vil|lage|Village|+ prov.-year|
|Sample selected||Full||Full|Hom.|Het.||Hom.|

Notes: The dependent variable is quota quantity in Columns (1) to (3) and Columns (6) to (7), and quota income in Columns (4) and (5) and Columns (8) and (9). The independent variables are the climatic indices for propensity to cultivate rice derived from weather data and from FAO data; the area cultivated in rice; the quota price; and both climatic indices interacted with the quota price. Fixed effects and clustering are as reported in the table. “Hom.” denotes villages that are homogeneous in rice and wheat cultivation, while “Het.” denotes villages that are heterogeneous. Asterisks indicate significance at the ten, five, and one percent level, respectively.

Source: Data from the RCRE household panel analyzed by the author.

Table 2: Summary statistics

|Variable|Mean|Standard deviation|Obs.|
|---|---|---|---|
|Household size|4.093|1.31|15657|
|Sown area|1.403|.92|16070|
|Grain sown area|1.233|.816|16115|
|Productive assets|.909|.287|16203|
|Non agri. assets|.263|.44|16203|
|Outside labor|.388|.487|16203|

Notes: Summary statistics are reported for variables of interest in the primary analysis sample, including only households in homogeneous cultivation villages. Sown area is measured in hectares. Productive assets is a dummy equal to one if the household reports any agricultural assets (tools, animals or machinery). Non-agricultural assets is a dummy equal to one if the household reports any non-agricultural assets. Outside labor is a dummy equal to one if the household reports any days worked outside the household, and associated wage income.

Source: Data from the RCRE household panel analyzed by the author.

36
# **IV. Some Illustrative Data**

We used data from the _World Development Indicators_ to map episodes of changes in shared prosperity. There are limited data available on the quintile shares of income by country. We came up with a total of 34 developing countries with at least two data points for the income share over the last 20 years.<sup>7</sup> We then used this (admittedly small) sample for a variety of descriptive figures to display some of the empirical “regularities” that are relevant to the above discussion of the shared prosperity indicator.

In the following graph (Figure 5), we plot the approximated decomposition of the percentage change in shared prosperity for the same set of countries. In some cases, the timespan between observations on shared prosperity is only five years; in other cases, it is longer. The criterion was to use the two most recent observations of the share for that country. To make the scatter points comparable across countries, we annualized the percentage change in the growth of GDP per capita and the growth in the share of total income accruing to the bottom 40 percent. Note that – at least for this limited set of countries – the vast majority experienced gains in shared prosperity: most data points are above the “negative” 45 degree line. There is a wide variety of combinations of increased “sharing” and “growth” contributions to increasing shared prosperity; however, many data points in this sample lie above the “positive” 45 degree line, indicating a larger contribution from growth than from increased “sharing.” One would certainly expect that over several decades, as noted in the simulation of the previous section; however, it is interesting that this phenomenon shows up over shorter periods (e.g., 5 to 15 years). Admittedly, there is only a very limited set of countries in this analysis.

> 7 The timespan for data explored was between 1990 and 2010, to be precise.  One can go directly to individual country household surveys and calculate shares and income growth directly from there, and perhaps double the sample of countries.   The intent here is to show the diversity of recent experience – in particular, in terms of the simple decomposition of factors.

11
links as possible to the domestic economy, i.e., to “reshore” supply chains, gained traction during that time.

It was not the first time that vulnerability of GVCs to local shocks became a cause for concern. Boehm, Flaaen, and Pandalai-Nayar (2019) and Carvalho et al. (2021) studied the 2011 earthquake in Japan and showed how this country-specific shock propagated through supply networks to affect firms located in other countries. Barrot and Sauvagnat (2016) studied firmlevel idiosyncratic shocks due to natural disasters and found that such shocks affected <mark>not only the companies nearby within the region, but also their customers who reported declines in sales growth in the medium run. However, neither the Japanese earthquake nor natural disasters led to the sustained criticism of GVCs and denouncing of globalization observed during the pandemic. This is surprising, especially in light of the fact that the COVID shock was not country-specific, but global, weakening the case for reshoring. A natural question is then whether the resilience argument reflects genuine worry as opposed to presenting a novel and convenient excuse for pursuing old-style protectionism motivated by the aforementioned concerns regarding the labor market effects of trade and its potential contribution to domestic inequality.</mark> <mark>To address this question, it is necessary to have a well-defined notion of resilience. A useful starting point is the definition provided by Markus Brunnermeier in his recent book “The Resilient Society.” According to Brunnermeier, resilient means to “bend but not break” in response to a shock. A comparison between the oak and the reed is illuminating. The oak is strong and “robust” to many shocks. But if the shock is strong enough, the oak may break. In contrast, the reed is not robust, but it is “resilient.” Even a light breeze makes it sway back and forth. But when hit with a big storm, it bends, it does not break, and proves therefore more resilient than the oak.</mark> <mark>Illuminating as this comparison may be, it still begs the question of how one would operationalize the concept of resilience in economics. A strict interpretation of the “bend but not break” criterion, would imply that an economic entity, be it a firm, an industry, an economic relationship, or an entire economy would be resilient if it survived an economic shock, and not resilient if it ceased to exist. A less strict interpretation might be taken to imply that even entities that survived a shock, are not resilient if it takes them a long time to completely recover. But this in turn begs the question of how long is long? It is becoming apparent that the concept of resilience raises several questions that remain unanswered in economics to date. Importantly, we</mark> 18
Folbre, Nancy. 1994. _Who Pays for the Kids: Gender and the Structures of Constraint._ New York: Routledge Press.

Friedemann-Sánchez, Greta. 2006a. _Assembling Flowers and Cultivating Homes: Labor and Gender in Colombia_ . Lanham: Lexington Books.

Friedemann-Sánchez, Greta. 2006b. “Assets in Intrahousehold Bargaining Among Cut-Flower Workers in Colombia.” _Feminist Economics_ 12 (1-2): 247-69.

Goldstein, Markus and Christopher Udry. 2008. “The Profits of Power: Land Rights and Agricultural Investment in Ghana.” _Journal of Political Economy_ 116 (6): 981-1022.

Hersch, Joni and Leslie Stratton. 1994. “Housework, wages, and the Division of Housework Time for Employed Spouses.” _American Economic Review_ 84 (2): 120-5.

Iversen, Vegard, Cecile Jackson, Bereket Kebede, Alistair Munro, and Arjan Verschoor, 2006. "What’s love got to do with it? An experimental test of household models in East Uganda," Working Paper 2006-01. Oxford: Centre for the Study of African Economies.

Jensen, Robert T. 2004. “Do private transfers 'displace' the benefits of public transfers? Evidence from South Africa,” _Journal of Public Economics_ 88 (1-2): 89-112.

Jones, Christine. 1983. “The Mobilization of Women's Labor for Cash Crop Production: A Game Theoretic Approach.” _American Journal of Agricultural Economics_ 65 (5): 1049-54.

Katz, Elizabeth G., 1995. “Gender and Trade within the Household: Observations from Rural Guatemala,” _World Development_ 23 (2): 327-42 Khemani, Stuti. 1999. “Inter-generational Transfers and Intra-Household Bargaining: Evidence from Indonesia.” Dissertation, Department of Economics, Cambridge, MA: Massachusetts Institute of Technology.

Kusago, Takayoshi and Bradford L. Barham. 2001. “Preference Heterogeneity, Power, and Intrahousehold Decision-Making in Rural Malaysia,” _World Development_ 29 (7): 1237-56.

Lim, <u>Stephen S, Lalit Dandona, Joseph A Hoisington, Spencer L James, Margaret C Hogan, and Emmanuela Gakidou. 2010. India's Janani Suraksha Yojana, a Conditional Cash Transfer</u> Programme to Increase Births in Health Facilities: An Impact Evaluation. _The Lancet_ <u>375 (9730): 2009-23.</u> Luke, Nancy and Kaivan Munshi. 2011. “Women as Agents of Change: Female Income and Mobility in India.” _Journal of Development Economics_ 94 (1): 1-17.

38
# 2.1. Tax Revenues

Total tax revenue for the year 2016/17 was 13.7 percent of GDP (see Table 1), a slight increase from the 13 percent observed in 2015/16 and the highest ratio reported since 2000. While higher by approximately two percentage points than Ethiopia, Uganda’s tax share is lower than in Ghana, Tanzania and Kenya’s (at 14.5 and 15.6, and 16.9 percent of GDP, respectively) and, much lower than South Africa’s (at 25.3 percent of GDP), as shown in Figure 2.<sup>8</sup> _Table 2. Uganda Government Revenues, 2016/17_<sup>_9_</sup>

||**UGX**<br>**(billions)**|**% of GDP**|**Included in Analysis**<br>**(Yes/No)**|
|---|---|---|---|
|**Total Revenue and Grants**|**13,897**|**15.2%**|**...**|
|Revenue|12,947|14.2%|...|
|Tax Revenue|12,593|13.7%|...|
|Direct taxes_of which_|4,279|4.7%|...|
|Personal Income Tax|2,115|2.3%|Yes|
|Corporate Income Tax|894|1.0%|No|
|Indirect Taxes_of which_|8,314|9.1%|...|
|VAT|3,904|4.3%|Yes|
|Excise Taxes|2,669|2.9%|Yes|
|Customs Duties|1,432|1.6%|No|
|Non-tax Revenue|354|0.4%|...|
|Grants|950|1.0%|...|

_Source: Uganda Annual Budget Performance Report 2016/17._ The tax system in Uganda is administered by the central government, through the Uganda Revenue Authority, and by local government regimes, and comprises both direct and indirect taxes. There are no social contributions funding social welfare or social security, or unemployment insurance (for example).

# 2.2. Indirect taxes

Over two-thirds of total tax revenue is collected through indirect taxes. Indirect taxes account for approximately 9.1 percent of GDP – a similar proportion as in Tanzania and South Africa (Figure 2), and greater than Kenya, Ghana and Ethiopia given the size of their economies (see Figure 3a). While _indirect taxes_ in Uganda are of a similar proportion of GDP as South Africa, South Africa compensates for this with significant social protection expenditures, thereby combatting undesirable impacts on poverty.

Indirect taxes include value added tax (VAT), excise taxes (mainly tobacco and alcohol) and custom duties (mainly petrol). The VAT on non-excisable goods and services is 18 percent. Goods and services such as

> 8 These are the countries for which there is a comparable CEQ fiscal incidence analysis. 9 These numbers coincide with the IMF Article IV – Feb 2019 figures (IMF, 2019).

8
2000; Khandker 2009). Therefore, the evaluation of PRIME must examine whether the provision of credit, along with other non-financial services, is capable of addressing both the human and physical capital needs of the ultra-poor for mitigating seasonal and hard-core poverty in a sustainable way.

# **3. Role of Credit in Seasonal and Hard-core Poverty: An Analytical Framework**

# <u>Theory</u>

We follow a standard inter-temporal utility maximization model of consumption smoothing and present the implications of borrowing restrictions following Deaton (1991). Each household _i_ is to maximize the following utility function, which is assumed to be increasing, strictly concave, and differentiable.

where _ci_ = a single aggregate consumption good and _β_ = the time discount rate.

This is maximized subject to the following dynamic asset constraint: where _yit_ = labor income that is risky, _Ait_ = real assets, and _r_ = real interest rate (assumed fixed over time).

11
the fraction of periods in which the coefficient is significantly greater or lower than zero at the ten-percent level. When considering all the active assets across classes (first row in column 1), we find evidence of significant _negative_ serial correlation in trades. Assets that are more intensively bought in a given month are significantly _less_ likely to be bought during the next month. Moreover, this significant negative coefficient is obtained in all one-month regressions. The rest of the results reported in column (1) indicate that the negative serial correlation is present in almost all asset classes, with domestic equities being the only asset class in which there is significant evidence of positive dynamic herding. One possible explanation for this finding is that pension funds cannot quickly adjust their positions in domestic equity markets because of the low trading activity of the stocks and, therefore, opt for a gradual change in positions towards their desired levels. In fact, equities are the domestic assets held by pension funds with the lowest annual turnover (trading over market capitalization) of around 15 percent, compared, for instance, with corporate and government bonds, with an annual turnover in 2004 of more than 100 and 400 percent, respectively.<sup>48</sup> Disentangling the extent to which pure herding drives the positive dynamic correlation in domestic equities trading by pension funds would require information on the overall trading activity of individual stocks, which is left for future research.

As in the case of contemporaneous herding, the results for dynamic herding may be driven by the prevalence of single trades. The statistics reported in columns (2) and (3) control for this concern by focusing only on assets that are traded by more than one PFA and more than half the number of PFAs in operation, respectively. The results indicate that indeed an important part of the negative serial correlation comes from single trades, which indicates that assets that are bought by only one PFA in a given month and are traded in the next month, are more likely to be sold (and vice-versa).

At the asset-class level, quotas of foreign investment and mutual funds show significant positive dynamic herding. Under the relatively safe assumption that these quotas are liquid assets, this finding could not be attributed to liquidity considerations unless changing positions in quotas of foreign investment and mutual funds required the gradual liquidation of other illiquid assets. This is unlikely for two reasons. First, unless

> 48 World Development Indicators (WDI) database, and Braun and Briones (2006).

23
- 25 -

|Bitumen (kg)|_bitumen_|106|4762.4|14122.5|0|79029.6|
|---|---|---|---|---|---|---|
|Earthworks (m3)|_earth_|106|6742.0|12665.7|0|93403.0|
|Brickworks (m3)|_brick_|106|45.2|165.5|0|<br>1204.1|
|Gabion (m3)|_gabion_|106|238.6|856.4|0|<br>8400.0|
|Excavation (m3)|_excavation_|106|2952.1|5319.8|0|29266.6|
|Cement concrete (m3)|_cement_|106|32.7|74.5|0|<br>507.8|
|Dummy variable for postqualification of<br>bids|D(_postqualify_)|106|0.92|0.28|0|<br>1|
|Bidder participation:|||||||
|Number of bidders purchasing bidding|_bdnum_|118|22.53|17.93|4|<br>107|
|documents|||||||
|Price of bidding documents|_bdcost_|118|1547.6|1044.6|290.5|<br>3576.1|
|Bid preparation period (days)|_bdpreptime_|118|25.94|9.25|6|<br>52|
|Efficiency in contract awarding:|||||||
|Number of days required to award a<br>contract|_dayscontracting_|154|64.66|47.46|1|<br>190|
|Contract management:|||||||
|Cost overrun rate (percent)|_overrun_|155|3.73|6.25|-14.83|<br>21.69|
|Project delay (days)|_delay_|152|107.25|208.09|-76|<br>1217|
|Number of security incidents during the<br>project implementation|_securityduring_|152|33.86|47.06|0|<br>280|
|Precipitation during the project<br>implementation (mm)|_rainduring_|155|1652.22|1724.13|0|<br>8774.2|
|Difference between the winning bid and the<br>second lowest bid (NRs million)|<br>_lowball_|142|-0.32|2.70|-29.60|<br>6.29|
|Project quality:|||||||
|Confortability rating (1 to 4)|_rate_|136|2.63|0.60|1|<br>4|
|Average traffic speed (km per houir)|_speed_|148|24.71|10.13|7.50|<br>57.00|
|Number of days for which a road has been<br>used since the project completion|_age_|136|794.32|497.60|11|<br>2656|
|Cumulative traffice since the project<br>completion|_traffic_|132|150802|153255|0|807884|
|Number of security incidents after the<br>project completion|_securityafter_|133|81.94|79.05|0|<br>377|
|Precipitation after the project<br>implementation (mm)|_rainafter_|136|4953.4|3591.9|0.0|18223.2|

# **_Bidder participation_**

One of the most important empirical issues in estimating Equation (1) is that the number of bidders, _num_ , is likely to be endogenous. Basic auction theory assumes it fixed. In reality, however, contractors may or may not enter the market for many reasons. Endogenous auction theory suggests that the entry cost would determine how many bidders would participate in