rafmacalaba/datause-encoder-data huggingface dataset:
As a robustness test, a more parsimonious model is also estimated, in which ActiveAid _ikt_ and CompletedAid _ikt_ are collapsed together into a single treatment variable that equals 1 in the year of commitment of the first project and in all subsequent years. The estimates of the coefficient on the interaction of interest (not reported) are again small and not significant. To sum up, the analysis in this section does not find any evidence of development outcomes being higher in cells with missions than in cells without missions, either after or during the implementation of aid projects.

There are a few caveats in this analysis. First, the exercise does not estimate a precise “zero effect,” so it is not possible to conclude definitively that aid does not at all work better in mission areas. Second, using measures of actual development as outcome variables (instead of project ratings) has some disadvantages: although _access_ to piped water and to electricity may be direct products of specific aid projects, the outcomes themselves measure ownership of private goods. The time span of the analysis may not be long enough for the effects of development aid to materialize into private consumption or investment. Furthermore, proxies based on DHS data may not capture the dimensions of aid that are most affected by the projects. Finally, note that if the “true” differences in aid success between cells with and without missions are small in magnitude, measurement error in several variables could result in too much attenuation bias to be able to estimate them.

# **3.3 Discussion**

The analyses above are derived from a corollary of a potential mechanism, which is that aid is allocated to mission areas because it is thought to be more effective there. However, the results from two different empirical exercises do not lend support to this hypothesis. This conclusion rests on the assumption that the variables considered are actually able to measure effectiveness, as well as on a number of assumptions about the validity of the test. There is also an implicit assumption that the allocating authorities have a reliable way to observe effectiveness, which may not be the case. If so, the allocation of aid to mission areas could be related to a prior belief that aid will be more effective there, a belief that is not updated because of a lack of evidence.

32
**Import** is the share of raw material imports in total material inputs.

**Importer** is a dummy variable that takes the value 1 if the firm imports and 0 otherwise.

**State ownership** is the share of ownership of a firm held by the government.

# **A.3 Definition of Industry-Level Variables**

**Dependence on external finance** ( _EFD_ ) is the median of the ratio of capital expenditures minus cash flow from operations over capital expenditures in US industries. Source: Rajan and Zingales (1998).

**Entry rate** ( _Entry_ ) in industry _j_ at time t is the number of all new firms in our data at time _t_ divided by the total number of firms at time _t – 1_ .

**Entry regulation** in industry _j_ is a dummy variable that equals 1 if industry was subject to entry restrictions (source: Presidential Decrees issued in 1993, 1995, and 2000 in Indonesia); it is an indicator of stringent entry regulation.

**Exit rate** ( _Exit_ ) in industry j at time _t_ is the number of all firm that do not exist in our data at time _t +1_ divided by the total number of firms at time _t_ .

**Export** in industry j is the share of total exports out of total output. **Foreign market share (** **_MS_ )** is the share of output in industry j produced by firms with majority foreign ownership: ∑ <u>, j</u> , where is a dummy variable indicating a firm with a majority of foreign ownership ∑ ii ii∈ FFii∙YYii and is the output of firm ii,ii∈jj YYii _i_ FF. ii **Herfindahl-Hirschman Index** ii _(HHI)_ in industry j at time _t_ is defined as the sum of the squared market YY 2 shares of firms in an industry: H = ∑ O <u>O i</u> . �∑ O i � ii ii **Import** in industry j is the share of total imported raw materials out of total material inputs. HHjjii ii,ii∈jj ii,ii∈jj iiO ii

**Import Labor (log)** is total number of paid workers in an industry. **Market share of the four largest firms** _(MS4)_ in industry j at time _t_ is defined as = ∑ <u>,2,3,4,∈j</u> O i ii=1∑ O iiOi ii ~~,~~ where _i_ indicates the rank of firms in sector _j_ with 1 representing the largest firms, 2 the MMSS4jjii second largest firm, 3 the third largest firm and 4 the fourth largest firm.   ii,ii∈jj iiO ii

**Natural rate of entry** is the percentage of new corporations (firms that are not more than one year old) in US industries, averaged over the period 1998-99. Source: Klapper et al. (2006).

**Number of firms** is the number of firms in industry _j_ in year _t._

**Output (log)** is total real output in an industry; nominal output was deflated using three-digit industrylevel deflators obtained from the Indonesian statistical office.

39
We also made a limited attempt to predict the distribution of commercial bank loans around the world. Here, we focused only on commercial banks because of data limitations. We did not attempt to estimate an aggregated figure for the number of loans because, unlike deposit service providers, there are a large number of non-regulated credit suppliers worldwide, rendering the estimation of a meaningful figure through regulatory data practically impossible.

# **_4.1 Counting the Number of Accounts_**

The broad coverage of our data allows us to develop a model to extrapolate out of sample to estimate the total number of bank accounts and their distribution around the world. To reach these estimates, we start with our survey data and develop a simple regression framework to impute values for some countries for which we don’t have commercial bank or cooperative data. In addition, we attempt to fill in gaps in the non-bank data with data from other sources, including the MIX, CGAPs Occasional paper no. 8, the World Council of Credit Unions annual survey, and country sources. In order to express the uncertainty in the estimate we calculate a conservative “low estimate”, a “preferred estimate” and a “high estimate” to give a sense of the range of values which may apply when we vary our assumptions. To generate the map and the count of accounts worldwide, we use the sum of the preferred estimates for commercial banks, cooperatives, government banks, and MFIs. The details of how numbers for each bank category were estimated are provided below, including the preferred estimate, and the high and low estimate reflecting different assumptions:

# **_4.1.1 Commercial banks_**

[ _Preferred estimate_ ] We first construct a set of regression models to fit the relationships between log of account penetration on the LHS and per capita income, size of financial sector, population density, value of deposits per capita, number of commercial bank branches per person, and whether the country is in the high-income OECD category on the RHS. This specification choice reflects a balance between wanting a parsimonious model with reasonable explanations as to why each variable should be included (so that we can think through whether the model will hold up well in conducting out of sample predictions) vs. the desire to get the best within sample fit 16
# Appendix I.A.10 Historical Demographic Data, Denmark 1801-1981

**-a**

|Year|1801|1840|1850|1860|1870|1880|1890|1901|
|---|---|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over_65_years old|17.37|11.49|9.76|11.76|10.54|9.77|8.42|8.96|
|Population aged 20 to 59/<br>population over 60 years old|5.33|5.75|5.38|6.09|5.52|4.93|4.51|4.73|
|Percentage of population<br>over 60 years old|9.40|8.42|9.33|8.07|8.80|9.65|10.78|9.82|
|Percentage of population<br>under 15 years old|40.50|32.81|31.81|33.73|33.41|33.79|34.85|33.97|
|Women over 60 years old/<br>Menover60yearsold|1.18|1.25|1.58|1.22|1.21|1.18|1.16|1.18|
|**Year**|**1911**|**1921**|**1930**|**1940**|**1950**|**1960**|**1970**|**1981**|
|Population aged 15 to 64/<br>population over 65 years old|8.98|9.05|8.96|12.72|7.08|5.67|5.26|4.50|
|Population aged 20 to_59/_<br>population over 60 years old|4.80|4.76|4.87|5.90|4.00|3.09|2.92|2.66|
|Percentage of population<br>over 60 years old|9.85|10.29|10.78|9.66|13.36|16.52|17.59|19.57|
|Percentage of population<br>under 15 years old|33.60|31.19|27.46|24.57|26.33|23.80|23.32|20.60|
|Women over 60 years old/|||||||||
|<br>Menover60yearsold|1.20|1.19|_1.15_|1.08|1.10|1.18|1.22|1.29|

Ratios calculated using data found in Mitchell, B. R. 1990 "International Historical Statistics: Europe 1750-1988".
**Table 1: Impact of cyclical fluctuations in male and female unemployment rates on IPV**

|||Ma|in Sample||Restrict<br>religio|ed sample:<br>n controls|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A: Physical Violence|||||||
|Ln(U<sup>f</sup>)|0.171|0.347*|-0.534***|-0.519***|-0.502***|-0.506***|
||(0.148)|(0.178)|(0.081)|(0.083)|(0.107)|(0.108)|
|Ln(U<sup>m</sup>)|-0.312**|-0.479**|0.508***|0.497***|0.458***|0.462***|
||(0.151)|(0.182)|(0.086)|(0.088)|(0.163)|(0.164)|
|Observations|381730|381730|381730|348165|206641|206641|
|P-value F-test|0.107|0.026|0.000|0.000|0.002|0.002|
|Mean Violence|0.182|0.182|0.182|0.181|0.187|0.187|
|MeanLn(U<sup>f</sup>)|0.018|0.018|0.018|0.024|0.062|0.062|
|MeanLn(U<sup>m</sup>)|0.008|0.008|0.008|0.012|0.019|0.019|
|B: Sexual Violence|||||||
|Ln(U<sup>f</sup>)|0.038|0.089|-0.172|-0.172|-0.159|-0.160|
||(0.126)|(0.142)|(0.107)|(0.109)|(0.116)|(0.115)|
|Ln(U<sup>m</sup>)|-0.150|-0.186|0.141|0.140|0.105|0.107|
||(0.112)|(0.135)|(0.107)|(0.107)|(0.159)|(0.159)|
|Observations|275718|275718|275718|268173|199929|199929|
|P-value F-test|0.436|0.325|0.150|0.154|0.336|0.332|
|Mean Violence|0.068|0.068|0.068|0.067|0.075|0.075|
|MeanLn(U<sup>f</sup>)|0.021|0.021|0.021|0.023|0.061|0.061|
|MeanLn(U<sup>m</sup>)|0.002|0.002|0.002|0.003|0.016|0.016|
|C: Any Violence|||||||
|Ln(U<sup>f</sup>)|0.204|0.326|-0.584***|-0.581***|-0.509***|-0.512***|
||(0.176)|(0.215)|(0.148)|(0.153)|(0.150)|(0.150)|
|Ln(U<sup>m</sup>)|-0.401**|-0.521**|0.535***|0.527***|0.426*|0.430*|
||(0.178)|(0.219)|(0.157)|(0.159)|(0.216)|(0.217)|
|Observations|274002|274002|274002|266517|198400|198400|
|P-value F-test|0.094|0.058|0.001|0.001|0.017|0.017|
|Mean Violence|0.198|0.198|0.198|0.198|0.208|0.208|
|MeanLn(U<sup>f</sup>)|0.021|0.021|0.021|0.022|0.061|0.061|
|MeanLn(U<sup>m</sup>)|0.002|0.002|0.002|0.003|0.017|0.017|
|Year FE||Y|Y|Y|Y|Y|
|Country FE|||Y|Y|Y|Y|
|X controls||||Y|Y|Y|
|Religion controls||||||Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. All estimates are of equation (1). The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (panel A), sexual (panel B) or either (panel C) in the last twelve months. _P-value F-test_ refers to the F-test that the coefficients on ln(U<sup>f</sup> ) and ln(U<sup>m</sup> ) are equal. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. The restricted sample is the sub-sample of surveys for which information on respondent religion is non-missing.

28
**Table 4. Tobit regression results**

||**SH**|**Gs who joi**|**ned af**|**ter VO form**<br>|**ation**<br>|||**All S**|**HGs**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|||||**APE on**|**APE on**||||**APE on**|**APE on**|
|**Variable**|**coeff**|**(se)**|**sig**|**P(y=0|X)**|**E(y|X)**|**coeff**|**(se)**|**sig**|**P(y=0|X)**|**E(y|X)**|
|**Loan characteristics**|||||||||||
|Amount (1,000Rs)|0.000|(0.001)||0.000|0.000|0.003|-0.001|***|-0.001|0.001|
|From bank|-0.683|(0.136)|***|0.186|-0.251|-0.688|-0.096|***|0.156|-0.209|
|From internal lending|0.025|(0.102)||-0.009|0.012|-0.218|-0.073|***|0.062|-0.083|
|From mixed fund|-0.165|(0.107)||0.057|-0.078|-0.037|-0.07||0.011|-0.015|
|Interest rate p.a. (%)|0.024|(0.011)|**|-0.009|0.006|0.009|-0.007||-0.003|0.002|
|Length (years)|0.409|(0.084)|***|-0.149|0.111|0.489|-0.059|***|-0.145|0.121|
|<br>Installments at least monthly|-0.332|<br>(0.095)|***|0.129|-0.176|-0.465|-0.065|***|0.151|-0.201|
|**Borrower characteristics**|||||||||||
|SHG size|-0.245|(0.072)|***|0.089|-0.066|-0.057|-0.048||0.017|-0.014|
|SHG size squared|0.009|(0.003)|***|||0.002|-0.002||||
|<br>SHG age|0.186|<br>(0.079)|**|-0.068|0.050|-0.018|-0.036||0.005|-0.004|
|SHG age squared|-0.020|(0.007)|***|||0|-0.003||||
|<br>Share of very poor members (%)|0.461|(0.161)|***|-0.167|0.125|0.351|-0.114|***|-0.105|0.087|
|Share of poor members (%)|0.288|(0.169)|*|-0.104|0.078|0.012|-0.117||-0.003|0.003|
|<br>Share of scheduled caste/tribe members (%)|0.219|<br>(0.179)||-0.080|0.059|0.317|-0.109|***|-0.094|0.079|
|Share of other backward caste members (%)|0.265|(0.175)||-0.096|0.072|0.214|-0.106|**|-0.064|0.053|
|Share of members from dominant caste (%)<br>**VO’s Management practices**|-0.285|(0.238)||0.104|-0.077|-0.391|-0.17|**|0.116|-0.097|
|<br>SHG regularly audited|-0.233|(0.091)|**|0.083|-0.113|-0.078|-0.058||0.023|-0.031|
|SHG required to present books at VO meeting|-0.275|(0.094)|***|0.095|-0.128|-0.191|-0.067|***|0.054|-0.073|
|fees for missing installment|-0.088|(0.084)||0.032|-0.043|-0.199|-0.057|***|0.059|-0.079|
|<br>loan recovery committee existed|-0.062|<br>(0.077)||0.023|-0.031|-0.102|-0.051|**|0.03|-0.04|
|VO collected thrift from SHGs|-0.541|(0.087)|***|0.200|-0.272|-0.429|-0.058|***|0.133|-0.178|
|VO employed trained book keeper|-0.149|(0.097)||0.056|-0.077|-0.166|-0.067|**|0.052|-0.07|
|<br>VO provided in-kind consumption credit|-0.371|(0.092)|***|0.127|-0.173|-0.353|-0.06|***|0.102|-0.137|
|<br>VO provided marketing services|0.003|(0.084)||-0.001|0.002|0|-0.055||0|0|
|**Other parameters**|||||||||||
|<br>Constant|0.861|(0.424)|**|||0.342|-0.311||||
|_σν_ (VO random effect)|0.010|<br>(0.278)||||0.525|-0.126|***|||
|<br>_σu_ (SHG random effect)|0.768|<br>(0.053)|***|||0.267|-0.268||||
|_σε_ (random error term)|0.306|(0.089)|***|||0.756|-0.134|***|||
|Numberofobservations||1,120|||||3,350||||

10
# Bank.

- Bussolo, M., De Hoyos R. and Medvedev D. Economic growth and income distribution: linking macroeconomic models with household survey data at the global level. International Journal of Microsimulation, pages 92-103, 2010.

- Bussolo, M. Rafael E. De Hoyos, Medvedev, D. and Mensbrugghe D. Global Growth and Distribution: Are China and India Reshaping the World? The World Bank Policy Research Working Paper, n. 4392, November 2007.

- Bussolo M., de Hoyos, R. E. & Medvedev, D., and Van der Mensbrugghe D., 2012. "Global Growth and Distribution: China, India, and the Emergence of a Global Middle Class," Journal of Globalization and Development, De Gruyter, vol. 2(2), pages 1-29, January.

- Card, D. and T. Lemieux (2001). Can Falling Supply Explain the Rising Return to College for Younger Men? A Cohort-Based Analysis. The Quarterly Journal of Economics 116(2), 705–746.

Cruz, M. and Milet, E. (2017) Skill premia in Developing Countries. World Bank, Manuscript.

- Cruz, M., Go, and Osório-Rodarte, I. (2017) Modeling the poverty and distribution effects of human capital formation across countries: Using a new database on labor skills and wages. World Bank, Manuscript.

- Cruz, M. and Ahmed, S. A. 2016. "On the impact of demographic change on growth, savings, and poverty," Policy Research Working Paper Series 7805, The World Bank.

- Deaton, A. and C. Paxson (1998). “Economies of Scale, Household Size, and the Demand for Food.” _The Journal of Political Economy_ , 106 (5): 897-930.

- De la Granville, O. and R.M. Solow (2009). “Capital-Labour Substitution and Economic Growth.” In O. de la Granville, _Economic Growth – A Unified Approach._ Cambridge: Cambridge University Press.

Ferreira, F.H., Lustig, N. & Teles, D. Journal of Economic Inequalality (2015) 13: 497.

Freeman, R. B. (2008). "The New Global Labor Market." _Focus_ 26(1) Summer-Fall 2008, pp. 1-6.

- Freeman, R. B. (2007). "Labor Market Institutions around the World," NBER Working Papers 13242, National Bureau of Economic Research, Inc.

- Freeman, R. B. (2007). "The Great Doubling: The Challenge of the New Global Labor Market." In J. Edwards, M. Crain, and A.L. Lalleberg, eds., _Ending Poverty in America: How to Restore the American Dream._ New York: The New Press.

Galbraigth, J.K. (2012). _Inequality and Instability._ Oxford: Oxford University Press.

- Harris, J.R. and M. Todaro (1970). “Migration, Unemployment and Development: A Two-Sector Analysis.” _American Economic Review_ 60: 126-42.

- Houthakker, H.S. (1955). “The Pareto Distribtuion and the Cobb-Douglas Production Function in Activity Analysis.” Review of Economic Studies 23: 27-31.

- Jones, C.I. (2009). “The Shape of Production Functions and the Direction of Technical Change.” Quarterly Journal of _Economics_ 2:517-549.

32
baseline and multiple follow-ups, than by doing a single baseline and single follow-up. For example, when ρ=0.25, power is greater doing one baseline and two follow-ups with _n=333_ , than doing a single baseline and single follow-up with _n=500_ , but also greater than doing one baseline and three follow-ups with _n=250._ As a final illustration, consider the power to detect an impact of microfinance on business profits. Banerjee et al. (2010) report a mean of 550 and standard deviation of 46604 for business profits. With such noisy data, the treatment and control group sample sizes required to detect a 10% increase in profits with 90 percent power using a single baseline and follow-up are over 15 million! Suppose then that they were able to consider a more homogeneous set of firms and measure profits more accurately<sup>8</sup> , reducing the standard deviation to 550. Then assuming an autocorrelation of profits of 0.25, with their sample of approximately 1150 treatment and 1150 control, the power for detecting a 10% increase in profits would be 0.669 with a single crosssection post-treatment, 0.697 with a baseline and single follow-up, 0.892 for a baseline and two follow-ups, and 0.952 with a baseline and three follow-ups.<sup>9</sup> Likewise, taking the example of Karlan and Zinman (2010), who have a control group mean of 17,000, if the standard deviation were the same as the mean, the power to detect a 10% increase in profits would rise from 0.68 with a single post-treatment survey as they have to 0.902 for a baseline and two follow-ups and 0.958 with a baseline and three follow-ups. In both cases then, assuming the standard deviation of profits can be kept or reduced to a reasonable level, the choice of a second or third posttreatment measure would allow them to detect a treatment effect of interest with considerably more power than possible with only a single survey round.

# **4. Recommendations for practice and other practical issues**

The above analysis has shown that there are gains to be had from moving beyond the paradigm of single baseline and follow-up. In this section I discuss first some basic guidelines for using these results in practice, and then discuss other practical issues that might affect the choice of how many survey waves to carry out.

> 8 They construct profits as revenue less expenses. De Mel et al. (2009) show that this leads to considerably noisier profit measures than directly asking for a single profits number in their experiments.

> 9 These power calculations ignore intra-cluster correlation for simplicity, and because it is typically low for an outcome like profits.

20
**Table 5. Spearman Rank Correlations between Different Indexes of Corruption**

||PACI1|PACI2|PACI3|PACI4|TI-CPI|WB-CCI|TI-GCB|
|---|---|---|---|---|---|---|---|
|PACI1|1|||||||
|PACI2|0.938|1||||||
||(123)|||||||
|PACI3|0.940|0.886|1|||||
||(123)|(123)||||||
|PACI4|0.885|0.945|0.941|1||||
||(123)|(123)|(123)|||||
|TI-CPI|-0.779|-0.736|-0.751|-0.729|1|||
||(123)|(123)|(123)|(123)||||
|WB-CC|-0.768|-0.711|-0.729|-0.692|0.954|1||
||(123)|(123)|(123)|(123)|(123)|||
|TI-GCB|0.755|0.731|0.769|0.747|-0.800|-0.767|1|
||(56)|(56)|(56)|(56)|(56)|(56)||

_Notes_ : PACI: Public Administration Corruption Index, 1998–2012. The subscript indicates: 1: All cases, all administrations (the same values shown in Table 4; our preferred index).

2: All cases, with the exclusion of health and telecom administration.

3: Only “positive” and “ongoing” cases, all administrations.

4: Only “positive” and “ongoing” cases, with the exclusion of health and telecom administrations. TI-CPI: Transparency International Corruption Perceptions Index, 2005. WB-CCI: World Bank Corruption Control Index, 2005. TI-GCB: Percentage of persons who answered “yes” to the question: “In the past 12 months, have you or anyone living in your household paid a bribe in any form?” Transparency International Global Corruption Barometer, 2005. Number of observations are between parentheses. All the estimated coefficients are significant at less than 1 percent. Countries for which _PACI_ z = 0 and Pr zero_ cases zPACI_ > 0.015 and countries for which _PACI_ z > 10000 have been excluded.

While the ranking based on the PACI is similar to the rankings based on the two most popular perception-based indices, the scales appear to be very different (Figure 1). Panel A shows a scatter diagram of the PACI together with the WB-CCI for 2005, while Panel B shows the same indices, with the PACI log-transformed. The Pearson correlation of log(PACI) and WB-CCI is quite high (equal to -0.841).

18
# **Graph 2: The regional inequalities in China**

# **(Coastal vs. inland regions)**

<!-- Start of picture text --> 0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>year 1980 1983 1986 1989 1992 1995 1998<br>inter-regional intra-regional<br><!-- End of picture text --> Source : Luo (2003). The value of the Theil index is equal to zero if the distribution (here, the distribution of real GDP per capita) is uniform; it is equal to 1 if the distribution is extremely unequal.

# **2.2. Polarization related to the process of industrialization**

Many theoretical and empirical works have tried to retrace the evolution of the regional inequalities and the trajectories of urban concentration, which characterize the major stages of development.

The empirical studies by Williamson (1965), Wheaton and Shishido (1981), Hansen (1990), Mac Kellar and Vining (1995), Ades and Glaeser (1995), Moomaw and Shatter (1996), Henderson (2000), and Henderson, Shalizi and Venables (2001), focus on the degree of urban concentration in the process of development, and suggest that, beyond the threshold of income per capita of US$5000 (the so-called “rule of $5000” by Kellar and Vining (1995)), the urban concentration in a country tends to decelerate or reduce.<sup>9</sup> Catin and Van Huffel (2003) argue that three major stages of development can be used to characterize the developing countries: the 1<sup>st</sup> stage, pre-industrial, where the per capita income and the urban concentration are weak; the 2<sup>nd</sup> stage, where the industrial process leads to the development and the polarization of the labor intensive activities; and the 3<sup>rd</sup> stage, where, with the concentration of the technological industries and the diffusion of the labor intensive activities, the widening of the regional disparities slows down and then decreases when the GDP per capita reaches the threshold of $5000 (constant 1985).

> 9 The critical threshold of $5000 (1985), beyond which the urban de-concentration appears, corresponds to the GDP per capita of $2000 (1970), as suggested by Wheaton and Shishido (1981).

7