rafmacalaba/datause-encoder-data huggingface dataset:
_Table 10: Heterogeneity in loss size by plot manager characteristics_

||Pool|ed|Ethi|opia|Mal|awi|M|ali|Nig|er|Nig|eria|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Dependent variable:**|||||||||||||
|Percent of harvest lost on plot|No controls|Controls|No controls|Controls|No controls|Controls|No controls|Controls|No controls|Controls|No controls|Controls|
|Female plot manager|4.405***|2.303**|3.345**|1.968|3.952|2.363|5.167|0.988|5.025***|3.789**|10.76**|-0.0518|
||(1.158)|(1.136)|(1.448)|(1.385)|(2.383)|(2.128)|(3.452)|(2.895)|(1.742)|(1.754)|(5.463)|(6.238)|
|Age of plot manager (decades)|0.231|0.285|0.373|0.407|-0.995|0.0282|-0.537|-0.385|-0.382|-0.0241|0.974|-0.303|
||(0.320)|(0.315)|(0.384)|(0.377)|(0.871)|(0.797)|(0.663)|(0.544)|(0.481)|(0.474)|(1.837)|(1.328)|
|Plot manager has primary educ|-0.0273|-1.641|-1.508|-3.215|0.497|1.915|-4.344|-1.184|-5.307*|-3.501|5.493|-1.332|
||(1.831)|(1.686)|(2.362)|(2.192)|(2.893)|(2.226)|(2.950)|(2.775)|(2.759)|(2.510)|(4.983)|(5.349)|
|Constant|49.31***|62.68***|48.94***|51.42***|55.91***|60.52***|49.98***|57.01***|65.99***|83.15***|46.19***|106.0***|
||(1.923)|(6.404)|(2.152)|(7.456)|(4.869)|(9.858)|(3.617)|(10.82)|(2.491)|(7.111)|(10.01)|(15.73)|
|Observations|30,230|30,230|14,680|14,680|1,739|1,739|7,100|7,100|6,027|6,027|684|684|
|Crop FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Country FE<br>Note: Results from OLS regression|YES<br>s with the share|YES<br>of potential h|NO<br>arvest lost as ou|NO<br>tcome variab|NO<br>le. Controls inclu|NO<br>de dummy v|NO<br>ariables for input|NO<br>use (any hire|NO<br>d labor, any inor|NO<br>ganic fertiliz|NO<br>er, any organic fe|NO<br>rtilizer), plot|

Note: Results from OLS regressions with the share of potential harvest lost as outcome variable. Controls include dummy variables for input use (any hired labor, any inorganic fertilizer, any organic fertilizer), plot characteristics (plot area, irrigration, intercropping, plot ownership, elevation, and a topographic wetness index), household characteristics (urban/rural residence, livestock farming, and electricity access) as well as main crop and country fixed effects. The estimates are weighted to be nationally representative. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 31
> <sup>24</sup>

since they do not tell us the extent to which financial intermediaries actually channel funds to the private sector. When the regressions in Tables 1 and 2 are repeated, substituting growth in liquid liabilities 1975 – 2000 or the level of liquid liabilities in 2000 for the corresponding private sector credit variables used earlier, the political variables are generally not significant in the ordinary least squares regressions. However, the two-stage least squares results are at least as strong as when the private sector credit variable is used. This pattern of results is consistent with the notion that liquid liabilities are a noisy measure of the financial sector, making results overall more sensitive to the noisiness of the independent variables. Since instrumental variables reduce the noise in the potentially endogenous variable, results improve in the two-stage least squares estimates.

**Table 2c:  The effects of political credibility and voter information, controlling for** **<u>political checks and balances</u>**

||Con<br>competi|tinuous ye<br>tive electio|ars of<br>ns(1975)|Average|Newspaper Ci<br>(1975-2000)|rculation|
|---|---|---|---|---|---|---|
|Lending to the<br>private sector by<br>banks and non-<br>bank financial<br>institutions|Growth<br>75 – 00<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|Growth<br>75 – 00<br>(_OLS_)|Level 2000<br>(_OLS_)|Level<br>2000<br>(_OLS_)|
|Political variable|.0003|.003|.01|.00007|.001|.002|
||(_.13_)|(_.31_)|(_0.0_)|(_.04_)|(_0.008_)|(_0.0_)|
|Checks and|.004|-.035|.06|.003|.04|.05|
|Balances|(_.20_)|(_.36_)|(_.05_)|(_.19_)|(_.13_)|(_.03_)|
|_R_<sup>_2_</sup>|.10|.36|.32|.12|.43|.42|
|_N_|66|73|88|78|93|114|

N.B. Table 2c uses the same specification as in Table 2a, with the addition of political checks and balances. _p_ -values are reported in parentheses, based on robust standard errors. Constants and conditioning variables not reported.

# **Politics, Property Rights and Financial Sector Development**

The arguments here echo the literature in claiming that the link from politics and political institutions to financial sector development passes through the security of property
lowering intermediation costs, and delivery of long-term credit. On supervision, the loan specified that the Central Banking Act be amended to include guidelines for emergency loans to banks in distress, transparent criteria for issuing cease and desist orders, new rules for lending to bank insiders, and liability insurance for central bank staff.5 With respect to deposit insurance, the Philippines Deposit Insurance Corporation (PDIC) increased its fee structure and increased its capital by three billion pesos (US$ 115 million). Intermediation costs remained high despite reduced taxes on intermediaries, fewer restrictions on foreign bank entry, and the elimination of branching restrictions.60 It is unlikely, however, that these developments had substantial impact on the increase in M2 three years after the intervention -- most of PDIC's capital increase was scheduled for 1991-2 and arrived late,<sup>61</sup> legislation liberalizing foreign bank entry was passed in 1994, and, although 148 new branches were _allowed_ to be established in 1990-91, it is unclear how many were actually in operation in those years.<sup>62</sup> Finally, with respect to delivery of long-term credit, directed credit schemes were transferred from government ministries to the Development Bank of Philippines (DBP), which was then scheduled to be privatized four years hence. Once again, it seems unlikely that this gradual change was responsible for any of the increase in M2 three years after the FSAL, although it may have contributed to subsequent increases.

S9. PAR, p. 10. 60 PAR, p. 1O. PCR, p. 22. World Bank, "Philippines FSAL: President's Report (PR)," April 6, 1989, p. 24. **61** PCR, p. 10, 32.

62 Of course, the mere threat of entry may change the behavior of incumbents. I assume, therefore, that the disciplinary effect of the announced entrants was not primarily responsible for the post-FSAL financial deepening.

42
Policy Research Working Paper 5919

# **Abstract**

A revenue-neutral switch from trade taxes to domestic consumption taxes is fraught with implementation challenges in countries with a large informal sector. It is shown for a sample of low-income countries over 25 years that they have had a mixed record of offsetting reductions in trade tax revenue. The paper then analyzes the specific case of Nepal, using a unique data set compiled from unpublished customs records of imports, tariffs and all other taxes levied at the border. It estimates changes to revenue and domestic production associated with two sets of reforms: i) proportional tariff cuts coordinated with a strictly enforced value-added tax; and ii) proposed tariff cuts under a regional free trade agreement. It is shown that a revenue-neutral tax reform is conditional on the effectiveness with which domestic taxes are enforced. Furthermore, loss of revenue as a result of intra-regional free trade can be minimized through judicious use of Sensitive Lists that still cover substantially all the trade as required by Article XXIV of the GATT.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at swarnim@post.harvard.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# PRE Working Paper Series

||Auftho||Contact<br>gape|
|---|---|---|---|
|WPS619 F6.ceign<br>Direct Investment in<br>Sub-Saharan Africa|Laurence Cockoroft<br>Roger C. Riddell|March 1991|S. King-Watson<br>33730|
|WPS620 Hovetommercial Banks<sup>Ignored</sup><br>History?|Sule Ozler|March 1991|S. King-Watsrn<br>33730|
|WPS621 Sensible Debt Buybacks<br>for Highly<br>Indebted Countries|Enrica Detragiache|March 1991|<br>S. King-Watson<br>33730|
|WPS622 How Factors in Creditor Countries<br>Affect Secondary Market Prices<br>for Developing<br>Country Debt|Sule Ozler<br>Harry Huizinga|March 1991|<br>S. King-Watson<br>N<br>33730|
|WPS623 World Bank-Supported<br>Adjustment<br>Programs: Country Performance 2<br>and Effectiveness<br>-.|ViftoriqCotbo<br>Patricio<sup>Rojas</sup>|March 1991|<br>A. Oropesa<br>39075|
|WPS624 Choosing Policy Intruments<sup>for</sup><br>Pollution.Control:<br>A Review<br>t|Gunnar Eskeland<br>Emmanuel<br>Jimenez|March 1991|<br>A.Bhalla<br>37699|
|WPS625 How Trade and Macroeconomic<br>Policies Affect Economic Growth<br>and Capital Accumulation<br>In.<br>Developing<br>Counfties|Ramon Lopez|March 1991|<br>K. Cabana<br>37946|
|WPS626 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Colombia|William Easterly|March 1991|<br>R. Luz<br>39059|
|WPS627 The>Role<br>of Institutions<br>In Poverty<br>Reduction: A Focus on the<br>Productive Sectors|Sharon L. Holt|March 1991|<br>WDR Office<br>31393|
|WPS28 The Indonesian<br>Family Planning<br>Program: An EconomicAwspective<br>14<br>¢<br>|Dov Chernichovsky<br> <sup>Henry Pardoko</sup><br>David De Leeuw<br>3<br>DPudjoRahardjo<br>Charles Lerman|<br>March 1,99|1<br>0. Nadora<br>31091|
|WPSO2 An Atheoretic Evaluation<br>ot SuccOs<br>in Structural<br>Adjustment|Patrick Conway|March 1991|<br>D. Ballantyne<br>,<br>37947|
|WPS630 The Spiritr't Capitnlisr and<sup>Long-ru</sup><br>Growth|<sup>n Heng-fu</sup><br>Zou|Marc<br>t<br>191|<br>, A. Shalla<br>37699|
|WPS631 The Macroeconomics<br>of the Public<br>Sector Deficit: The Case of<br>Morocco|Rlccard Falni|March 199|1<br>R. Lux,<br>34303|
<!-- Start of picture text --> Joint tiling default for married couples<br>Timeline by legal tradition<br>- ae<br>oa<br>o TT<br>+ ——__————;*<br>f<br>p---------!<br>=] oe i<br>ol rrr ee oe<br>1960 1970 1980 1990 2000 2010<br>year<br>—— civil —---- hybrid<br>----- common -—- — -- socialist<br><!-- End of picture text --> <!-- Start of picture text --> Adult married women head of household<br>Timeline by legal tradition<br>eee<br>axe<br>---<br>f<br>poco co<br>ioe) f<br>f<br>=e<br>BL<br>1960 1970 1980 1990 2000 2010<br>year<br>—— civil —---—- hybrid<br>----- common -—-- - -- socialist<br><!-- End of picture text -->
through two districts. Overall, the data are more complete than for sectors.

# **4.3 Results**

Having decided on the methodology and datasets we ran the regressions based on Equation 1, both with and without lags. There were two different sets of regressions, one with the 12 sectors as dependent variables and another one with the four categories being the dependent variables. In both cases, the damage indices were independent variables. In addition we controlled for the fixed effects, potential time trends and potential regional time trends. The datasets used were the panels missing no more than 2 years of reporting.<sup>11</sup> Note that lags have not been included for the tsunami data. The reason for this is that the tsunami data are already lagged. The tsunami happened on 26 December 2004, hence a lot of the expenses incurred and any shifts in spending are likely to not be realized until the fiscal year of 2005. As mentioned in the report by The Global Facility for Disaster Reduction and Recovery (2011) the budget allocation for 2005 had already been approved, so major changes were needed during the mid-year budget revision of 2005. Furthermore, the earthquake data does not start until 2005, so to be able to control for all disasters at the same time, the starting year is 2005. As for the other lags, they are added to check whether any spending patterns or redistribution of expenses occur the year after the disaster has struck.

To better understand the results and the timing, it is worth noting how disaster relief is financed in Indonesia. In short, from the report by The Global Facility for Disaster Reduction

> 11We also ran the regressions based on balanced data and 1 year of reporting missing. Overall the results were quite similar, but for some of the lesser reported sectors they differed more.

16
# **2. The welfare effects of improved technology: theoretical insights**

Trade liberalization measures have opened new markets for small-scale irrigation equipment in Bangladesh and led to a substantial growth in rice productivity, so in this paper we focus on links between productivity-enhancing trade liberalization measures and household welfare. Ceteris paribus, productivity gains improve producer welfare because their positive effect on profits. However, productivity improvements may also translate into lower output prices, for example when domestic markets are segmented, which in turn has a negative effect on producer welfare.

Households may purchase or sell rice at different times of year, but over the course of the year they can be defined as net rice suppliers or net rice consumers. In order to explain the implications of increasing agricultural productivity and declining rice prices on household welfare, we make a distinction between net sellers and net buyers of rice. We also define one additional category of rural households: agricultural wage earners. Conceptually, the welfare effects of productivity improvements will be different for these three categories.

The first link between trade liberalization and welfare can be established through the direct effect of trade-related measures on crop prices and productivity, and we refer to this channel as direct first-round effect. The direct first-round effect of a single price change on household welfare is proportional to its net supply position in that good, valued at current prices and as a share of total expenditure. Declining price levels have a positive impact on net buyers and a negative impact on net sellers of rice. Formally, this has been described by Deaton (1989) in his pioneering study of the welfare effects of an increase in rice prices in Thailand and subsequently used in many empirical studies of welfare effects of price changes on farm households (e.g., Benjamin and Deaton 1993, Barrett and Dorosh 1995, Seshan 2005).

A productivity improvement has no direct effect on net buyers, but it affects profit levels of net sellers. For net sellers, technical change causes income to increase as long as elasticity of output with respect to technical change is greater than elasticity of price with respect to technical change (e.g., Barret and Minten 2005). Intuitively, if output increases faster than the price falls in response to technical change, net sellers will enjoy higher income and welfare even if some of the gains accrue to net buyers. If market prices have 5
# **Appendix Table I. 8: Organized Sector Log (Employment) & Log (Output) and Unorganized Household Sector Spillovers**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Emplo|yment|Real O|utput|
|Hitl Elt Sill|_Other-_|_sector Employm_|_ent/Output Spillo_|_vers_|
|orzona mpoymen povers|||||
|from HH Unorganized Sector|0.1176***|0.0278*|0.0923***|0.0136|
||(0.0176)|(0.0142)|(0.0183)|(0.0165)|
|Forward Employment Spillovers from|||||
|HH Unorganized Sector|-0.1351|2.6231***|-0.1038|1.2486|
||(0.1786)|(0.8784)|(0.3361)|(1.4487)|
|Backward Employment Spillovers|||||
|from HH Unorganized Sector|0.5319***|1.0595***|-0.5947*|0.3273|
||(0.1454)|(0.3767)|(0.3380)|(1.0033)|
||_Ot_|_her-sector Prod_|_uctivity Spillover_|_s_|
|Average TFP of Horizontal-linked|||||
|HH Unorganized Plants|-0.0119|0.0094|0.1908|0.2104|
||(0.0717)|(0.0688)|(0.2242)|(0.2201)|
|Average TFP of Forward-linked HH|||||
|Unorganized Plants|-0.1392|0.1837|-0.3507|1.9189|
||(0.1831)|(0.8283)|(0.5559)|(2.5368)|
|Average TFP of Backward-linked HH|||||
|Unorganized Plants|0.0895|-2.6008***|0.6370+|-6.4893***|
||(0.1247)|(0.5928)|(0.3890)|(2.2855)|
||_Own-_|_sector Employm_|_ent/Output Spillo_|_vers_|
|Forward Employment Spillovers from|||||
|Organized Sector|1.6742***|1.2021*|4.0599***|1.0867|
||(0.2889)|(0.6488)|(0.7956)|(1.5263)|
|Backward Employment Spillovers|||||
|from Organized Sector|1.3647***|1.2371***|1.7996***|2.0410++|
||(0.1673)|(0.2380)|(0.4986)|(1.5262)|
|Constant|-32.4282***|-55.8741***|-112.3751***|-79.4029+|
||(4.4233)|(15.4678)|(19.6836)|(48.4015)|
|Observations|5888|5888|5888|5888|
|R-squared|0.1258|0.3353|0.0918|0.2843|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2 and 4 include state-year and industry-year fixed effects while Columns 1 and 3 do not. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***. HH stands for the sub-sample of household-based unorganized plants._ 61
displacement. 55% of the returnees reported to have been employed before the crisis and 36% in June 2014.

Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100% reporting no employment during January and February.

**Figure 7: Employment status over time (%)**

<!-- Start of picture text --> 11<br>21 22 23<br>30 28 29 24 27<br>44 35 53 51 41 52 40 33 45 40 50 45<br>59<br>64<br>77 80 76 74 83 83<br>97 100 100<br>89<br>79 78 77<br>70 72 71 76 73<br>56 65 47 49 59 48 60 67 55 60 50 55<br>41<br>36<br>23 20 24 26 17 17<br>3<br>IDPs Refugees Niger Refugees Mauritania Returnees<br>Yes No<br>Before 14-Jun 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb Before 14-Jun 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb Before 14-Jun 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb Before 14-Jun 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014 and 2015.

The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU)<sup>12</sup> owned prior to the crisis and in June 2014 as well as the percentage of ‘yes’ responses on a question whether a given asset was owned by the household.<sup>13</sup> The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90% and 75% of their animals.

> 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition.

> 13 This was a ‘yes/no’ question meaning that if 56% of the IDPs owned a ventilator before the crisis, 46% did not, etc.

14
# **<u>Table I-2 Direct, indirect and capital stock losses for selected disaster events (continued)</u>**

|**Hurricane Mitch in Nicaragua 1998**<br>**(million USD)**||Losses||
|---|---|---|---|
|Sector|Direct|Indirect|Total|
|Social(housing,education,health)|225|45|270|
|Productive(agriculture,industry,commerce,tourism)|128|57|185|
|Infrastructure (water and sanitation, electricity,<br>transport)|159|147|306|
|Environment|na|Na|-|
|Emergencyand relief expenditure||227|227|
|Total(ECLAC)|512|249|988|
|Total loss accordingto EMDAT|||988|
|Capital stock losses (productive sector and<br>infrastructure)as a share of total loss|29%|||
|**Capital stock losses (productive sector and**<br>**infrastructure) as a share of total loss (EMDAT)**|**29%**|||
|**Earthquake in El Salvador, 2001**<br>**(million USD)**||Losses||
|Sector|Direct|Indirect|Total|
|Social(housing,education,health)|496|120|616|
|Productive(agriculture,industry,commerce,tourism)|244|96|340|
|Infrastructure (water and sanitation, electricity,<br>transport)|97|375|472|
|Environment|102|1|103|
|Emergencyand relief expenditure||73|73|
|Total(ECLAC)|939|591|1,604|
|Total loss accordingto EMDAT|||1,500|
|Capital stock losses (productive sector and<br>infrastructure)as a share of total loss|21%|||
|**Capital stock losses (productive sector and**<br>**infrastructure) as a share of total loss (EMDAT)**|23%|||

Sources: ECLAC, 1999, 2002; Telford et al., 2004; CRED, 2009 29
more severe for extreme values of the distribution of prevalence (i.e zero). Nevertheless, we will also check our results to the inclusion of the zeroes and show that they remain largely unaffected.

By correlating the prevalence of the phenomenon with cross-country measures of institutional quality, we find that it is significantly more prevalent in countries where institutions that limit the powers of the government vis-à-vis the citizens, and where institutions that prevent corruption are less developed (even after controlling for GDP per capita and population size, see Table 4, columns one and two). The power of financial regulators is also positively correlated with the prevalence of banker-politicians (Column (3)). The relation is not driven by a few outliers but seems a robust feature of the data (see both panels of Figure 1). Furthermore, the result stands pretty much unchanged when one considers just the private banks when measuring _PREVALENCE_ (columns (4) to (6)).

The economic effect is large: the difference in the measure of corruption between the UK and Russia is commensurate to their difference in terms of the prevalence of the banker-politician arrangement. Although one cannot rule out that this strong cross-country correlation is not causal, at least it is clear that the phenomenon does not appear to be neutral but rather to be associated to other bad outcomes traditionally related with institutional weakness.

As mentioned above, our baseline measure of prevalence disregards the zeroes by construction. The correlations reported above are, therefore, conditional on exhibiting a nonempty intersection between the sets of politicians and bankers, and are akin to those obtained in a truncated regression. Nevertheless, results obtained including countries with a ratio of actual to expected matches equal to zero are qualitatively similar to those reported above (unreported).

# **_B. Financial Regulation_**

Here we search for a link between banking sector regulation and the prevalence of the banker-politician phenomenon. The private interest story, after all, depends critically on both parties having something to win from their exchange. Regulation that favors incumbents in the banking system is the obvious service that politicians have to exchange for a seat on a bank’s board.

Caprio et al (2007) provide data on the way countries regulate their financial systems on five dimensions: restrictions to bank activities, entry regulation, supervisory powers, private monitoring and self-regulation, and capital requirements. Each of these broad ways of regulating is assigned a score based on answers to surveys conducted to regulators in each country in 2001 and 2007.

13