rafmacalaba/datause-encoder-data huggingface dataset:
## 4.5 Public Sector and the Balance of Payments

Government expenditures consist of government consumption, which only has demand-side effects, and public investment, which has both demand- and supply-side effects. Public investment consists of investment in infrastructure, education, and health. Investment in infrastructure is defined as the expenditure affecting the accumulation of public infrastructure capital, which includes public assets such as roads, power plants and railroads. Investment in education affects the stock of public education capital, which consists of assets such as school buildings and other infrastructure affecting skills acquisition. In a similar fashion, investment in health adds to the stock of public assets such as hospitals and other government infrastructure affecting health. Infrastructure and health capital affect the production process in the private sector as they both combine to produce the stock of government capital.

All value added in the production of public goods is distributed as wages. The government fiscal balance is thus defined as total tax revenues minus the wage bill on school teachers, government transfers to households, total employment subsidies to firms in the private formal sector, other real current expenditures on goods and services, real investment spending, and interest payments on loans from abroad. Total tax revenues consist of revenue generated by import tariffs, sales taxes, income taxes, and payroll taxes.

The external constraint implies that any current account surplus (or deficit) must be compensated by a net outflow (or inflow) of foreign capital, given by the sum of changes in net foreign borrowing by the government and private firms. In the simulations reported below, we assume that public foreign borrowing is exogenous, and that private foreign borrowing adjusts to equilibrate the balance of payments.

# 5 Comparing Policy Shocks with Alternative Linkages

Mini-IMMPA can be used to analyze a variety of policy and exogenous shocks. To compare the performance of the three alternative approaches to micro-macro linkages discussed above, the growth, employment and poverty effects of two types of labor market policies are examined in this section: a cut in the minimum wage and an increase in the employment subsidy on unskilled labor. Both experiments relate to critical policy issues in devel20
42 items were foodstuffs for every quintile group. Expenditures on all food items were deflated using a Laspeyres index provided in the survey data set.

The survey asked a wide range of questions concerning fuel use, allowing an unusually rich picture of LPG use:

1. What is the main primary type of appliance used for cooking? The options include a gas cooker.

2. What are the household’s main and secondary sources of cooking fuel? Collected firewood was the most commonly used cooking fuel, followed by kerosene and charcoal. The secondary choice of fuel for cooking was not identified by two thirds of households, but kerosene and charcoal were the most frequently cited alternatives (Figure A2.2).

3. In the last year, has your household used LPG?

4. During the last month, how many units of LPG did you use? Units are defined by a range of specified cylinder sizes. This question elicited an integer for every response, suggesting that the respondents might have cited the number of cylinders the household had in operation (or possibly owned) during the recall period rather than an estimate of the actual amount of LPG consumed. Table A2.9 summarizes the data on the distribution of LPG cylinder use by size in kg and quintile group. LPG use was almost entirely confined to the top quintile. The spread of cylinder sizes used was relatively even and suggests that households could vary their choice of cylinder to suit their convenience and budget. Households reporting the cylinder size used also reported the prices paid. The unit price was highest for the smallest cylinder size, as expected, but for other sizes there was no trend with relation to size.

5. What was the cost of those units that you used during the last month?

6. During the last month how much (expenditure and quantity) was purchased of LPG?

Figure A2.2: Main sources of energy for cooking across all households in Kenya <!-- Start of picture text --> Collected firewood 58<br>3.2<br>Purchased firewood 11<br>4.9<br>13<br>Kerosene<br>7.4<br>Charcoal 13<br>16<br>LPG 3.3<br>1.2 Primary cooking fuel<br>Electricity 0.50.6 Secondary cooking fuel<br>Biomass residue 0.3<br>0.7<br>Grass 0.1<br>0.05<br>Biogas 0.040.07<br>Other/not reported 0.6 66<br>0 10 20 30 40 50 60 70<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.
**<u>Table 7: Compare effects by gender and number of right holders across surveys</u>**

||<br>**Orga**<br>|<br>**nic manure**<br>|<br>**applied**<br>|**Ca**<br>|**sh crop pl**<br>|**anted**<br>|
|---|---|---|---|---|---|---|
||**IHPS**|**IHS4**|**IHS4 rest.**|**IHPS**|**IHS4**|**IHS4 rest.**|
|Women have the right to bequeath (_β_11)|0.165***|0.025|0.027|0.045|-0.042*|-0.088**|
||[0.002]|[0.320]|[0.545]|[0.535]|[0.076]|[0.034]|
|Women have the right to sell (_β_12)|0.047|0.015|-0.008|-0.047|0.035*|0.009|
||[0.388]|[0.684]|[0.889]|[0.594]|[0.098]|[0.760]|
|Men have the right to bequeath (_β_21)|-0.013|0.047|0.070|0.199***|-0.002|0.001|
||[0.916]|[0.301]|[0.301]|[0.002]|[0.969]|[0.990]|
|Men have the right to sell (_β_22)|0.019|-0.024|-0.010|0.027|-0.013|-0.003|
||[0.820]|[0.449]|[0.789]|[0.659]|[0.696]|[0.951]|
|Observations|2,011|11,472|9,668|2,011|11,472|9,668|
|R-squared|0.779|0.817|0.834|0.587|0.619|0.635|
|Mean of dependent variable|0.233|0.214|0.216|0.070|0.055|0.055|
|Sd. of dependent variable|(0.416)|(0.404)|(0.406)|(0.238)|(0.217)|(0.216)|
|Mean of women’s right to bequeath|0.454|0.488|0.407|0.454|0.488|0.407|
|Sd. of women’s right to bequeath|(0.498)|(0.500)|(0.491)|(0.498)|(0.500)|(0.491)|
|Mean of women’s right to sell|0.402|0.395|0.333|0.402|0.395|0.333|
|Sd. of women’s right to sell|(0.490)|(0.489)|(0.471)|(0.490)|(0.489)|(0.471)|
|Mean of men’s right to bequeath|0.334|0.375|0.321|0.334|0.375|0.321|
|Sd. of men’s right to bequeath|(0.472)|(0.484)|(0.467)|(0.472)|(0.484)|(0.467)|
|Mean of men’s right to sell|0.299|0.329|0.281|0.299|0.329|0.281|
|Sd. of men’s right to sell|(0.458)|(0.470)|(0.449)|(0.458)|(0.470)|(0.449)|

Notes: Regressions are for owned parcels. Household fixed effects, parcel attributes including size, soil type (sandy, between sandy and clay, clay), slope (flat, slight slope, moderate slope, hilly), and whether land is in swamp/wetland, and interactions between women’ or men’s ownership are controlled for throughout. Robust standard errors are clustered by district. Figures in square brackets are p-values from wild cluster bootstrap (Roodman, 2018) with Rademacher weights and 1000 replications. *** significant at 1%; significant at 5%; * significant at 10%.

22
39 Breheny, M., 1992. Sustainable development and urban form. Pion, London.

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# **I. Introduction**

Household finance has always been an important lever to influence key policy outcomes. China, for instance, is hoping to increase household consumption to reduce its reliance on exports for growth, and household finance is clearly instrumental for purchase decisions for housing, durables, education, and medical care. Another key policy objective emphasized by the Chinese government in the past two years has been the facilitation of creation of new businesses, especially small and medium-sized ones. Again, only with good access to finance can households start businesses. Yet, partly due to the lack of detailed data on household usage of financial services, we have little in-depth knowledge about household financial arrangements in developing countries such as China.

In this paper, we rely on a comprehensive new national data set to provide a more complete description of Chinese household finance. In particular, we address what determines a household’s access to finance. Why do certain segments of the society such as rural residents have significantly less access to finance? How do households use formal and informal finance differently? Do those factors that facilitate access to finance also facilitate lower costs of finance? What are the roles of political and private networks in facilitating access to finance? Does financial knowledge affect access to finance? The data we use, the Chinese Household Finance Survey (CHFS), is designed to be representative of China through a multi-stage stratified random sampling process. It covers 29 provinces, 262 counties, 28,100 households and 98,000 individuals, and represents the best data source available for studying the above questions.

Our study contributes to an emerging literature on the relative importance of informal versus formal finance in explaining China’s recent growth. Some studies ascribe more importance to the role and efficiency of informal sources of finance in explaining the growth of Chinese firms (Allen et al., 2005) than others (Ayyagari et al., 2010; Cull and Xu, 2005; Cull 2
participation in government programs. Individual data consist of information on demographics, education, employment, health, and migration. It should be noted that the VHLSS contain data on the year and month but not the full date of birth of individuals.<sup>4</sup>

# **<mark>3. Childcare systems and descriptive analysis</mark>**

# _3.1. Childcare in Vietnam_

In Vietnam, kindergartens are generally available for children age 3 to 5, but some <mark>kindergartens also admit children age 18 months or older.</mark> Vietnam’s law on universal primary education require that children 6 years old and older attend a primary school, with the exception of children with health problems or those living in isolated areas. It should be noted that the admission age for school entrance in Vietnam reflects a child’s current age rather than completed age. Following this practice, hereafter, when we discuss a child’s age for admission to childcare, we refer to running age rather than completed age.

Children under the age of three can attend early childcare centers, but access to early childhood care centers remains limited in Vietnam, with only 26% of villages in rural Vietnam providing such centers.<sup>5</sup> Kindergartens are more widely available, with 49% of villages having at least a kindergarten. The VHLSS does not collect data on the availability of childcare centers for urban areas. However, using individual-level data, we can compute the proportion of urban and rural children attending childcare centers and kindergartens. In 2016, 44% of urban children age below 6 attended childcare centers and kindergartens, while this rate for rural children was lower at 35%.

In Vietnam, children are entitled to a place in a kindergarten from the age of 3 through just before attending primary school. School, including kindergarten, in the country starts from September each year. Thus, for example, if a child was born in 2000 (regardless of birth

> 4 We acknowledge that if the VHLSSs do not capture the top-income population groups, our analysis may not be relevant to these groups.

> 5 Unless noted otherwise, our estimates are based on the 2016 VHLSS. Vietnam includes 63 provinces and provincial-level cities. Each province (and provincial-level city) is split into districts, and each district is split further into communes. Communes are the smallest administrative units in Vietnam. In 2016, there were 713 districts and 11,164 communes in Vietnam. Each commune contains around 3-15 villages. In urban areas, communes are called wards.

6
of using this standard model as the starting point is to create a tractable setup for the effect of productivity on tariffs and then, having accounted for the economy-wide trade reform, to ultimately obtain structural equations for econometric analysis.

It is often argued that trade reform may be used as an exogenous change and this uniform shift in policy helps identify the effect of trade policy on productivity without worrying too much about its endogeneity. In order to account for this argument, I model a unilateral trade liberalization shock which is common across sectors. I find that under such a common exogenous shock, the reduction in tariffs is lower for the sectors that experience a higher productivity gain (or a lower productivity decline) as compared to the other sectors in the presence of political economy. Initially, I keep the additional channel simple making sure that it is clearly the political economy consideration that is driving the results. Next, I give more structure to the extra channel of protection and to the way the liberalization shock manifests itself by allowing for an infant industry argument.<sup>3</sup> This argument led to the widespread use of import substitution policies in most developing countries until the mid-1980s and its dismissal was the source of much unilateral trade liberalization since then.

In my two period model, the government initially believes that there exists a learning-by-doing (LBD) process and decides about the current tariffs by considering both the political economy effects and the effect of these tariffs on future welfare through LBD. In the second period, the government realizes that LBD does not actually exist and it is a false perception, and thus initiates a trade reform. Given that political economy forces still determine tariffs, the extent of the liberalization differs across sectors (due to political economy) despite this common shock across sectors. The implication from the models I present is that, assuming cross-sectional differences in trade policy to be independent of cross-sectional differences in productivity is incorrect.

I employ Colombian data for the empirical tests of my theory. Colombia has been used in various studies (for example, Roberts 1996; Fernandes 2003; Melendez, Seim and Medina 2003) given that it provides a great natural experiment environment. Colombia experienced a drastic trade reform in the early 1990s and had a stable economy in this period without major crises. Given the existence of other studies using the same country, I get the chance to test my predictions with a comparable dataset. In Section 3, I discuss the trade policy in Colombia during the sample period of 1983 through 1998, and describe how we can rule out a uniform change in tariffs across

> 3For example, the infant industry argument is mentioned as an important motive for protection in Grossman and Horn (1988).

2
# : **Impacts of Disasters in Conflict Settings** Evidence from Mozambique and Nigeria

Novembre 20, 2024 Karima Ben Bih, World Bank Chloe Desjonqueres, World Bank Bramka Jarafino, World Bank Elodie Blanc, Motu Economic and Public Policy Research Center Solene Masson, World Bank VIIRS; Floods; Nigeria; Mozambique; Conflicts influence on Disaster Impacts and Recovery; GDP.

The authors are grateful to Stephane Hallegatte, Oscar Ishizawa, and Jun Rentschler for their thoughtful comments, suggestions, and guidance.
from the BFL model to the national figure using ENCEL 1997, the total cost of the program is estimated to be around 6.08 billion pesos, which is very close to the actual program cost of 6.06 billion pesos in January 2002 from the official program data (Table 5).

**Table 5: Estimated and Observed** **_PROGRESA_ 's Cost**

||_BFL Simulation_<sup>_1,2_</sup>|_January 2002_<sup>_1,3_</sup>|
|---|---|---|
|Number of Families with Children Receiving Benefits|4,567|1,681,254|
|Average Monthly Transfer for Families with Children|$301|$300|
|Estimated Total Annual Transfer|$16,517,580|$6,061,221,243|
|Scaling Up Total Transfer: Simulated average times<br>families in 2002|$6,080,632,364|n.a.|

_Note_ :

|1: November 1999 prices|
|---|
|2: Estimated by BFL model using Baseline survey of 1997, Rounds 1-4|
|3: National official numbers from:|
|http://www.oportunidades.gob.mx/indicadores_gestion/ene_feb_02/indice.htm|

Thus the BFL model applied to the _PROGRESA_ program in Mexico appears to have strong predictive power for the impact of the program on outcome indicators (i.e. enrollment rate, child labor and poverty) as well as the cost of the program, when compared against relevant benchmarks – namely AIT impacts estimated by _ex post_ evaluations using experimental methods and actual or observed program cost. Since _ex post_ AIT estimates represent a lower bound of the impact on households that actually _received_ treatment (for reasons explained earlier), the _ex ante_ estimates of program impact from BFL should also be seen as a _lower bound_ for the actual impacts of the program on beneficiary households.

# _The impact of Bono de Desarrollo Humano (BDH)_

As before, we compare the _ex ante_ estimates of program impact from the BFL model with _ex post_ findings from Schady and Araújo (2008), henceforth SA. For _ex ante_ simulations, we use the baseline survey of BDH collected in Carchi, Imbabura, Cotopaxi and Tungurahaua and described in section IV above, which is also used by SA as the baseline data in analyzing impact. It is relevant to note that by the authors’ own admission, the results from SA should be treated with some caution as they are not based entirely on random assignment.

26
# **Table 8. Growth and the strength of legal institutions supporting business regulations**

|VARIABLES|(1)<br>growth|(2)<br>growth|(3)<br>growth|(4)<br>growth|(5)<br>growth|(6)<br>growth|(7)<br>growth|(8)<br>growth|(9)<br>growth|
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|-4.998***<br>(-3.222)|-4.999***<br>(-3.224)|-4.025**<br>(-2.170)|-5.400***<br>(-2.723)|-5.353***<br>(-2.668)|-5.416***<br>(-2.692)|-6.799***<br>(-3.202)|-6.678***<br>(-2.961)|-8.940***<br>(-3.214)|
|FDI||0.000725|0.000865|7.41e-05|0.000217|8.97e-05|-4.47e-05|3.98e-05|0.00215|
|||(0.575)|(0.921)|(0.0811)|(0.237)|(0.100)|(-0.0419)|(0.0379)|(1.127)|
|Country risk (ICRG)|||0.114*|0.0805|0.0752|0.0773|0.0694|0.0715|0.131|
||||(1.727)|(1.177)|(1.148)|(1.213)|(1.066)|(1.096)|(1.560)|
|Government consumption||||<br>-0.253***|<br>-0.223***|<br>-0.218***|<br>-0.159**|<br>-0.170**|<br>-0.163*|
|||||(-3.020)|(-2.657)|(-2.625)|(-2.025)|(-2.117)|(-1.868)|
|Rents from natural resources|||||0.0344|0.0344|-0.00560|0.00593|-0.0292|
|Political freedom and civil<br>rights (FH)|||||(1.194)|(1.186)<br>0.290|(-0.183)<br>0.335|(0.190)<br>0.347|(-0.889)<br>-0.0393|
|||||||(0.646)|(0.787)|(0.797)|(-0.0922)|
|Trade (X and M)|||||||0.0611***<br>(3.475)|0.0586***<br>(3.385)|0.0538***<br>(3.050)|
|Corruption (CPI)||||||||<br>-0.0925<br>(-0.281)|<br>-0.325<br>(-0.749)|
|Credit to private sector|||||||||<br>-0.0280<br>1173|
||||||||||-.|
|Lagged GDP growth|0.443***<br>(10.73)|0.443***<br>(10.74)|0.425***<br>(6.861)|0.366***<br>(4.921)|0.358***<br>(5.117)|0.360***<br>(5.103)|0.330***<br>(4.857)|0.328***<br>(4.806)|<br>0.273***<br>(3.121)|
|Delta DTF (legal)|<br>0.0374|<br>0.0368|<br>0.0901**|<br>0.0966**|<br>0.0958**|<br>0.0956**|<br>0.0859**|<br>0.0873**|<br>0.0871**|
||(1.464)|(1.438)|(2.218)|(2.318)|(2.340)|(2.308)|(2.188)|(2.197)|(1.982)|
|Constant|<br>45.35***<br>(3.300)|<br>45.36***<br>(3.302)|<br>32.05**<br>(2.019)|<br>50.62***<br>(2.853)|<br>49.60***<br>(2.793)|<br>49.06***<br>(2.700)|<br>56.01***<br>(2.976)|<br>55.48***<br>(2.884)|<br>77.64***<br>(3.233)|
|Observations|855|852|633|567|561|557|552|545|428|
|Number of cod2|175|174|130|122|122|121|120|118|115|
|chi2|153.7|154.5|175.1|136.1|165.7|161.5|113.9|119.2|99.85|

Robust z-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

**Notes:** ***(**)(*) denotes significance at the 1(5)(10) percent level. Standard errors reported in parentheses. This table reports results from Arellano-Bond dynamic panel estimator regressions of growth on the change in the distance to the legal regulatory frontier. The estimations reported include controls for lagged GDP per capita growth, per-capita GDP, trade openness, corruption, credit to the private sector, FDI inflow, economic and political risk as well as civil freedoms. All specifications include time dummies to control for any global changes in the macroeconomic environment.

24
**Figure 1: Overall trends in PPI Levels in five years periods, by sector (US$ millions, 1991-2010)**

<!-- Start of picture text --> 400,000 900,000<br>350,000 800,000<br>700,000<br>300,000<br>600,000<br>250,000<br>500,000<br>200,000<br>400,000<br>150,000<br>300,000<br>100,000<br>200,000<br>50,000 100,000<br>0 0<br>1991-1995 1996-2000 2001-2005 2006-2010<br>Energy Telecom Transport Water and sewerage Total<br> Millions US$ in 2012 prices<br> Millions US$ in 2012 prices<br><!-- End of picture text --> Source: World Bank PPI Database For policy makers trying to leverage private capital and attract operational efficiencies in infrastructure and basic service provision, understanding the underlying factors that influence levels of PPI is of central importance. That understanding can influence the timing of market offerings, the prioritization and sequencing of sectors or projects, decisions about the use of credit enhancements, expectations for investment commitments, and the form of contract pursued by governments. Moreover, if a relationship between public policies and levels of investment in PPI can be demonstrated, it raises the visibility of PPI success or failure above the level of line agencies. It can focus senior officials from ministries of finance, economy and planning as well as legislators and offices of the president, on public policy shortcomings—such as decisions on national debt restructuring, rules governing repatriation of capital, or expropriation practices—which previously may have seemed irrelevant to the considerations of market interest in a single investment opportunity.

This raises the question of whether country risk ratings—which agglomerate several political, economic, credit and financial conditions and behaviors at the sovereign level—can be used to explain a part of the differences among countries trying to attract investments, and, if so, whether that difference is significant over the years. Simply asked, does sovereign risk play a significant role as a predictor of PPI in developing countries? With panel data on both risk ratings and investment commitments, it is possible to identify which are the most sensitive sectors and forms of PPI to changes in country risk ratings. It is then possible to contrast the effects of risk on investments in energy, telecommunications, water and transport. Likewise, we can test the relative sensitivity of greenfield investments as well as concessions of existing assets to country risk.

3