rafmacalaba/datause-encoder-data huggingface dataset:
ratio of urban to rural national poverty lines to derive the corresponding rural poverty line in local currency units (Chen and Ravallion 2008). For India the ICP included rural areas, but they were underrepresented. We derived urban and rural poverty lines consistent with both the urbanrural differential in the national poverty lines and the relevant features of the design of the ICP samples for India; further details can be found in Ravallion (2008b). No adjustments were made for Brazil.

Given the changes in data and methodology between ICP rounds, PPPs for different benchmark years cannot be easily compared, and cannot be expected to be consistent with national data sources (Dalgaard and Sørensen, 2002; World Bank, 2008b). We follow common practice in letting the national data override the ICP data for inter-temporal comparisons; this is the most reasonable position to take given the changes in methodology between different ICP rounds (World Bank, 2008b). Thus the PPP conversion is only done once for each country, and all estimates are revised back in time consistently with the data for that country.

29
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25
19 Table 1. Growth Regressions with Diversification and Quality Indices

||**Gro**|<br>**wth Regre**|**ssion, GL**|**S fixed effe**|**cts**||||
|---|---|---|---|---|---|---|---|---|
|||**AllCo**|**untries**||**East**|**Asia**|**Sout **|**h Asia**|
|**VARIABLES**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Lagged GDP|-5.363***|-6.027***|-5.898***|-5.897***|-5.604***|-4.886***|-3.882|-7.313***|
||(0.439)|(0.464)|(0.454)|(0.471)|(1.697)|(1.551)|(3.488)|(2.173)|
|Education|0.124***|0.139***|0.146***|0.137***|0.236**|0.217**|0.203|0.208*|
||(0.023)|(0.023)|(0.023)|(0.023)|(0.101)|(0.100)|(0.125)|(0.112)|
|Investment|3.599***|3.523***|3.513***|3.374***|4.520***|4.324***|4.046**|4.436***|
||(0.433)|(0.429)|(0.429)|(0.436)|(1.428)|(1.408)|(1.626)|(1.401)|
|Population growth|-0.053|-0.194|-0.238|-0.118|0.869|0.670|-1.346|-1.759|
||(0.229)|(0.227)|(0.228)|(0.227)|(0.738)|(0.736)|(2.749)|(2.248)|
|Diversification Index|-0.608**||||||||
||(0.279)||||||||
|Quality upgrade index||8.761***|||13.660*||||
|||(2.124)|||(6.878)||||
|Quality Index, Agriculture|||9.687***|||21.036**|-8.572||
||||(2.348)|||(8.361)|(15.365)||
|Quality Index, Manufacture||||7.646***||||48.638**|
|||||(2.485)||||(18.846)|
|Constant|35.550***|31.748***|29.948***|31.842***|16.662*|6.508|22.728|0.110|
||(3.720)|(3.534)|(3.614)|(3.570)|(9.267)|(9.840)|(19.191)|(18.537)|
|_Observations_|790|789|789|789|75|75|46|46|
|_R-squared_|0.234|0.250|0.250|0.241|0.291|0.317|0.295|0.402|
|_Number of countrycode_|113|113|113|113|10|10|6|6|

# _Source_ : Author calculations.

_Note_ : Both country and time fixed effects are used in the estimations. For Asian country groups, the estimated coefficients on the diversification index are not statistically significant. Standard errors in parentheses. *** denotes p < 0.01, ** denotes p < 0.05, * denotes p < 0.1.
<u>Table 5: Estimation results using alternative measures of export diversification. >$100k >$lm Manuf. Only Lambda — HH Index</u> Entry Cost -0.254*** -0.314*** -0.142** -0.017** — 0.052** [0.086] [0.094] [0.056] [0.007] [0.022] Export Cost -0.506*** -0.644*** = -0.327*** -0.039*** 0.161*** [0.107] [0.088] [0.095] [0.012] [0.034] Distance -0.537*** —-0.569*** -0.456*** -0.050*** 0.165*** [0.127] [0.141] [0.089] [0.012] [0.034] Tariff (EU) -1.413 -0.518 -0.406 0.309 -0.678 [2.355] [2.850] [1.947] [0.213] [0.736] Tariff(Own) 0.424 0.462 0.335 0.075 -0.092 [0.727] [0.845] [0.709] [0.056] [0.174] GDP 0.604*** 0.672*** 0.461*** 0.058*** -0.166*** [0.039] [0.036] [0.029] [0.004] [0.012] GDPPC 0.058 -0.004 0.094 0.014* -0.021 [0.075] [0.071] [0.058] [0.008] [0.023] Constant -0.102 -0.125 1.188 0.021 -0.512 [1.602] [1.612] [1.305] [0.149] [0.438] Observations 11328 11328 8496 7659 7642

1. Estimation in columns 4-5 is by OLS with dependent variables lambda_cn2 and hh_ index_cn2. Estimation in columns 1-3 is by Poisson, with dependent variable lines_cn2. Columns 1 and 2 only count export flows greater than $100k and $1m respectively, while column 3 excludes agricultural products (HS chapters 1-24). All models include fixed effects by 2-digit sector.

2. Only the independent variables are in logarithms in columns 1-3, while both the dependent and independent variables are transformed in columns 4-5.

3. Robust standard errors, adjusted for clustering by exporter, are in square brackets. Statistical significance is indicated by * (10%), ** (5%), and *** (1%).

37
including wage labor in agriculture. The logit coefficients are all statistically significant at 1 percent level. The logit results imply that the extra labor endowment in a village due to imposition of land restrictions is absorbed mostly in self employment in agriculture compared with all other activities.

The multinomial logit coefficients in Table 3 do not reflect the way probability of participation in an activity responds to a change in land restrictions. To measure the magnitudes of these responses, we estimate the marginal effects (evaluated at the mean) which are reported in the upper part of each panel in Table 3. The estimates of the marginal effects in the first panel show that the probability of participation in all types of non-farm activities decreases with an increase in the percentage of land under restrictions. Land restrictions have opposite effects on the probability of participation in self and wage employment in agriculture. The marginal effect is precisely estimated for different types of wage employment.

The main conclusion that emerges from Table 3 is that agriculture absorbs a disproportionate share of the increased labor supply resulting from land restrictions. The estimates of coefficients of land under restriction could however be biased if unobserved land and labor productivity heterogeneity is correlated with proportion of land under restrictions. In the following subsection, we tackle this omitted variable bias using a control function approach.

# **5.2 Employment Choices and Land Restrictions: Control Function Results**

As noted earlier in the empirical strategy section (3), a control function is needed to be included to correct for the possible omitted variable biases in the probit and multinomial logit estimates. The control function term is defined from the residuals of the first stage regression where the endogenous variable (proportion of land under restrictions) is regressed on the instrument and rest of the regressors in the model. Panel A of Table 4 shows the control function estimates of the effects of land restrictions on employment probabilities, and panel B reports the first stage regressions and diagnostics for the strength of the instrument. According to the first stage result in panel B, the instrument has the expected positive sign and is statistically significant at the 1 percent level; higher historical malaria in 1935 led to higher incidence of land restrictions. The Kleibergen-Paap F statistics is 24.50 which is more than twice the StockYogo critical value of 9.08 for 10 percent maximal relative IV bias. This confirms that the instrument has excellent strength to identify the effects of land restrictions. The control function term is defined from the 19
# **1. Introduction**

The formation of human capital requires a long time, starting from the prenatal stage and extending to an individual’s entry into the labor market. The impact of an early-stage investment can be potentially large if investments at different stages are complementary and/or the outcome of an early stage is an important input for the following stage of production. Rapidly accumulating literature shows that investments in prenatal and early childhood stages have long-term impacts on schooling and labor market outcomes (Alderman et al. 2001; Alderman, Hoddinott, and Kinsey 2006; Maluccio et al. 2009; Yamauchi 2008; Cunha et al. 2006), though some catching up is certainly possible later on (Mani 2008). The sequential nature of human capital formation poses the question of the extent to which human capital investments have lasting effects on human life.

In this paper, we use a nationwide intervention in elementary schools implemented during the period 2000 to 2006 in the Philippines, named the Third Elementary Education Project (TEEP), to evaluate the long-term impact of improved school quality on individual outcomes in high school and college, migration decisions, and labor market earnings. TEEP was implemented in 23 poor provinces (education divisions)<sup>4</sup> identified in the Social Reform Agenda (Ramos 1995; Philippines, Congress 1998). It has several components: school building construction and renovation, textbook distribution, teacher training, school-based management, and some other modules to improve school facilities.<sup>5</sup> Yamauchi and Liu (2011) demonstrate that the program significantly increased national achievement test scores—by a magnitude of 15 points over the six-year exposure.<sup>6</sup> By improving the quality of teachers and relaxing resource constraints at school (for example, by providing more classrooms and textbooks), TEEP was expected to increase the quality of human capital formed as well as the efficiency with which it was formed. These improvements were expected to augment the returns to schooling in the labor market and ultimately increase schooling investments. Such a program may also increase the general ability of students, which can directly increase current labor market wages and may decrease educational attainment due to increased

> 4Officially, the division is an administrative unit under the Department of Education. In many cases, the education division coincides with the province, but there are more than one division in some provinces. For example, the province of Leyte has Leyte division and the Tacloban City division (Leyte’s capital city).

> 5See, for example, Case and Deaton (1999) and Hoxby (2000) for the effects of class size on educational outcomes.

> 6See Khattri, Ling, and Jha (2010) for the impact of school-based management on students’ learning outcomes.

2
Tables 4 and 5 report the two components of the revenue from financial repression: interest-rate differentials and changes in exchange rates (inclusive of the "dollar revaluation effect"). In the majority of countries the time-variation of exchange-rate changes account for most of time-variation of the gains from financial repression. This is confirmed by the decomposition of the variance (over the sample for each country) of financial repression: on average the variance of the exchange-rate component is three times the variance of the interest-differential component.<sup>2 8</sup> We do not report separate data on the dollar revaluation effect since it is small (less than one-half of one percent) for most years and for all countries if averaged over time. This is due to the fact that in the majority of countries almost all foreign commercial debt is denominated in dollars.<sup>29</sup> The evidence in tables 4 and 5 is suggestive of the way financial repression works in practice. Domestic interest-rate ceilings are maintained with high rates of currency depreciation and high domestic inflation. With free capital markets, this source of revenue would be limited, since expectations of exchange-rate changes would be reflected in the differential between domestic and foreign interest rates. Table 6 reports information about the geographic and time distribution of financial-repression tax revenue. The data in the table should be interpreted cautiously, since the non-uniform time coverage of the countries in the sample makes regional averages contain different time periods in different regions, while timeaverages contain different countries in different subperiods. Yet table 6 seems to reveal a number of suggestive facts, worthy of closer inspection in future research: financial repression appears to be more relevant in African countries (including North Africa), and least important in Asian countries. It is also much more evident in the recent years (1979-1987), than in the early part of the sample. This last phenomenon is associated with the growth of fiscal imbalances among LDCs in the 1980s.

In section 2.1 we showed that financial repression would not be used by an optimizing government, if all the assumptions of the

> 2 8 The results of **this** variance decomposition are available from us on request. 29 We verified this by looking at data on the currency of denomination of external debt from the World Bank DRS. Detailed data on the dollar revaluation effect are available on request.

16
ii) the average radiance ( _R_ ) of the pixels, measured in _DN_ , within the chosen geographic space; and iii) the “sum of lights” ( _SoL_ ) which equals the sum of the digital numbers of all illuminated pixels.<sup>7</sup> The sum of lights, _SoL_ , also equals the area of lights, _AoL_ , multiplied by the average radiance, _R_ .<sup>8</sup> Figure 2 provides an illustration for an area composed of 25 square pixels. The numbers within the pixels are digital numbers representing observed radiance. In this figure, _AoL_ includes 6 pixels, _R_ = 23.3, and _SoL_ =140. The properties of these three variables is presented in more detail in Section V below.

**Figure 2:  An Example of Night-time Lights Data**

<!-- Start of picture text --> 10<br>10 30<br>10 30 50<br><!-- End of picture text -->

**<u>Table 1:  Selected Night-Lights Outcomes, Period Averages, 1992-2011</u>**

||||Average Area|of Lights 1/|Aver|age Radiance|2/|Sum of L|ights 1/|
|---|---|---|---|---|---|---|---|---|---|
||Land Area<br>(Km2)|Percent<br>Illum.|Growth|St. Dev.<br>Growth|Level|Growth|St. Dev.<br>Growth|Growth|St. Dev.<br>Growth|
|Azerbaijan|82,658|8.2|-1.4|20.0|8.8|0.0|9.3|-1.4|13.2|
|China|9,388,211|7.2|4.8|10.1|11.4|0.6|6.5|5.4|5.1|
|Egypt|995,450|6.4|1.9|3.5|20.8|1.4|4.0|3.4|3.2|
|France|547,557|59.4|1.3|7.4|9.4|-0.4|4.5|0.8|8.0|
|Indonesia|1,811,570|5.6|2.7|21.1|11.3|-0.6|7.2|2.2|15.0|
|Japan|364,550|56.5|-0.5|8.8|15.9|-0.4|5.6|-0.8|4.6|
|Kyrgyzstan|191,800|4.8|-0.8|21.4|7.9|-1.2|6.4|-2.0|18.6|
|Luxembourg|2,590|99.5|0.1|0.6|12.3|-0.1|14.4|0.0|14.6|
|Malaysia|328,550|13.4|4.1|23.6|15.2|0.8|10.0|4.9|14.5|
|Rep. Korea|97,230|80.0|0.4|6.0|14.9|0.9|3.7|1.3|7.1|
|Tajikistan|139,960|6.0|-4.2|23.6|8.0|-1.3|5.4|-5.4|21.9|
|Ukraine|579,320|24.0|-5.2|18.7|6.8|0.1|7.6|-5.1|16.7|
|Vietnam|310,070|10.2|10.7|17.6|10.5|0.9|6.4|11.7|12.7|
|Yemen|527,970|2.2|5.2|17.7|11.3|0.1|6.5|5.3|12.8|

1. Number of illuminated pixels with _DN_ >=6 within the borders of a country. 2. Average _DN_ within all illuminated pixels. _Sources:_ NGDC v4, World Development Indicators for land area, and author’s calculations.

Long-run patterns in _AoL_ and _R_ are consistent with some country circumstances. For example, as shown in Table 1, a country with a rapidly expanding _AoL_ is more likely to be a country with a high urban population growth rate. China, Indonesia, Malaysia, Vietnam, and Yemen are examples. A country with shrinking _AoL_ could be in the early, painful stages of transition from a planned economy to a market economy. Azerbaijan, Tajikistan and Ukraine are examples. Countries with growing average radiance, _R_ ,

> 7 There are additional possibilities.  For example, one could calculate the illuminated area as a share of total area. Researchers starting with raw data can generate additional variables.  For example, one could calculate the standard deviation for the average annual radiance of a country or sub-region.  One could also record the average number of observations per _AoL_ per year or the fraction of a year that an _AoL_ is illuminated.  Measures such as these would be useful in assessing the accuracy of the data.

> 8 In essence, _SoL_ is the volume of light.
**Table 5: General Government Fiscal Trends (% of GDP)**

||85/86-<br>89/90<br>Avg.|8th<br>Plan<br>Avg.|9th<br>Plan<br>Avg.|10th<br>Plan<br>02/03-<br>05/06<br>Avg.|02/03|03/04|04/05|05/06<sup>1/</sup>|
|---|---|---|---|---|---|---|---|---|
|_Revenues_|19.4|17.9|17.0|18.6|17.8|18.1|19.1|19.6|
|_Current expenditure_<sup>2/</sup>|22.0|21.5|22.9|23.7|24.6|24.0|23.5|22.9|
|Social services|5.4|5.0|5.5|5.3|5.3|5.1|5.4|5.3|
|Economic services|6.5|5.8|5.5|6.0|6.3|6.4|5.7|5.6|
|General services|9.5|10.3|11.6|12.1|12.6|12.1|12.0|11.7|
|_Capital expenditure_ <sup>3/</sup>|6.6|3.7|3.7|3.4|2.9|3.1|3.3|4.1|
|_Gross fiscal deficit_|9.2|7.2|9.7|8.5|9.8|9.1|7.6|7.4|
|**Memo**|||||||||
|Primary deficit|5.4|2.1|4.0|2.2|3.2|2.7|1.4|1.3|
|Revenue deficit|2.6|3.6|6.0|5.1|6.8|6.0|4.4|3.3|
|Interest|3.8|5.1|5.7|6.3|6.5|6.4|6.2|6.1|
|Interest / Revenue %|19.4|28.5|33.6|34.0|36.8|35.6|32.5|31.3|
|(Irrigation+power+transport)/GDP|4.7|3.5|3.4|4.1|3.8|4.9|4.1|3.6|
|(Interest+ admin.+ pensions)/GDP|6.3|8.1|9.2|9.9|10.2|9.9|9.8|9.8|
|(Interest+admin.+pensions)/Revenue<br>Notes:|32.6|45.1|54.2|53.5|57.6|54.9|51.5|50.0|

1/ Revised estimates for the center and budget estimates for the states 2/ Refers to Revenue expenditure in the budget 3/ Refers to Capital outlay and net loans and advances from the center to the states Sources: GOI budget documents, Handbook of Statistics on Indian Economy (RBI) Today, India’s tax system exhibits the following salient features:<sup>30</sup> (i) a high reliance on indirect taxes (notwithstanding the large decline); (ii) low average effective tax rates;<sup>31</sup> and (iii) high marginal tax rates. An encouraging feature of the 1990s is that AETRs on labor and capital have been gradually increasing even though statutory tax rates have been lowered. In other words, tax administration has improved and compliance has gone up. Nevertheless, there is still considerable scope for raising AETRs, which are low because tax productivity is low. For example, calculations for 2001 reported in Poirson (2006, Table 3a) show that corporate income tax (CIT) productivity measured as the ratio of the effective to the statutory CIT rate was less than 10 percent compared to an OECD average of 30 percent!

> 30 The discussion here is chiefly a summary of main results from Poirson (2006).

> 31 The AETR is actual tax collections divided by the potential tax base; or, equivalently, the statutory tax rate times the actual tax base divided by the potential tax base.  If the ratio of the actual tax base to the potential tax base is very small, then raising this substantially could increase AETRs even if statutory (and hence marginal) tax rates are decreased, which typifies the Indian case.

18