rafmacalaba/datause-encoder-data huggingface dataset:
In light of the discussion in section 5, finding a convincing source of exogenous variation in program receipt is a daunting task. The data used consist of four pooled yearly cross-sections (2010–13) of Sisben-eligible households with at least one age-eligible member. The basic setup may be represented as follows: where � is the intercept, � the parameter of interest, and � other parameters of no direct interest. The outcome is denoted by _Y_ ; _CM_ denotes the variable of interest (living in a household with a Colombia Mayor beneficiary); and _X_ denotes an additional control variable. The subscripts refer to individual _i_ observed in year _t_ and living in municipality _m_ . The error term has three components: municipalityspecific, time invariant unobserved factors (��); year-specific unobserved shocks (��); and individual specific factors (����). We directly control the first two of these through municipality and year fixed effects so that the only error term of concern is the last one. The individual in question is not restricted to the potential beneficiary, but the effect of the program on other household members is also analyzed.

The main concern with this model is the endogeneity of the variable of interest ( _CM_ ). This seems to arise not so much because of reverse causation (that is, being a beneficiary is partially determined by the outcome), but rather by selection into the program. It is ex ante unclear which unobserved individual- or household-specific factors determine enrollment into the program, but, to the extent that the factors also (partially) determine the outcome variable, the estimate of � will be biased. Given that other identification strategies are not available, the most common way to address this concern is to find an IV for _CM_ . Because we have controlled for municipal and year fixed effects, the natural choice for the IV would be a measure of the level of program rollout in the municipality in each year. Yet, in section 5, we explain at great length that the available administrative data do not provide a valid instrument. The problem is that, according to the official data, the program was rolled out in similar increments in almost all municipalities each year so that almost all the variation in the data is accounted for by the two sets of fixed effects.

We do, however, have serious concerns about the quality of the official data because there appear to be only two types of municipalities: those that have the same increments as almost all others, and those that exhibit unreasonable jumps. While the latter are clearly errors in the data, the former seem 21
Table 10: Model with 2010 - 2002 Difference

|||_Gi_||| ̄_Ii_||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Panel A: OLS Estima|tes||||||
|∆Votes (2010-2002)|0.037***|0.046***|0.045***|0.016***|0.019***|0.018***|
||(0.005)|(0.006)|(0.006)|(0.002)|(0.002)|(0.002)|
|Num.Obs.|557|557|557|557|557|557|
|R<sup>2</sup>|0.215|0.216|0.214|0.459|0.312|0.287|
|Panel B: GAM Estim|ates||||||
|∆Votes (2010-2002)|0.035***<br>(0.005)|0.045***<br>(0.006)|0.048***<br>(0.006)|0.019***<br>(0.002)|0.018***<br>(0.003)|0.019***<br>(0.003)|
|Num.Obs.|557|557|557|557|557|557|
|R<sup>2</sup>|0.396|0.408|0.421|0.568|0.451|0.443|
|_S_(lat, lon)|0.000|0.000|0.000|0.000|0.000|0.000|

Note: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Columns (1) and (4) correspond to the Fully Optimal scenario, columns (2) and (5) to the Efficiency+Coordination scenario, column (3) to the Efficiency (Electricity) scenario, and column (6) to the Efficiency (Roads) scenario. All regressions control for the change in population. Panel A controls for latitude and longitude. Panel B shows semiparametric regressions with thin-plate spline in latitude and longitude, _S_ (lat, lon) shows the approximate p- value of the thin-plate spline.

37
**Table 2 – Financial Dependency and BNDES Credit on Distortions: Main results**

|||(1)|(2)|
|---|---|---|---|
||VARIABLES|ln(1 - tyit)|ln(1 + tkit)|
|(a)|BNDES Creditit|0.0195***|-0.0346***|
|||(0.00450)|(0.00464)|
|(b)|BNDES Cred * Fin.Depit|0.00183|-0.00797**|
|||(0.00338)|(0.00349)|
|(c)|Financial Depit|-0.0391***|0.129***|
|||(0.00186)|(0.00192)|
||Observations|104,092|104,092|
||Number of ii|36,598|36,598|
||R-squared|0.138|0.113|
||BNDES Cred. Contrib to Var|-0.13%|0.09%|
||Median Fin Dep.|0.166|0.166|
||BNDES Credit Eff.|0.0198|-0.0359|
||p-value|0.000|0.000|

Note: authors ́ calculation based on raw IBGE manufacturing data, 2003-2014. BNDES credit is a dummy which takes value 1 if firm _i_ used BNDES/FINAME credit in year _t_ and zero otherwise; Financial Dep equals investment outlays / cash flow, where cash flow is value added minus wage bill; Firm and Year dummies included; lagged log revenue and log value added included as controls. BNDES Cred. Contrib. is the proportion of the variance of the dependent variable explained by BNDES Credit variables, following eq. (7). BNDES Credit Eff. is BNDES credit effect on distortion evaluated for the median firm, i.e., BNDES Credit coefficient plus the interaction coefficient times Median Financial Dependency; P-value is a significance test p-value of BNDES Credit Eff. Standard errors given in parentheses: *** p<0.01, ** p<0.05, * p<0.1.

Line (c) of Table 2 provides information on the effect of financial dependency on distortions for firms that do not use BNDES credit (the BNDES dummy is null). The estimates suggest that firms that use relatively less internal funds for their investment, i.e., a high finance dependency variable, have a lower output distortion and a higher capital distortion. The opposing directions of the effect on distortions suggest that an increase in credit use to finance investments worsens misallocation and decreases aggregate TFP.

When we compare the effect of financial dependency on distortions across firms that use and do not use BNDES credit for their equipment outlays (line (b) of coefficients), the financial dependency effect on distortions is dampened, i.e., we find a relatively lower capital distortion and a smaller (in absolute value) negative effect of financial dependency on output distortions (although the interaction effect is not significant). The dampening effect of BNDES credit on financial dependency effects should, therefore, ease the negative effect of financial dependency on TFP.

In Table 3 we present the BNDES/FINAME credit effects and the financial dependency effects for large and small firms, according to the BNDES sector classification. Large firms are those with yearly turnover of at least R$60 million up to 2009 and R$90 million from 2010 onwards. Results are reported in columns (3)-(6). At the bottom of the table note that larger firms have a higher financial dependency variable, i.e., 0.219 compared to 0.161 for smaller firms. This is expected as larger firms may find it easier to finance their investment through credit. Coefficients in lines (a) and (b) suggest that BNDES credit use effects on 15
number of workers is 2.44 per household.<sup>19</sup> The average number of years of education is 5.89, which is equal to the level of the primary school completion. The average land surface per household is 10.48 _mus_ ,<sup>20</sup> and the per capita level is only 2.92 _mus_ . In other words, land is a scarce resource in rural China, only 0.19 hectares per capita. Compared to the households that participate only in farm activity, the households that participate in non-farm activity have a larger number of workers, a higher education level, a lower level land surface, and live closer to urban centers / hubs of transport.

# **4 Methodologies**

Based on the LSMS data, we use two methods to study the impacts of non-farm income on rural inequality. First, we consider non-farm income as an “exogenous transfer”, which adds to pre-existing total household income, to decompose total household income and study the distribution of each income source and its contribution to total income inequality. Second, we consider non-farm income as a “potential substitute” for farm income, and compare the level and distribution of observed total household income with those without non-farm income.

# **4.1 Method I: Decomposition of the Gini index**

The method of the decomposition of the Gini index is often used to analyze income inequality (Pyatt et al _._ , 1980; Stark, 1991). Suppose that _y_ 1, _y_ 2 , L , _y K_ stand for the _K K_ components of household income and<sup>_y_</sup> 0<sup>the total income,</sup> _y_ 0 = ∑<sup>_y_</sup> _k_ . The Gini index of the _k_ = 1 total income, _G_ 0 , can be decomposed as follows: where _S k_ stands for the share of the component _k_ in the total income, _Gk_ the Gini index corresponding to the component _k_ ; and _Rk_ the correlation between the Gini indices of the component _k_ and the total income. Hence, the Gini index _G_ 0 is decomposed into three components: (i) the share of the component _k_ in total income,<sup>_S_</sup> _k_<sup>; (ii) the inequality of the</sup>

> 19 Here “workers” are the household members who are at least 15 years old and can participate in productive activity.

> 20 1 _mu_ = 1/15 hectares.

8
residual category in official Bank of Italy statistics and mainly comprises holdings by Italian households, experienced a persistent decline, falling from 43 percent of total debt in 1966 to 15 percent in 1975. A slight recovery in the share of the household sector to 16 percent was observed in 1976 after the initial reforms of the debt markets. General government was relying on the banking system for 38 percent of its total debt in 1966, but ten years later it was dependent on it for almost 80 percent of the total.

**Table 2.1: Holders of Italian Public Debt Securities (1966-1975)**

|_Holders_|_1966_|_1970_|_1975_|
|---|---|---|---|
|Bank of Italy|12.3|32.9|48.3|
|Banks|35.8|32.1|31.7|
|Economy and foreign investors|42.6|29.4|15.3|
|Other financial institutions*|9.3|5.6|4.7|
|Total|100.0|100.0|100.0|

*Includes Mortgage Banks, Special Credit Institutes, Public Insurance Agency and Insurance Companies Source: Bank of Italy

# **2.3 The Search for Investor Diversity and Market Finance (1977-1987)**

The necessity to shift the focus of the system from intermediaries (mainly banks) to markets and to enlarge and diversify the investor base characterizes the period between 1977 and 1987. The reforms followed different tracks: from an increase in transparency in public debt management to financial innovation and introduction of new types of securities to attract new investors, the refinement of issuing techniques, and the amelioration of the enabling environment.

## **_Increased Transparency_**

Given the ties between the two institutions, and the serious consequences of denying funds to the Treasury, the Bank of Italy suffered income losses in 1981, having been obliged to sell bonds below their purchase price (Passacantando 1996:87-91). Furthermore, the overdraft facility acted as a portfolio constraint on the Bank of Italy’s balance sheet, rising in the early 1980s from 24.5% to 45.8% of total assets. In an attempt to strengthen central bank independence in the conduct of monetary policy and reinforce transparency in public debt management, a “divorce” between the Treasury and the Bank of Italy was initiated in 1981.

The “divorce” resulted in the Bank of Italy ceasing to act as a residual buyer at Treasury bill auctions. The central bank gradually adopted a more independent attitude despite the growing funding needs of the Treasury. However, at the end of 1982, the Bank of Italy refused to finance the deficit, thus highlighting the fragility of the system. An extraordinary law, enacted by Parliament, required the extra funding from the central bank and put an end to this controversy. This incident underlined the importance of granting total independence to the central bank from the authorities responsible for public spending.

A number of other reforms took place between the end of the 1970s and the early 1980s. In 1979, securities repurchase agreements with banks (repos) were introduced as an instrument of liquidity management. In December 1982, the system of reserve requirements was changed.

11
With regard to SOFIs’ countercyclical policy, just one in four SOFIs in our sample has a specific policy for increasing lending during economic recessions or financial crises and this is predominantly among Development SOFIs. In relative terms, one in five Commercial SOFIs has a specific counter-cyclical policy while for Development SOFIs this is almost one in three.

# **_5.3 Instruments and portfolio_**

# _5.3.1 Instruments_

About 20 percent of Commercial SOFIs do on-lending (a.k.a. wholesale lending, second tier, or apex lending) alongside direct lending (a.k.a. retail lending), and almost 70 percent of Development SOFIs do a combination of direct and on-lending. East ECA, characterized by more Commercial SOFIs, has a lower incidence of on-lending compared to the other two granular regions (Figure 6a).

# **Figure 6: Instruments**

## **6a. Means of lending By type**

## **By granular region**

<!-- Start of picture text --> Commercial Development<br>Direct loans On-lending<br>Direct and on-lend<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>Direct loans On-lending<br>Direct and on-lend<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> 19
Table A2: Regressions on transfer within group

||Amount transferred (std)|Transferre|d nothing|
|---|---|---|---|
||(1)<br>(2)|(3)|(4)|
|Cooperation|0.232<sup>_∗∗∗_</sup><br>0.237<sup>_∗∗∗_</sup>|-0.122<sup>_∗∗∗_</sup>|-0.123<sup>_∗∗∗_</sup>|
||(0.089)<br>(0.090)|(0.044)|(0.044)|
|Republican|-0.075||0.042|
||(0.102)||(0.050)|
|High age|-0.030||0.129<sup>_∗∗∗_</sup>|
||(0.090)||(0.045)|
|Female|0.012||-0.014|
||(0.091)||(0.044)|
|High income|-0.156<sup>_∗_</sup>||0.087<sup>_∗_</sup>|
||(0.093)||(0.046)|
|Constant|0.796<sup>_∗∗∗_</sup><br>0.883<sup>_∗∗∗_</sup>|0.524<sup>_∗∗∗_</sup>|0.420<sup>_∗∗∗_</sup>|
||(0.063)<br>(0.088)|(0.031)|(0.047)|
|Observations|508<br>508|508|508|
|_R_<sup>2</sup>|0.013<br>0.021|0.015|0.045|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 _Note:_ The table reports OLS regressions on transfers when the two workers belong to the same group. The dependent variable in columns (1) and (2) is the standardized amount transferred to the unlucky worker. The dependent variable in columns (3) and (4) is an indicator for the spectator transferring nothing to the unlucky worker. “Cooperation” is an indicator for the spectator being assigned to a treatment where the workers cooperate within group. “Republican” is an indicator for the spectator self-reporting voting Republican. “High age” is an indicator for the spectator being above median age in the sample. “Female” is an indicator variable for the spectator being female. “High income” is an indicator for the spectator having an income above the median income in the sample. Standard errors in parentheses, where * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

16
<!-- Start of picture text --> Afghan exit from Iran (number)<br>250000<br>200000<br>150000<br>100000<br>yearly growth rate of USD to IRR<br>200<br>150<br>100<br>50<br>0<br>2015 2016 2017 2018 2019<br>quarter<br><!-- End of picture text --> _Figure 7 - Economic shock and number of Afghan refugees exiting from Iranian border. The data of Afghan exit from two main borders of Iran (Herat and Nimrouz) is drawn from the website of International Organization for Migration._ <!-- Start of picture text --> nationality=Iran<br> nationality=Afghan<br>-.2 0 .2 .4 .6 .8<br> nationality=Iran<br> nationality=Afghan<br>-.2 0 .2 .4 .6 .8<br>Cyclical Xrate yearly difference (measure of negative shock)<br>14.8<br>14.7<br>14.6<br>monthly expenditure per capita<br>14.5<br>17<br>16<br>15<br>14<br>Free items received by household<br><!-- End of picture text --> _Figure 8 - The bin-scatter graph for regression results of the matched sample. The sample is divided to 20 equalized bins for better illustration._ 21
**Table 9**

**HIES 2016/17 allocation of PSUs by stratum**

|**Stratum no**|**Stratum name**|**No. of PSUs**|
|---|---|---|
|1|Barisal Rural|162|
|2|Barisal Urban|54|
|3|Chittagong Rural|308|
|4|Chittagong Urban|60|
|5|Chittagong City Corporation|28|
|6|Dhaka Rural|262|
|7|Dhaka Urban|178|
|8|Dhaka City Corporation|28|
|9|Khulna Rural|236|
|10|Khulna Urban|98|
|11|Khulna City Corporation|26|
|12|Mymensingh Rural|100|
|13|Mymensingh Urban|44|
|14|Rajshahi Rural|211|
|15|Rajshahi Urban|49|
|16|Rajshahi City Corporation|28|
|17|Rangpur Rural|219|
|18|Rangpur Urban|69|
|19|Sylhet Rural|107|
|20|Sylhet Urban|37|
||**Total:**|2,304|

**Table 10 HIES 2016/17 allocation of PSUs for urban and rural areas**

|**Sector**|**HIES 2016**<br>**No. of PSUs**|**/17 sample**<br>**Percentage**|**Population**<br>**share, Census of**<br>**Population and**<br>**Housing 2011**|
|---|---|---|---|
|Urban|699|30.3%|23.1%|
|Rural|1,605|69.7%|76.9%|
|Total|2,304|100.0%|100.0%|

26
Policy Research Working Paper 9078

# **Abstract**

The ability to manage financial risk is especially important for people earning their living through agriculture. Many farmers only get paid once or twice a year, and households need to stretch their earnings across the year by saving or borrowing money. Moreover, agricultural production faces a variety of risks related to both production and markets because of their exposure to weather and disease shocks. Households engaged in agriculture may thus especially benefit from financial inclusion—access to and use of formal financial services. This paper explores the topic of financial risk management in agriculture—how adults who rely on growing crops or raising livestock as their household’s main source of income manage financial risk and use financial services. The paper summarizes new data based on a nationally representative survey of about 15,000 adults in 15 lower-middle- and low-income Sub-Saharan African economies collected as part of the World Bank’s Global Findex database. The majority of these adults reported suffering a bad harvest or significant livestock loss in the past five years, and most bear the entire financial risk of such a loss. Most adults in agricultural households lack the financial tools—such as insurance, accounts, savings, and credit—that could help them manage financial risks.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at lklapper@worldbank.org and dsinger@worldbank.org.

_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
specifications, non-discrimination and women’s employment show a statistically insignificant relationship (at 10 percent level) for countries that are below a critical level of income and women’s population; the same holds for large firms for most of the specifications. We note that the results for the interaction terms for firm-size and income level are conditional on controls for industry fixed effects and education gap, respectively; also, the interaction term for women’s population is insignificance (at the 10 percent level) if we control for all the three interaction terms simultaneously.<sup>7</sup>

# 5.4 _Falsification test_

A falsification test was performed under the assumption that non-discrimination should be positively correlated with _Absolute women employment_ but uncorrelated with _Absolute men employment_ . Regression results shown in Table 7 clearly pass this test. That is, while women’s absolute employment (columns 1-3) is positively and significantly (at the 10 percent level) correlated with non-discrimination, men’s absolute employment shows a weak (statistically insignificant at the 10 percent level) and negative relationship with non-discrimination. These results help raise our confidence against the omitted variable bias problem with our main results.

# 5.5 _An alternative measure of women’s rights_

We experimented by replacing our non-discrimination variable with a broader measure of women’s rights obtained from CIRI database. We would like to mention that the CIRI data has been discontinued (since 2011) but we were able to obtain data for the earlier (pre 2011) years.

> 7 We also experimented by interacting the non-discrimination variable with industry concentration ratios under the assumption that the positive effect of non-discrimination on women’s employment is likely to be bigger (more positive) in sectors that are less competitive. The results confirm this conjecture and are available in an on-line version of the article at:

19
Perhaps it is the lower inflation rate which has been observed since the 1990s. Despite all the talk of ratchet effects, disinflation certainly does seem to have the potential to lower dollarization ratios. This appears to have been the case in Lao PDR which had the sharpest decline in dollarization soon after the stabilization of 3-digit inflation in 1998-9. Earlier episodes of disinflation from high levels, as in Egypt and in certain East European countries, were also accompanied by a fall in dollarization ratios. Although current and recent inflation does not enter in a statistically significant way in the estimated regression equations, it would not be surprising if such an effect were masked in the analysis by cross-country variations in the size and timing of the response to inflation. It is less clear why the inflationary downturn should have had a sudden effect in 2002.

Local interest rates might also capture changing confidence effects regarding future inflation and exchange rates. Although these too do not enter strongly in the equations, this may reflect the general difficulty of modeling short-term confidence effects. As an instance of this difficulty, consider the experience of many Transition economies which were simultaneously working through the inflationary – and dollarization – implications of price liberalization while also starting to build the institutions that would stabilize their macroeconomies (including, for example, central bank independence).

<!-- Start of picture text --> Dollarization net of depreciation impact<br>Mean of balanced sample (76 countries)<br>37<br>35<br>33<br>31<br>Actual<br>Net of estimated impact<br>29<br>Net of theoretical impact<br>27<br>25<br>1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Figure 8 _Adjusted dollarization figures showing underlying trend (a) 76 country mean_ Even if these calculations serve to qualify the degree to which growth in dollarization shares has been reversed, they do not alter the fact that there has break in trend. Will 2001 be seen as a high point of dollarization, or have the last few years merely been a pause in what had been an inexorable worldwide trend?

17