rafmacalaba/datause-encoder-data huggingface dataset:
than that in South Asia (0.34). But the share is identical in these two regions (76 percent) according to the Bingley and Cappellari (2019) estimates.

The disaggregated country level estimates of the intergenerational share are reported in Table 3 using the Bingley and Cappellari (2019) method. The estimated share is high in most of the countries (more than 60 percent in every case), and there are some countries where 80 percent or more of the sibling correlation can be attributed to the intergenerational transmission between father and the children. They are Philippines (0.81) and Vietnam (0.82) in South East Asia, Bangladesh (0.82) and Pakistan (0.81) in South Asia, and Benin (0.82), cameroon (0.85), the Republic of Congo (0.85), Madagascar (0.85), Mozambique (0.82), Senegal (0.80), and Togo (0.82) in Sub-Saharan Africa. Interestingly, none of the countries in the Latin America and Caribbean region have such a high share of the intergenerational transmission even though some of these countries have very high sibling correlation.

# (6.2) Evolution of Intergenerational Share

We next look at the evolution of the intergenerational share across the three birth cohorts in the six regions. Figure 4 presents the results based on the Bingley and Cappellari (2019) method. It is striking that in every region, the share of intergenerational transmission declined from the 1970s cohort to the 1990s cohort, even though in some cases the magnitude is negligible (for example, Latin America and Caribbean where the share was 74 percent in the 1970s and 73 percent in the 1990s) . This can be interpreted as a declining role of parents in shaping the educational opportunities of children over time. The evolution of the share over time oers some contrasting patterns when compared to the evidence on sibling correlation across cohorts discussed earlier. The share of the intergenerational transmission remained virtually unchanged in the Latin America and Caribbean despite the substantial decline in the sibling correlation we discussed above. In the Middle East and North Africa region, the intergenerational share declined substantially across the cohorts which stands in sharp contrast to the monotonically increasing magnitudes of sibling correlation.

The individual country level estimates of the intergenerational share across cohorts show a variety of mobility experiences. Although the share of intergenerational transmission declined in most of the cases, there are some countries, especially in the Sub-Saharan Africa, which experienced a higher share in the 1990s (out of the 13 countries with a higher share, 11 are 23
**<u>Table A.35. The district fixed-effect RDD-DD regressions of households’ income using districts with region-specific time trends</u>**

|||||Dependent variables<br>|<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program *Post-program period|-0.0211|0.1414|0.5139|-0.3346*|0.3496|0.6362*|0.9971**|
||(0.0836)|(0.4732)|(0.3495)|(0.1950)|(0.5840)|(0.3254)|(0.3931)|
|(Poverty rate – 50) *Post-program|0.0028|-0.0747*|-0.0494|0.0079|-0.0121|-0.0430|-0.0085|
|period|(0.0081)|(0.0408)|(0.0378)|(0.0230)|(0.0588)|(0.0311)|(0.0387)|
|Program * (Poverty rate – 50) *Post-|-0.0065|0.0329|0.0389|-0.0037|-0.0227|0.0343|0.0139|
|program period|(0.0086)|(0.0465)|(0.0398)|(0.0237)|(0.0632)|(0.0371)|(0.0435)|
|Age of household heads|0.0263***|0.0479***|0.0553***|0.1331***|-0.0460***|-0.0943***|-0.0600***|
||(0.0021)|(0.0133)|(0.0080)|(0.0127)|(0.0090)|(0.0100)|(0.0106)|
|Age squared of household heads|-0.0002***|-0.0007***|-0.0006***|-0.0013***|0.0007***|0.0012***|0.0007***|
||(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head (male=1,|0.0038|-0.0244|0.1982***|0.6801***|-0.4093***|-0.0864|-0.3757***|
|female=0)|(0.0181)|(0.0980)|(0.0625)|(0.0753)|(0.0505)|(0.0558)|(0.0669)|
|Ethnic minorities (yes=1, Kinh=0)|-0.6194***|-0.2434|-1.7851***|0.8629***|-0.5281***|0.6340***|-0.7120***|
||(0.0484)|(0.1838)|(0.1596)|(0.1221)|(0.1361)|(0.1185)|(0.1015)|
|Region-specific time trend|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.6160***|2.9543***|1.5280***|3.7686***|4.7509***|1.9300***|3.1246***|
||(0.0838)|(0.3548)|(0.2404)|(0.3276)|(0.3100)|(0.3053)|(0.2559)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.367|0.167|0.109|0.149|0.229|0.152|0.093|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

85
# 6. References

Antweiler, Werner and Daniel Trefler.<sup>2002. " Increasing Returns and All That: A View</sup> from Trade." American Economic Review.<sup>92(1):93-119</sup> Arellano, Manuel, and Stephen Bond.<sup>1991. "Some Tests of Specification for Panel Data:</sup> Montecarlo Evidence and an Application<sup>to Employment Equations." Review of</sup> Economic Studies 58(2): 277-297.

Arellano, Manuel, and Olympia Bover.<sup>1995. "Another Look at the Instrumental-</sup> Variable Estimation of Error Component<sup>Models." Journal of Econometrics 68(1): 29-52.</sup> Arellano, Manuel, and Stephen Bond. 1998.<sup>"Dynamic Panel Data Estimation Using</sup> DPD98 for Gauss a Guide for Users".

Auty, Richard M. 1998. Resource Abundance<sup>and Economic Development. Helsinki:</sup> UNU World Institute for Development Economics<sup>Research.</sup> Barro, Robert J. 1991. "Economic Growth<sup>in a Cross Section of Countries." Quarterly</sup> Journal of Economics 106: 407-444.

Barro, Robert J. 2001. "Human Capital:<sup>Growth". The American Economic Review:</sup> Papers and Proceedings. May 2001: 12-17.

Blundell, Richard and Bond, Stephen.<sup>1998 "Initial Conditions and Moment Restrictions</sup> in Dynamic Panel Data Model". Journal<sup>of Econometrics 87:115-143.</sup> Bond, Stephen R., Hoeffler, Anke. And Temple,<sup>Jonathan: 2001. "GMM Estimation of</sup> Empirical Growth Models". CEPR discussion<sup>paper series. N 3048.</sup> Bruno, Michael and William Easterly. 1998.<sup>"Inflation crises and long-run growth"</sup> Journal of Monetary Economics 41(1): 3-26.

Caselli, Francesco, Gerardo Esquivel, and<sup>Fernando Lefort. 1996. "Reopening the</sup> Convergence Debate: A New Look at Cross-Country<sup>Growth Empirics." Journal of</sup> Economic Growth 1(3): 363-389.

Cuddington, John T., Rodney Ludema and<sup>Shamila A. Jayasuriya (2001), "Prebisch-</sup> Singer Redux" mimeo, Georgetown University.

De Ferranti, David, Guillermo Perry, Daniel<sup>Lederman, and William Maloney. 2002.</sup> From Natural Resources to the Knowledge<sup>Economy: Trade and Job Quality. The World</sup> Bank: Washington, DC.

De Long, Bradford, and Lawrence Summers.<sup>1992. "Equipment Investment and</sup> Economic Growth." Quarterly Journal of Economics<sup>106(2): 455-502.</sup> 16
Table A.2. Characterizing Documented vs. Undocumented Migrants

||PEP|Non-PEP<br>M|ean Diff. (P-value)|
|---|---|---|---|
||(1)|(2)|(3)|
|Female [=1]|0.496<br>|0.499<br>|0.135|
||(0.500)|(0.500)||
|Age (Years)|26.986<br>|23.988<br>|0.000|
||(14.141)|(15.121)||
|Primary or Less [=1]|0.294<br>|0.423<br>|0.000|
||(0.456)|(0.494)||
|Secondary [=1]|0.592|0.506|0.000|
||(0.492)|(0.500)||
|University or more [=1]|0.115|0.071|0.000|
||(0.318)|(0.257)||
|Married or cohabitating [=1]|0.377|0.294|0.000|
||(0.485)|(0.456)||
|Pregnant [=1]|0.037|0.038|0.196|
||(0.188)|(0.191)||
|Sector: Services and Sales|0.815|0.822|0.002|
||(0.388)|(0.382)||
|Sector: Manufacturing|0.060|0.054|0.000|
||(0.237)|(0.226)||
|Sector: Extraction and Transp.|0.078|0.077|0.226|
||(0.269)|(0.266)||
|Sector: Finance and Adm.|0.009<br>|0.009<br>|0.417|
||(0.093)|(0.095)||
|Sector: Other|0.038<br>|0.038<br>|0.758|
||(0.190)|(0.191)||
|Occupation: Formally Employed (% of Pop.)|0.008<br>|0.007<br>|0.582|
||(0.087)|(0.086)||
|Occupation: Informally Employed (% of Pop.)|0.264<br>|0.188<br>|0.000|
||(0.441)|(0.391)||
|Occupation: Self-Employed (% of Pop.)|0.225|0.207|0.000|
||(0.418)|(0.405)||
|Occupation: Unemployed (% of Pop.)|0.186|0.174|0.000|
||(0.389)|(0.379)||
|Occupation: Student (% of Pop.)|0.071|0.088|0.000|
||(0.257)|(0.283)||
|Occupation: Housework (% of Pop.)|0.093|0.104|0.000|
||(0.291)|(0.305)||
|Family size|3.507|3.517|0.134|
||(2.042)|(2.092)||
|Family in Colombia [=1]|0.413|0.473|0.000|
||(0.492)|(0.499)||
|Family in Venezuela [=1]|0.666|0.652|0.000|
||(0.472)|(0.476)||
|Migration Intent: Stay in Colombia|0.889<br>|0.903<br>|0.000|
||(0.315)|(0.296)||
|Migration Intent: Move to another country|0.019<br>|0.021<br>|0.000|
||(0.136)|(0.143)||
|Migration Intent: Return to Venezuela|0.092<br>|0.076<br>|0.000|
||(0.290)|(0.265)||
|Observations|281,307|161,310|442,617|

_Source:_ RAMV census.

57
Another question is whether schemes should be targeted on specific sectors or be rather broad. Targeting specific sectors or geographic areas might be in the interest of policy makers that want to focus the fund on alleviating financing constraints of specific disadvantaged groups and thus maximize the additionality of such a scheme. Too specific targeting, on the other hand, might increase the bureaucratic costs of running such a fund – e.g. verification costs – and again limit take-up. Further, such restrictions might distort the lending market and lead to net weigh losses (Zia, 2008).

This paper does not test whether these different characteristics discussed so far are related to the performance of credit guarantee schemes, as our aggregate cross-sectional data do not allow us to do so. Rather, using survey results for 46 developed and developing countries, we will show the variation in 76 PCG funds around the world. We will provide descriptive statistics and correlation analysis to see how these different mechanisms and features vary across countries and are linked with each other in a systematic way. We will focus on two areas – the role of government in partial credit schemes and the role of different mechanisms to reduce risks. Finally, we will provide some indication of how loan losses vary across schemes with different characteristics.

# **3. The Sample and Some General Characteristics**

While sections 4 and 5 focus on two specific dimensions along which partial credit guarantee schemes differ across countries, this section provides some general information about the survey on which our analysis is based, and the sample of PCG funds on which we have information.

Our sample includes 76 PCG funds across 46 countries, both developing and developed economies. Specifically, we have information on PCG funds in 20 highincome, 25 middle-income, and 1 low-income country (India). In terms of regional distribution among developing countries, we have information on six schemes in Asia, 24 in Latin America, 11 in transition economies and one in Africa (Egypt).<sup>4</sup> In general, we include all national PCG funds if a country has more than one fund; this sometimes includes both publicly and privately operated funds (e.g. Argentina), in other cases – such

> 4 See Appendix 3 for the complete list of countries and funds.

7
17 <!-- Start of picture text --> Figure 4: World Bank and Dooley Combined with Trade Misinvoicing (Annual Flows)<br><!-- End of picture text --> <!-- Start of picture text --> BSllhone of USt<br>160 T<br>100<br>X\<br>50<br>° --0 - / ---- ------<br>71 72 73 74 78- -1_ 77 78 79 80 81 82 83 84 86 8e 87 88 89 90 91<br>World Bank Residual + Trade Misinvoicin - Dooley + Trade Misinvoicing<br><!-- End of picture text --> Over a long period (in this case, 20 years) stock figures of flight capital are more interesting that annual flow figures. According to the World Bank Residual method, at the end of 1991, the accumulated stock of flight capital was $500 billion, or about 44 percent of their stock of external debt. The Dooley measure shows capitalflight stock of about $140 billion (12 percent of debt stock). The difference between the two (about $360 billion) is equal to the imputed stock of reported assets held abroad.

**Figure 5: Stocks of Flight Capital (with and without interest compounding)**

<!-- Start of picture text --> 600<br>4C0<br>300<br>200<br>100<br>0 .t : * - . -_. . . . .<br>71 72 73 -74A, 75 76 77 78 79 90 8 82 83 84 85 86 87 88 89 90i 6<br>-100<br>- - - -World Bank Dooley + Trade World Bank Dooley + Trade<br>Residual + Misinvoicing Residual + Misinvoicing (no<br>Trade (with interes:) Trade interest)<br>Misinvoicing Misinvoicing (no<br>(with interest) interest)<br><!-- End of picture text -->
**-** **<u>Table 6: Treatment regressions estimated program effects on area cultivated with summer crops</u>**

||<br>**Overall effect**|<br>**Front distance**|<br>**Heterogeneity by**<br>**Farm size**|**Formality**|
|---|---|---|---|---|
|PSG|0.1748***<br>(0.0626)||||
|PSG * < 50 km to front||0.7429***<br>(0.1107)|||
|PSG * 50 - 100 km to front||0.0905<br>(0.1013)|||
|PSG * > 100 km to front||-0.1166<br>(0.0846)|||
|PSG * farm size < 20 ha|||0.0809***<br>(0.0261)||
|PSG * farm size 20 - 50ha|||0.1656<br>(0.1904)||
|PSG * farm size > 50ha|||0.6773*<br>(0.3982)||
|PSG * Individual||||0.0924**<br>(0.0452)|
|PSG * FOP||||0.3918***<br>(0.1357)|
|PSG * Legal entity||||0.3175*<br>(0.1876)|
|Winter crop area|-0.8566***|-0.8566***|-0.8567***|<br>-0.8567***|
||(0.0123)|(0.0123)|(0.0123)|(0.0123)|
|No. of obs (farms)|<br>215,015|<br>215,015|<br>215,015|<br>215,015|
|R-squared|0.9554|0.9554|0.9554|0.9554|
|Mean of dep. var.|10.8791|10.8791|10.8791|10.8791|
|SDofdep. var.|17.5184|17.5184|17.5184|17.5184|

_Note:_ Results are based on weights derived using entropy matching. Dependent area is the area cultivated with summer crops in panel A and the area cultivated with winter crops in panel B. For summer crop regressions in panel A, the level and square of weather variables, in particular GDD (>5°C), total rainfall, and the number of zero precipitation days during the planting and growing seasons, winter crop area, and a constant are included but not reported. In panel B, the cut-off for GDD is >0°C and all weather variables (and their squares) are for planting, dormancy, and growing seasons. Robust standard errors clustered at rayon level in parentheses: * p<0.10, ** p<0.05, *** p<0.010.

22
<!-- Start of picture text --> 0.250<br>0.225<br>0.200<br>0.175<br>0.150<br>0.125<br>0.100<br>0.075<br>0.050<br>1982 1985 1988 1991 1994 1997 2000 2003 2006 2009<br>%GDP<br><!-- End of picture text --> Figure 7: Govenment consumption spending plus transfers as %GDP <!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>% of total spending<br><!-- End of picture text --> Figure 8: Investment expenditures as % of total spending
45 _Now you have got terms of trade improving for developlng countries, not as much as they did apparently Li the 1965 to 1986 perlod, but, it still looks pretty good._

**_Hlgh_** _and_ **_Low_** _Grovth Scenarios..._

_MR. VERSIWJYSEN: My lnterventlon Is more out of parochial self-Interest for the WDR (World Development Report 1988). To the extent we make projections [which are not forecasts] in the WDR, I could see a base case emerging where a recession doesn't necessarily take place, but world economy gets locked Into a low growth scenario In the near-term, which Is then extrapolated in the longer term through the end of the century also in low growth. It is difficult to see how high growth can result after a period of low or no growth._ _This may sound paradoxical, but then if we have a low case and a high case deviation from the central scenario, I could see the high case even more paradoxical, subject zo a number of qualifications; that the high case would in fact presuppose in the short-run a recession, but a recession which can then only lead to a high case without the qualifications of technology and possible other systemic shocks. But where will the main systemic shock come from? Out of a recession come two things: that the individual OECD countries would not try each to grow out of the recession on its own but in a concerted fashion, and that, somehow, greater flnancial disclpllne, potentially through greater aggravation In the financial m&Jkets, particularly the foreign exchange markets, would put checks and balances on the likelihood that the dollar would bottom out in the very near future and then start escalating again within the next 18 months._ _So in a sense the shock that comes out of the recession should be a systemic shock which also brings about, galvanizes the wisdom and the ability of the various governments to actually cooperate with one another._ _MR. LARSEN: Obviously, the question of the linkage between growth in the developing countries and in the industrial countries is an extremely crucial aspect of this whole discussion._ _More on the_ _More on the I wonder if it would not be useful to begin perhaps to_ **_linkage......._** _explore the possibility that the link may be less strong than we believed in the past. And, indeed, as I read the numbers, I think it will be very difficult to defend the hypothesis that high growth in the industrial countries is a necessary and sufficient condition for high growth in the developing countries._ _Indeed, I think high growth in the industrial countries is neither a necessary nor a sufficient condition. I .hink what is interesting to examine is the kind of trade performanc che develop-_ WP/SOLTHO/SF:Ig
# **I. Introduction**

Micro-firms account for roughly 50% of employment in Latin America and substantially more in Africa and Southeast Asia. Raising their productivity therefore ranks high as a development goal and eliminating impediments to their dynamism as a central concern of policy. Using a very detailed micro-firm data set with a panel dimension from Mexico, this paper examines how micro firm performance is affected by several dimensions of their business environment: access to formal and informal credit, training programs, business associations or guilds, and government taxation and services. Following the convention of the literature on evaluation in quasi-experimental contexts, we conceive of engaging with these institutions as _treatments_ and attempt to measure their impact.

In our sample the share of treated firms is low: less than 10% of micro-firms have received credit or training services and less than 17% participate in industry or trade associations. This is often seen as evidence of supply bottlenecks that limit access to financing, human capital, and information. Releasing those constraints would permit firms to approach the steady state size dictated by their intrinsic entrepreneurial ability.<sup>1</sup> However, it may also be that for many firms, the steady state size is very small and thus there is, in fact, little demand for these treatments. For example, a sizable literature dating from Harris and Todaro (1970) argues that most informal micro-firms are run by individuals who did not enter the sector due to their higher entrepreneurial ability or other cost advantages, but rather are essentially queuing for entry into salaried jobs and are unlikely to be thinking of future growth. In sum, while in one approach increasing the supply of credit, training and information services would lead to improved business performance, in the other it would amount to pushing on a string and have little impact.

Empirically, estimating the magnitude of any treatment impact is not trivial. Both views imply self selection into the treatments considered which potentially imparts serious bias to naïve ordinary least squares (OLS) estimates. As an example, applying for

> 1 In Lucas’(1978) model, for instance, factors such as location and entrepreneurial ability determine firmspecific costs structures and optimal firm sizes. Newly created firms would tend to converge to their steady state sizes, provided that they are not supply-constrained in their access to factor or goods markets.

2
# **Table 2: Descriptive Statistics**

## **Descriptive Statistics Ben Ali Firms vs Other firms – 2010**

<u>Only firms with Y>0 and L>0</u>

||||**_Descriptiv_**<br>|**_e Statistic_**<br>|**_s_**<br>||**_Diff_**<br>|**_erentials_**<br>|
|---|---|---|---|---|---|---|---|---|
||<br>_(To_|**_Other Firm_**<br>_tal N=_565|**_s_**<br>65_)_|**_B_**<br> _( _|**_en Ali Firm_**<br>_Total N=15_|**_s_**<br>_5)_|Ben<br>(averag|Ali - Other<br>e) (average)|
||||||||**Deme**|**aned**|
||**N**|**Mean**|**St. Dev.**|**N**|**Mean**|**St. Dev.**|**_No_**|**_5-digit_**|
|**_Performance_**<sup>_x_</sup>|||||||||
|logL|56565|1.095|1.457|155|2.777|1.929|1.682***|1.182***|
|logY|56565|18.849|1.914|155|21.568|2.372|2.718***|1.678***|
|Profits Z-score|56445|0.008|1.124|155|1.760|14.148|1.752*|1.681*|
|Profitable<sup>#</sup>|56565|0.768|0.422|155|0.574|0.496|-0.194***|-0.062|
|Market Share<sup>i</sup>|56565|0.009|0.055|155|0.063|0.159|0.055***|0.038***|
|**_Firm Characteristics_**|||||||||
|Age|56558|13.400|10.325|155|13.019|12.945|-0.381|-0.178|
|Offshore|56565|0.083|0.275|155|0.071|0.258|-0.012|-0.024|
|Foreign|56565|0.040|0.196|155|0.039|0.194|-0.001|-0.007|
|**_Regulation_**|||||||||
|Highly Regulated (de Jure)<sup>^</sup>|29090|0.100|0.300|90|0.300|0.461|0.200*||
|HighlyRegulated(de Facto)<sup>+</sup>|29090|0.015|0.124|90|0.122|0.329|0.107*||

*, **, and *** in the last two columns indicate that the differential between Ben Ali and non-Ben Ali firms is significant at the 10%, 5% and 1% level respectively. The reported differentials are the coefficients _βBA_ obtained from regressions of the form: _Yijt= βBA*Ben Ali+ uj +_ ε _it_ where _Ben Ali_ is a dummy taking the value 1 for connected firms and 0 otherwise and _uj_ is a vector of 5-digit sector dummies (which are not included when no demeaning is taking place). Standard errors are clustered at the 5-digit sector level.

> _+_ The profit margin is defined as the ratio of profits to output. Values greater than 1 or smaller than -1 are excluded.

> i _Market share_ is measured at the 5 digit level.

> # _Profitable_ is a dummy variable taking the value 1 if a firm reports positive profits and 0 otherwise.

> _^ Highly Regulated (de Jure)_ is a dummy variable indicating whether the particular 5 digit sub-sector the firm operates in is subject to both authorization requirements and FDI restrictions at time t.

> + _Highly Regulated (de Facto)_ is  dummy variable taking the value 1 if the 5 digit sector in which the firm is operating is subject to an authorization which is de facto difficult to obtain according to one of Tunisia’s leading lawfirms, and 0 otherwise.

29