rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1.4. Trends of Different Definitions of Household Income and Consumption, RLMS 1994-2015**

<!-- Start of picture text --> Panel A: Real values Panel B: Logarithm<br>Total HH income per capita<br>HH Labor income per adult<br>HH Disposable income per capita<br>HH Calculated income per capita<br>IND Labor income<br>HH Pensions per capita<br>HH Government subsidies per capita<br>199419951996 1998 2000200120022003200420052006200720082009201020112012201320142015 199419951996 1998 2000200120022003200420052006200720082009201020112012201320142015<br>10<br>20000<br>15000<br>8<br>10000<br>Log of income<br>Income (rubles)<br>6<br>5000<br>0 4<br><!-- End of picture text -->

**Note:** We use the repeated cross sections for each year.  The estimation sample is restricted to individuals who are 18 years old and older. Household labor income per adult is calculated using average household labor earnings per month. Disposable household income includes average household labor earnings, net private transfers, public transfers and financial income (see Gorodnichenko _et al._ (2010) for detailed description). Calculated household income is based on the sum of average household labor earnings, received private transfers, public transfers and financial income (see Lukiyanova and Oshchepkov (2012) for detailed description). Individual labor income includes money and payment in kind received last month from primary job and secondary job + money received last month from regular individual economic activities (see Gorodnichenko _et al._ (2010) for detailed description). Government subsidies include stipends, unemployment benefits, fuel and state child subsidies. All income measures are converted to a monthly base. To keep the income variable consistent over time, we exclude income categories that became available after 1994. All numbers are deflated with December to December regional CPIs and weighted with population weights.

63
# **3. Data and Stylized Facts**

This paper uses data from the International Monetary Fund–International Financial Statistics (IMF-IFS) database. Lack of data forced the exclusion of Algeria, the Comoros, Lebanon, Libya, Oman, Qatar, Somalia, and the United Arab Emirates. Many data points were also missing for other countries, including Djibouti, Morocco, Saudi Arabia, the Syrian Arab Republic, and Tunisia.

The countries’ foreign assets and liabilities grew rapidly over the past four decades, increasing from about 20 percent of GDP in 1970 to 205 percent in 2016, as is shown in figure 1. NFA rose from about 20 percent of GDP in 1970 to about 90 percent in 1994 before falling to 40 percent in 2015 (figure 2).

**Figure 1 Global ratio of total assets and liabilities to GDP, 1970–2015**

**Figure 2 Global ratio of net foreign assets to GDP, 1970–2015**

<!-- Start of picture text --> 2.5 0<br>2.0<br>‐20<br>1.5<br>‐40<br>1.0<br>0.5 ‐60<br>0.0 ‐80<br>‐100<br>Total assets/GDP Total liabilities/GDP<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015<br>Percent of GDP<br>percent of GDP<br>Net foreign assets as<br>1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015<br><!-- End of picture text --> _Source_ : International Monetary Fund– International Financial Statistics (IMF-IFS) database.

_Source_ : International Monetary Fund– International Financial Statistics (IMF-IFS) database.

Net foreign assets and/or liabilities are seen as the reflection of a country’s external position and trade and investment with the rest of the world. Central banks traditionally define NFA as the change in net foreign assets as the current account balance. This definition is a bit narrow and more of a short-term nature. A country’s external sector also involves portfolio investments, direct investments as well as debt instruments. This broader context is more reliable. It is captured in the definition proposed by Lane and Ferretti (2001), which relies on two identities. The first identity is _NFA_ = _FDIA_ + _EQA_ + _DEBTA_ + _FX_ – _FDIL_ – _EQL_ – _DEBTL_ (1) where _FX_ = foreign exchange reserves, _FDI_ = the stock of direct investment, _EQ_ = portfolio equity investment, _DEBT_ = debt, _A_ = assets, and _L_ = liabilities.

5
**Table 17a: Specification with Log of annual sales as dependent variable – Doing Business City only**

|**DB City only**|**(1)-**|**Basic**|**(2) = (1) +**<br>**fina**|**access to**<br>**nce**|**(3) = (2) +**<br>**firms' inte**<br>**trade eng**|**degree of**<br>**rnational**<br>**agement***|**(4) = (**<br>**interacti**|**3) plus**<br>**on terms**|
|---|---|---|---|---|---|---|---|---|
|Variables|Firm|Industry|Firm|Industry|Firm|Industry|Firm|Industry|
|Log of past sales|0.687***<br>(0.04)|0.450***<br>(0.06)|0.710***<br>(0.05)|0.410***<br>(0.07)|0.683***<br>(0.07)|0.334***<br>(0.09)|0.690***<br>(0.06)|0.328***<br>(0.09)|
|Rank on the ease of Getting<br>Electricity|-0.0307***|0.0637***|-0.0344***|-0.03|-0.218***|-0.0913***|-0.231***|-0.0908***|
||(0.01)|(0.01)|(0.00)|(0.02)|(0.02)|(0.02)|(0.02)|(0.03)|
|Number of power outages per<br>month|0.0012|0.0048|0.0010|0.0068|0.0009|0.0037|0.0010|0.0051|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|(0.00)|(0.01)|
|Log of Labor Input|0.346***|0.676***|0.318***|0.745***|0.309***|0.790***|0.297***|0.779***|
||(0.05)|(0.10)|(0.05)|(0.11)|(0.06)|(0.12)|(0.06)|(0.13)|
|Capital Input|0***|-0***|0***|-0***|0**|0.00|0*|0.00|
||0.00|<br>0.00|<br>0.00|0.00|<br>0.00|<br>0.00|0.00|<br>0.00|
|Access to finance Dummy|-|-|0.05|0.12|<br>0.07|<br>0.27|0.06|<br>0.28|
||-|-|(0.03)|(0.12)|(0.05)|(0.18)|(0.05)|(0.19)|
|Direct Export 10% Dummy|-|-|-|-|0.1260|0.3160|0.1070|0.2980|
||-|-|-|-|(0.12)|(0.32)|(0.12)|(0.35)|
|Direct Import Dummy|-|-|-|-|0.219***|0.417***|0.237***|0.469***|
||-|-|-|-|(0.06)|(0.14)|(0.06)|(0.14)|
|Two-way Trader Dummy|-|-|-|-|(0.13)|0.04|(0.13)|(0.02)|
|L f l GDP i|-<br>#N/A|-<br>#N/A|-<br>#N/A|-<br>#N/A|(0.12)<br>#N/A|(0.27)<br>#N/A|(0.12)<br>#N/A|(0.32)<br>#N/A|
|og o rea per capta<br>Income Group Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Interaction Terms:<br>Industry*Getting Electricity|No|No|No|No|No|No|Yes|Yes|
|<br>Constant|4.873***|4.551***|4.802***|7.550***|43.70***|10.09***|45.57***|9.980***|
||(0.44)|(0.54)|(0.45)|(0.73)|(2.76)|(1.21)|(2.71)|(1.40)|
|Observations|3,043|875|2,749|679|1,270|468|1,270|468|
|R-squared<br>Robust standard errors in paren<br>* measured by whether the firm|0.93<br>theses ***<br>exports direc|0.86<br>p<0.01, ** p<0.<br>tly at least 10%|0.94<br>05, * p<0.1<br>of its sales, im|0.87<br> <br>ports directly|0.95<br>at least 10%|0.90<br>of its inputs o|0.95<br>r does both o|0.91<br>f these.|

49
I measure TFP as the residual of a Cobb-Douglas production function, which has an output that is a function of the inputs the firm employs — the same equation of basic specifications as equation (3), but without an innovation term in the RHS.

The empirical specification of estimation TFP can be written as follows: ln ����= ����+ ��ln ���� + ��ln ���� + ��ln ���� + �� + �� + ���� (14)

- ���� : The real output of firm _i_ operating in sector _j_ at time _t,_ which is calculated by the sales revenue deflated by subindustry-level deflators from the Producer Price Index.

- ���� : The value of fixed assets at the beginning of the year, deflated by GFCF, from the National Account.

- ���� : Number of employees.

- ����: The value of material inputs adjusted for changes in material inventories, deflated by deflators based on input-output tables.

- ����: The Hicks neutral efficiency level of the firm (TFP of firm) measured as the residual of the model/stochastic error term.

- �� : Time dummies for 1997–2007.

- ��: Firm fixed effect.

- ���� : S tandard i.i.d. error term capturing unanticipated shocks to production and measurement error.

I use the generalized method of moments (GMM) in TFP estimation for consistency, by controlling simultaneous input selection issues when input is correlated with the error term (����). The INS data set has a relatively short time dimension ( _T_ = 11 max, after omitting information after 2008, but most firms have less than 11 years of observations) and a larger firm dimension (more than 16,000 manufacturing firm observations, although many are repeated several times), therefore, is suitable for GMM.

With INS data, for each of the NACE two-digit industries in manufacturing (NACE 15–36), I obtained TFP estimation, with non-dynamic system GMM with 2 and 3 lags for instruments. One drawback of using a weakly balanced INS data set is that the dynamic model will 30
The paper confirms the historically close association between poverty and agricultural wage employment, and finds that there has been no decline – until the most recent period of 1999/00 to 2004/05 – in the share of the adult population with primary occupation in agricultural wage labor. As a result, changes in poverty are found to correlate well with changes in agricultural wages at the region (within state) level. There is little evidence of broad, across the board, acceleration in agricultural wage growth during the 1990s. In a majority of regions, real agricultural wage growth between 1993/4 and 2004/5 is lower than what was observed during the 1980s. The period post-1999 witnessed a sharp deceleration in the growth rate of agricultural wages, and in some regions real wages actually declined.

A sector that is often looked to for impetus in rural poverty reduction is the rural non-farm sector. This sector accounts for nearly half of rural household income in a significant number of states in India. The sector is highly heterogeneous and can be crudely divided into three sub-sectors comprising: regular salaried non-farm employment; casual wage labor in the non-farm sector; and non-agricultural selfemployment activities. The former sub-sector is most clearly associated with relatively high and stable incomes, while the latter two are more heterogeneous and can comprise both productive as well as residual activities. NSS data indicate that during the reference period there are signs of modest growth, on aggregate, in the size of the non-farm sector. While overall employment levels in the non-farm sector remained remarkably stable between 1983 and 1999/0, there is evidence that this has picked up between 1999/0 and 2004/5.

However, throughout this period the poor are hardly represented at all in the regular non-farm sector and are only slightly more involved in non-farm selfemployment. Only in the case of casual wage-labor in the non-farm sector do the poor appear to have a somewhat greater involvement in the non-farm sector than do the nonpoor.

Despite evidence of only modest poverty reduction or diversification out of agriculture at the national level, National Sample Survey (NSS) data reveal a great deal of heterogeneity in these outcomes at the sub-state region-level. This regional variation can be exploited to study econometrically the relationship between poverty, agricultural 3
Table V Social Support Programs

|||Childallowance|**I**<br>**Pare**|**ntal**allowance|
|---|---|---|---|---|
||Numberof<br>recipients|Expenditure<br>(in**millionSK)**|Numberof<br>recipients|Expenditure<br>**(in**million**SK)**|
|1995|682,045|10,002|154,012|2,519|
|1996|653,938|9,982|144,101|2,758|
|1997|594,219|9,119|142,134|4,116|
|1998|603,445|9,925|139,876|4,479|
|1999|568,951|9,190|137,931|4,417|

Source: _Social Policy._ Ministry of Labor, Social Affairs and Family.

||TableVIDistributionofSpells||
|---|---|---|
|Spellsperindividual|Numberofindividuals|Percentageofallindividuals|
|1|10,287|56.71|
|2|4,537|25.01|
|3|2,186|12.05|
|4|860|4.74|
|5|271|1.49|
|Total|18,141|100.00|

Source: Unemployment Registry, 1990-2000.

Table VII Distribution of Unem **lo** ment S ells **by** Duration

|Durationofspell(in months)|Numberofspells|Percentof**all**spells|
|---|---|---|
|Lessthan3|11,180|36.40|
|**4**to6|5,629|18.33|
|6to12|5,985|19.49|
|12to24|4,622|15.05|
|Morethan24|3,298|10.74|
|Total|30,714|100.00|

**Source:** Unemployment Registry, 1990-2000.

50
# **1. INTRODUCTION**

A crucial policy question in developing countries is whether to provide basic social services individually or through an integrated approach in a bundled fashion. Today, most development agencies and Governments finance service provision through large sector programs, providing services to the beneficiary population on an individual basis, i.e. through separate water supply, electricity, or telecommunications projects. Such sector programs typically have little coordination between them. Similarly, even social or infrastructure fund projects, that offer a menu of different services to the beneficiary population, typically only finance one type of service for participating communities. Most of these fund projects follow a menu-approach with local populations choosing the type of service they consider most important for their development. But in most such projects, the beneficiary community will be excluded from renewed funding, at least until other, comparable communities have also benefited in a similar fashion.

Such singular provision of services could imply that two potential gains of bundling service provision are foregone: first, important savings could materialize due to economies of scale in the supply of bundled service delivery due to lower per unit connection costs of households. Second, and the subject of this study, bundling could also lead to higher household welfare improvements if the joint provision of services holds higher returns for the household than if services are provided individually. For example, clean water access could improve household’s well-being somewhat but might do so substantially only if it were to come _together_ with sanitation. Health risks might decline with water access only but the real benefits might only materialize if the services are provided together.

Empirical evidence on the impact of bundling public services on the welfare of the beneficiary population in developing countries is rare, largely due to lack of adequate data. We overcome this problem by using recent national household surveys and constructing a threeperiod panel for Peru. This country is one of a few that has panel availability for household surveys during a relatively extended period (1994-2000). Further, availability of panel data coincides with a period of relative stability (Saavedra and Chong, 1999) which can help identify service delivery welfare effects. The availability of household panel information for Peru has allowed some researchers to study dynamic patterns of consumption and income in the country. Glewwe and Hall (1998) use a limited panel data between 1986 and 1990 to study the decline in 2
# <u>Fragile Caribbean (Haiti)</u>

Haiti’s connectivity with the rest of the world is focused primarily on the U.S. and on the Caribbean overall. There has been surprisingly steady growth since 2005, despite the fragile state of the country. Long term growth rates have been over 3 percent annually (Table 21). The majority of Haitian diaspora reside in the New York City area, and in the Miami area, which are also the airport pairs carrying 79% of the international passengers flowing traveling in and out of Haiti, which may explain some of the growth. However, the two spikes in growth between 2002 and 2004, and between 2010 and 2012, are most likely disaster-relief related, as those dates coincide with a major flooding incident in 2002, and the earthquake of 2010.

|**Table 21: Ext**<br>**Country**|**ra-regional T**<br>**Est.**<br>**Seats**<br>**2013**|**raffic wit**<br>**Share**|**h Haiti**<br>**Annual**<br>**Growth**<br>**01-13**|**Annual**<br>**Growth**<br>**08-13**|
|---|---|---|---|---|
|US|1,130,233|86.1%|3.3%|1.4%|
|Panama|42,380|3.2%|5.0%|10.4%|
|Guadeloupe|40,469|3.1%|6.8%|7.4%|
|Curaçao|23,400|1.8%|-2.4%|77.6%|
|Turks And Caicos Islands|22,308|1.7%|-0.7%|-8.1%|
|Canada|21,944|1.7%|-0.8%|2.6%|
|Cuba|14,352|1.1%|8.5%|13.1%|
|Sint Maarten|10,608|0.8%||-2.2%|
|France|6,942|0.5%|||
|Total|1,312,636|100.0%|3.2%|2.1%|

_Source: Authors’ calculation based on data by Diio (2014)._ The number of airport pairs being served has oscillated between 13 and 16 since 2003 (Figure 25). The most predominant three airlines serving Haiti are from the US, with American Airlines holding a 56.6% share, Delta Airlines 11.0 percent, and the more recent Spirit airline (since 2007) 6.8%. In spite of the strong linguistic connection, Air France, at 4.7% of the market share, has only a minor market share compared to the 74.4% share held by the U.S. carriers.

**Figure 25: Haiti’s extra-regional Seat Capacities and Direct Airport Pairs**

<!-- Start of picture text --> 1,600 17<br> 1,400 15<br>13<br> 1,200<br>11<br> 1,000<br>9<br> 800<br>7<br> 600<br>5<br> 400<br>3<br> 200 1<br> - -1<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats, thousands<br>Extra-Regional Airports Served<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ 54
_Table A3.2 Intention‐to‐treat effects of the original intervention on main psychological and behavioral outcomes by gender_

||(1)|(2)|(3)<br>(4)|(5)|
|---|---|---|---|---|
||ITT for|males|ITT for females|Coefficient<br>|
||(no controls)|(controls)|(no controls)<br>(controls)|equality (2)vs(4)|
||_Main o_<br>|_utcomes – Psychologi_<br>|_cal and behavioral_<br> <br>||
||0.099|0.079|‐0.071<br>‐0.045|‐0.125|
|Psychological wellbeing index|(0.204)|(0.306)|(0.216)<br>(0.414)|(0.190)|
|Risk index|0.004|‐0.003|‐0.069<br>‐0.061|‐0.058|
||(0.960)|(0.964)|(0.391)<br>(0.430)|(0.565)|
|Trust index|0.038|‐0.004|‐0.076<br>‐0.102|‐0.098|
||(0.653)|(0.963)|(0.235)<br>(0.127)|(0.407)|
|Crime and violence index|0.007|0.007|‐0.129**<br>‐0.152**|‐0.159*|
||(0.939)|(0.927)|(0.024)<br>(0.011)|(0.087)|
|Migration index|‐0.050|‐0.002|‐0.013<br>‐0.025|‐0.022|
||(0.478)|(0.975)|(0.845)<br>(0.716)|(0.834)|
||_Main outcome_|_s(experiments) – Psy_|_chological and behavioral_||
|List experiment cattle index|0.269*|0.258|0.108<br>0.117|‐0.142|
||(0.094)|(0.174)|(0.382)<br>(0.206)|(0.507)|
|List experiment argument index|0.140|0.135|‐0.303**<br>‐0.304**|‐0.439**|
||(0.378)|(0.419)|(0.017)<br>(0.014)|(0.038)|
|Observations|555|547|968<br>948||

_Note: P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐ food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between baseline and endline. Column (5) reports tests for coefficient equality between estimates from males and females in column (2) and (4). Displayed are differences of coefficient p‐values of the test in parenthesis._ 44
5.74 years, so our boat owners are broadly representative of the education levels prevailing in the developing world (Barro and Lee, 2000). The boat owners all started fishing as laborers and eventually became owners. On average, they have been boat owners for the past 14 years. Twenty-six percent of the sample own a katumarram, 66 percent a single fiber boat, and the remaining 8 percent own more than one fiber boat.

# **2.2. The Visual Aid**

We designed a short module on subjective expectations which followed the typical format used in many developing country surveys: a visual aid was introduced to conceptualize probabilities, respondents were then asked several questions which measure whether they understand basic concepts of probabilities, and then expectations about a future outcome of interest (in this case fish catches) were asked. Within this basic design we vary three specific elements by randomizing respondents into 8 different groups (Table 2), which we describe in more detail below.

Respondents were asked to express their expectations about future events by picking out beans and placing them on a sheet of paper in accordance with their subjective probability of an event occurring. Specifically, the interviewer started out by explaining the concept of probability to the respondents using the introduction from Delavande and Kohler (2009) as follows: _I will ask you several questions about the chance or likelihood that certain events are going to happen. Here are 10 beans. I would like you to choose some beans out of these 10 beans and put them on the sheet of paper to express what you think the likelihood or chance is of a specific event happening. One bean represents one chance out of 10. If you do not put any beans on the sheet of paper, it means you are sure that the event will NOT happen. As you add beans, it means that you think the likelihood that the event happen increases. For example, if you put one or two beans, it means you think the event is not likely to happen but it is still possible. If you pick 5 beans, it means that it is just as likely it happens as it does not happen (fifty-fifty). If you pick 6 beans, it means the event is slightly more_ 7
tutored (row 3). PTAs that meet more often (row 4) decreases tutoring but increases teacher after-school work hours.

Finally, if the most frequently discussed topics in the PTA are related to school facilities, budget and expenses, and fund raising, teachers are more likely to provide tutoring but do not increase number of students tutored or after-school work hours (row 5). When the most frequently discussed topics are instructional methods, teacher promotion and pay, and student performance, teachers are more likely to increase their after-school work hours and the number of tutored students, but the likelihood of teachers offering tutoring does not increase.

[Table 6 about here] Interestingly enough, independent of our study findings, the Lao Government has been implementing school-based management with features related to finance and governance in a number of provinces (World Bank, 2009). While rigorous impact evaluation of this reform remains to be implemented, our estimation results, as well as studies from other parts of the world, provide supporting evidence for the reform. Enhanced community participation in monitoring and governance at the school level tends to improve service delivery and quality of schools in India (Banerjee et al., 2010) and health clinics in Uganda (Bjorkman and Svensson, 2009), as well as in a number of other developing countries (Bruns, Filmer and Patrinos, 2011).

# **VIII. Summary**

Teacher effort and performance affect student performance, one way or another. In turn, teacher effort is shaped by a variety of factors including the incentives that are embedded in the education system and those that are particular to a school. In this paper, we examine the influence of pecuniary and non-pecuniary incentives that vary across schools using data from a special school survey in Lao PDR that was linked to a national household consumption and 27